From b3f8af4e230aff00d7f1e425645cefa21405ddc0 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Mon, 5 May 2025 18:24:10 -0400 Subject: [PATCH 01/29] [WIP] Add documentation for failure stores. TBD on recipes. Most links are not complete and need updating from "???". --- .../data-store/data-streams/failure-store.md | 607 ++++++++++++++++++ manage-data/toc.yml | 1 + 2 files changed, 608 insertions(+) create mode 100644 manage-data/data-store/data-streams/failure-store.md diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md new file mode 100644 index 000000000..501718fd0 --- /dev/null +++ b/manage-data/data-store/data-streams/failure-store.md @@ -0,0 +1,607 @@ +--- +applies_to: + stack: ga 8.19.0 + serverless: ga 9.1.0 +--- + +# Failure store [failure-store] + +Failure stores are a secondary set of indices inside a data stream dedicated to storing failed documents. Failed documents are any documents that cause ingest pipeline exceptions or have a structure that conflicts with a data stream's mappings. These failures normally cause the indexing operation to fail, returning the error message in the response. When a data stream's failure store is enabled, these failures are instead captured and persisted to be analysed later, returning a successful response to the client in the meantime. + +## Set up a data stream failure store [set-up-failure-store] + +Each data stream has its own failure store that can be enabled to accept failures. By default, this failure store is disabled and any ingestion problems are raised in the response to write operations. + +### Set up for new data streams [set-up-failure-store-new] + +You can specify on a data stream's template if it should enable the failure store when it is first created. The `data_stream_options` field in a [template](../templates.md) contains the settings required to enable a data stream's failure store. + +:::{note} +Unlike the `settings` and `mappings` fields on an [index template](../templates.md) which are repeatedly applied to new data stream write indices over time, the `data_stream_options` section of a template is applied to a data stream only once when the data stream is first created. To configure existing data streams, use the put data stream options API. +::: + +To enable the failure store on a new data stream, enable it in the `data_stream_options` of the template: + +```console +PUT _index_template/my-index-template +{ + "index_patterns": ["my-datastream-*"], + "data_stream": { }, + "template": { + "data_stream_options": { <1> + "failure_store": { + "enabled": true <2> + } + } + } +} +``` + +1. The options for a data stream to be applied at creation time. +2. The failure store feature will be enabled for new data streams that match this template. + + +After a matching data stream is created, its failure store will be enabled. + +### Set up for existing data streams [set-up-failure-store-existing] + +Enabling the failure store via [index templates](../templates.md) can only affect data streams that are newly created. Existing data streams that use a template will not apply any changes to the template's `data_stream_options` after they have been created. + +To modify an existing data stream's options, use the [put data stream options](???) API: + +```console +PUT my-datastream-existing/_options +{ + "failure_store": { + "enabled": true <1> + } +} +``` + +1. The failure store option will now be enabled. + + +The failure store redirection can be suspended using this API as well. When the failure store is disabled, only failed document redirection is halted. Any existing failure data in the data stream will remain until removed by deletion or by retention. + +```console +PUT my-datastream-existing/_options +{ + "failure_store": { + "enabled": false <1> + } +} +``` + +1. Redirecting failed documents into the failure store will now be disabled. + +### Enable failure store via cluster setting [set-up-failure-store-cluster-setting] + +If you have a large number of existing data streams you may want an easier way to control if failures should be redirected. Instead of enabling the failure store using the [put data stream options](???) API, you can instead configure a set of patterns in the [cluster settings](???) which will enable the failure store feature by default. + +Configure a list of patterns using the `data_streams.failure_store.enabled` dynamic cluster setting. If a data stream matches a pattern in this setting and does not have the failure store explicitly disabled in its options, then the failure store will default to being enabled for that matching data stream. + +```console +PUT _cluster/settings +{ + "persistent" : { + "data_streams.failure_store.enabled" : [ "my-datastream-*", "logs-*" ] <1> + } +} +``` + +1. Indices that match `my-datastream-*` or `logs-*` will redirect failures to the failure store unless explicitly disabled. + +## Using a failure store [use-failure-store] + +The failure store is meant to ease the burden of detecting and handling failures when ingesting data to {{es}}. Clients are less likely to encounter unrecoverable failures when writing documents, and developers are more easily able to troubleshoot faulty pipelines and mappings. + +### Failure redirection [use-failure-store-redirect] + +Once a failure store is enabled for a data stream it will begin redirecting documents that fail due to common ingestion problems instead of returning errors in write operations. Clients are notified in a non-intrusive way when a document is redirected to the failure store. + +Each data stream's failure store is made up of a list of indices that are dedicated to storing failed documents. These indices function much like a data stream's normal backing indices: There is a write index that accepts failed documents, they can be rolled over, and are automatically cleaned up over time subject to a lifecycle policy. + +When a document bound for a data stream encounters a problem during its ingestion, the response is annotated with the `failure_store` field which describes how {{es}} responded to that problem. The `failure_store` field is present on both the [bulk](???) and [index](???) API responses when applicable. Clients can use this information to augment their behavior based on the response from {{es}}. + +Here we have a bulk operation that sends two documents. Both are writing to the `id` field which is mapped as a `long` field type. The first document will be accepted, but the second document would cause a failure because the value `invalid_text` cannot be parsed as a `long`. This second document will be redirected to the failure store: + +```console +POST my-datastream/_bulk +{"create":{}} +{"@timestamp": "2025-05-01T00:00:00Z", "id": 1234} <1> +{"create":{}} +{"@timestamp": "2025-05-01T00:00:00Z", "id": "invalid_text"} <2> +``` +1. A correctly formatted document. +2. Invalid document that cannot be parsed using the current mapping. + +```console-result +{ + "errors": false, <1> + "took": 400, + "items": [ + { + "create": { + "_index": ".ds-my-datastream-2025.05.01-000001", <2> + "_id": "YUvQipYB_ZAKuDfZRosB", + "_version": 1, + "result": "created", + "_shards": { + "total": 1, + "successful": 1, + "failed": 0 + }, + "_seq_no": 3, + "_primary_term": 1, + "status": 201 + } + }, + { + "create": { + "_index": ".fs-my-datastream-2025.05.01-000002", <3> + "_id": "lEu8jZYB_ZAKuDfZNouU", + "_version": 1, + "result": "created", + "_shards": { + "total": 1, + "successful": 1, + "failed": 0 + }, + "_seq_no": 10, + "_primary_term": 1, + "failure_store": "used", <4> + "status": 201 + } + } + ] +} +``` + +1. The response code is 200 OK, and the response body does not report any errors encountered. +2. The first document is accepted into the data stream's write index. +3. The second document encountered a problem during ingest and was redirected to the data stream's failure store. +4. The response is annotated with a field indicating that the failure store was used to persist the second document. + + +If the document was redirected to a data stream's failure store due to a problem, then the `failure_store` field on the response will be `used`, and the response will not return any error information: + +```console-result +{ + "_index": ".fs-my-datastream-2025.05.01-000002", <1> + "_id": "lEu8jZYB_ZAKuDfZNouU", + "_version": 1, + "result": "created", + "_shards": { + "total": 1, + "successful": 1, + "failed": 0 + }, + "_seq_no": 11, + "_primary_term": 1, + "failure_store": "used" <2> +} +``` + +1. The document for this index operation was sent to the failure store's write index. +2. The response is annotated with a flag indicating the document was redirected. + + +If the document could have been redirected to a data stream's failure store but the failure store was disabled, then the `failure_store` field on the response will be `not_enabled`, and the response will display the error encountered as normal. + +```console-result +{ + "error": { + "root_cause": [ <1> + { + "type": "document_parsing_exception", + "reason": "[1:53] failed to parse field [id] of type [long] in document with id 'Y0vQipYB_ZAKuDfZR4sR'. Preview of field's value: 'invalid_text'" + } + ], + "type": "document_parsing_exception", + "reason": "[1:53] failed to parse field [id] of type [long] in document with id 'Y0vQipYB_ZAKuDfZR4sR'. Preview of field's value: 'invalid_text'", + "caused_by": { + "type": "illegal_argument_exception", + "reason": "For input string: \"invalid_text\"" + }, + "failure_store": "not_enabled" <2> + }, + "status": 400 <3> +} +``` + +1. The failure is returned to the client as normal when the failure store is not enabled. +2. The response is annotated with a flag indicating the failure store could have accepted the document, but it was not enabled. +3. Status of 400 Bad Request due to the mapping problem. + + +If the document was redirected to a data stream's failure store but that failed document could not be stored (e.g. due to shard unavailability or a similar problem), then the `failure_store` field on the response will be `failed`, and the response will display the error for the original failure, as well as a suppressed error detailing why the failure could not be stored: + +```console-result +{ + "error": { + "root_cause": [ + { + "type": "document_parsing_exception", <1> + "reason": "[1:53] failed to parse field [id] of type [long] in document with id 'Y0vQipYB_ZAKuDfZR4sR'. Preview of field's value: 'invalid_text'", + "suppressed": [ + { + "type": "cluster_block_exception", <2> + "reason": "index [.fs-my-datastream-2025.05.01-000002] blocked by: [FORBIDDEN/5/index read-only (api)];" + } + ] + } + ], + "type": "document_parsing_exception", <3> + "reason": "[1:53] failed to parse field [id] of type [long] in document with id 'Y0vQipYB_ZAKuDfZR4sR'. Preview of field's value: 'invalid_text'", + "caused_by": { + "type": "illegal_argument_exception", + "reason": "For input string: \"invalid_text\"" + }, + "suppressed": [ + { + "type": "cluster_block_exception", + "reason": "index [.fs-my-datastream-2025.05.01-000002] blocked by: [FORBIDDEN/5/index read-only (api)];" + } + ], + "failure_store": "failed" <4> + }, + "status": 400 <5> +} +``` + +1. The root cause of the problem was a mapping mismatch. +2. The document could not be redirected because the failure store was not able to accept writes at this time due to an unforeseeable issue. +3. The complete exception tree is present on the response. +4. The response is annotated with a flag indicating the failure store would have accepted the document, but it was not able to. +5. Status of 400 Bad Request due to the original mapping problem. + + +### Searching failures [use-failure-store-searching] + +Once you have accumulated some failures, they can be searched much like a regular index. + +:::{warning} +Documents redirected to the failure store in the event of a failed ingest pipeline will be stored in their original, unprocessed form. If an ingest pipeline normally redacts sensitive information from a document, then failed documents in their original, unprocessed form may contain sensitive information. + +Furthermore, failed documents are likely to be structured differently than normal data in a data stream, and thus are not supported by [document level security](???) or [field level security](???). + +To limit visibility on potentially sensitive data, users require the [`read_failure_store`](???) index privilege for a data stream in order to search that data stream's failure store data. +::: + +Searching a data stream's failure store can be done by making use of the existing search APIs available in {{es}}. + +To indicate that the search should be performed on failure store data, use the [index component selector syntax](???) to indicate which part of the data stream to target in the search operation. Appending the `::failures` suffix to the name of the data stream indicates that the operation should be performed against that data stream's failure store instead of its regular backing indices. + +:::::{tab-set} + +::::{tab-item} {{esql}} +```console +POST _query?format=txt +{ + "query": """FROM my-datastream::failures | DROP error.stack_trace | LIMIT 1""" <1> +} +``` +1. We drop the `error.stack_trace` field here just to keep the example free of newlines. + +An example of a search result with the failed document present: + +```console-result + @timestamp | document.id |document.index |document.routing| error.message |error.pipeline |error.pipeline_trace|error.processor_tag|error.processor_type| error.type +------------------------+--------------------+---------------+----------------+-------------------------------------------------------------------------------------------------------------------------------------+---------------+--------------------+-------------------+--------------------+-------------------------- +2025-05-01T12:00:00.000Z|Y0vQipYB_ZAKuDfZR4sR|my-datastream |null |[1:45] failed to parse field [id] of type [long] in document with id 'Y0vQipYB_ZAKuDfZR4sR'. Preview of field's value: 'invalid_text'|null |null |null |null |document_parsing_exception +``` + +:::{note} +Because the `document.source` field is unmapped, it is absent from the {{esql}} results. +::: + +:::: + +::::{tab-item} _search API +```console +GET my-datastream::failures/_search +``` + +An example of a search result with the failed document present: + +```console-result +{ + "took": 0, + "timed_out": false, + "_shards": { + "total": 1, + "successful": 1, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 1, + "relation": "eq" + }, + "max_score": 1, + "hits": [ + { + "_index": ".fs-my-datastream-2025.05.01-000002", <1> + "_id": "lEu8jZYB_ZAKuDfZNouU", + "_score": 1, + "_source": { + "@timestamp": "2025-05-01T12:00:00.000Z", <2> + "document": { <3> + "id": "Y0vQipYB_ZAKuDfZR4sR", + "index": "my-datastream", + "source": { + "@timestamp": "2025-05-01T00:00:00Z", + "id": "invalid_text" + } + }, + "error": { <4> + "type": "document_parsing_exception", + "message": "[1:53] failed to parse field [id] of type [long] in document with id 'Y0vQipYB_ZAKuDfZR4sR'. Preview of field's value: 'invalid_text'", + "stack_trace": """o.e.i.m.DocumentParsingException: [1:53] failed to parse field [id] of type [long] in document with id 'Y0vQipYB_ZAKuDfZR4sR'. Preview of field's value: 'invalid_text' + at o.e.i.m.FieldMapper.rethrowAsDocumentParsingException(FieldMapper.java:241) + at o.e.i.m.FieldMapper.parse(FieldMapper.java:194) + ... 24 more +Caused by: j.l.IllegalArgumentException: For input string: "invalid_text" + at o.e.x.s.AbstractXContentParser.toLong(AbstractXContentParser.java:189) + at o.e.x.s.AbstractXContentParser.longValue(AbstractXContentParser.java:210) + ... 31 more +""" + } + } + } + ] + } +} +``` + +1. The document belongs to a failure store index on the data stream. +2. The failure document timestamp is when the failure occurred in {{es}}. +3. The document that was sent is captured inside the failure document. Failure documents capture the id of the document at time of failure, along with which data stream the document was being written to, and the contents of the document. The `document.source` fields are unmapped to ensure failures are always captured. +4. The failure document captures information about the error encountered, like the type of error, the error message, and a compressed stack trace. +:::: + +::::{tab-item} SQL +```console +POST _sql?format=txt +{ + "query": """SELECT * FROM "my-datastream::failures" LIMIT 1""" +} +``` + +An example of a search result with the failed document present: + +```console-result + @timestamp | document.id |document.index |document.routing| error.message |error.pipeline |error.pipeline_trace|error.processor_tag|error.processor_type| error.stack_trace | error.type +------------------------+--------------------+---------------+----------------+-------------------------------------------------------------------------------------------------------------------------------------+---------------+--------------------+-------------------+--------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-------------------------- +2025-05-05T20:49:10.899Z|sXk1opYBL1dfU_1htCAE|my-datastream |null |[1:45] failed to parse field [id] of type [long] in document with id 'sXk1opYBL1dfU_1htCAE'. Preview of field's value: 'invalid_text'|null |null |null |null |o.e.i.m.DocumentParsingException: [1:45] failed to parse field [id] of type [long] in document with id 'sXk1opYBL1dfU_1htCAE'. Preview of field's value: 'invalid_text' + at o.e.i.m.FieldMapper.rethrowAsDocumentParsingException(FieldMapper.java:241) + at o.e.i.m.FieldMapper.parse(FieldMapper.java:194) + ... 19 more +Caused by: j.l.IllegalArgumentException: For input string: "invalid_text" + at o.e.x.s.AbstractXContentParser.toLong(AbstractXContentParser.java:189) + at o.e.x.s.AbstractXContentParser.longValue(AbstractXContentParser.java:210) + ... 26 more +|document_parsing_exception +``` + +:::{note} +Because the `document.source` field is unmapped, it is absent from the SQL results. +::: +:::: +::::: + +Failure documents have a uniform structure that is handled internally by {{es}}. + +`@timestamp` +: (`date`) The timestamp at which the document encountered a failure in {{es}}. + +`document` +: (`object`) The document at time of failure. If the document failed in an ingest pipeline, then the document will be the unprocessed version of the document as it arrived in the original indexing request. If the document failed due to a mapping issue, then the document will be as it was after any ingest pipelines were applied to it. + + `document.id` + : (`keyword`) The id of the original document at the time of failure. + + `document.routing` + : (`keyword`, optional) The routing of the original document at the time of failure if it was specified. + + `document.index` + : (`keyword`) The index that the document was being written to when it failed. + + `document.source` + : (unmapped object) The body of the document. This field is unmapped and unindexed to ensure failures are indexed reliably. If you need to include fields from the document source in your queries, use [runtime fields](???) on the search request. + +`error` +: (`object`) Information about the failure that prevented this document from being indexed. + + `error.message` + : (`match_only_text`) The error message that describes the failure. + + `error.stack_trace` + : (`text`) A compressed stack trace from {{es}} for the failure. + + `error.type` + : (`keyword`) The type classification of failure. Values are the same type returned within failed indexing API responses. + + `error.pipeline` + : (`keyword`, optional) If the failure occurred in an ingest pipeline, this will contain the name of the pipeline. + + `error.pipeline_trace` + : (`keyword`, optional array) If the failure occurred in an ingest pipeline, this will contain the list of pipelines that the document had visited up until the failure. + + `error.processor_tag` + : (`keyword`, optional) If the failure occurred in an ingest processor that is annotated with a [tag](???), the tag contents will be present here. + + `error.processor_type` + : (`keyword`, optional) If the failure occurred in an ingest processor, this will contain the processor type. (e.g. `script`, `append`, `enrich`, etc.) + + +## Manage a data stream's failure store [manage-failure-store] + +Failure data can accumulate in a data stream over time. To help manage this accumulation, most administrative operations that can be done on a data stream can be applied to the data stream's failure store. + +### Failure store rollover [manage-failure-store-rollover] + +A data stream treats its failure store much like a secondary set of [backing indices](???). Multiple dedicated hidden indices serve search requests for the failure store, while one index acts as the current write index. You can use the [rollover](???) API to rollover the failure store. Much like the regular indices in a data stream, a new write index will be created in the failure store to accept new failure documents. + +```console +POST my-datastream::failures/_rollover +``` + +```console-result +{ + "acknowledged": true, + "shards_acknowledged": true, + "old_index": ".fs-my-datastream-2025.05.01-000002", + "new_index": ".fs-my-datastream-2025.05.01-000003", + "rolled_over": true, + "dry_run": false, + "lazy": false, + "conditions": {} +} +``` + +### Failure store lifecycle [manage-failure-store-lifecycle] + +Failure stores have their retention managed using an internal [data stream lifecycle](???). A thirty day (30d) retention is applied to failure store data. You can view the active lifecycle for a failure store index by calling the [get data stream API](???): + +```console +GET _data_stream/my-datastream +``` + +```console-result +{ + "data_streams": [ + { + "name": "my-datastream", + "timestamp_field": { + "name": "@timestamp" + }, + "indices": [ + { + "index_name": ".ds-my-datastream-2025.05.01-000001", + "index_uuid": "jUbUNf-8Re-Nca8vJkHnkA", + "managed_by": "Data stream lifecycle", + "prefer_ilm": true, + "index_mode": "standard" + } + ], + "generation": 2, + "status": "GREEN", + "template": "my-datastream-template", + "lifecycle": { + "enabled": true + }, + "next_generation_managed_by": "Data stream lifecycle", + "prefer_ilm": true, + "hidden": false, + "system": false, + "allow_custom_routing": false, + "replicated": false, + "rollover_on_write": false, + "index_mode": "standard", + "failure_store": { <1> + "enabled": true, + "rollover_on_write": false, + "indices": [ + { + "index_name": ".fs-my-datastream-2025.05.05-000002", + "index_uuid": "oYS2WsjkSKmdazWuS4RP9Q", + "managed_by": "Data stream lifecycle" <2> + } + ], + "lifecycle": { + "enabled": true, + "effective_retention": "30d", <3> + "retention_determined_by": "default_failures_retention" <4> + } + } + } + ] +} +``` +1. Information about the failure store is presented in the response under its own field. +2. Indices are managed by data stream lifecycles by default. +3. An effective retention period of thirty days (30d) is present by default. +4. The retention is currently determined by the default. + +:::{note} +The default retention respects any maximum retention values. If [maximum retention](???) is configured lower than thirty days then the maximum retention will be used as the default value. +::: + +You can update the default retention period for failure stores in your deployment by updating the `data_streams.lifecycle.retention.failures_default` cluster setting. Data streams that have no retention configured on their failure stores will use this value to determine their retention period. + +```console +PUT _cluster/settings +{ + "persistent": { + "data_streams.lifecycle.retention.failures_default": "15d" + } +} +``` + +You can also specify the failure store retention period for a data stream on its data stream options. These can be specified via the index template for new data streams, or via the [put data stream options](???) API for existing data streams. + +```console +PUT _data_stream/my-datastream/_options +{ + "failure_store": { + "enabled": true, <1> + "lifecycle": { + "data_retention": "10d" <2> + } + } +} +``` +1. Ensure that the failure store remains enabled. +2. Set only this data stream's failure store retention to ten days. + +### Add and remove from failure store [manage-failure-store-indices] + +Failure stores support adding and removing indices from them using the [modify data stream](???) API. + +```console +POST _data_stream/_modify +{ + "actions":[ + { + "remove_backing_index": { <1> + "data_stream": "my-datastream", + "index": ".fs-my-datastream-2025.05.05-000002", <2> + "failure_store": true <3> + } + }, + { + "add_backing_index": { <4> + "data_stream": "my-datastream", + "index": "restored-failure-index", <5> + "failure_store": true <6> + } + } + ] +} +``` +1. Action to remove a backing index. +2. The name of an auto-generated failure store index that should be removed. +3. Set `failure_store` to true to have the modify API target operate on the data stream's failure store. +4. Action to add a backing index. +5. The name of an index that should be added to the failure store. +6. Set `failure_store` to true to have the modify API target operate on the data stream's failure store. + +This API gives you fine grained control over the indices in your failure store, allowing you to manage backup and restoration operations as well as isolate failure data for later remediation. + +## Failure store recipes and use cases [recipes] + +TBD + +### Troubleshooting ingest pipelines effectively [recipes-ingest-troubleshoot] + +TBD + +### Alerting on failed ingestion [recipes-alerting] + +TBD + +### Data remediation [recipes-remediation] + +TBD diff --git a/manage-data/toc.yml b/manage-data/toc.yml index 9275294ab..72cafaace 100644 --- a/manage-data/toc.yml +++ b/manage-data/toc.yml @@ -20,6 +20,7 @@ toc: - file: data-store/data-streams/run-downsampling-using-data-stream-lifecycle.md - file: data-store/data-streams/reindex-tsds.md - file: data-store/data-streams/logs-data-stream.md + - file: data-store/data-streams/failure-store.md - file: data-store/mapping.md children: - file: data-store/mapping/dynamic-mapping.md From 32d93367c191a334c72b45919324c133fc9c9eac Mon Sep 17 00:00:00 2001 From: James Baiera Date: Tue, 6 May 2025 02:44:15 -0400 Subject: [PATCH 02/29] Change ??? to "wip" --- .../data-store/data-streams/failure-store.md | 28 ++++++++++--------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index 501718fd0..beb1c87b2 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -47,7 +47,7 @@ After a matching data stream is created, its failure store will be enabled. Enabling the failure store via [index templates](../templates.md) can only affect data streams that are newly created. Existing data streams that use a template will not apply any changes to the template's `data_stream_options` after they have been created. -To modify an existing data stream's options, use the [put data stream options](???) API: +To modify an existing data stream's options, use the [put data stream options](wip) API: ```console PUT my-datastream-existing/_options @@ -76,7 +76,7 @@ PUT my-datastream-existing/_options ### Enable failure store via cluster setting [set-up-failure-store-cluster-setting] -If you have a large number of existing data streams you may want an easier way to control if failures should be redirected. Instead of enabling the failure store using the [put data stream options](???) API, you can instead configure a set of patterns in the [cluster settings](???) which will enable the failure store feature by default. +If you have a large number of existing data streams you may want an easier way to control if failures should be redirected. Instead of enabling the failure store using the [put data stream options](wip) API, you can instead configure a set of patterns in the [cluster settings](wip) which will enable the failure store feature by default. Configure a list of patterns using the `data_streams.failure_store.enabled` dynamic cluster setting. If a data stream matches a pattern in this setting and does not have the failure store explicitly disabled in its options, then the failure store will default to being enabled for that matching data stream. @@ -101,7 +101,7 @@ Once a failure store is enabled for a data stream it will begin redirecting docu Each data stream's failure store is made up of a list of indices that are dedicated to storing failed documents. These indices function much like a data stream's normal backing indices: There is a write index that accepts failed documents, they can be rolled over, and are automatically cleaned up over time subject to a lifecycle policy. -When a document bound for a data stream encounters a problem during its ingestion, the response is annotated with the `failure_store` field which describes how {{es}} responded to that problem. The `failure_store` field is present on both the [bulk](???) and [index](???) API responses when applicable. Clients can use this information to augment their behavior based on the response from {{es}}. +When a document bound for a data stream encounters a problem during its ingestion, the response is annotated with the `failure_store` field which describes how {{es}} responded to that problem. The `failure_store` field is present on both the [bulk](wip) and [index](wip) API responses when applicable. Clients can use this information to augment their behavior based on the response from {{es}}. Here we have a bulk operation that sends two documents. Both are writing to the `id` field which is mapped as a `long` field type. The first document will be accepted, but the second document would cause a failure because the value `invalid_text` cannot be parsed as a `long`. This second document will be redirected to the failure store: @@ -263,14 +263,14 @@ Once you have accumulated some failures, they can be searched much like a regula :::{warning} Documents redirected to the failure store in the event of a failed ingest pipeline will be stored in their original, unprocessed form. If an ingest pipeline normally redacts sensitive information from a document, then failed documents in their original, unprocessed form may contain sensitive information. -Furthermore, failed documents are likely to be structured differently than normal data in a data stream, and thus are not supported by [document level security](???) or [field level security](???). +Furthermore, failed documents are likely to be structured differently than normal data in a data stream, and thus are not supported by [document level security](wip) or [field level security](wip). -To limit visibility on potentially sensitive data, users require the [`read_failure_store`](???) index privilege for a data stream in order to search that data stream's failure store data. +To limit visibility on potentially sensitive data, users require the [`read_failure_store`](wip) index privilege for a data stream in order to search that data stream's failure store data. ::: Searching a data stream's failure store can be done by making use of the existing search APIs available in {{es}}. -To indicate that the search should be performed on failure store data, use the [index component selector syntax](???) to indicate which part of the data stream to target in the search operation. Appending the `::failures` suffix to the name of the data stream indicates that the operation should be performed against that data stream's failure store instead of its regular backing indices. +To indicate that the search should be performed on failure store data, use the [index component selector syntax](wip) to indicate which part of the data stream to target in the search operation. Appending the `::failures` suffix to the name of the data stream indicates that the operation should be performed against that data stream's failure store instead of its regular backing indices. :::::{tab-set} @@ -409,7 +409,7 @@ Failure documents have a uniform structure that is handled internally by {{es}}. : (`keyword`) The index that the document was being written to when it failed. `document.source` - : (unmapped object) The body of the document. This field is unmapped and unindexed to ensure failures are indexed reliably. If you need to include fields from the document source in your queries, use [runtime fields](???) on the search request. + : (unmapped object) The body of the document. This field is unmapped and unindexed to ensure failures are indexed reliably. If you need to include fields from the document source in your queries, use [runtime fields](wip) on the search request. `error` : (`object`) Information about the failure that prevented this document from being indexed. @@ -430,7 +430,7 @@ Failure documents have a uniform structure that is handled internally by {{es}}. : (`keyword`, optional array) If the failure occurred in an ingest pipeline, this will contain the list of pipelines that the document had visited up until the failure. `error.processor_tag` - : (`keyword`, optional) If the failure occurred in an ingest processor that is annotated with a [tag](???), the tag contents will be present here. + : (`keyword`, optional) If the failure occurred in an ingest processor that is annotated with a [tag](wip), the tag contents will be present here. `error.processor_type` : (`keyword`, optional) If the failure occurred in an ingest processor, this will contain the processor type. (e.g. `script`, `append`, `enrich`, etc.) @@ -442,7 +442,7 @@ Failure data can accumulate in a data stream over time. To help manage this accu ### Failure store rollover [manage-failure-store-rollover] -A data stream treats its failure store much like a secondary set of [backing indices](???). Multiple dedicated hidden indices serve search requests for the failure store, while one index acts as the current write index. You can use the [rollover](???) API to rollover the failure store. Much like the regular indices in a data stream, a new write index will be created in the failure store to accept new failure documents. +A data stream treats its failure store much like a secondary set of [backing indices](wip). Multiple dedicated hidden indices serve search requests for the failure store, while one index acts as the current write index. You can use the [rollover](wip) API to rollover the failure store. Much like the regular indices in a data stream, a new write index will be created in the failure store to accept new failure documents. ```console POST my-datastream::failures/_rollover @@ -463,7 +463,7 @@ POST my-datastream::failures/_rollover ### Failure store lifecycle [manage-failure-store-lifecycle] -Failure stores have their retention managed using an internal [data stream lifecycle](???). A thirty day (30d) retention is applied to failure store data. You can view the active lifecycle for a failure store index by calling the [get data stream API](???): +Failure stores have their retention managed using an internal [data stream lifecycle](wip). A thirty day (30d) retention is applied to failure store data. You can view the active lifecycle for a failure store index by calling the [get data stream API](wip): ```console GET _data_stream/my-datastream @@ -526,7 +526,7 @@ GET _data_stream/my-datastream 4. The retention is currently determined by the default. :::{note} -The default retention respects any maximum retention values. If [maximum retention](???) is configured lower than thirty days then the maximum retention will be used as the default value. +The default retention respects any maximum retention values. If [maximum retention](wip) is configured lower than thirty days then the maximum retention will be used as the default value. ::: You can update the default retention period for failure stores in your deployment by updating the `data_streams.lifecycle.retention.failures_default` cluster setting. Data streams that have no retention configured on their failure stores will use this value to determine their retention period. @@ -540,7 +540,7 @@ PUT _cluster/settings } ``` -You can also specify the failure store retention period for a data stream on its data stream options. These can be specified via the index template for new data streams, or via the [put data stream options](???) API for existing data streams. +You can also specify the failure store retention period for a data stream on its data stream options. These can be specified via the index template for new data streams, or via the [put data stream options](wip) API for existing data streams. ```console PUT _data_stream/my-datastream/_options @@ -558,7 +558,7 @@ PUT _data_stream/my-datastream/_options ### Add and remove from failure store [manage-failure-store-indices] -Failure stores support adding and removing indices from them using the [modify data stream](???) API. +Failure stores support adding and removing indices from them using the [modify data stream](wip) API. ```console POST _data_stream/_modify @@ -605,3 +605,5 @@ TBD ### Data remediation [recipes-remediation] TBD + +# WIP [wip] From 356cd6208788533ebc70ec404fbc1866eaee0363 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Tue, 6 May 2025 10:03:05 -0400 Subject: [PATCH 03/29] Fix wip links --- .../data-store/data-streams/failure-store.md | 28 ++++++++++--------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index beb1c87b2..f432859ed 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -47,7 +47,7 @@ After a matching data stream is created, its failure store will be enabled. Enabling the failure store via [index templates](../templates.md) can only affect data streams that are newly created. Existing data streams that use a template will not apply any changes to the template's `data_stream_options` after they have been created. -To modify an existing data stream's options, use the [put data stream options](wip) API: +To modify an existing data stream's options, use the [put data stream options](failure-store#wip) API: ```console PUT my-datastream-existing/_options @@ -76,7 +76,7 @@ PUT my-datastream-existing/_options ### Enable failure store via cluster setting [set-up-failure-store-cluster-setting] -If you have a large number of existing data streams you may want an easier way to control if failures should be redirected. Instead of enabling the failure store using the [put data stream options](wip) API, you can instead configure a set of patterns in the [cluster settings](wip) which will enable the failure store feature by default. +If you have a large number of existing data streams you may want an easier way to control if failures should be redirected. Instead of enabling the failure store using the [put data stream options](failure-store#wip) API, you can instead configure a set of patterns in the [cluster settings](failure-store#wip) which will enable the failure store feature by default. Configure a list of patterns using the `data_streams.failure_store.enabled` dynamic cluster setting. If a data stream matches a pattern in this setting and does not have the failure store explicitly disabled in its options, then the failure store will default to being enabled for that matching data stream. @@ -101,7 +101,7 @@ Once a failure store is enabled for a data stream it will begin redirecting docu Each data stream's failure store is made up of a list of indices that are dedicated to storing failed documents. These indices function much like a data stream's normal backing indices: There is a write index that accepts failed documents, they can be rolled over, and are automatically cleaned up over time subject to a lifecycle policy. -When a document bound for a data stream encounters a problem during its ingestion, the response is annotated with the `failure_store` field which describes how {{es}} responded to that problem. The `failure_store` field is present on both the [bulk](wip) and [index](wip) API responses when applicable. Clients can use this information to augment their behavior based on the response from {{es}}. +When a document bound for a data stream encounters a problem during its ingestion, the response is annotated with the `failure_store` field which describes how {{es}} responded to that problem. The `failure_store` field is present on both the [bulk](failure-store#wip) and [index](failure-store#wip) API responses when applicable. Clients can use this information to augment their behavior based on the response from {{es}}. Here we have a bulk operation that sends two documents. Both are writing to the `id` field which is mapped as a `long` field type. The first document will be accepted, but the second document would cause a failure because the value `invalid_text` cannot be parsed as a `long`. This second document will be redirected to the failure store: @@ -263,14 +263,14 @@ Once you have accumulated some failures, they can be searched much like a regula :::{warning} Documents redirected to the failure store in the event of a failed ingest pipeline will be stored in their original, unprocessed form. If an ingest pipeline normally redacts sensitive information from a document, then failed documents in their original, unprocessed form may contain sensitive information. -Furthermore, failed documents are likely to be structured differently than normal data in a data stream, and thus are not supported by [document level security](wip) or [field level security](wip). +Furthermore, failed documents are likely to be structured differently than normal data in a data stream, and thus are not supported by [document level security](failure-store#wip) or [field level security](failure-store#wip). -To limit visibility on potentially sensitive data, users require the [`read_failure_store`](wip) index privilege for a data stream in order to search that data stream's failure store data. +To limit visibility on potentially sensitive data, users require the [`read_failure_store`](failure-store#wip) index privilege for a data stream in order to search that data stream's failure store data. ::: Searching a data stream's failure store can be done by making use of the existing search APIs available in {{es}}. -To indicate that the search should be performed on failure store data, use the [index component selector syntax](wip) to indicate which part of the data stream to target in the search operation. Appending the `::failures` suffix to the name of the data stream indicates that the operation should be performed against that data stream's failure store instead of its regular backing indices. +To indicate that the search should be performed on failure store data, use the [index component selector syntax](failure-store#wip) to indicate which part of the data stream to target in the search operation. Appending the `::failures` suffix to the name of the data stream indicates that the operation should be performed against that data stream's failure store instead of its regular backing indices. :::::{tab-set} @@ -409,7 +409,7 @@ Failure documents have a uniform structure that is handled internally by {{es}}. : (`keyword`) The index that the document was being written to when it failed. `document.source` - : (unmapped object) The body of the document. This field is unmapped and unindexed to ensure failures are indexed reliably. If you need to include fields from the document source in your queries, use [runtime fields](wip) on the search request. + : (unmapped object) The body of the document. This field is unmapped and unindexed to ensure failures are indexed reliably. If you need to include fields from the document source in your queries, use [runtime fields](failure-store#wip) on the search request. `error` : (`object`) Information about the failure that prevented this document from being indexed. @@ -430,7 +430,7 @@ Failure documents have a uniform structure that is handled internally by {{es}}. : (`keyword`, optional array) If the failure occurred in an ingest pipeline, this will contain the list of pipelines that the document had visited up until the failure. `error.processor_tag` - : (`keyword`, optional) If the failure occurred in an ingest processor that is annotated with a [tag](wip), the tag contents will be present here. + : (`keyword`, optional) If the failure occurred in an ingest processor that is annotated with a [tag](failure-store#wip), the tag contents will be present here. `error.processor_type` : (`keyword`, optional) If the failure occurred in an ingest processor, this will contain the processor type. (e.g. `script`, `append`, `enrich`, etc.) @@ -442,7 +442,7 @@ Failure data can accumulate in a data stream over time. To help manage this accu ### Failure store rollover [manage-failure-store-rollover] -A data stream treats its failure store much like a secondary set of [backing indices](wip). Multiple dedicated hidden indices serve search requests for the failure store, while one index acts as the current write index. You can use the [rollover](wip) API to rollover the failure store. Much like the regular indices in a data stream, a new write index will be created in the failure store to accept new failure documents. +A data stream treats its failure store much like a secondary set of [backing indices](failure-store#wip). Multiple dedicated hidden indices serve search requests for the failure store, while one index acts as the current write index. You can use the [rollover](failure-store#wip) API to rollover the failure store. Much like the regular indices in a data stream, a new write index will be created in the failure store to accept new failure documents. ```console POST my-datastream::failures/_rollover @@ -463,7 +463,7 @@ POST my-datastream::failures/_rollover ### Failure store lifecycle [manage-failure-store-lifecycle] -Failure stores have their retention managed using an internal [data stream lifecycle](wip). A thirty day (30d) retention is applied to failure store data. You can view the active lifecycle for a failure store index by calling the [get data stream API](wip): +Failure stores have their retention managed using an internal [data stream lifecycle](failure-store#wip). A thirty day (30d) retention is applied to failure store data. You can view the active lifecycle for a failure store index by calling the [get data stream API](failure-store#wip): ```console GET _data_stream/my-datastream @@ -526,7 +526,7 @@ GET _data_stream/my-datastream 4. The retention is currently determined by the default. :::{note} -The default retention respects any maximum retention values. If [maximum retention](wip) is configured lower than thirty days then the maximum retention will be used as the default value. +The default retention respects any maximum retention values. If [maximum retention](failure-store#wip) is configured lower than thirty days then the maximum retention will be used as the default value. ::: You can update the default retention period for failure stores in your deployment by updating the `data_streams.lifecycle.retention.failures_default` cluster setting. Data streams that have no retention configured on their failure stores will use this value to determine their retention period. @@ -540,7 +540,7 @@ PUT _cluster/settings } ``` -You can also specify the failure store retention period for a data stream on its data stream options. These can be specified via the index template for new data streams, or via the [put data stream options](wip) API for existing data streams. +You can also specify the failure store retention period for a data stream on its data stream options. These can be specified via the index template for new data streams, or via the [put data stream options](failure-store#wip) API for existing data streams. ```console PUT _data_stream/my-datastream/_options @@ -558,7 +558,7 @@ PUT _data_stream/my-datastream/_options ### Add and remove from failure store [manage-failure-store-indices] -Failure stores support adding and removing indices from them using the [modify data stream](wip) API. +Failure stores support adding and removing indices from them using the [modify data stream](failure-store#wip) API. ```console POST _data_stream/_modify @@ -607,3 +607,5 @@ TBD TBD # WIP [wip] + +Placeholder link \ No newline at end of file From 6c18018dbf39826ca75d64e110d333fb33c7373d Mon Sep 17 00:00:00 2001 From: James Baiera Date: Tue, 6 May 2025 13:35:10 -0400 Subject: [PATCH 04/29] fix wip please? --- .../data-store/data-streams/failure-store.md | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index f432859ed..d9e9c99d4 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -47,7 +47,7 @@ After a matching data stream is created, its failure store will be enabled. Enabling the failure store via [index templates](../templates.md) can only affect data streams that are newly created. Existing data streams that use a template will not apply any changes to the template's `data_stream_options` after they have been created. -To modify an existing data stream's options, use the [put data stream options](failure-store#wip) API: +To modify an existing data stream's options, use the [put data stream options](#wip) API: ```console PUT my-datastream-existing/_options @@ -76,7 +76,7 @@ PUT my-datastream-existing/_options ### Enable failure store via cluster setting [set-up-failure-store-cluster-setting] -If you have a large number of existing data streams you may want an easier way to control if failures should be redirected. Instead of enabling the failure store using the [put data stream options](failure-store#wip) API, you can instead configure a set of patterns in the [cluster settings](failure-store#wip) which will enable the failure store feature by default. +If you have a large number of existing data streams you may want an easier way to control if failures should be redirected. Instead of enabling the failure store using the [put data stream options](#wip) API, you can instead configure a set of patterns in the [cluster settings](#wip) which will enable the failure store feature by default. Configure a list of patterns using the `data_streams.failure_store.enabled` dynamic cluster setting. If a data stream matches a pattern in this setting and does not have the failure store explicitly disabled in its options, then the failure store will default to being enabled for that matching data stream. @@ -101,7 +101,7 @@ Once a failure store is enabled for a data stream it will begin redirecting docu Each data stream's failure store is made up of a list of indices that are dedicated to storing failed documents. These indices function much like a data stream's normal backing indices: There is a write index that accepts failed documents, they can be rolled over, and are automatically cleaned up over time subject to a lifecycle policy. -When a document bound for a data stream encounters a problem during its ingestion, the response is annotated with the `failure_store` field which describes how {{es}} responded to that problem. The `failure_store` field is present on both the [bulk](failure-store#wip) and [index](failure-store#wip) API responses when applicable. Clients can use this information to augment their behavior based on the response from {{es}}. +When a document bound for a data stream encounters a problem during its ingestion, the response is annotated with the `failure_store` field which describes how {{es}} responded to that problem. The `failure_store` field is present on both the [bulk](#wip) and [index](#wip) API responses when applicable. Clients can use this information to augment their behavior based on the response from {{es}}. Here we have a bulk operation that sends two documents. Both are writing to the `id` field which is mapped as a `long` field type. The first document will be accepted, but the second document would cause a failure because the value `invalid_text` cannot be parsed as a `long`. This second document will be redirected to the failure store: @@ -263,14 +263,14 @@ Once you have accumulated some failures, they can be searched much like a regula :::{warning} Documents redirected to the failure store in the event of a failed ingest pipeline will be stored in their original, unprocessed form. If an ingest pipeline normally redacts sensitive information from a document, then failed documents in their original, unprocessed form may contain sensitive information. -Furthermore, failed documents are likely to be structured differently than normal data in a data stream, and thus are not supported by [document level security](failure-store#wip) or [field level security](failure-store#wip). +Furthermore, failed documents are likely to be structured differently than normal data in a data stream, and thus are not supported by [document level security](#wip) or [field level security](#wip). -To limit visibility on potentially sensitive data, users require the [`read_failure_store`](failure-store#wip) index privilege for a data stream in order to search that data stream's failure store data. +To limit visibility on potentially sensitive data, users require the [`read_failure_store`](#wip) index privilege for a data stream in order to search that data stream's failure store data. ::: Searching a data stream's failure store can be done by making use of the existing search APIs available in {{es}}. -To indicate that the search should be performed on failure store data, use the [index component selector syntax](failure-store#wip) to indicate which part of the data stream to target in the search operation. Appending the `::failures` suffix to the name of the data stream indicates that the operation should be performed against that data stream's failure store instead of its regular backing indices. +To indicate that the search should be performed on failure store data, use the [index component selector syntax](#wip) to indicate which part of the data stream to target in the search operation. Appending the `::failures` suffix to the name of the data stream indicates that the operation should be performed against that data stream's failure store instead of its regular backing indices. :::::{tab-set} @@ -409,7 +409,7 @@ Failure documents have a uniform structure that is handled internally by {{es}}. : (`keyword`) The index that the document was being written to when it failed. `document.source` - : (unmapped object) The body of the document. This field is unmapped and unindexed to ensure failures are indexed reliably. If you need to include fields from the document source in your queries, use [runtime fields](failure-store#wip) on the search request. + : (unmapped object) The body of the document. This field is unmapped and unindexed to ensure failures are indexed reliably. If you need to include fields from the document source in your queries, use [runtime fields](#wip) on the search request. `error` : (`object`) Information about the failure that prevented this document from being indexed. @@ -430,7 +430,7 @@ Failure documents have a uniform structure that is handled internally by {{es}}. : (`keyword`, optional array) If the failure occurred in an ingest pipeline, this will contain the list of pipelines that the document had visited up until the failure. `error.processor_tag` - : (`keyword`, optional) If the failure occurred in an ingest processor that is annotated with a [tag](failure-store#wip), the tag contents will be present here. + : (`keyword`, optional) If the failure occurred in an ingest processor that is annotated with a [tag](#wip), the tag contents will be present here. `error.processor_type` : (`keyword`, optional) If the failure occurred in an ingest processor, this will contain the processor type. (e.g. `script`, `append`, `enrich`, etc.) @@ -442,7 +442,7 @@ Failure data can accumulate in a data stream over time. To help manage this accu ### Failure store rollover [manage-failure-store-rollover] -A data stream treats its failure store much like a secondary set of [backing indices](failure-store#wip). Multiple dedicated hidden indices serve search requests for the failure store, while one index acts as the current write index. You can use the [rollover](failure-store#wip) API to rollover the failure store. Much like the regular indices in a data stream, a new write index will be created in the failure store to accept new failure documents. +A data stream treats its failure store much like a secondary set of [backing indices](#wip). Multiple dedicated hidden indices serve search requests for the failure store, while one index acts as the current write index. You can use the [rollover](#wip) API to rollover the failure store. Much like the regular indices in a data stream, a new write index will be created in the failure store to accept new failure documents. ```console POST my-datastream::failures/_rollover @@ -463,7 +463,7 @@ POST my-datastream::failures/_rollover ### Failure store lifecycle [manage-failure-store-lifecycle] -Failure stores have their retention managed using an internal [data stream lifecycle](failure-store#wip). A thirty day (30d) retention is applied to failure store data. You can view the active lifecycle for a failure store index by calling the [get data stream API](failure-store#wip): +Failure stores have their retention managed using an internal [data stream lifecycle](#wip). A thirty day (30d) retention is applied to failure store data. You can view the active lifecycle for a failure store index by calling the [get data stream API](#wip): ```console GET _data_stream/my-datastream @@ -526,7 +526,7 @@ GET _data_stream/my-datastream 4. The retention is currently determined by the default. :::{note} -The default retention respects any maximum retention values. If [maximum retention](failure-store#wip) is configured lower than thirty days then the maximum retention will be used as the default value. +The default retention respects any maximum retention values. If [maximum retention](#wip) is configured lower than thirty days then the maximum retention will be used as the default value. ::: You can update the default retention period for failure stores in your deployment by updating the `data_streams.lifecycle.retention.failures_default` cluster setting. Data streams that have no retention configured on their failure stores will use this value to determine their retention period. @@ -540,7 +540,7 @@ PUT _cluster/settings } ``` -You can also specify the failure store retention period for a data stream on its data stream options. These can be specified via the index template for new data streams, or via the [put data stream options](failure-store#wip) API for existing data streams. +You can also specify the failure store retention period for a data stream on its data stream options. These can be specified via the index template for new data streams, or via the [put data stream options](#wip) API for existing data streams. ```console PUT _data_stream/my-datastream/_options @@ -558,7 +558,7 @@ PUT _data_stream/my-datastream/_options ### Add and remove from failure store [manage-failure-store-indices] -Failure stores support adding and removing indices from them using the [modify data stream](failure-store#wip) API. +Failure stores support adding and removing indices from them using the [modify data stream](#wip) API. ```console POST _data_stream/_modify From 075abe26a898cbbde82092590877bf946e245eba Mon Sep 17 00:00:00 2001 From: James Baiera Date: Tue, 6 May 2025 15:44:45 -0400 Subject: [PATCH 05/29] fix --- .../data-store/data-streams/failure-store.md | 28 +++++++++---------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index d9e9c99d4..beb26bcf0 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -47,7 +47,7 @@ After a matching data stream is created, its failure store will be enabled. Enabling the failure store via [index templates](../templates.md) can only affect data streams that are newly created. Existing data streams that use a template will not apply any changes to the template's `data_stream_options` after they have been created. -To modify an existing data stream's options, use the [put data stream options](#wip) API: +To modify an existing data stream's options, use the [put data stream options](./failure-store.md) API: ```console PUT my-datastream-existing/_options @@ -76,7 +76,7 @@ PUT my-datastream-existing/_options ### Enable failure store via cluster setting [set-up-failure-store-cluster-setting] -If you have a large number of existing data streams you may want an easier way to control if failures should be redirected. Instead of enabling the failure store using the [put data stream options](#wip) API, you can instead configure a set of patterns in the [cluster settings](#wip) which will enable the failure store feature by default. +If you have a large number of existing data streams you may want an easier way to control if failures should be redirected. Instead of enabling the failure store using the [put data stream options](./failure-store.md) API, you can instead configure a set of patterns in the [cluster settings](./failure-store.md) which will enable the failure store feature by default. Configure a list of patterns using the `data_streams.failure_store.enabled` dynamic cluster setting. If a data stream matches a pattern in this setting and does not have the failure store explicitly disabled in its options, then the failure store will default to being enabled for that matching data stream. @@ -101,7 +101,7 @@ Once a failure store is enabled for a data stream it will begin redirecting docu Each data stream's failure store is made up of a list of indices that are dedicated to storing failed documents. These indices function much like a data stream's normal backing indices: There is a write index that accepts failed documents, they can be rolled over, and are automatically cleaned up over time subject to a lifecycle policy. -When a document bound for a data stream encounters a problem during its ingestion, the response is annotated with the `failure_store` field which describes how {{es}} responded to that problem. The `failure_store` field is present on both the [bulk](#wip) and [index](#wip) API responses when applicable. Clients can use this information to augment their behavior based on the response from {{es}}. +When a document bound for a data stream encounters a problem during its ingestion, the response is annotated with the `failure_store` field which describes how {{es}} responded to that problem. The `failure_store` field is present on both the [bulk](./failure-store.md) and [index](./failure-store.md) API responses when applicable. Clients can use this information to augment their behavior based on the response from {{es}}. Here we have a bulk operation that sends two documents. Both are writing to the `id` field which is mapped as a `long` field type. The first document will be accepted, but the second document would cause a failure because the value `invalid_text` cannot be parsed as a `long`. This second document will be redirected to the failure store: @@ -263,14 +263,14 @@ Once you have accumulated some failures, they can be searched much like a regula :::{warning} Documents redirected to the failure store in the event of a failed ingest pipeline will be stored in their original, unprocessed form. If an ingest pipeline normally redacts sensitive information from a document, then failed documents in their original, unprocessed form may contain sensitive information. -Furthermore, failed documents are likely to be structured differently than normal data in a data stream, and thus are not supported by [document level security](#wip) or [field level security](#wip). +Furthermore, failed documents are likely to be structured differently than normal data in a data stream, and thus are not supported by [document level security](./failure-store.md) or [field level security](./failure-store.md). -To limit visibility on potentially sensitive data, users require the [`read_failure_store`](#wip) index privilege for a data stream in order to search that data stream's failure store data. +To limit visibility on potentially sensitive data, users require the [`read_failure_store`](./failure-store.md) index privilege for a data stream in order to search that data stream's failure store data. ::: Searching a data stream's failure store can be done by making use of the existing search APIs available in {{es}}. -To indicate that the search should be performed on failure store data, use the [index component selector syntax](#wip) to indicate which part of the data stream to target in the search operation. Appending the `::failures` suffix to the name of the data stream indicates that the operation should be performed against that data stream's failure store instead of its regular backing indices. +To indicate that the search should be performed on failure store data, use the [index component selector syntax](./failure-store.md) to indicate which part of the data stream to target in the search operation. Appending the `::failures` suffix to the name of the data stream indicates that the operation should be performed against that data stream's failure store instead of its regular backing indices. :::::{tab-set} @@ -409,7 +409,7 @@ Failure documents have a uniform structure that is handled internally by {{es}}. : (`keyword`) The index that the document was being written to when it failed. `document.source` - : (unmapped object) The body of the document. This field is unmapped and unindexed to ensure failures are indexed reliably. If you need to include fields from the document source in your queries, use [runtime fields](#wip) on the search request. + : (unmapped object) The body of the document. This field is unmapped and unindexed to ensure failures are indexed reliably. If you need to include fields from the document source in your queries, use [runtime fields](./failure-store.md) on the search request. `error` : (`object`) Information about the failure that prevented this document from being indexed. @@ -430,7 +430,7 @@ Failure documents have a uniform structure that is handled internally by {{es}}. : (`keyword`, optional array) If the failure occurred in an ingest pipeline, this will contain the list of pipelines that the document had visited up until the failure. `error.processor_tag` - : (`keyword`, optional) If the failure occurred in an ingest processor that is annotated with a [tag](#wip), the tag contents will be present here. + : (`keyword`, optional) If the failure occurred in an ingest processor that is annotated with a [tag](./failure-store.md), the tag contents will be present here. `error.processor_type` : (`keyword`, optional) If the failure occurred in an ingest processor, this will contain the processor type. (e.g. `script`, `append`, `enrich`, etc.) @@ -442,7 +442,7 @@ Failure data can accumulate in a data stream over time. To help manage this accu ### Failure store rollover [manage-failure-store-rollover] -A data stream treats its failure store much like a secondary set of [backing indices](#wip). Multiple dedicated hidden indices serve search requests for the failure store, while one index acts as the current write index. You can use the [rollover](#wip) API to rollover the failure store. Much like the regular indices in a data stream, a new write index will be created in the failure store to accept new failure documents. +A data stream treats its failure store much like a secondary set of [backing indices](./failure-store.md). Multiple dedicated hidden indices serve search requests for the failure store, while one index acts as the current write index. You can use the [rollover](./failure-store.md) API to rollover the failure store. Much like the regular indices in a data stream, a new write index will be created in the failure store to accept new failure documents. ```console POST my-datastream::failures/_rollover @@ -463,7 +463,7 @@ POST my-datastream::failures/_rollover ### Failure store lifecycle [manage-failure-store-lifecycle] -Failure stores have their retention managed using an internal [data stream lifecycle](#wip). A thirty day (30d) retention is applied to failure store data. You can view the active lifecycle for a failure store index by calling the [get data stream API](#wip): +Failure stores have their retention managed using an internal [data stream lifecycle](./failure-store.md). A thirty day (30d) retention is applied to failure store data. You can view the active lifecycle for a failure store index by calling the [get data stream API](./failure-store.md): ```console GET _data_stream/my-datastream @@ -526,7 +526,7 @@ GET _data_stream/my-datastream 4. The retention is currently determined by the default. :::{note} -The default retention respects any maximum retention values. If [maximum retention](#wip) is configured lower than thirty days then the maximum retention will be used as the default value. +The default retention respects any maximum retention values. If [maximum retention](./failure-store.md) is configured lower than thirty days then the maximum retention will be used as the default value. ::: You can update the default retention period for failure stores in your deployment by updating the `data_streams.lifecycle.retention.failures_default` cluster setting. Data streams that have no retention configured on their failure stores will use this value to determine their retention period. @@ -540,7 +540,7 @@ PUT _cluster/settings } ``` -You can also specify the failure store retention period for a data stream on its data stream options. These can be specified via the index template for new data streams, or via the [put data stream options](#wip) API for existing data streams. +You can also specify the failure store retention period for a data stream on its data stream options. These can be specified via the index template for new data streams, or via the [put data stream options](./failure-store.md) API for existing data streams. ```console PUT _data_stream/my-datastream/_options @@ -558,12 +558,12 @@ PUT _data_stream/my-datastream/_options ### Add and remove from failure store [manage-failure-store-indices] -Failure stores support adding and removing indices from them using the [modify data stream](#wip) API. +Failure stores support adding and removing indices from them using the [modify data stream](./failure-store.md) API. ```console POST _data_stream/_modify { - "actions":[ + "actions":[ { "remove_backing_index": { <1> "data_stream": "my-datastream", From eac400518baacf1454fce36baccc859bc504feef Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 8 May 2025 15:43:39 -0400 Subject: [PATCH 06/29] Update manage-data/data-store/data-streams/failure-store.md Co-authored-by: Lee Hinman --- manage-data/data-store/data-streams/failure-store.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index beb26bcf0..92750a1c9 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -6,7 +6,7 @@ applies_to: # Failure store [failure-store] -Failure stores are a secondary set of indices inside a data stream dedicated to storing failed documents. Failed documents are any documents that cause ingest pipeline exceptions or have a structure that conflicts with a data stream's mappings. These failures normally cause the indexing operation to fail, returning the error message in the response. When a data stream's failure store is enabled, these failures are instead captured and persisted to be analysed later, returning a successful response to the client in the meantime. +Failure stores are a secondary set of indices inside a data stream dedicated to storing failed documents. Failed documents are any documents that cause ingest pipeline exceptions or have a structure that conflicts with a data stream's mappings. These failures normally cause the indexing operation to fail, returning the error message in the response. When a data stream's failure store is enabled, these failures are instead captured in a separate index and persisted to be analysed later, returning a successful response to the client in the meantime. ## Set up a data stream failure store [set-up-failure-store] From dfbefc1ff9d349e39fc9537ab8f1b6eec883faaa Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 8 May 2025 15:49:30 -0400 Subject: [PATCH 07/29] clarify that we do not redirect backpressure or version conflicts --- manage-data/data-store/data-streams/failure-store.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index 92750a1c9..17cefdf2e 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -6,7 +6,7 @@ applies_to: # Failure store [failure-store] -Failure stores are a secondary set of indices inside a data stream dedicated to storing failed documents. Failed documents are any documents that cause ingest pipeline exceptions or have a structure that conflicts with a data stream's mappings. These failures normally cause the indexing operation to fail, returning the error message in the response. When a data stream's failure store is enabled, these failures are instead captured in a separate index and persisted to be analysed later, returning a successful response to the client in the meantime. +Failure stores are a secondary set of indices inside a data stream dedicated to storing failed documents. Failed documents are any documents that cause ingest pipeline exceptions or have a structure that conflicts with a data stream's mappings. These failures normally cause the indexing operation to fail, returning the error message in the response. When a data stream's failure store is enabled, these failures are instead captured in a separate index and persisted to be analysed later, returning a successful response to the client in the meantime. Failure stores do not capture failures caused by backpressure or document version conflicts. These failures are always returned as-is since they warrant specific action by the client. ## Set up a data stream failure store [set-up-failure-store] @@ -588,7 +588,7 @@ POST _data_stream/_modify 5. The name of an index that should be added to the failure store. 6. Set `failure_store` to true to have the modify API target operate on the data stream's failure store. -This API gives you fine grained control over the indices in your failure store, allowing you to manage backup and restoration operations as well as isolate failure data for later remediation. +This API gives you fine-grained control over the indices in your failure store, allowing you to manage backup and restoration operations as well as isolate failure data for later remediation. ## Failure store recipes and use cases [recipes] From 20193676da9877a81e266699e82cb38bf6dd7a0b Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 8 May 2025 15:52:19 -0400 Subject: [PATCH 08/29] Update manage-data/data-store/data-streams/failure-store.md Co-authored-by: Lee Hinman --- manage-data/data-store/data-streams/failure-store.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index 17cefdf2e..fe989e77f 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -17,7 +17,7 @@ Each data stream has its own failure store that can be enabled to accept failure You can specify on a data stream's template if it should enable the failure store when it is first created. The `data_stream_options` field in a [template](../templates.md) contains the settings required to enable a data stream's failure store. :::{note} -Unlike the `settings` and `mappings` fields on an [index template](../templates.md) which are repeatedly applied to new data stream write indices over time, the `data_stream_options` section of a template is applied to a data stream only once when the data stream is first created. To configure existing data streams, use the put data stream options API. +Unlike the `settings` and `mappings` fields on an [index template](../templates.md) which are repeatedly applied to new data stream write indices on rollover, the `data_stream_options` section of a template is applied to a data stream only once when the data stream is first created. To configure existing data streams, use the put data stream options API. ::: To enable the failure store on a new data stream, enable it in the `data_stream_options` of the template: From 2847fe94c8bb03b2f771a1bcaf6ea161623c3dc0 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 8 May 2025 15:52:29 -0400 Subject: [PATCH 09/29] Update manage-data/data-store/data-streams/failure-store.md Co-authored-by: Lee Hinman --- manage-data/data-store/data-streams/failure-store.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index fe989e77f..2c6d274a9 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -61,7 +61,7 @@ PUT my-datastream-existing/_options 1. The failure store option will now be enabled. -The failure store redirection can be suspended using this API as well. When the failure store is disabled, only failed document redirection is halted. Any existing failure data in the data stream will remain until removed by deletion or by retention. +The failure store redirection can be suspended using this API as well. When the failure store is disabled, only failed document redirection is halted. Any existing failure data in the data stream will remain until removed by manual deletion or by retention. ```console PUT my-datastream-existing/_options From 840a8bef9d7495e55cdbd6229e1142f8baf4bd50 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 8 May 2025 15:57:46 -0400 Subject: [PATCH 10/29] suspended -> disabled --- manage-data/data-store/data-streams/failure-store.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index 2c6d274a9..6d9ff1567 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -61,7 +61,7 @@ PUT my-datastream-existing/_options 1. The failure store option will now be enabled. -The failure store redirection can be suspended using this API as well. When the failure store is disabled, only failed document redirection is halted. Any existing failure data in the data stream will remain until removed by manual deletion or by retention. +The failure store redirection can be disabled using this API as well. When the failure store is deactivated, only failed document redirection is halted. Any existing failure data in the data stream will remain until removed by manual deletion or by retention. ```console PUT my-datastream-existing/_options From 1591e7bbbf8f132dff0995b91b729006d3545885 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 8 May 2025 16:27:52 -0400 Subject: [PATCH 11/29] reword document.source docs --- manage-data/data-store/data-streams/failure-store.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index 6d9ff1567..b7e091a16 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -409,7 +409,7 @@ Failure documents have a uniform structure that is handled internally by {{es}}. : (`keyword`) The index that the document was being written to when it failed. `document.source` - : (unmapped object) The body of the document. This field is unmapped and unindexed to ensure failures are indexed reliably. If you need to include fields from the document source in your queries, use [runtime fields](./failure-store.md) on the search request. + : (unmapped object) The body of the original document. This field is unmapped and only present in the failure document's source. This prevents mapping conflicts in the failure store when redirecting failed documents. If you need to include fields from the original document's source in your queries, use [runtime fields](./failure-store.md) on the search request. `error` : (`object`) Information about the failure that prevented this document from being indexed. From cec305f90adc5f0426147e91cadcadf35b6140b1 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 8 May 2025 16:29:37 -0400 Subject: [PATCH 12/29] clarify default retention application --- manage-data/data-store/data-streams/failure-store.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index b7e091a16..d6e9dcca3 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -529,7 +529,7 @@ GET _data_stream/my-datastream The default retention respects any maximum retention values. If [maximum retention](./failure-store.md) is configured lower than thirty days then the maximum retention will be used as the default value. ::: -You can update the default retention period for failure stores in your deployment by updating the `data_streams.lifecycle.retention.failures_default` cluster setting. Data streams that have no retention configured on their failure stores will use this value to determine their retention period. +You can update the default retention period for failure stores in your deployment by updating the `data_streams.lifecycle.retention.failures_default` cluster setting. New and existing data streams that have no retention configured on their failure stores will use this value to determine their retention period. ```console PUT _cluster/settings From f1cb727ab544fe408ed0bd25b18debed997f3ea4 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 8 May 2025 16:32:50 -0400 Subject: [PATCH 13/29] Add placeholder link for put data streams api --- manage-data/data-store/data-streams/failure-store.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index d6e9dcca3..9c9413905 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -17,7 +17,7 @@ Each data stream has its own failure store that can be enabled to accept failure You can specify on a data stream's template if it should enable the failure store when it is first created. The `data_stream_options` field in a [template](../templates.md) contains the settings required to enable a data stream's failure store. :::{note} -Unlike the `settings` and `mappings` fields on an [index template](../templates.md) which are repeatedly applied to new data stream write indices on rollover, the `data_stream_options` section of a template is applied to a data stream only once when the data stream is first created. To configure existing data streams, use the put data stream options API. +Unlike the `settings` and `mappings` fields on an [index template](../templates.md) which are repeatedly applied to new data stream write indices on rollover, the `data_stream_options` section of a template is applied to a data stream only once when the data stream is first created. To configure existing data streams, use the put [data stream options API](./failure-store.md). ::: To enable the failure store on a new data stream, enable it in the `data_stream_options` of the template: From 28287fe40db256eb68c0f7dfa57162fa5069bd53 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 8 May 2025 17:10:31 -0400 Subject: [PATCH 14/29] Mention flags in the intro --- manage-data/data-store/data-streams/failure-store.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index 9c9413905..761c0de09 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -6,7 +6,9 @@ applies_to: # Failure store [failure-store] -Failure stores are a secondary set of indices inside a data stream dedicated to storing failed documents. Failed documents are any documents that cause ingest pipeline exceptions or have a structure that conflicts with a data stream's mappings. These failures normally cause the indexing operation to fail, returning the error message in the response. When a data stream's failure store is enabled, these failures are instead captured in a separate index and persisted to be analysed later, returning a successful response to the client in the meantime. Failure stores do not capture failures caused by backpressure or document version conflicts. These failures are always returned as-is since they warrant specific action by the client. +Failure stores are a secondary set of indices inside a data stream dedicated to storing failed documents. Failed documents are any documents that cause ingest pipeline exceptions or have a structure that conflicts with a data stream's mappings. These failures normally cause the indexing operation to fail, returning the error message in the response. + +When a data stream's failure store is enabled, these failures are instead captured in a separate index and persisted to be analysed later. Clients receive a successful response with a flag indicating the failure was redirected. Failure stores do not capture failures caused by backpressure or document version conflicts. These failures are always returned as-is since they warrant specific action by the client. ## Set up a data stream failure store [set-up-failure-store] From 08b2ac31923ebc65010a7a59e17e881b846ae867 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 8 May 2025 17:15:12 -0400 Subject: [PATCH 15/29] Note lazy creation --- manage-data/data-store/data-streams/failure-store.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index 761c0de09..c65bdd5fd 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -101,7 +101,7 @@ The failure store is meant to ease the burden of detecting and handling failures Once a failure store is enabled for a data stream it will begin redirecting documents that fail due to common ingestion problems instead of returning errors in write operations. Clients are notified in a non-intrusive way when a document is redirected to the failure store. -Each data stream's failure store is made up of a list of indices that are dedicated to storing failed documents. These indices function much like a data stream's normal backing indices: There is a write index that accepts failed documents, they can be rolled over, and are automatically cleaned up over time subject to a lifecycle policy. +Each data stream's failure store is made up of a list of indices that are dedicated to storing failed documents. These failure indices function much like a data stream's normal backing indices: There is a write index that accepts failed documents, they can be rolled over, and are automatically cleaned up over time subject to a lifecycle policy. Failure indices are lazily created the first time they are needed to store a failed document. When a document bound for a data stream encounters a problem during its ingestion, the response is annotated with the `failure_store` field which describes how {{es}} responded to that problem. The `failure_store` field is present on both the [bulk](./failure-store.md) and [index](./failure-store.md) API responses when applicable. Clients can use this information to augment their behavior based on the response from {{es}}. From e978f6e9974fa9a565a458b5abe5013a9c6e15c5 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Fri, 9 May 2025 01:42:24 -0400 Subject: [PATCH 16/29] edit configuration section for more clarity --- .../data-store/data-streams/failure-store.md | 30 +++++++++++++++---- 1 file changed, 24 insertions(+), 6 deletions(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index c65bdd5fd..1534bd666 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -52,7 +52,7 @@ Enabling the failure store via [index templates](../templates.md) can only affec To modify an existing data stream's options, use the [put data stream options](./failure-store.md) API: ```console -PUT my-datastream-existing/_options +PUT _data_stream/my-datastream-existing/_options { "failure_store": { "enabled": true <1> @@ -66,7 +66,7 @@ PUT my-datastream-existing/_options The failure store redirection can be disabled using this API as well. When the failure store is deactivated, only failed document redirection is halted. Any existing failure data in the data stream will remain until removed by manual deletion or by retention. ```console -PUT my-datastream-existing/_options +PUT _data_stream/my-datastream-existing/_options { "failure_store": { "enabled": false <1> @@ -78,9 +78,7 @@ PUT my-datastream-existing/_options ### Enable failure store via cluster setting [set-up-failure-store-cluster-setting] -If you have a large number of existing data streams you may want an easier way to control if failures should be redirected. Instead of enabling the failure store using the [put data stream options](./failure-store.md) API, you can instead configure a set of patterns in the [cluster settings](./failure-store.md) which will enable the failure store feature by default. - -Configure a list of patterns using the `data_streams.failure_store.enabled` dynamic cluster setting. If a data stream matches a pattern in this setting and does not have the failure store explicitly disabled in its options, then the failure store will default to being enabled for that matching data stream. +If you have a large number of existing data streams you may want to enable their failure stores in one place. Instead of updating each of their options individually, set `data_streams.failure_store.enabled` to a list of index patterns in the [cluster settings](./failure-store.md). Any data streams that match one of these patterns will operate with their failure store enabled. ```console PUT _cluster/settings @@ -90,9 +88,29 @@ PUT _cluster/settings } } ``` - 1. Indices that match `my-datastream-*` or `logs-*` will redirect failures to the failure store unless explicitly disabled. +Matching data streams will ignore this configuration if the failure store is explicitly enabled or disabled in their [data stream options](./failure-store.md). + +```console +PUT _cluster/settings +{ + "persistent" : { + "data_streams.failure_store.enabled" : [ "my-datastream-*", "logs-*" ] <1> + } +} +``` +```console +PUT _data_stream/my-datastream-1/_options +{ + "failure_store": { + "enabled": false <2> + } +} +``` +1. Enabling the failure stores for `my-datastream-*` and `logs-*` +2. The failure store for `my-datastream-1` is disabled even though it matches `my-datastream-*`. + ## Using a failure store [use-failure-store] The failure store is meant to ease the burden of detecting and handling failures when ingesting data to {{es}}. Clients are less likely to encounter unrecoverable failures when writing documents, and developers are more easily able to troubleshoot faulty pipelines and mappings. From 2db178a1e0dc2e3bab149da96733c8b89a6865ef Mon Sep 17 00:00:00 2001 From: James Baiera Date: Fri, 9 May 2025 01:48:01 -0400 Subject: [PATCH 17/29] mention override directly --- manage-data/data-store/data-streams/failure-store.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index 1534bd666..fa2fb0b35 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -109,7 +109,7 @@ PUT _data_stream/my-datastream-1/_options } ``` 1. Enabling the failure stores for `my-datastream-*` and `logs-*` -2. The failure store for `my-datastream-1` is disabled even though it matches `my-datastream-*`. +2. The failure store for `my-datastream-1` is disabled even though it matches `my-datastream-*`. The data stream options override the cluster setting. ## Using a failure store [use-failure-store] From 87f3a471f5c3b9b2e3423d6c2151c28722a65712 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Wed, 28 May 2025 15:47:55 -0400 Subject: [PATCH 18/29] Add recipes to the failure store narrative docs --- .../data-store/data-streams/failure-store.md | 1391 ++++++++++++++++- ...ailure_store_alerting_create_data_view.png | Bin 0 -> 153959 bytes ...ent_failure_store_alerting_create_rule.png | Bin 0 -> 150846 bytes ...-management_failure_store_alerting_dsl.png | Bin 0 -> 300586 bytes ...management_failure_store_alerting_esql.png | Bin 0 -> 126803 bytes ...nagement_failure_store_alerting_finish.png | Bin 0 -> 98604 bytes ...-management_failure_store_alerting_kql.png | Bin 0 -> 271556 bytes 7 files changed, 1381 insertions(+), 10 deletions(-) create mode 100644 manage-data/images/elasticsearch-reference-management_failure_store_alerting_create_data_view.png create mode 100644 manage-data/images/elasticsearch-reference-management_failure_store_alerting_create_rule.png create mode 100644 manage-data/images/elasticsearch-reference-management_failure_store_alerting_dsl.png create mode 100644 manage-data/images/elasticsearch-reference-management_failure_store_alerting_esql.png create mode 100644 manage-data/images/elasticsearch-reference-management_failure_store_alerting_finish.png create mode 100644 manage-data/images/elasticsearch-reference-management_failure_store_alerting_kql.png diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index fa2fb0b35..0ebd10af6 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -411,6 +411,8 @@ Because the `document.source` field is unmapped, it is absent from the SQL resul :::: ::::: +### Failure document structure [use-failure-store-document] + Failure documents have a uniform structure that is handled internally by {{es}}. `@timestamp` @@ -454,7 +456,242 @@ Failure documents have a uniform structure that is handled internally by {{es}}. `error.processor_type` : (`keyword`, optional) If the failure occurred in an ingest processor, this will contain the processor type. (e.g. `script`, `append`, `enrich`, etc.) - + +#### Failure document source [use-failure-store-document-source] + +The contents of a failure's `document` field is dependent on when the failure occurred in ingestion. When sending data to a data stream, documents can fail in two different phases: during an ingest pipeline and during indexing. +1. Documents that fail during an ingest pipeline will store the source of the document as it was originally sent to {{es}}. Changes from pipelines are discarded before redirecting the failure. +2. Documents that fail during indexing will store the source of the document as it was during the index operation. Any changes from pipelines will be reflected in the source of the document that is redirected. + +To help demonstrate the differences between these kinds of failures, we will use the following pipeline and template definition. + +```console +PUT _ingest/pipeline/my-datastream-example-pipeline +{ + "processors": [ + { + "set": { <1> + "override": false, + "field": "@timestamp", + "copy_from": "_ingest.timestamp" + } + }, + { + "set": { <2> + "field": "published", + "copy_from": "data" + } + } + ] +} +``` +1. We use this processor to add a `@timestamp` to the document if one is missing. +2. A simple processor that copies the `data` field to the `published` field. + +```console +PUT _index_template/my-datastream-example-template +{ + "index_patterns": ["my-datastream-ingest*"], + "data_stream": {}, + "template": { + "settings": { + "index.default_pipeline": "my-datastream-example-pipeline" // Calling the pipeline by default. + }, + "mappings": { + "properties": { + "published": { // A field of type long to hold our result. + "type": "long" + } + } + }, + "data_stream_options": { + "failure_store": { + "enabled": true // Failure store is enabled. + } + } + } +} +``` + +During ingestion, documents are first processed by any applicable ingest pipelines. This process modifies a copy of the document and only saves the changes to the original document after all pipelines have completed. If a document is sent to the failure store because of a failure during an ingest pipeline, any changes to the document made by the pipelines it has been through will be discarded before redirecting the failure. This means that the document will be in the same state as when it was originally sent by the client. This has the benefit of being able to see the document before any pipelines have run on it, and allows for the original document to be used in simulate operations to further troubleshoot any problems in the ingest pipeline. + +Using the pipeline and template defined above, we will send a document that is missing a required field for the pipeline. The document will fail: + +```console +POST my-datastream-ingest/_doc +{ + "random": 42 // Not the field we're looking for. +} +``` + +```console-result +{ + "_index": ".fs-my-datastream-ingest-2025.05.09-000002", + "_id": "eXS-tpYBwrYNjPmat9Cx", + "_version": 1, + "result": "created", + "_shards": { + "total": 1, + "successful": 1, + "failed": 0 + }, + "_seq_no": 0, + "_primary_term": 1, + "failure_store": "used" // The document failed and went to the failure store. +} +``` + +Inspecting the corresponding failure document will show the document in its original form as it was sent to {{es}}. + +```console +GET my-datastream-ingest::failures/_search +``` + +```console-result +{ + "took": 0, + "timed_out": false, + "_shards": { + "total": 1, + "successful": 1, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 1, + "relation": "eq" + }, + "max_score": 1, + "hits": [ + { + "_index": ".fs-my-datastream-ingest-2025.05.09-000002", + "_id": "eXS-tpYBwrYNjPmat9Cx", + "_score": 1, + "_source": { + "@timestamp": "2025-05-09T20:31:13.759Z", + "document": { <1> + "index": "my-datastream-ingest", + "source": { + "random": 42 + } + }, + "error": { + "type": "illegal_argument_exception", + "message": "field [data] not present as part of path [data]", <2> + "stack_trace": """j.l.IllegalArgumentException: field [data] not present as part of path [data] + at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) + at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) + ... 14 more +""", + "pipeline_trace": [ + "my-datastream-example-pipeline" + ], + "pipeline": "my-datastream-example-pipeline", + "processor_type": "set" + } + } + } + ] + } +} +``` +1. The `document` field shows the state of the document is from before any pipeline executions. +2. The pipeline failed after the timestamp would have been added. + +We can see that the document failed on the second processor in the pipeline. The first processor would have added a `@timestamp` field. Since the pipeline failed, we find that it has no `@timestamp` field added because it did not save any changes from before the pipeline failed. + +The second place failures can occur is during indexing. After the documents have been processed by any applicable pipelines, they are parsed using the index mappings before being indexed into the shard. If a document is sent to the failure store due to a failure in this process, then it will be stored as it was after any ingestion had occurred. This is because the original document is overwritten by the ingest pipeline changes by this point. This has the benefit of being able to see what the document looked like during the mapping and indexing phase of the write operation. + +Building on the example above, we send a document that has a text value where we expect a numeric value: + +```console +POST my-datastream-ingest/_doc +{ + "data": "this field is invalid" <1> +} +``` +1. The mappings above expect this field to have been a numeric value. + +```console-result +{ + "_index": ".fs-my-datastream-ingest-2025.05.09-000002", + "_id": "sXTVtpYBwrYNjPmaFNAY", + "_version": 1, + "result": "created", + "_shards": { + "total": 1, + "successful": 1, + "failed": 0 + }, + "_seq_no": 0, + "_primary_term": 1, + "failure_store": "used" <1> +} +``` +1. The document failed and was sent to the failure store. + +If we obtain the corresponding failure document, we can see that the document stored has had the default pipeline applied to it. + +```console +GET my-datastream-ingest::failures/_search +``` + +```console-result +{ + "took": 0, + "timed_out": false, + "_shards": { + "total": 1, + "successful": 1, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 1, + "relation": "eq" + }, + "max_score": 1, + "hits": [ + { + "_index": ".fs-my-datastream-ingest-2025.05.09-000002", + "_id": "sXTVtpYBwrYNjPmaFNAY", + "_score": 1, + "_source": { + "@timestamp": "2025-05-09T20:55:38.943Z", + "document": { <1> + "id": "sHTVtpYBwrYNjPmaEdB5", + "index": "my-datastream-ingest", + "source": { + "@timestamp": "2025-05-09T20:55:38.362486755Z", + "data": "this field is invalid", + "published": "this field is invalid" + } + }, + "error": { + "type": "document_parsing_exception", <2> + "message": "[1:91] failed to parse field [published] of type [long] in document with id 'sHTVtpYBwrYNjPmaEdB5'. Preview of field's value: 'this field is invalid'", + "stack_trace": """o.e.i.m.DocumentParsingException: [1:91] failed to parse field [published] of type [long] in document with id 'sHTVtpYBwrYNjPmaEdB5'. Preview of field's value: 'this field is invalid' + at o.e.i.m.FieldMapper.rethrowAsDocumentParsingException(FieldMapper.java:241) + at o.e.i.m.FieldMapper.parse(FieldMapper.java:194) + ... 24 more +Caused by: j.l.IllegalArgumentException: For input string: "this field is invalid" + at o.e.x.s.AbstractXContentParser.toLong(AbstractXContentParser.java:189) + at o.e.x.s.AbstractXContentParser.longValue(AbstractXContentParser.java:210) + ... 31 more +""" + } + } + } + ] + } +} +``` +1. The `document` field reflects the document after the ingest pipeline has run. +2. The document failed to be indexed because of a mapping mismatch. + +The `document` field attempts to show the effective input to whichever process led to the failure occurring. This gives you all the information you need to reproduce the problem. ## Manage a data stream's failure store [manage-failure-store] @@ -612,20 +849,1154 @@ This API gives you fine-grained control over the indices in your failure store, ## Failure store recipes and use cases [recipes] -TBD +When something goes wrong during ingestion it is often not an isolated event. Included for your convenience are some examples of how you can use the failure store to quickly respond to ingestion failures and get your indexing back on track. -### Troubleshooting ingest pipelines effectively [recipes-ingest-troubleshoot] +### Troubleshooting nested ingest pipelines [recipes-nested-ingest-troubleshoot] -TBD +When a document fails in an ingest pipeline it can be difficult to figure out exactly what when wrong and where. When these failures are captured by the failure store during this part of the ingestion process, they will contain additional debugging information. Failed documents will note the type of processor and which pipeline was executing when the failure occurred. Failed documents will also contain a pipeline trace which keeps track of any nested pipeline calls that the document was in at time of failure. -### Alerting on failed ingestion [recipes-alerting] +To demonstrate this, we will follow a failed document through an unfamiliar data stream and ingest pipeline: +```console +POST my-datastream-ingest/_doc +{ + "@timestamp": "2025-04-21T00:00:00Z", + "important": { + "info": "The rain in Spain falls mainly on the plain" + } +} +``` + +```console-result +{ + "_index": ".fs-my-datastream-ingest-2025.05.09-000001", + "_id": "F3S3s5YBwrYNjPmayMr9", + "_version": 1, + "result": "created", + "_shards": { + "total": 1, + "successful": 1, + "failed": 0 + }, + "_seq_no": 2, + "_primary_term": 1, + "failure_store": "used" // The document was sent to the failure store +} +``` -TBD +Now we search the failure store to check the failure document to see what went wrong. +```console +GET my-datastream-ingest::failures/_search +``` -### Data remediation [recipes-remediation] +```console-result +{ + "took": 0, + "timed_out": false, + "_shards": { + "total": 1, + "successful": 1, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 1, + "relation": "eq" + }, + "max_score": 1, + "hits": [ + { + "_index": ".fs-my-datastream-ingest-2025.05.09-000001", + "_id": "F3S3s5YBwrYNjPmayMr9", + "_score": 1, + "_source": { + "@timestamp": "2025-05-09T06:24:48.381Z", + "document": { + "index": "my-datastream-ingest", + "source": { // When an ingest pipeline fails, the document stored is what was originally sent to the cluster. + "important": { + "info": "The rain in Spain falls mainly on the plain" // The important info that we failed to find was originally present on the document. + }, + "@timestamp": "2025-04-21T00:00:00Z" + } + }, + "error": { + "type": "illegal_argument_exception", + "message": "field [info] not present as part of path [important.info]", // The info field was not present when the failure occurred. + "stack_trace": """j.l.IllegalArgumentException: field [info] not present as part of path [important.info] + at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) + at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) + ... 19 more +""", + "pipeline_trace": [ // The first pipeline called the second pipeline. + "ingest-step-1", + "ingest-step-2" + ], + "pipeline": "ingest-step-2", // The document failed in the second pipeline. + "processor_type": "set" // It failed in the pipeline's set processor. + } + } + } + ] + } +} +``` + +Despite not knowing the pipelines beforehand, we have some places to start looking. The `ingest-step-2` pipeline cannot find the `important.info` field despite it being present on the document that was sent to the cluster. If we pull that pipeline definition we find the following: + +```console +GET _ingest/pipeline/ingest-step-2 +``` + +```console-result +{ + "ingest-step-2": { + "processors": [ + { + "set": { // There is only one processor here. + "field": "copy.info", + "copy_from": "important.info" // This field was missing from the document at this point. + } + } + ] + } +} +``` + +There is only a set processor in the `ingest-step-2` pipeline so this is likely not where the root problem is. Remembering the `pipeline_trace` field on the failure we find that `ingest-step-1` was the original pipeline called for this document. It is likely the data stream's default pipeline. Pulling its definition we find the following: + +```console +GET _ingest/pipeline/ingest-step-1 +``` + +```console-result +{ + "ingest-step-1": { + "processors": [ + { + "remove": { + "field": "important.info" // A remove processor that is incorrectly getting rid of our important field. + } + }, + { + "pipeline": { + "name": "ingest-step-2" // The call to the second pipeline. + } + } + ] + } +} +``` + +We find a remove processor in the first pipeline that is the root cause of the problem! The pipeline should be updated to not remove important data, or the downstream pipeline should be changed to not expect the important data to be always present. -TBD +### Troubleshooting complicated ingest pipelines [recipes-complicated-ingest-troubleshoot] -# WIP [wip] +Ingest processors can be labeled with [tags](./failure-store.md). These tags are user provided information that names or describes the processor's purpose in the pipeline. When documents are redirected to the failure store due to a processor issue, they capture the tag from the processor in which the failure occurred if it exists. Because of this, it is a good practice to tag the processors in your pipeline so that the location of a failure can be identified quickly. + +Here we have a needlessly complicated pipeline. It is made up of several set and remove processors. Beneficially, they are all tagged with descriptive names. +```console +PUT _ingest/pipeline/complicated-processor +{ + "processors": [ + { + "set": { + "tag": "initialize counter", + "field": "counter", + "value": "1" + } + }, + { + "set": { + "tag": "copy counter to new", + "field": "new_counter", + "copy_from": "counter" + } + }, + { + "remove": { + "tag": "remove old counter", + "field": "counter" + } + }, + { + "set": { + "tag": "transfer counter back", + "field": "counter", + "copy_from": "new_counter" + } + }, + { + "remove": { + "tag": "remove counter again", + "field": "counter" + } + }, + { + "set": { + "tag": "copy to new counter again", + "field": "new_counter", + "copy_from": "counter" + } + } + ] +} +``` + +We ingest some data and find that it was sent to the failure store +```console +POST my-datastream-ingest/_doc?pipeline=complicated-processor +{ + "@timestamp": "2025-04-21T00:00:00Z", + "counter_name": "test" +} +``` + +```console-result +{ + "_index": ".fs-my-datastream-ingest-2025.05.09-000001", + "_id": "HnTJs5YBwrYNjPmaFcri", + "_version": 1, + "result": "created", + "_shards": { + "total": 1, + "successful": 1, + "failed": 0 + }, + "_seq_no": 1, + "_primary_term": 1, + "failure_store": "used" +} +``` + +Upon checking the failure, we can quickly identify the tagged processor that caused the problem +```console +GET my-datastream-ingest::failures/_search +``` + +```console-result +{ + "took": 0, + "timed_out": false, + "_shards": { + "total": 1, + "successful": 1, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 1, + "relation": "eq" + }, + "max_score": 1, + "hits": [ + { + "_index": ".fs-my-datastream-ingest-2025.05.09-000001", + "_id": "HnTJs5YBwrYNjPmaFcri", + "_score": 1, + "_source": { + "@timestamp": "2025-05-09T06:41:24.775Z", + "document": { + "index": "my-datastream-ingest", + "source": { + "@timestamp": "2025-04-21T00:00:00Z", + "counter_name": "test" + } + }, + "error": { + "type": "illegal_argument_exception", + "message": "field [counter] not present as part of path [counter]", + "stack_trace": """j.l.IllegalArgumentException: field [counter] not present as part of path [counter] + at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) + at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) + ... 14 more +""", + "pipeline_trace": [ + "complicated-processor" + ], + "pipeline": "complicated-processor", + "processor_type": "set", // Helpful, but which set processor on the pipeline could it be? + "processor_tag": "copy to new counter again" // The tag of the exact processor that it failed on. + } + } + } + ] + } +} +``` + +Without tags in place it would not be as clear where in the pipeline we encountered the problem. Tags provide a unique identifier for a processor that can be quickly referenced in case of an ingest failure. + +### Alerting on failed ingestion [recipes-alerting] + +Since failure stores can be searched just like a normal data stream, we can use them as inputs to [alerting rules](./failure-store.md) in Kibana. Here is a simple alerting example to trigger on more than ten failures in the last five minutes for a data stream: + +:::::{stepper} + +::::{step} Create a failure store data view +If you want to use KQL or Lucene query types, you should first create a data view for your failure store data. +If you plan to use {{esql}} or the Query DSL query types, this step is not required. + +Navigate to the data view page in Kibana and add a new data view. Set the index pattern to your failure store using the selector syntax. + +:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_create_data_view.png +:alt: create a data view using the failure store syntax in the index name +::: +:::: + +::::{step} Create new rule +Navigate to Management / Alerts and Insights / Rules. Create a new rule. Choose the Elasticsearch query option. + +:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_create_rule.png +:alt: create a new alerting rule and select the elasticsearch query option +::: +:::: + +::::{step} Pick your query type +Choose which query type you wish to use + +For KQL/Lucene queries, reference the data view that contains your failure store. + +:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_kql.png +:alt: use the data view created in the previous step as the input to the kql query +::: + +For Query DSL queries, use the `::failures` suffix on your data stream name. + +:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_dsl.png +:alt: use the ::failures suffix in the data stream name in the query dsl +::: + +For {{esql}} queries, use the `::failures` suffix on your data stream name in the `FROM` command. + +:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_esql.png +:alt: use the ::failures suffix in the data stream name in the from command +::: +:::: + +::::{step} Test +Configure schedule, actions, and details of the alert before saving the rule. + +:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_finish.png +:alt: complete the rule configuration and save it +::: +:::: + +::::{step} Done +:::: + +::::: + +### Data remediation [recipes-remediation] + +If you've encountered a long span of ingestion failures you may find that a sizeable gap of events has appeared in your data stream. If the failure store is enabled, the documents that should fill those gaps would be tucked away in the data stream's failure store. Because failure stores are made up of regular indices and the failure documents contain the document source that failed, the failure documents can often times be replayed into your production data streams. + +::::{warning} +Care should be taken when replaying data into a data stream from a failure store. Any failures during the replay process may generate new failures in the failure store which can duplicate and obscure the original events. +:::: + +We recommend a few best practices for remediating failure data. + +**Separate your failures beforehand.** As described in the [failure document source](#use-failure-store-document-source) section above, failure documents are structured differently depending on when the document failed during ingestion. We recommend to separate documents by ingest pipeline failures and indexing failures at minimum. Ingest pipeline failures often need to have the original pipeline re-executed, while index failures should skip any pipelines. Further separating failures by index or specific failure type may also be beneficial. + +**Perform a failure store rollover.** Consider rolling over the failure store before attempting to remediate failures. This will create a new failure index that will collect any new failures during the remediation process. + +**Use an ingest pipeline to convert failure documents back into their original document.** Failure documents store failure information along with the document that failed ingestion. The first step for remediating documents should be to use an ingest pipeline to extract the original source from the failure document and discard any other info on it. + +**Simulate first to avoid repeat failures.** If you must execute a pipeline as part of your remediation process, it is best to simulate the pipeline against the failure first. This will catch any unforeseen issues that may fail the document a second time. Remember, ingest pipeline failures will capture the document before an ingest pipeline was applied to it, which can further complicate remediation when a failure document becomes nested inside a new failure. + +#### Remediating ingest node failures [recipes-remediation-ingest] + +Failures that occurred during an ingest processor will be stored as they were before any pipelines were executed. To replay the document into the data stream we will need to rerun any applicable pipelines for the document. + +:::::{stepper} + +::::{step} Separate out which failures to replay + +Start off by constructing a query that can be used to consistently identify which failures will be remediated. + +```console +POST my-datastream-ingest-example::failures/_search +{ + "query": { + "bool": { + "must": [ + { + "exists": { <1> + "field": "error.pipeline" + } + }, + { + "match": { <2> + "document.index": "my-datastream-ingest-example" + } + }, + { + "match": { <3> + "error.type": "illegal_argument_exception" + } + }, + { + "range": { <4> + "@timestamp": { + "gt": "2025-05-01T00:00:00Z", + "lte": "2025-05-02T00:00:00Z" + } + } + } + ] + } + } +} +``` +1. Require the `error.pipeline` field to exist. This filters to ingest pipeline failures only. +2. Filter on the data stream name to remediate documents headed for a specific index. +3. Further narrow which kind of failure you are attempting to remediate. In this example we are targeting a specific type of error. +4. Filter on timestamp to only retrieve failures before a certain point in time. This provides a stable set of documents. + +Take note of the documents that are returned. We can use these to simulate that our remediation logic makes sense +```console-result +{ + "took": 14, + "timed_out": false, + "_shards": { + "total": 2, + "successful": 2, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 1, + "relation": "eq" + }, + "max_score": 2.575364, + "hits": [ + { + "_index": ".fs-my-datastream-ingest-example-2025.05.16-000001", + "_id": "cOnR2ZYByIwDXH-g6GpR", + "_score": 2.575364, + "_source": { + "@timestamp": "2025-05-01T15:58:53.522Z", <1> + "document": { + "index": "my-datastream-ingest-example", + "source": { + "@timestamp": "2025-05-01T00:00:00Z", + "data": { + "counter": 42 <2> + } + } + }, + "error": { + "type": "illegal_argument_exception", + "message": "field [id] not present as part of path [data.id]", <3> + "stack_trace": """j.l.IllegalArgumentException: field [id] not present as part of path [data.id] + at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) + at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) + ... 14 more +""", + "pipeline_trace": [ + "my-datastream-default-pipeline" + ], + "pipeline": "my-datastream-default-pipeline", <4> + "processor_type": "set" + } + } + } + ] + } +} +``` +1. This document is what we'll use for our simulations. +2. It had a counter value. +3. The document was missing a required field. +4. The document failed in the `my-data-stream-default-pipeline` +:::: + +::::{step} Fix the original problem +Because ingest pipeline failures need to be reprocessed by their original pipelines, any problems with those pipeline should be fixed before remediating failures. Investigating the pipeline mentioned in the example above shows that there is a processor that expects a field to be present that is not always present. + +```console-result +{ + "my-datastream-default-pipeline": { + "processors": [ + { + "set": { <1> + "field": "identifier", + "copy_from": "data.id" + } + } + ] + } +} +``` +1. The `data.id` field is expected to be present. If it isn't present this pipeline will fail. + +Fixing a failure's root cause is a often a bespoke process. In this example, instead of discarding the data, we will make this identifier field optional. + +```console +PUT _ingest/pipeline/my-datastream-default-pipeline +{ + "processors": [ + { + "set": { + "field": "identifier", + "copy_from": "data.id", + "if": "ctx.data?.id != null" <1> + } + } + ] +} +``` +1. Only conditionally run the processor if the field exists. + +:::: + +::::{step} Create a pipeline to convert failure documents + +We must convert our failure documents back into their original forms and send them off to be reprocessed. We will create a pipeline to do this: + +```console +PUT _ingest/pipeline/my-datastream-remediation-pipeline +{ + "processors": [ + { + "script": { + "lang": "painless", + "source": """ + ctx._index = ctx.document.index; <1> + ctx._routing = ctx.document.routing; + def s = ctx.document.source; <2> + ctx.remove("error"); <3> + ctx.remove("document"); <4> + for (e in s.entrySet()) { <5> + ctx[e.key] = e.value; + }""" + } + }, + { + "reroute": { <6> + "destination": "my-datastream-ingest-example" + } + } + ] +} +``` +1. Copy the original index name from the failure document over into the document's metadata. If you use custom document routing, copy that over too. +2. Capture the source of the original document. +3. Discard the `error` field since it wont be needed for the remediation. +4. Also discard the `document` field. +5. We extract all the fields from the original document's source back to the root of the document. +6. Since the pipeline that failed was the default pipeline on `my-datastream-ingest-example`, we will use the `reroute` processor to send any remediated documents to that data stream's default pipeline again to be reprocessed. + +:::: + +::::{step} Test your pipelines +Before sending data off to be reindexed, be sure to test the pipelines in question with an example document to make sure they work. First, test to make sure the resulting document from the remediation pipeline is shaped how you expect. We can use the [simulate pipeline API](./failure-store.md) for this. + +```console +POST _ingest/pipeline/_simulate +{ + "pipeline": { <1> + "processors": [ + { + "script": { + "lang": "painless", + "source": """ + ctx._index = ctx.document.index; + ctx._routing = ctx.document.routing; + def s = ctx.document.source; + ctx.remove("error"); + ctx.remove("document"); + for (e in s.entrySet()) { + ctx[e.key] = e.value; + }""" + } + }, + { + "reroute": { + "destination": "my-datastream-ingest-example" + } + } + ] + }, + "docs": [ <2> + { + "_index": ".fs-my-datastream-ingest-example-2025.05.16-000001", + "_id": "cOnR2ZYByIwDXH-g6GpR", + "_source": { + "@timestamp": "2025-05-01T15:58:53.522Z", + "document": { + "index": "my-datastream-ingest-example", + "source": { + "@timestamp": "2025-05-01T00:00:00Z", + "data": { + "counter": 42 + } + } + }, + "error": { + "type": "illegal_argument_exception", + "message": "field [id] not present as part of path [data.id]", + "stack_trace": """j.l.IllegalArgumentException: field [id] not present as part of path [data.id] + at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) + at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) + ... 14 more +""", + "pipeline_trace": [ + "my-datastream-default-pipeline" + ], + "pipeline": "my-datastream-default-pipeline", + "processor_type": "set" + } + } + } + ] +} +``` +1. The contents of the remediation pipeline written in the previous step. +2. The contents of an example failure document we identified in the previous steps. + +```console-result +{ + "docs": [ + { + "doc": { + "_index": "my-datastream-ingest-example", <1> + "_version": "-3", + "_id": "cOnR2ZYByIwDXH-g6GpR", <2> + "_source": { <3> + "data": { + "counter": 42 + }, + "@timestamp": "2025-05-01T00:00:00Z" + }, + "_ingest": { + "timestamp": "2025-05-01T20:58:03.566210529Z" + } + } + } + ] +} +``` +1. The index has been updated via the reroute processor. +2. The id has stayed the same. +3. The source should cleanly match what the original document should have been. + +Now that the remediation pipeline has been tested, be sure to test the end to end ingestion to verify that no further problems will arise. To do this, we will use the [simulate ingestion API](./failure-store.md) to test multiple pipeline executions. + +```console +POST _ingest/_simulate?pipeline=my-datastream-remediation-pipeline <1> +{ + "pipeline_substitutions": { + "my-datastream-remediation-pipeline": { <2> + "processors": [ + { + "script": { + "lang": "painless", + "source": """ + ctx._index = ctx.document.index; + ctx._routing = ctx.document.routing; + def s = ctx.document.source; + ctx.remove("error"); + ctx.remove("document"); + for (e in s.entrySet()) { + ctx[e.key] = e.value; + }""" + } + }, + { + "reroute": { + "destination": "my-datastream-ingest-example" + } + } + ] + } + }, + "docs": [ <3> + { + "_index": ".fs-my-datastream-ingest-example-2025.05.16-000001", + "_id": "cOnR2ZYByIwDXH-g6GpR", + "_source": { + "@timestamp": "2025-05-01T15:58:53.522Z", + "document": { + "index": "my-datastream-ingest-example", + "source": { + "@timestamp": "2025-05-01T00:00:00Z", + "data": { + "counter": 42 + } + } + }, + "error": { + "type": "illegal_argument_exception", + "message": "field [id] not present as part of path [data.id]", + "stack_trace": """j.l.IllegalArgumentException: field [id] not present as part of path [data.id] + at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) + at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) + ... 14 more +""", + "pipeline_trace": [ + "my-datastream-default-pipeline" + ], + "pipeline": "my-datastream-default-pipeline", + "processor_type": "set" + } + } + } + ] +} +``` +1. Set the pipeline to be the remediation pipeline name, otherwise, the default pipeline for the document's index is used. +2. The contents of the remediation pipeline in previous steps. +3. The contents of the previously identified example failure document. + +```console-result +{ + "docs": [ + { + "doc": { + "_id": "cOnR2ZYByIwDXH-g6GpR", + "_index": "my-datastream-ingest-example", <1> + "_version": -3, + "_source": { <2> + "@timestamp": "2025-05-01T00:00:00Z", + "data": { + "counter": 42 + } + }, + "executed_pipelines": [ <3> + "my-datastream-remediation-pipeline", + "my-datastream-default-pipeline" + ] + } + } + ] +} +``` +1. The index name has been updated. +2. The source is as expected after the default pipeline has run. +3. Ensure that both the new remediation pipeline and the original default pipeline have successfully run. + +:::: + +::::{step} Reindex the failure documents +Combine the remediation pipeline with the failure store query together in a [reindex operation](./failure-store.md) to replay the failures. + +```console +POST _reindex +{ + "source": { + "index": "my-datastream-ingest-example::failures", <1> + "query": { + "bool": { <2> + "must": [ + { + "exists": { + "field": "error.pipeline" + } + }, + { + "match": { + "document.index": "my-datastream-ingest-example" + } + }, + { + "match": { + "error.type": "illegal_argument_exception" + } + }, + { + "range": { + "@timestamp": { + "gt": "2025-05-01T00:00:00Z", + "lte": "2025-05-17T00:00:00Z" + } + } + } + ] + } + } + }, + "dest": { + "index": "my-datastream-ingest-example", <3> + "op_type": "create", + "pipeline": "my-datastream-remediation-pipeline" <4> + } +} +``` +1. Read from the failure store. +2. Only reindex failure documents that match the ones we are replaying. +3. Set the destination to the data stream the failures originally were sent to. +4. Replace the pipeline with the remediation pipeline. + +```console-result +{ + "took": 469, + "timed_out": false, + "total": 1, + "updated": 0, + "created": 1, <1> + "deleted": 0, + "batches": 1, + "version_conflicts": 0, + "noops": 0, + "retries": { + "bulk": 0, + "search": 0 + }, + "throttled_millis": 0, + "requests_per_second": -1, + "throttled_until_millis": 0, + "failures": [] +} +``` +1. The failures have been remediated. + +:::{tip} +Since the failure store is enabled on this data stream, it would be wise to check it for any further failures from the reindexing process. Failures that happen at this point in the process may end up as nested failures in the failure store. Remediating nested failures can quickly become a hassle as the original document gets nested multiple levels deep in the failure document. For this reason, it is suggested to remediate data during a quiet period where no other failures will arise. Furthermore, rolling over the failure store before executing the remediation would allow easier discarding of any new nested failures and only operate on the original failure documents. +::: + +::::{step} Done +:::: + +::::: + +#### Remediating mapping and shard failures [recipes-remediation-mapping] + +As described in the [failure document source](#use-failure-store-document-source) section above, failures that occur due to a mapping or indexing issue will be stored as they were after any pipelines had executed. This means that to replay the document into the data stream we will need to make sure to skip any pipelines that have already run. + +:::{tip} +You can greatly simplify this remediation process by writing any ingest pipelines to be idempotent. In that case, any document that has already be processed that passes through a pipeline again would be unchanged. +::: + +:::::{stepper} + +::::{step} Separate out which failures to replay + +Start off by constructing a query that can be used to consistently identify which failures will be remediated. + +```console +POST my-datastream-indexing-example::failures/_search +{ + "query": { + "bool": { + "must_not": [ + { + "exists": { <1> + "field": "error.pipeline" + } + } + ], + "must": [ + { + "match": { <2> + "document.index": "my-datastream-indexing-example" + } + }, + { + "match": { <3> + "error.type": "document_parsing_exception" + } + }, + { + "range": { <4> + "@timestamp": { + "gt": "2025-05-01T00:00:00Z", + "lte": "2025-05-02T00:00:00Z" + } + } + } + ] + } + } +} +``` +1. Require the `error.pipeline` field to not exist. This filters out any ingest pipeline failures, and only returns indexing failures. +2. Filter on the data stream name to remediate documents headed for a specific index. +3. Further narrow which kind of failure you are attempting to remediate. In this example we are targeting a specific type of error. +4. Filter on timestamp to only retrieve failures before a certain point in time. This provides a stable set of documents. + +Take note of the documents that are returned. We can use these to simulate that our remediation logic makes sense +```console-result +{ + "took": 1, + "timed_out": false, + "_shards": { + "total": 1, + "successful": 1, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 1, + "relation": "eq" + }, + "max_score": 1.5753641, + "hits": [ + { + "_index": ".fs-my-datastream-indexing-example-2025.05.16-000002", + "_id": "_lA-GJcBHLe506UUGL0I", + "_score": 1.5753641, + "_source": { <1> + "@timestamp": "2025-05-02T18:53:31.153Z", + "document": { + "id": "_VA-GJcBHLe506UUFL2i", + "index": "my-datastream-indexing-example", + "source": { + "processed": true, <2> + "data": { + "counter": 37 + } + } + }, + "error": { + "type": "document_parsing_exception", <3> + "message": "[1:40] failed to parse: data stream timestamp field [@timestamp] is missing", + "stack_trace": """o.e.i.m.DocumentParsingException: [1:40] failed to parse: data stream timestamp field [@timestamp] is missing + at o.e.i.m.DocumentParser.wrapInDocumentParsingException(DocumentParser.java:265) + at o.e.i.m.DocumentParser.internalParseDocument(DocumentParser.java:162) + ... 19 more +Caused by: j.l.IllegalArgumentException: data stream timestamp field [@timestamp] is missing + at o.e.i.m.DataStreamTimestampFieldMapper.extractTimestampValue(DataStreamTimestampFieldMapper.java:210) + at o.e.i.m.DataStreamTimestampFieldMapper.postParse(DataStreamTimestampFieldMapper.java:223) + ... 20 more +""" + } + } + } + ] + } +} +``` +1. This document is what we'll use for our simulations. +2. The document was missing a required `@timestamp` field. +3. The document failed with a `document_parsing_exception` because of the missing timestamp. + +:::: + +::::{step} Fix the original problem + +There are a broad set of possible indexing failures. Most of these problems stem from incorrect values for a particular mapping. Sometimes a large number of new fields are dynamically mapped and the maximum number of mapping fields is reached and no more can be added. In our example above, the document being indexed is missing a required timestamp. + +These problems can occur in a number of places: Data sent from a client may be incomplete, ingest pipelines may not be producing the correct result, or the index mapping may need to be updated to account for changes in data. + +Once all clients and pipelines are producing complete and correct documents, and your mappings are correctly configured for your incoming data, proceed with the remediation. + +:::: + +::::{step} Create a pipeline to convert failure documents + +We must convert our failure documents back into their original forms and send them off to be reprocessed. We will create a pipeline to do this. Since the example failure was due to not having a timestamp on the document, we will simply use the timestamp at the time of failure for the document since the original timestamp is missing. This solution assumes that the documents we are remediating were created very closely to when the failure occurred. Your remediation process may need adjustments if this is not applicable for you. + +```console +PUT _ingest/pipeline/my-datastream-remediation-pipeline +{ + "processors": [ + { + "script": { + "lang": "painless", + "source": """ + ctx._index = ctx.document.index; <1> + ctx._routing = ctx.document.routing; + def s = ctx.document.source; <2> + ctx.remove("error"); <3> + ctx.remove("document"); <4> + for (e in s.entrySet()) { <5> + ctx[e.key] = e.value; + }""" + } + } + ] +} +``` +1. Copy the original index name from the failure document over into the document's metadata. If you use custom document routing, copy that over too. +2. Capture the source of the original document. +3. Discard the `error` field since it wont be needed for the remediation. +4. Also discard the `document` field. +5. We extract all the fields from the original document's source back to the root of the document. The `@timestamp` field is not overwritten and thus will be present in the final document. + +:::{important} +Remember that a document that has failed during indexing has already been processed by the ingest processor! It shouldn't need to be processed again unless you made changes to your pipeline to fix the original problem. Make sure that any fixes applied to the ingest pipeline is reflected in the pipeline logic here. +::: + +:::: + +::::{step} Test your pipeline +Before sending data off to be reindexed, be sure to test the remedial pipeline with an example document to make sure it works. Most importantly, make sure the resulting document from the remediation pipeline is shaped how you expect. We can use the [simulate pipeline API](./failure-store.md) for this. + +```console +POST _ingest/pipeline/_simulate +{ + "pipeline": { <1> + "processors": [ + { + "script": { + "lang": "painless", + "source": """ + ctx._index = ctx.document.index; + ctx._routing = ctx.document.routing; + def s = ctx.document.source; + ctx.remove("error"); + ctx.remove("document"); + for (e in s.entrySet()) { + ctx[e.key] = e.value; + }""" + } + } + ] + }, + "docs": [ <2> + { + "_index": ".fs-my-datastream-indexing-example-2025.05.16-000002", + "_id": "_lA-GJcBHLe506UUGL0I", + "_score": 1.5753641, + "_source": { + "@timestamp": "2025-05-02T18:53:31.153Z", + "document": { + "id": "_VA-GJcBHLe506UUFL2i", + "index": "my-datastream-indexing-example", + "source": { + "processed": true, + "data": { + "counter": 37 + } + } + }, + "error": { + "type": "document_parsing_exception", + "message": "[1:40] failed to parse: data stream timestamp field [@timestamp] is missing", + "stack_trace": """o.e.i.m.DocumentParsingException: [1:40] failed to parse: data stream timestamp field [@timestamp] is missing + at o.e.i.m.DocumentParser.wrapInDocumentParsingException(DocumentParser.java:265) + at o.e.i.m.DocumentParser.internalParseDocument(DocumentParser.java:162) + ... 19 more +Caused by: j.l.IllegalArgumentException: data stream timestamp field [@timestamp] is missing + at o.e.i.m.DataStreamTimestampFieldMapper.extractTimestampValue(DataStreamTimestampFieldMapper.java:210) + at o.e.i.m.DataStreamTimestampFieldMapper.postParse(DataStreamTimestampFieldMapper.java:223) + ... 20 more +""" + } + } + } + ] +} +``` +1. The contents of the remediation pipeline written in the previous step. +2. The contents of an example failure document we identified in the previous steps. + +```console-result +{ + "docs": [ + { + "doc": { + "_index": "my-datastream-indexing-example", <1> + "_version": "-3", + "_id": "_lA-GJcBHLe506UUGL0I", + "_source": { <2> + "processed": true, + "@timestamp": "2025-05-28T18:53:31.153Z", <3> + "data": { + "counter": 37 + } + }, + "_ingest": { + "timestamp": "2025-05-28T19:14:50.457560845Z" + } + } + } + ] +} +``` +1. The index has been updated via the script processor. +2. The source should reflect any fixes and match the expected document shape for the final index. +3. In this example case, we find that the failure timestamp has stayed in the source. + +:::: + +::::{step} Reindex the failure documents +Combine the remediation pipeline with the failure store query together in a [reindex operation](./failure-store.md) to replay the failures. + +```console +POST _reindex +{ + "source": { + "index": "my-datastream-indexing-example::failures", <1> + "query": { + "bool": { <2> + "must_not": [ + { + "exists": { + "field": "error.pipeline" + } + } + ], + "must": [ + { + "match": { + "document.index": "my-datastream-indexing-example" + } + }, + { + "match": { + "error.type": "document_parsing_exception" + } + }, + { + "range": { + "@timestamp": { + "gt": "2025-05-01T00:00:00Z", + "lte": "2025-05-28T19:00:00Z" + } + } + } + ] + } + } + }, + "dest": { + "index": "my-datastream-indexing-example", <3> + "op_type": "create", + "pipeline": "my-datastream-remediation-pipeline" <4> + } +} +``` +1. Read from the failure store. +2. Only reindex failure documents that match the ones we are replaying. +3. Set the destination to the data stream the failures originally were sent to. The remediation pipeline above updates the index to be the correct one, but a destination is still required. +4. Replace the pipeline with the remediation pipeline. This will keep any default pipelines from running. + +```console-result +{ + "took": 469, + "timed_out": false, + "total": 1, + "updated": 0, + "created": 1, <1> + "deleted": 0, + "batches": 1, + "version_conflicts": 0, + "noops": 0, + "retries": { + "bulk": 0, + "search": 0 + }, + "throttled_millis": 0, + "requests_per_second": -1, + "throttled_until_millis": 0, + "failures": [] +} +``` +1. The failures have been remediated. + +:::{tip} +Since the failure store is enabled on this data stream, it would be wise to check it for any further failures from the reindexing process. Failures that happen at this point in the process may end up as nested failures in the failure store. Remediating nested failures can quickly become a hassle as the original document gets nested multiple levels deep in the failure document. For this reason, it is suggested to remediate data during a quiet period where no other failures will arise. Furthermore, rolling over the failure store before executing the remediation would allow easier discarding of any new nested failures and only operate on the original failure documents. +::: + +::::{step} Done +:::: + +::::: -Placeholder link \ No newline at end of file +Once any failures have been remediated, you may wish to purge the failures from the failure store to clear up space and to avoid warnings about failed data that has already been replayed. 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Update manage-data/data-store/data-streams/failure-store.md Co-authored-by: David Kilfoyle <41695641+kilfoyle@users.noreply.github.com> --- manage-data/data-store/data-streams/failure-store.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index 0ebd10af6..8f5671810 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -6,7 +6,7 @@ applies_to: # Failure store [failure-store] -Failure stores are a secondary set of indices inside a data stream dedicated to storing failed documents. Failed documents are any documents that cause ingest pipeline exceptions or have a structure that conflicts with a data stream's mappings. These failures normally cause the indexing operation to fail, returning the error message in the response. +A failure store is a secondary set of indices inside a data stream, dedicated to storing failed documents. A failed document is any document that, without the failure store enabled, would cause an ingest pipeline exception or that has a structure that conflicts with a data stream's mappings. In the absence of the failure store, a failed document would cause the indexing operation to fail, with an error message returned in the operation response. When a data stream's failure store is enabled, these failures are instead captured in a separate index and persisted to be analysed later. Clients receive a successful response with a flag indicating the failure was redirected. Failure stores do not capture failures caused by backpressure or document version conflicts. These failures are always returned as-is since they warrant specific action by the client. From 0003ca2fa6f826fb896386b642c1168bc09bb3a8 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 29 May 2025 03:55:08 -0400 Subject: [PATCH 20/29] Update manage-data/data-store/data-streams/failure-store.md Co-authored-by: David Kilfoyle <41695641+kilfoyle@users.noreply.github.com> --- manage-data/data-store/data-streams/failure-store.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index 8f5671810..597208e32 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -16,7 +16,7 @@ Each data stream has its own failure store that can be enabled to accept failure ### Set up for new data streams [set-up-failure-store-new] -You can specify on a data stream's template if it should enable the failure store when it is first created. The `data_stream_options` field in a [template](../templates.md) contains the settings required to enable a data stream's failure store. +You can specify in a data stream's [index template](../templates.md) if it should enable the failure store when it is first created. :::{note} Unlike the `settings` and `mappings` fields on an [index template](../templates.md) which are repeatedly applied to new data stream write indices on rollover, the `data_stream_options` section of a template is applied to a data stream only once when the data stream is first created. To configure existing data streams, use the put [data stream options API](./failure-store.md). From 39cf08fb6ff972c735d1f99a921f0ffad43e6b77 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 29 May 2025 03:55:40 -0400 Subject: [PATCH 21/29] Update manage-data/data-store/data-streams/failure-store.md Co-authored-by: David Kilfoyle <41695641+kilfoyle@users.noreply.github.com> --- manage-data/data-store/data-streams/failure-store.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index 597208e32..ef7982c82 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -47,7 +47,7 @@ After a matching data stream is created, its failure store will be enabled. ### Set up for existing data streams [set-up-failure-store-existing] -Enabling the failure store via [index templates](../templates.md) can only affect data streams that are newly created. Existing data streams that use a template will not apply any changes to the template's `data_stream_options` after they have been created. +Enabling the failure store via [index templates](../templates.md) can only affect data streams that are newly created. Existing data streams that use a template are not affected by changes to the template's `data_stream_options` field. To modify an existing data stream's options, use the [put data stream options](./failure-store.md) API: From 7cb071eb7ab6f86734ee8726fe03d848f664e2de Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 29 May 2025 03:55:54 -0400 Subject: [PATCH 22/29] Update manage-data/data-store/data-streams/failure-store.md Co-authored-by: David Kilfoyle <41695641+kilfoyle@users.noreply.github.com> --- manage-data/data-store/data-streams/failure-store.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index ef7982c82..62e256bb6 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -12,7 +12,7 @@ When a data stream's failure store is enabled, these failures are instead captur ## Set up a data stream failure store [set-up-failure-store] -Each data stream has its own failure store that can be enabled to accept failures. By default, this failure store is disabled and any ingestion problems are raised in the response to write operations. +Each data stream has its own failure store that can be enabled to accept failed documents. By default, this failure store is disabled and any ingestion problems are raised in the response to write operations. ### Set up for new data streams [set-up-failure-store-new] From 2f4d02680fdf262a84ffb2e8f8845b9cdfc4a4ea Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 29 May 2025 03:58:57 -0400 Subject: [PATCH 23/29] Update manage-data/data-store/data-streams/failure-store.md Co-authored-by: David Kilfoyle <41695641+kilfoyle@users.noreply.github.com> --- manage-data/data-store/data-streams/failure-store.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index 62e256bb6..899dc2792 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -119,7 +119,7 @@ The failure store is meant to ease the burden of detecting and handling failures Once a failure store is enabled for a data stream it will begin redirecting documents that fail due to common ingestion problems instead of returning errors in write operations. Clients are notified in a non-intrusive way when a document is redirected to the failure store. -Each data stream's failure store is made up of a list of indices that are dedicated to storing failed documents. These failure indices function much like a data stream's normal backing indices: There is a write index that accepts failed documents, they can be rolled over, and are automatically cleaned up over time subject to a lifecycle policy. Failure indices are lazily created the first time they are needed to store a failed document. +Each data stream's failure store is made up of a list of indices that are dedicated to storing failed documents. These failure indices function much like a data stream's normal backing indices: There is a write index that accepts failed documents, the indices can be rolled over, and they're automatically cleaned up over time subject to a lifecycle policy. Failure indices are lazily created the first time they are needed to store a failed document. When a document bound for a data stream encounters a problem during its ingestion, the response is annotated with the `failure_store` field which describes how {{es}} responded to that problem. The `failure_store` field is present on both the [bulk](./failure-store.md) and [index](./failure-store.md) API responses when applicable. Clients can use this information to augment their behavior based on the response from {{es}}. From 1ac9c09f661cf8a843dd6dccc335b35a777b2e4d Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 29 May 2025 04:02:14 -0400 Subject: [PATCH 24/29] Apply suggestions from code review Co-authored-by: David Kilfoyle <41695641+kilfoyle@users.noreply.github.com> --- .../data-store/data-streams/failure-store.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index 899dc2792..280c8dc39 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -177,7 +177,7 @@ POST my-datastream/_bulk } ``` -1. The response code is 200 OK, and the response body does not report any errors encountered. +1. The response code is `200 OK`, and the response body does not report any errors encountered. 2. The first document is accepted into the data stream's write index. 3. The second document encountered a problem during ingest and was redirected to the data stream's failure store. 4. The response is annotated with a field indicating that the failure store was used to persist the second document. @@ -231,7 +231,7 @@ If the document could have been redirected to a data stream's failure store but 1. The failure is returned to the client as normal when the failure store is not enabled. 2. The response is annotated with a flag indicating the failure store could have accepted the document, but it was not enabled. -3. Status of 400 Bad Request due to the mapping problem. +3. The response status is `400 Bad Request` due to the mapping problem. If the document was redirected to a data stream's failure store but that failed document could not be stored (e.g. due to shard unavailability or a similar problem), then the `failure_store` field on the response will be `failed`, and the response will display the error for the original failure, as well as a suppressed error detailing why the failure could not be stored: @@ -273,7 +273,7 @@ If the document was redirected to a data stream's failure store but that failed 2. The document could not be redirected because the failure store was not able to accept writes at this time due to an unforeseeable issue. 3. The complete exception tree is present on the response. 4. The response is annotated with a flag indicating the failure store would have accepted the document, but it was not able to. -5. Status of 400 Bad Request due to the original mapping problem. +5. The response status is `400 Bad Request` due to the original mapping problem. ### Searching failures [use-failure-store-searching] @@ -377,7 +377,7 @@ Caused by: j.l.IllegalArgumentException: For input string: "invalid_text" 1. The document belongs to a failure store index on the data stream. 2. The failure document timestamp is when the failure occurred in {{es}}. -3. The document that was sent is captured inside the failure document. Failure documents capture the id of the document at time of failure, along with which data stream the document was being written to, and the contents of the document. The `document.source` fields are unmapped to ensure failures are always captured. +3. The document that was sent is captured inside the failure document. Failure documents capture the ID of the document at time of failure, along with which data stream the document was being written to, and the contents of the document. The `document.source` fields are unmapped to ensure failures are always captured. 4. The failure document captures information about the error encountered, like the type of error, the error message, and a compressed stack trace. :::: @@ -422,7 +422,7 @@ Failure documents have a uniform structure that is handled internally by {{es}}. : (`object`) The document at time of failure. If the document failed in an ingest pipeline, then the document will be the unprocessed version of the document as it arrived in the original indexing request. If the document failed due to a mapping issue, then the document will be as it was after any ingest pipelines were applied to it. `document.id` - : (`keyword`) The id of the original document at the time of failure. + : (`keyword`) The ID of the original document at the time of failure. `document.routing` : (`keyword`, optional) The routing of the original document at the time of failure if it was specified. @@ -443,7 +443,7 @@ Failure documents have a uniform structure that is handled internally by {{es}}. : (`text`) A compressed stack trace from {{es}} for the failure. `error.type` - : (`keyword`) The type classification of failure. Values are the same type returned within failed indexing API responses. + : (`keyword`) The type classification of the failure. Values are the same type returned within failed indexing API responses. `error.pipeline` : (`keyword`, optional) If the failure occurred in an ingest pipeline, this will contain the name of the pipeline. @@ -601,7 +601,7 @@ GET my-datastream-ingest::failures/_search We can see that the document failed on the second processor in the pipeline. The first processor would have added a `@timestamp` field. Since the pipeline failed, we find that it has no `@timestamp` field added because it did not save any changes from before the pipeline failed. -The second place failures can occur is during indexing. After the documents have been processed by any applicable pipelines, they are parsed using the index mappings before being indexed into the shard. If a document is sent to the failure store due to a failure in this process, then it will be stored as it was after any ingestion had occurred. This is because the original document is overwritten by the ingest pipeline changes by this point. This has the benefit of being able to see what the document looked like during the mapping and indexing phase of the write operation. +The second time when failures can occur is during indexing. After the documents have been processed by any applicable pipelines, they are parsed using the index mappings before being indexed into the shard. If a document is sent to the failure store due to a failure in this process, then it will be stored as it was after any ingestion had occurred. This is because, by this point, the original document has already been overwritten by the ingest pipeline changes. This has the benefit of allowing you to see what the document looked like during the mapping and indexing phase of the write operation. Building on the example above, we send a document that has a text value where we expect a numeric value: @@ -1146,7 +1146,7 @@ Navigate to the data view page in Kibana and add a new data view. Set the index :::: ::::{step} Create new rule -Navigate to Management / Alerts and Insights / Rules. Create a new rule. Choose the Elasticsearch query option. +Navigate to Management / Alerts and Insights / Rules. Create a new rule. Choose the {{es}} query option. :::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_create_rule.png :alt: create a new alerting rule and select the elasticsearch query option From 8869cda3c78ac93e1b3dd33d6badf26bc6fafc3d Mon Sep 17 00:00:00 2001 From: James Baiera Date: Thu, 29 May 2025 04:06:20 -0400 Subject: [PATCH 25/29] Split recipes into sub-page --- .../data-streams/failure-store-recipes.md | 1153 +++++++++++++++++ .../data-store/data-streams/failure-store.md | 1153 ----------------- manage-data/toc.yml | 2 + 3 files changed, 1155 insertions(+), 1153 deletions(-) create mode 100644 manage-data/data-store/data-streams/failure-store-recipes.md diff --git a/manage-data/data-store/data-streams/failure-store-recipes.md b/manage-data/data-store/data-streams/failure-store-recipes.md new file mode 100644 index 000000000..1f394336d --- /dev/null +++ b/manage-data/data-store/data-streams/failure-store-recipes.md @@ -0,0 +1,1153 @@ +# Failure store recipes and use cases [failure-store-recipes] + +When something goes wrong during ingestion it is often not an isolated event. Included for your convenience are some examples of how you can use the failure store to quickly respond to ingestion failures and get your indexing back on track. + +## Troubleshooting nested ingest pipelines [failure-store-recipes-nested-ingest-troubleshoot] + +When a document fails in an ingest pipeline it can be difficult to figure out exactly what when wrong and where. When these failures are captured by the failure store during this part of the ingestion process, they will contain additional debugging information. Failed documents will note the type of processor and which pipeline was executing when the failure occurred. Failed documents will also contain a pipeline trace which keeps track of any nested pipeline calls that the document was in at time of failure. + +To demonstrate this, we will follow a failed document through an unfamiliar data stream and ingest pipeline: +```console +POST my-datastream-ingest/_doc +{ + "@timestamp": "2025-04-21T00:00:00Z", + "important": { + "info": "The rain in Spain falls mainly on the plain" + } +} +``` + +```console-result +{ + "_index": ".fs-my-datastream-ingest-2025.05.09-000001", + "_id": "F3S3s5YBwrYNjPmayMr9", + "_version": 1, + "result": "created", + "_shards": { + "total": 1, + "successful": 1, + "failed": 0 + }, + "_seq_no": 2, + "_primary_term": 1, + "failure_store": "used" // The document was sent to the failure store +} +``` + +Now we search the failure store to check the failure document to see what went wrong. +```console +GET my-datastream-ingest::failures/_search +``` + +```console-result +{ + "took": 0, + "timed_out": false, + "_shards": { + "total": 1, + "successful": 1, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 1, + "relation": "eq" + }, + "max_score": 1, + "hits": [ + { + "_index": ".fs-my-datastream-ingest-2025.05.09-000001", + "_id": "F3S3s5YBwrYNjPmayMr9", + "_score": 1, + "_source": { + "@timestamp": "2025-05-09T06:24:48.381Z", + "document": { + "index": "my-datastream-ingest", + "source": { // When an ingest pipeline fails, the document stored is what was originally sent to the cluster. + "important": { + "info": "The rain in Spain falls mainly on the plain" // The important info that we failed to find was originally present on the document. + }, + "@timestamp": "2025-04-21T00:00:00Z" + } + }, + "error": { + "type": "illegal_argument_exception", + "message": "field [info] not present as part of path [important.info]", // The info field was not present when the failure occurred. + "stack_trace": """j.l.IllegalArgumentException: field [info] not present as part of path [important.info] + at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) + at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) + ... 19 more +""", + "pipeline_trace": [ // The first pipeline called the second pipeline. + "ingest-step-1", + "ingest-step-2" + ], + "pipeline": "ingest-step-2", // The document failed in the second pipeline. + "processor_type": "set" // It failed in the pipeline's set processor. + } + } + } + ] + } +} +``` + +Despite not knowing the pipelines beforehand, we have some places to start looking. The `ingest-step-2` pipeline cannot find the `important.info` field despite it being present on the document that was sent to the cluster. If we pull that pipeline definition we find the following: + +```console +GET _ingest/pipeline/ingest-step-2 +``` + +```console-result +{ + "ingest-step-2": { + "processors": [ + { + "set": { // There is only one processor here. + "field": "copy.info", + "copy_from": "important.info" // This field was missing from the document at this point. + } + } + ] + } +} +``` + +There is only a set processor in the `ingest-step-2` pipeline so this is likely not where the root problem is. Remembering the `pipeline_trace` field on the failure we find that `ingest-step-1` was the original pipeline called for this document. It is likely the data stream's default pipeline. Pulling its definition we find the following: + +```console +GET _ingest/pipeline/ingest-step-1 +``` + +```console-result +{ + "ingest-step-1": { + "processors": [ + { + "remove": { + "field": "important.info" // A remove processor that is incorrectly getting rid of our important field. + } + }, + { + "pipeline": { + "name": "ingest-step-2" // The call to the second pipeline. + } + } + ] + } +} +``` + +We find a remove processor in the first pipeline that is the root cause of the problem! The pipeline should be updated to not remove important data, or the downstream pipeline should be changed to not expect the important data to be always present. + +## Troubleshooting complicated ingest pipelines [failure-store-recipes-complicated-ingest-troubleshoot] + +Ingest processors can be labeled with [tags](./failure-store.md). These tags are user provided information that names or describes the processor's purpose in the pipeline. When documents are redirected to the failure store due to a processor issue, they capture the tag from the processor in which the failure occurred if it exists. Because of this, it is a good practice to tag the processors in your pipeline so that the location of a failure can be identified quickly. + +Here we have a needlessly complicated pipeline. It is made up of several set and remove processors. Beneficially, they are all tagged with descriptive names. +```console +PUT _ingest/pipeline/complicated-processor +{ + "processors": [ + { + "set": { + "tag": "initialize counter", + "field": "counter", + "value": "1" + } + }, + { + "set": { + "tag": "copy counter to new", + "field": "new_counter", + "copy_from": "counter" + } + }, + { + "remove": { + "tag": "remove old counter", + "field": "counter" + } + }, + { + "set": { + "tag": "transfer counter back", + "field": "counter", + "copy_from": "new_counter" + } + }, + { + "remove": { + "tag": "remove counter again", + "field": "counter" + } + }, + { + "set": { + "tag": "copy to new counter again", + "field": "new_counter", + "copy_from": "counter" + } + } + ] +} +``` + +We ingest some data and find that it was sent to the failure store +```console +POST my-datastream-ingest/_doc?pipeline=complicated-processor +{ + "@timestamp": "2025-04-21T00:00:00Z", + "counter_name": "test" +} +``` + +```console-result +{ + "_index": ".fs-my-datastream-ingest-2025.05.09-000001", + "_id": "HnTJs5YBwrYNjPmaFcri", + "_version": 1, + "result": "created", + "_shards": { + "total": 1, + "successful": 1, + "failed": 0 + }, + "_seq_no": 1, + "_primary_term": 1, + "failure_store": "used" +} +``` + +Upon checking the failure, we can quickly identify the tagged processor that caused the problem +```console +GET my-datastream-ingest::failures/_search +``` + +```console-result +{ + "took": 0, + "timed_out": false, + "_shards": { + "total": 1, + "successful": 1, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 1, + "relation": "eq" + }, + "max_score": 1, + "hits": [ + { + "_index": ".fs-my-datastream-ingest-2025.05.09-000001", + "_id": "HnTJs5YBwrYNjPmaFcri", + "_score": 1, + "_source": { + "@timestamp": "2025-05-09T06:41:24.775Z", + "document": { + "index": "my-datastream-ingest", + "source": { + "@timestamp": "2025-04-21T00:00:00Z", + "counter_name": "test" + } + }, + "error": { + "type": "illegal_argument_exception", + "message": "field [counter] not present as part of path [counter]", + "stack_trace": """j.l.IllegalArgumentException: field [counter] not present as part of path [counter] + at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) + at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) + ... 14 more +""", + "pipeline_trace": [ + "complicated-processor" + ], + "pipeline": "complicated-processor", + "processor_type": "set", // Helpful, but which set processor on the pipeline could it be? + "processor_tag": "copy to new counter again" // The tag of the exact processor that it failed on. + } + } + } + ] + } +} +``` + +Without tags in place it would not be as clear where in the pipeline we encountered the problem. Tags provide a unique identifier for a processor that can be quickly referenced in case of an ingest failure. + +## Alerting on failed ingestion [failure-store-recipes-alerting] + +Since failure stores can be searched just like a normal data stream, we can use them as inputs to [alerting rules](./failure-store.md) in Kibana. Here is a simple alerting example to trigger on more than ten failures in the last five minutes for a data stream: + +:::::{stepper} + +::::{step} Create a failure store data view +If you want to use KQL or Lucene query types, you should first create a data view for your failure store data. +If you plan to use {{esql}} or the Query DSL query types, this step is not required. + +Navigate to the data view page in Kibana and add a new data view. Set the index pattern to your failure store using the selector syntax. + +:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_create_data_view.png +:alt: create a data view using the failure store syntax in the index name +::: +:::: + +::::{step} Create new rule +Navigate to Management / Alerts and Insights / Rules. Create a new rule. Choose the {{es}} query option. + +:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_create_rule.png +:alt: create a new alerting rule and select the elasticsearch query option +::: +:::: + +::::{step} Pick your query type +Choose which query type you wish to use + +For KQL/Lucene queries, reference the data view that contains your failure store. + +:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_kql.png +:alt: use the data view created in the previous step as the input to the kql query +::: + +For Query DSL queries, use the `::failures` suffix on your data stream name. + +:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_dsl.png +:alt: use the ::failures suffix in the data stream name in the query dsl +::: + +For {{esql}} queries, use the `::failures` suffix on your data stream name in the `FROM` command. + +:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_esql.png +:alt: use the ::failures suffix in the data stream name in the from command +::: +:::: + +::::{step} Test +Configure schedule, actions, and details of the alert before saving the rule. + +:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_finish.png +:alt: complete the rule configuration and save it +::: +:::: + +::::{step} Done +:::: + +::::: + +## Data remediation [failure-store-recipes-remediation] + +If you've encountered a long span of ingestion failures you may find that a sizeable gap of events has appeared in your data stream. If the failure store is enabled, the documents that should fill those gaps would be tucked away in the data stream's failure store. Because failure stores are made up of regular indices and the failure documents contain the document source that failed, the failure documents can often times be replayed into your production data streams. + +::::{warning} +Care should be taken when replaying data into a data stream from a failure store. Any failures during the replay process may generate new failures in the failure store which can duplicate and obscure the original events. +:::: + +We recommend a few best practices for remediating failure data. + +**Separate your failures beforehand.** As described in the [failure document source](#use-failure-store-document-source) section above, failure documents are structured differently depending on when the document failed during ingestion. We recommend to separate documents by ingest pipeline failures and indexing failures at minimum. Ingest pipeline failures often need to have the original pipeline re-executed, while index failures should skip any pipelines. Further separating failures by index or specific failure type may also be beneficial. + +**Perform a failure store rollover.** Consider rolling over the failure store before attempting to remediate failures. This will create a new failure index that will collect any new failures during the remediation process. + +**Use an ingest pipeline to convert failure documents back into their original document.** Failure documents store failure information along with the document that failed ingestion. The first step for remediating documents should be to use an ingest pipeline to extract the original source from the failure document and discard any other info on it. + +**Simulate first to avoid repeat failures.** If you must execute a pipeline as part of your remediation process, it is best to simulate the pipeline against the failure first. This will catch any unforeseen issues that may fail the document a second time. Remember, ingest pipeline failures will capture the document before an ingest pipeline was applied to it, which can further complicate remediation when a failure document becomes nested inside a new failure. + +### Remediating ingest node failures [failure-store-recipes-remediation-ingest] + +Failures that occurred during an ingest processor will be stored as they were before any pipelines were executed. To replay the document into the data stream we will need to rerun any applicable pipelines for the document. + +:::::{stepper} + +::::{step} Separate out which failures to replay + +Start off by constructing a query that can be used to consistently identify which failures will be remediated. + +```console +POST my-datastream-ingest-example::failures/_search +{ + "query": { + "bool": { + "must": [ + { + "exists": { <1> + "field": "error.pipeline" + } + }, + { + "match": { <2> + "document.index": "my-datastream-ingest-example" + } + }, + { + "match": { <3> + "error.type": "illegal_argument_exception" + } + }, + { + "range": { <4> + "@timestamp": { + "gt": "2025-05-01T00:00:00Z", + "lte": "2025-05-02T00:00:00Z" + } + } + } + ] + } + } +} +``` +1. Require the `error.pipeline` field to exist. This filters to ingest pipeline failures only. +2. Filter on the data stream name to remediate documents headed for a specific index. +3. Further narrow which kind of failure you are attempting to remediate. In this example we are targeting a specific type of error. +4. Filter on timestamp to only retrieve failures before a certain point in time. This provides a stable set of documents. + +Take note of the documents that are returned. We can use these to simulate that our remediation logic makes sense +```console-result +{ + "took": 14, + "timed_out": false, + "_shards": { + "total": 2, + "successful": 2, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 1, + "relation": "eq" + }, + "max_score": 2.575364, + "hits": [ + { + "_index": ".fs-my-datastream-ingest-example-2025.05.16-000001", + "_id": "cOnR2ZYByIwDXH-g6GpR", + "_score": 2.575364, + "_source": { + "@timestamp": "2025-05-01T15:58:53.522Z", <1> + "document": { + "index": "my-datastream-ingest-example", + "source": { + "@timestamp": "2025-05-01T00:00:00Z", + "data": { + "counter": 42 <2> + } + } + }, + "error": { + "type": "illegal_argument_exception", + "message": "field [id] not present as part of path [data.id]", <3> + "stack_trace": """j.l.IllegalArgumentException: field [id] not present as part of path [data.id] + at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) + at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) + ... 14 more +""", + "pipeline_trace": [ + "my-datastream-default-pipeline" + ], + "pipeline": "my-datastream-default-pipeline", <4> + "processor_type": "set" + } + } + } + ] + } +} +``` +1. This document is what we'll use for our simulations. +2. It had a counter value. +3. The document was missing a required field. +4. The document failed in the `my-data-stream-default-pipeline` + :::: + +::::{step} Fix the original problem +Because ingest pipeline failures need to be reprocessed by their original pipelines, any problems with those pipeline should be fixed before remediating failures. Investigating the pipeline mentioned in the example above shows that there is a processor that expects a field to be present that is not always present. + +```console-result +{ + "my-datastream-default-pipeline": { + "processors": [ + { + "set": { <1> + "field": "identifier", + "copy_from": "data.id" + } + } + ] + } +} +``` +1. The `data.id` field is expected to be present. If it isn't present this pipeline will fail. + +Fixing a failure's root cause is a often a bespoke process. In this example, instead of discarding the data, we will make this identifier field optional. + +```console +PUT _ingest/pipeline/my-datastream-default-pipeline +{ + "processors": [ + { + "set": { + "field": "identifier", + "copy_from": "data.id", + "if": "ctx.data?.id != null" <1> + } + } + ] +} +``` +1. Only conditionally run the processor if the field exists. + +:::: + +::::{step} Create a pipeline to convert failure documents + +We must convert our failure documents back into their original forms and send them off to be reprocessed. We will create a pipeline to do this: + +```console +PUT _ingest/pipeline/my-datastream-remediation-pipeline +{ + "processors": [ + { + "script": { + "lang": "painless", + "source": """ + ctx._index = ctx.document.index; <1> + ctx._routing = ctx.document.routing; + def s = ctx.document.source; <2> + ctx.remove("error"); <3> + ctx.remove("document"); <4> + for (e in s.entrySet()) { <5> + ctx[e.key] = e.value; + }""" + } + }, + { + "reroute": { <6> + "destination": "my-datastream-ingest-example" + } + } + ] +} +``` +1. Copy the original index name from the failure document over into the document's metadata. If you use custom document routing, copy that over too. +2. Capture the source of the original document. +3. Discard the `error` field since it wont be needed for the remediation. +4. Also discard the `document` field. +5. We extract all the fields from the original document's source back to the root of the document. +6. Since the pipeline that failed was the default pipeline on `my-datastream-ingest-example`, we will use the `reroute` processor to send any remediated documents to that data stream's default pipeline again to be reprocessed. + +:::: + +::::{step} Test your pipelines +Before sending data off to be reindexed, be sure to test the pipelines in question with an example document to make sure they work. First, test to make sure the resulting document from the remediation pipeline is shaped how you expect. We can use the [simulate pipeline API](./failure-store.md) for this. + +```console +POST _ingest/pipeline/_simulate +{ + "pipeline": { <1> + "processors": [ + { + "script": { + "lang": "painless", + "source": """ + ctx._index = ctx.document.index; + ctx._routing = ctx.document.routing; + def s = ctx.document.source; + ctx.remove("error"); + ctx.remove("document"); + for (e in s.entrySet()) { + ctx[e.key] = e.value; + }""" + } + }, + { + "reroute": { + "destination": "my-datastream-ingest-example" + } + } + ] + }, + "docs": [ <2> + { + "_index": ".fs-my-datastream-ingest-example-2025.05.16-000001", + "_id": "cOnR2ZYByIwDXH-g6GpR", + "_source": { + "@timestamp": "2025-05-01T15:58:53.522Z", + "document": { + "index": "my-datastream-ingest-example", + "source": { + "@timestamp": "2025-05-01T00:00:00Z", + "data": { + "counter": 42 + } + } + }, + "error": { + "type": "illegal_argument_exception", + "message": "field [id] not present as part of path [data.id]", + "stack_trace": """j.l.IllegalArgumentException: field [id] not present as part of path [data.id] + at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) + at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) + ... 14 more +""", + "pipeline_trace": [ + "my-datastream-default-pipeline" + ], + "pipeline": "my-datastream-default-pipeline", + "processor_type": "set" + } + } + } + ] +} +``` +1. The contents of the remediation pipeline written in the previous step. +2. The contents of an example failure document we identified in the previous steps. + +```console-result +{ + "docs": [ + { + "doc": { + "_index": "my-datastream-ingest-example", <1> + "_version": "-3", + "_id": "cOnR2ZYByIwDXH-g6GpR", <2> + "_source": { <3> + "data": { + "counter": 42 + }, + "@timestamp": "2025-05-01T00:00:00Z" + }, + "_ingest": { + "timestamp": "2025-05-01T20:58:03.566210529Z" + } + } + } + ] +} +``` +1. The index has been updated via the reroute processor. +2. The id has stayed the same. +3. The source should cleanly match what the original document should have been. + +Now that the remediation pipeline has been tested, be sure to test the end to end ingestion to verify that no further problems will arise. To do this, we will use the [simulate ingestion API](./failure-store.md) to test multiple pipeline executions. + +```console +POST _ingest/_simulate?pipeline=my-datastream-remediation-pipeline <1> +{ + "pipeline_substitutions": { + "my-datastream-remediation-pipeline": { <2> + "processors": [ + { + "script": { + "lang": "painless", + "source": """ + ctx._index = ctx.document.index; + ctx._routing = ctx.document.routing; + def s = ctx.document.source; + ctx.remove("error"); + ctx.remove("document"); + for (e in s.entrySet()) { + ctx[e.key] = e.value; + }""" + } + }, + { + "reroute": { + "destination": "my-datastream-ingest-example" + } + } + ] + } + }, + "docs": [ <3> + { + "_index": ".fs-my-datastream-ingest-example-2025.05.16-000001", + "_id": "cOnR2ZYByIwDXH-g6GpR", + "_source": { + "@timestamp": "2025-05-01T15:58:53.522Z", + "document": { + "index": "my-datastream-ingest-example", + "source": { + "@timestamp": "2025-05-01T00:00:00Z", + "data": { + "counter": 42 + } + } + }, + "error": { + "type": "illegal_argument_exception", + "message": "field [id] not present as part of path [data.id]", + "stack_trace": """j.l.IllegalArgumentException: field [id] not present as part of path [data.id] + at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) + at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) + ... 14 more +""", + "pipeline_trace": [ + "my-datastream-default-pipeline" + ], + "pipeline": "my-datastream-default-pipeline", + "processor_type": "set" + } + } + } + ] +} +``` +1. Set the pipeline to be the remediation pipeline name, otherwise, the default pipeline for the document's index is used. +2. The contents of the remediation pipeline in previous steps. +3. The contents of the previously identified example failure document. + +```console-result +{ + "docs": [ + { + "doc": { + "_id": "cOnR2ZYByIwDXH-g6GpR", + "_index": "my-datastream-ingest-example", <1> + "_version": -3, + "_source": { <2> + "@timestamp": "2025-05-01T00:00:00Z", + "data": { + "counter": 42 + } + }, + "executed_pipelines": [ <3> + "my-datastream-remediation-pipeline", + "my-datastream-default-pipeline" + ] + } + } + ] +} +``` +1. The index name has been updated. +2. The source is as expected after the default pipeline has run. +3. Ensure that both the new remediation pipeline and the original default pipeline have successfully run. + +:::: + +::::{step} Reindex the failure documents +Combine the remediation pipeline with the failure store query together in a [reindex operation](./failure-store.md) to replay the failures. + +```console +POST _reindex +{ + "source": { + "index": "my-datastream-ingest-example::failures", <1> + "query": { + "bool": { <2> + "must": [ + { + "exists": { + "field": "error.pipeline" + } + }, + { + "match": { + "document.index": "my-datastream-ingest-example" + } + }, + { + "match": { + "error.type": "illegal_argument_exception" + } + }, + { + "range": { + "@timestamp": { + "gt": "2025-05-01T00:00:00Z", + "lte": "2025-05-17T00:00:00Z" + } + } + } + ] + } + } + }, + "dest": { + "index": "my-datastream-ingest-example", <3> + "op_type": "create", + "pipeline": "my-datastream-remediation-pipeline" <4> + } +} +``` +1. Read from the failure store. +2. Only reindex failure documents that match the ones we are replaying. +3. Set the destination to the data stream the failures originally were sent to. +4. Replace the pipeline with the remediation pipeline. + +```console-result +{ + "took": 469, + "timed_out": false, + "total": 1, + "updated": 0, + "created": 1, <1> + "deleted": 0, + "batches": 1, + "version_conflicts": 0, + "noops": 0, + "retries": { + "bulk": 0, + "search": 0 + }, + "throttled_millis": 0, + "requests_per_second": -1, + "throttled_until_millis": 0, + "failures": [] +} +``` +1. The failures have been remediated. + +:::{tip} +Since the failure store is enabled on this data stream, it would be wise to check it for any further failures from the reindexing process. Failures that happen at this point in the process may end up as nested failures in the failure store. Remediating nested failures can quickly become a hassle as the original document gets nested multiple levels deep in the failure document. For this reason, it is suggested to remediate data during a quiet period where no other failures will arise. Furthermore, rolling over the failure store before executing the remediation would allow easier discarding of any new nested failures and only operate on the original failure documents. +::: + +::::{step} Done +:::: + +::::: + +### Remediating mapping and shard failures [failure-store-recipes-remediation-mapping] + +As described in the [failure document source](#use-failure-store-document-source) section above, failures that occur due to a mapping or indexing issue will be stored as they were after any pipelines had executed. This means that to replay the document into the data stream we will need to make sure to skip any pipelines that have already run. + +:::{tip} +You can greatly simplify this remediation process by writing any ingest pipelines to be idempotent. In that case, any document that has already be processed that passes through a pipeline again would be unchanged. +::: + +:::::{stepper} + +::::{step} Separate out which failures to replay + +Start off by constructing a query that can be used to consistently identify which failures will be remediated. + +```console +POST my-datastream-indexing-example::failures/_search +{ + "query": { + "bool": { + "must_not": [ + { + "exists": { <1> + "field": "error.pipeline" + } + } + ], + "must": [ + { + "match": { <2> + "document.index": "my-datastream-indexing-example" + } + }, + { + "match": { <3> + "error.type": "document_parsing_exception" + } + }, + { + "range": { <4> + "@timestamp": { + "gt": "2025-05-01T00:00:00Z", + "lte": "2025-05-02T00:00:00Z" + } + } + } + ] + } + } +} +``` +1. Require the `error.pipeline` field to not exist. This filters out any ingest pipeline failures, and only returns indexing failures. +2. Filter on the data stream name to remediate documents headed for a specific index. +3. Further narrow which kind of failure you are attempting to remediate. In this example we are targeting a specific type of error. +4. Filter on timestamp to only retrieve failures before a certain point in time. This provides a stable set of documents. + +Take note of the documents that are returned. We can use these to simulate that our remediation logic makes sense +```console-result +{ + "took": 1, + "timed_out": false, + "_shards": { + "total": 1, + "successful": 1, + "skipped": 0, + "failed": 0 + }, + "hits": { + "total": { + "value": 1, + "relation": "eq" + }, + "max_score": 1.5753641, + "hits": [ + { + "_index": ".fs-my-datastream-indexing-example-2025.05.16-000002", + "_id": "_lA-GJcBHLe506UUGL0I", + "_score": 1.5753641, + "_source": { <1> + "@timestamp": "2025-05-02T18:53:31.153Z", + "document": { + "id": "_VA-GJcBHLe506UUFL2i", + "index": "my-datastream-indexing-example", + "source": { + "processed": true, <2> + "data": { + "counter": 37 + } + } + }, + "error": { + "type": "document_parsing_exception", <3> + "message": "[1:40] failed to parse: data stream timestamp field [@timestamp] is missing", + "stack_trace": """o.e.i.m.DocumentParsingException: [1:40] failed to parse: data stream timestamp field [@timestamp] is missing + at o.e.i.m.DocumentParser.wrapInDocumentParsingException(DocumentParser.java:265) + at o.e.i.m.DocumentParser.internalParseDocument(DocumentParser.java:162) + ... 19 more +Caused by: j.l.IllegalArgumentException: data stream timestamp field [@timestamp] is missing + at o.e.i.m.DataStreamTimestampFieldMapper.extractTimestampValue(DataStreamTimestampFieldMapper.java:210) + at o.e.i.m.DataStreamTimestampFieldMapper.postParse(DataStreamTimestampFieldMapper.java:223) + ... 20 more +""" + } + } + } + ] + } +} +``` +1. This document is what we'll use for our simulations. +2. The document was missing a required `@timestamp` field. +3. The document failed with a `document_parsing_exception` because of the missing timestamp. + +:::: + +::::{step} Fix the original problem + +There are a broad set of possible indexing failures. Most of these problems stem from incorrect values for a particular mapping. Sometimes a large number of new fields are dynamically mapped and the maximum number of mapping fields is reached and no more can be added. In our example above, the document being indexed is missing a required timestamp. + +These problems can occur in a number of places: Data sent from a client may be incomplete, ingest pipelines may not be producing the correct result, or the index mapping may need to be updated to account for changes in data. + +Once all clients and pipelines are producing complete and correct documents, and your mappings are correctly configured for your incoming data, proceed with the remediation. + +:::: + +::::{step} Create a pipeline to convert failure documents + +We must convert our failure documents back into their original forms and send them off to be reprocessed. We will create a pipeline to do this. Since the example failure was due to not having a timestamp on the document, we will simply use the timestamp at the time of failure for the document since the original timestamp is missing. This solution assumes that the documents we are remediating were created very closely to when the failure occurred. Your remediation process may need adjustments if this is not applicable for you. + +```console +PUT _ingest/pipeline/my-datastream-remediation-pipeline +{ + "processors": [ + { + "script": { + "lang": "painless", + "source": """ + ctx._index = ctx.document.index; <1> + ctx._routing = ctx.document.routing; + def s = ctx.document.source; <2> + ctx.remove("error"); <3> + ctx.remove("document"); <4> + for (e in s.entrySet()) { <5> + ctx[e.key] = e.value; + }""" + } + } + ] +} +``` +1. Copy the original index name from the failure document over into the document's metadata. If you use custom document routing, copy that over too. +2. Capture the source of the original document. +3. Discard the `error` field since it wont be needed for the remediation. +4. Also discard the `document` field. +5. We extract all the fields from the original document's source back to the root of the document. The `@timestamp` field is not overwritten and thus will be present in the final document. + +:::{important} +Remember that a document that has failed during indexing has already been processed by the ingest processor! It shouldn't need to be processed again unless you made changes to your pipeline to fix the original problem. Make sure that any fixes applied to the ingest pipeline is reflected in the pipeline logic here. +::: + +:::: + +::::{step} Test your pipeline +Before sending data off to be reindexed, be sure to test the remedial pipeline with an example document to make sure it works. Most importantly, make sure the resulting document from the remediation pipeline is shaped how you expect. We can use the [simulate pipeline API](./failure-store.md) for this. + +```console +POST _ingest/pipeline/_simulate +{ + "pipeline": { <1> + "processors": [ + { + "script": { + "lang": "painless", + "source": """ + ctx._index = ctx.document.index; + ctx._routing = ctx.document.routing; + def s = ctx.document.source; + ctx.remove("error"); + ctx.remove("document"); + for (e in s.entrySet()) { + ctx[e.key] = e.value; + }""" + } + } + ] + }, + "docs": [ <2> + { + "_index": ".fs-my-datastream-indexing-example-2025.05.16-000002", + "_id": "_lA-GJcBHLe506UUGL0I", + "_score": 1.5753641, + "_source": { + "@timestamp": "2025-05-02T18:53:31.153Z", + "document": { + "id": "_VA-GJcBHLe506UUFL2i", + "index": "my-datastream-indexing-example", + "source": { + "processed": true, + "data": { + "counter": 37 + } + } + }, + "error": { + "type": "document_parsing_exception", + "message": "[1:40] failed to parse: data stream timestamp field [@timestamp] is missing", + "stack_trace": """o.e.i.m.DocumentParsingException: [1:40] failed to parse: data stream timestamp field [@timestamp] is missing + at o.e.i.m.DocumentParser.wrapInDocumentParsingException(DocumentParser.java:265) + at o.e.i.m.DocumentParser.internalParseDocument(DocumentParser.java:162) + ... 19 more +Caused by: j.l.IllegalArgumentException: data stream timestamp field [@timestamp] is missing + at o.e.i.m.DataStreamTimestampFieldMapper.extractTimestampValue(DataStreamTimestampFieldMapper.java:210) + at o.e.i.m.DataStreamTimestampFieldMapper.postParse(DataStreamTimestampFieldMapper.java:223) + ... 20 more +""" + } + } + } + ] +} +``` +1. The contents of the remediation pipeline written in the previous step. +2. The contents of an example failure document we identified in the previous steps. + +```console-result +{ + "docs": [ + { + "doc": { + "_index": "my-datastream-indexing-example", <1> + "_version": "-3", + "_id": "_lA-GJcBHLe506UUGL0I", + "_source": { <2> + "processed": true, + "@timestamp": "2025-05-28T18:53:31.153Z", <3> + "data": { + "counter": 37 + } + }, + "_ingest": { + "timestamp": "2025-05-28T19:14:50.457560845Z" + } + } + } + ] +} +``` +1. The index has been updated via the script processor. +2. The source should reflect any fixes and match the expected document shape for the final index. +3. In this example case, we find that the failure timestamp has stayed in the source. + +:::: + +::::{step} Reindex the failure documents +Combine the remediation pipeline with the failure store query together in a [reindex operation](./failure-store.md) to replay the failures. + +```console +POST _reindex +{ + "source": { + "index": "my-datastream-indexing-example::failures", <1> + "query": { + "bool": { <2> + "must_not": [ + { + "exists": { + "field": "error.pipeline" + } + } + ], + "must": [ + { + "match": { + "document.index": "my-datastream-indexing-example" + } + }, + { + "match": { + "error.type": "document_parsing_exception" + } + }, + { + "range": { + "@timestamp": { + "gt": "2025-05-01T00:00:00Z", + "lte": "2025-05-28T19:00:00Z" + } + } + } + ] + } + } + }, + "dest": { + "index": "my-datastream-indexing-example", <3> + "op_type": "create", + "pipeline": "my-datastream-remediation-pipeline" <4> + } +} +``` +1. Read from the failure store. +2. Only reindex failure documents that match the ones we are replaying. +3. Set the destination to the data stream the failures originally were sent to. The remediation pipeline above updates the index to be the correct one, but a destination is still required. +4. Replace the pipeline with the remediation pipeline. This will keep any default pipelines from running. + +```console-result +{ + "took": 469, + "timed_out": false, + "total": 1, + "updated": 0, + "created": 1, <1> + "deleted": 0, + "batches": 1, + "version_conflicts": 0, + "noops": 0, + "retries": { + "bulk": 0, + "search": 0 + }, + "throttled_millis": 0, + "requests_per_second": -1, + "throttled_until_millis": 0, + "failures": [] +} +``` +1. The failures have been remediated. + +:::{tip} +Since the failure store is enabled on this data stream, it would be wise to check it for any further failures from the reindexing process. Failures that happen at this point in the process may end up as nested failures in the failure store. Remediating nested failures can quickly become a hassle as the original document gets nested multiple levels deep in the failure document. For this reason, it is suggested to remediate data during a quiet period where no other failures will arise. Furthermore, rolling over the failure store before executing the remediation would allow easier discarding of any new nested failures and only operate on the original failure documents. +::: + +::::{step} Done +:::: + +::::: + +Once any failures have been remediated, you may wish to purge the failures from the failure store to clear up space and to avoid warnings about failed data that has already been replayed. Otherwise, your failures will stay available until the maximum failure store retention should you need to reference them. \ No newline at end of file diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index 280c8dc39..e00f971e5 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -847,1156 +847,3 @@ POST _data_stream/_modify This API gives you fine-grained control over the indices in your failure store, allowing you to manage backup and restoration operations as well as isolate failure data for later remediation. -## Failure store recipes and use cases [recipes] - -When something goes wrong during ingestion it is often not an isolated event. Included for your convenience are some examples of how you can use the failure store to quickly respond to ingestion failures and get your indexing back on track. - -### Troubleshooting nested ingest pipelines [recipes-nested-ingest-troubleshoot] - -When a document fails in an ingest pipeline it can be difficult to figure out exactly what when wrong and where. When these failures are captured by the failure store during this part of the ingestion process, they will contain additional debugging information. Failed documents will note the type of processor and which pipeline was executing when the failure occurred. Failed documents will also contain a pipeline trace which keeps track of any nested pipeline calls that the document was in at time of failure. - -To demonstrate this, we will follow a failed document through an unfamiliar data stream and ingest pipeline: -```console -POST my-datastream-ingest/_doc -{ - "@timestamp": "2025-04-21T00:00:00Z", - "important": { - "info": "The rain in Spain falls mainly on the plain" - } -} -``` - -```console-result -{ - "_index": ".fs-my-datastream-ingest-2025.05.09-000001", - "_id": "F3S3s5YBwrYNjPmayMr9", - "_version": 1, - "result": "created", - "_shards": { - "total": 1, - "successful": 1, - "failed": 0 - }, - "_seq_no": 2, - "_primary_term": 1, - "failure_store": "used" // The document was sent to the failure store -} -``` - -Now we search the failure store to check the failure document to see what went wrong. -```console -GET my-datastream-ingest::failures/_search -``` - -```console-result -{ - "took": 0, - "timed_out": false, - "_shards": { - "total": 1, - "successful": 1, - "skipped": 0, - "failed": 0 - }, - "hits": { - "total": { - "value": 1, - "relation": "eq" - }, - "max_score": 1, - "hits": [ - { - "_index": ".fs-my-datastream-ingest-2025.05.09-000001", - "_id": "F3S3s5YBwrYNjPmayMr9", - "_score": 1, - "_source": { - "@timestamp": "2025-05-09T06:24:48.381Z", - "document": { - "index": "my-datastream-ingest", - "source": { // When an ingest pipeline fails, the document stored is what was originally sent to the cluster. - "important": { - "info": "The rain in Spain falls mainly on the plain" // The important info that we failed to find was originally present on the document. - }, - "@timestamp": "2025-04-21T00:00:00Z" - } - }, - "error": { - "type": "illegal_argument_exception", - "message": "field [info] not present as part of path [important.info]", // The info field was not present when the failure occurred. - "stack_trace": """j.l.IllegalArgumentException: field [info] not present as part of path [important.info] - at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) - at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) - ... 19 more -""", - "pipeline_trace": [ // The first pipeline called the second pipeline. - "ingest-step-1", - "ingest-step-2" - ], - "pipeline": "ingest-step-2", // The document failed in the second pipeline. - "processor_type": "set" // It failed in the pipeline's set processor. - } - } - } - ] - } -} -``` - -Despite not knowing the pipelines beforehand, we have some places to start looking. The `ingest-step-2` pipeline cannot find the `important.info` field despite it being present on the document that was sent to the cluster. If we pull that pipeline definition we find the following: - -```console -GET _ingest/pipeline/ingest-step-2 -``` - -```console-result -{ - "ingest-step-2": { - "processors": [ - { - "set": { // There is only one processor here. - "field": "copy.info", - "copy_from": "important.info" // This field was missing from the document at this point. - } - } - ] - } -} -``` - -There is only a set processor in the `ingest-step-2` pipeline so this is likely not where the root problem is. Remembering the `pipeline_trace` field on the failure we find that `ingest-step-1` was the original pipeline called for this document. It is likely the data stream's default pipeline. Pulling its definition we find the following: - -```console -GET _ingest/pipeline/ingest-step-1 -``` - -```console-result -{ - "ingest-step-1": { - "processors": [ - { - "remove": { - "field": "important.info" // A remove processor that is incorrectly getting rid of our important field. - } - }, - { - "pipeline": { - "name": "ingest-step-2" // The call to the second pipeline. - } - } - ] - } -} -``` - -We find a remove processor in the first pipeline that is the root cause of the problem! The pipeline should be updated to not remove important data, or the downstream pipeline should be changed to not expect the important data to be always present. - -### Troubleshooting complicated ingest pipelines [recipes-complicated-ingest-troubleshoot] - -Ingest processors can be labeled with [tags](./failure-store.md). These tags are user provided information that names or describes the processor's purpose in the pipeline. When documents are redirected to the failure store due to a processor issue, they capture the tag from the processor in which the failure occurred if it exists. Because of this, it is a good practice to tag the processors in your pipeline so that the location of a failure can be identified quickly. - -Here we have a needlessly complicated pipeline. It is made up of several set and remove processors. Beneficially, they are all tagged with descriptive names. -```console -PUT _ingest/pipeline/complicated-processor -{ - "processors": [ - { - "set": { - "tag": "initialize counter", - "field": "counter", - "value": "1" - } - }, - { - "set": { - "tag": "copy counter to new", - "field": "new_counter", - "copy_from": "counter" - } - }, - { - "remove": { - "tag": "remove old counter", - "field": "counter" - } - }, - { - "set": { - "tag": "transfer counter back", - "field": "counter", - "copy_from": "new_counter" - } - }, - { - "remove": { - "tag": "remove counter again", - "field": "counter" - } - }, - { - "set": { - "tag": "copy to new counter again", - "field": "new_counter", - "copy_from": "counter" - } - } - ] -} -``` - -We ingest some data and find that it was sent to the failure store -```console -POST my-datastream-ingest/_doc?pipeline=complicated-processor -{ - "@timestamp": "2025-04-21T00:00:00Z", - "counter_name": "test" -} -``` - -```console-result -{ - "_index": ".fs-my-datastream-ingest-2025.05.09-000001", - "_id": "HnTJs5YBwrYNjPmaFcri", - "_version": 1, - "result": "created", - "_shards": { - "total": 1, - "successful": 1, - "failed": 0 - }, - "_seq_no": 1, - "_primary_term": 1, - "failure_store": "used" -} -``` - -Upon checking the failure, we can quickly identify the tagged processor that caused the problem -```console -GET my-datastream-ingest::failures/_search -``` - -```console-result -{ - "took": 0, - "timed_out": false, - "_shards": { - "total": 1, - "successful": 1, - "skipped": 0, - "failed": 0 - }, - "hits": { - "total": { - "value": 1, - "relation": "eq" - }, - "max_score": 1, - "hits": [ - { - "_index": ".fs-my-datastream-ingest-2025.05.09-000001", - "_id": "HnTJs5YBwrYNjPmaFcri", - "_score": 1, - "_source": { - "@timestamp": "2025-05-09T06:41:24.775Z", - "document": { - "index": "my-datastream-ingest", - "source": { - "@timestamp": "2025-04-21T00:00:00Z", - "counter_name": "test" - } - }, - "error": { - "type": "illegal_argument_exception", - "message": "field [counter] not present as part of path [counter]", - "stack_trace": """j.l.IllegalArgumentException: field [counter] not present as part of path [counter] - at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) - at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) - ... 14 more -""", - "pipeline_trace": [ - "complicated-processor" - ], - "pipeline": "complicated-processor", - "processor_type": "set", // Helpful, but which set processor on the pipeline could it be? - "processor_tag": "copy to new counter again" // The tag of the exact processor that it failed on. - } - } - } - ] - } -} -``` - -Without tags in place it would not be as clear where in the pipeline we encountered the problem. Tags provide a unique identifier for a processor that can be quickly referenced in case of an ingest failure. - -### Alerting on failed ingestion [recipes-alerting] - -Since failure stores can be searched just like a normal data stream, we can use them as inputs to [alerting rules](./failure-store.md) in Kibana. Here is a simple alerting example to trigger on more than ten failures in the last five minutes for a data stream: - -:::::{stepper} - -::::{step} Create a failure store data view -If you want to use KQL or Lucene query types, you should first create a data view for your failure store data. -If you plan to use {{esql}} or the Query DSL query types, this step is not required. - -Navigate to the data view page in Kibana and add a new data view. Set the index pattern to your failure store using the selector syntax. - -:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_create_data_view.png -:alt: create a data view using the failure store syntax in the index name -::: -:::: - -::::{step} Create new rule -Navigate to Management / Alerts and Insights / Rules. Create a new rule. Choose the {{es}} query option. - -:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_create_rule.png -:alt: create a new alerting rule and select the elasticsearch query option -::: -:::: - -::::{step} Pick your query type -Choose which query type you wish to use - -For KQL/Lucene queries, reference the data view that contains your failure store. - -:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_kql.png -:alt: use the data view created in the previous step as the input to the kql query -::: - -For Query DSL queries, use the `::failures` suffix on your data stream name. - -:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_dsl.png -:alt: use the ::failures suffix in the data stream name in the query dsl -::: - -For {{esql}} queries, use the `::failures` suffix on your data stream name in the `FROM` command. - -:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_esql.png -:alt: use the ::failures suffix in the data stream name in the from command -::: -:::: - -::::{step} Test -Configure schedule, actions, and details of the alert before saving the rule. - -:::{image} /manage-data/images/elasticsearch-reference-management_failure_store_alerting_finish.png -:alt: complete the rule configuration and save it -::: -:::: - -::::{step} Done -:::: - -::::: - -### Data remediation [recipes-remediation] - -If you've encountered a long span of ingestion failures you may find that a sizeable gap of events has appeared in your data stream. If the failure store is enabled, the documents that should fill those gaps would be tucked away in the data stream's failure store. Because failure stores are made up of regular indices and the failure documents contain the document source that failed, the failure documents can often times be replayed into your production data streams. - -::::{warning} -Care should be taken when replaying data into a data stream from a failure store. Any failures during the replay process may generate new failures in the failure store which can duplicate and obscure the original events. -:::: - -We recommend a few best practices for remediating failure data. - -**Separate your failures beforehand.** As described in the [failure document source](#use-failure-store-document-source) section above, failure documents are structured differently depending on when the document failed during ingestion. We recommend to separate documents by ingest pipeline failures and indexing failures at minimum. Ingest pipeline failures often need to have the original pipeline re-executed, while index failures should skip any pipelines. Further separating failures by index or specific failure type may also be beneficial. - -**Perform a failure store rollover.** Consider rolling over the failure store before attempting to remediate failures. This will create a new failure index that will collect any new failures during the remediation process. - -**Use an ingest pipeline to convert failure documents back into their original document.** Failure documents store failure information along with the document that failed ingestion. The first step for remediating documents should be to use an ingest pipeline to extract the original source from the failure document and discard any other info on it. - -**Simulate first to avoid repeat failures.** If you must execute a pipeline as part of your remediation process, it is best to simulate the pipeline against the failure first. This will catch any unforeseen issues that may fail the document a second time. Remember, ingest pipeline failures will capture the document before an ingest pipeline was applied to it, which can further complicate remediation when a failure document becomes nested inside a new failure. - -#### Remediating ingest node failures [recipes-remediation-ingest] - -Failures that occurred during an ingest processor will be stored as they were before any pipelines were executed. To replay the document into the data stream we will need to rerun any applicable pipelines for the document. - -:::::{stepper} - -::::{step} Separate out which failures to replay - -Start off by constructing a query that can be used to consistently identify which failures will be remediated. - -```console -POST my-datastream-ingest-example::failures/_search -{ - "query": { - "bool": { - "must": [ - { - "exists": { <1> - "field": "error.pipeline" - } - }, - { - "match": { <2> - "document.index": "my-datastream-ingest-example" - } - }, - { - "match": { <3> - "error.type": "illegal_argument_exception" - } - }, - { - "range": { <4> - "@timestamp": { - "gt": "2025-05-01T00:00:00Z", - "lte": "2025-05-02T00:00:00Z" - } - } - } - ] - } - } -} -``` -1. Require the `error.pipeline` field to exist. This filters to ingest pipeline failures only. -2. Filter on the data stream name to remediate documents headed for a specific index. -3. Further narrow which kind of failure you are attempting to remediate. In this example we are targeting a specific type of error. -4. Filter on timestamp to only retrieve failures before a certain point in time. This provides a stable set of documents. - -Take note of the documents that are returned. We can use these to simulate that our remediation logic makes sense -```console-result -{ - "took": 14, - "timed_out": false, - "_shards": { - "total": 2, - "successful": 2, - "skipped": 0, - "failed": 0 - }, - "hits": { - "total": { - "value": 1, - "relation": "eq" - }, - "max_score": 2.575364, - "hits": [ - { - "_index": ".fs-my-datastream-ingest-example-2025.05.16-000001", - "_id": "cOnR2ZYByIwDXH-g6GpR", - "_score": 2.575364, - "_source": { - "@timestamp": "2025-05-01T15:58:53.522Z", <1> - "document": { - "index": "my-datastream-ingest-example", - "source": { - "@timestamp": "2025-05-01T00:00:00Z", - "data": { - "counter": 42 <2> - } - } - }, - "error": { - "type": "illegal_argument_exception", - "message": "field [id] not present as part of path [data.id]", <3> - "stack_trace": """j.l.IllegalArgumentException: field [id] not present as part of path [data.id] - at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) - at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) - ... 14 more -""", - "pipeline_trace": [ - "my-datastream-default-pipeline" - ], - "pipeline": "my-datastream-default-pipeline", <4> - "processor_type": "set" - } - } - } - ] - } -} -``` -1. This document is what we'll use for our simulations. -2. It had a counter value. -3. The document was missing a required field. -4. The document failed in the `my-data-stream-default-pipeline` -:::: - -::::{step} Fix the original problem -Because ingest pipeline failures need to be reprocessed by their original pipelines, any problems with those pipeline should be fixed before remediating failures. Investigating the pipeline mentioned in the example above shows that there is a processor that expects a field to be present that is not always present. - -```console-result -{ - "my-datastream-default-pipeline": { - "processors": [ - { - "set": { <1> - "field": "identifier", - "copy_from": "data.id" - } - } - ] - } -} -``` -1. The `data.id` field is expected to be present. If it isn't present this pipeline will fail. - -Fixing a failure's root cause is a often a bespoke process. In this example, instead of discarding the data, we will make this identifier field optional. - -```console -PUT _ingest/pipeline/my-datastream-default-pipeline -{ - "processors": [ - { - "set": { - "field": "identifier", - "copy_from": "data.id", - "if": "ctx.data?.id != null" <1> - } - } - ] -} -``` -1. Only conditionally run the processor if the field exists. - -:::: - -::::{step} Create a pipeline to convert failure documents - -We must convert our failure documents back into their original forms and send them off to be reprocessed. We will create a pipeline to do this: - -```console -PUT _ingest/pipeline/my-datastream-remediation-pipeline -{ - "processors": [ - { - "script": { - "lang": "painless", - "source": """ - ctx._index = ctx.document.index; <1> - ctx._routing = ctx.document.routing; - def s = ctx.document.source; <2> - ctx.remove("error"); <3> - ctx.remove("document"); <4> - for (e in s.entrySet()) { <5> - ctx[e.key] = e.value; - }""" - } - }, - { - "reroute": { <6> - "destination": "my-datastream-ingest-example" - } - } - ] -} -``` -1. Copy the original index name from the failure document over into the document's metadata. If you use custom document routing, copy that over too. -2. Capture the source of the original document. -3. Discard the `error` field since it wont be needed for the remediation. -4. Also discard the `document` field. -5. We extract all the fields from the original document's source back to the root of the document. -6. Since the pipeline that failed was the default pipeline on `my-datastream-ingest-example`, we will use the `reroute` processor to send any remediated documents to that data stream's default pipeline again to be reprocessed. - -:::: - -::::{step} Test your pipelines -Before sending data off to be reindexed, be sure to test the pipelines in question with an example document to make sure they work. First, test to make sure the resulting document from the remediation pipeline is shaped how you expect. We can use the [simulate pipeline API](./failure-store.md) for this. - -```console -POST _ingest/pipeline/_simulate -{ - "pipeline": { <1> - "processors": [ - { - "script": { - "lang": "painless", - "source": """ - ctx._index = ctx.document.index; - ctx._routing = ctx.document.routing; - def s = ctx.document.source; - ctx.remove("error"); - ctx.remove("document"); - for (e in s.entrySet()) { - ctx[e.key] = e.value; - }""" - } - }, - { - "reroute": { - "destination": "my-datastream-ingest-example" - } - } - ] - }, - "docs": [ <2> - { - "_index": ".fs-my-datastream-ingest-example-2025.05.16-000001", - "_id": "cOnR2ZYByIwDXH-g6GpR", - "_source": { - "@timestamp": "2025-05-01T15:58:53.522Z", - "document": { - "index": "my-datastream-ingest-example", - "source": { - "@timestamp": "2025-05-01T00:00:00Z", - "data": { - "counter": 42 - } - } - }, - "error": { - "type": "illegal_argument_exception", - "message": "field [id] not present as part of path [data.id]", - "stack_trace": """j.l.IllegalArgumentException: field [id] not present as part of path [data.id] - at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) - at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) - ... 14 more -""", - "pipeline_trace": [ - "my-datastream-default-pipeline" - ], - "pipeline": "my-datastream-default-pipeline", - "processor_type": "set" - } - } - } - ] -} -``` -1. The contents of the remediation pipeline written in the previous step. -2. The contents of an example failure document we identified in the previous steps. - -```console-result -{ - "docs": [ - { - "doc": { - "_index": "my-datastream-ingest-example", <1> - "_version": "-3", - "_id": "cOnR2ZYByIwDXH-g6GpR", <2> - "_source": { <3> - "data": { - "counter": 42 - }, - "@timestamp": "2025-05-01T00:00:00Z" - }, - "_ingest": { - "timestamp": "2025-05-01T20:58:03.566210529Z" - } - } - } - ] -} -``` -1. The index has been updated via the reroute processor. -2. The id has stayed the same. -3. The source should cleanly match what the original document should have been. - -Now that the remediation pipeline has been tested, be sure to test the end to end ingestion to verify that no further problems will arise. To do this, we will use the [simulate ingestion API](./failure-store.md) to test multiple pipeline executions. - -```console -POST _ingest/_simulate?pipeline=my-datastream-remediation-pipeline <1> -{ - "pipeline_substitutions": { - "my-datastream-remediation-pipeline": { <2> - "processors": [ - { - "script": { - "lang": "painless", - "source": """ - ctx._index = ctx.document.index; - ctx._routing = ctx.document.routing; - def s = ctx.document.source; - ctx.remove("error"); - ctx.remove("document"); - for (e in s.entrySet()) { - ctx[e.key] = e.value; - }""" - } - }, - { - "reroute": { - "destination": "my-datastream-ingest-example" - } - } - ] - } - }, - "docs": [ <3> - { - "_index": ".fs-my-datastream-ingest-example-2025.05.16-000001", - "_id": "cOnR2ZYByIwDXH-g6GpR", - "_source": { - "@timestamp": "2025-05-01T15:58:53.522Z", - "document": { - "index": "my-datastream-ingest-example", - "source": { - "@timestamp": "2025-05-01T00:00:00Z", - "data": { - "counter": 42 - } - } - }, - "error": { - "type": "illegal_argument_exception", - "message": "field [id] not present as part of path [data.id]", - "stack_trace": """j.l.IllegalArgumentException: field [id] not present as part of path [data.id] - at o.e.i.IngestDocument.getFieldValue(IngestDocument.java:202) - at o.e.i.c.SetProcessor.execute(SetProcessor.java:86) - ... 14 more -""", - "pipeline_trace": [ - "my-datastream-default-pipeline" - ], - "pipeline": "my-datastream-default-pipeline", - "processor_type": "set" - } - } - } - ] -} -``` -1. Set the pipeline to be the remediation pipeline name, otherwise, the default pipeline for the document's index is used. -2. The contents of the remediation pipeline in previous steps. -3. The contents of the previously identified example failure document. - -```console-result -{ - "docs": [ - { - "doc": { - "_id": "cOnR2ZYByIwDXH-g6GpR", - "_index": "my-datastream-ingest-example", <1> - "_version": -3, - "_source": { <2> - "@timestamp": "2025-05-01T00:00:00Z", - "data": { - "counter": 42 - } - }, - "executed_pipelines": [ <3> - "my-datastream-remediation-pipeline", - "my-datastream-default-pipeline" - ] - } - } - ] -} -``` -1. The index name has been updated. -2. The source is as expected after the default pipeline has run. -3. Ensure that both the new remediation pipeline and the original default pipeline have successfully run. - -:::: - -::::{step} Reindex the failure documents -Combine the remediation pipeline with the failure store query together in a [reindex operation](./failure-store.md) to replay the failures. - -```console -POST _reindex -{ - "source": { - "index": "my-datastream-ingest-example::failures", <1> - "query": { - "bool": { <2> - "must": [ - { - "exists": { - "field": "error.pipeline" - } - }, - { - "match": { - "document.index": "my-datastream-ingest-example" - } - }, - { - "match": { - "error.type": "illegal_argument_exception" - } - }, - { - "range": { - "@timestamp": { - "gt": "2025-05-01T00:00:00Z", - "lte": "2025-05-17T00:00:00Z" - } - } - } - ] - } - } - }, - "dest": { - "index": "my-datastream-ingest-example", <3> - "op_type": "create", - "pipeline": "my-datastream-remediation-pipeline" <4> - } -} -``` -1. Read from the failure store. -2. Only reindex failure documents that match the ones we are replaying. -3. Set the destination to the data stream the failures originally were sent to. -4. Replace the pipeline with the remediation pipeline. - -```console-result -{ - "took": 469, - "timed_out": false, - "total": 1, - "updated": 0, - "created": 1, <1> - "deleted": 0, - "batches": 1, - "version_conflicts": 0, - "noops": 0, - "retries": { - "bulk": 0, - "search": 0 - }, - "throttled_millis": 0, - "requests_per_second": -1, - "throttled_until_millis": 0, - "failures": [] -} -``` -1. The failures have been remediated. - -:::{tip} -Since the failure store is enabled on this data stream, it would be wise to check it for any further failures from the reindexing process. Failures that happen at this point in the process may end up as nested failures in the failure store. Remediating nested failures can quickly become a hassle as the original document gets nested multiple levels deep in the failure document. For this reason, it is suggested to remediate data during a quiet period where no other failures will arise. Furthermore, rolling over the failure store before executing the remediation would allow easier discarding of any new nested failures and only operate on the original failure documents. -::: - -::::{step} Done -:::: - -::::: - -#### Remediating mapping and shard failures [recipes-remediation-mapping] - -As described in the [failure document source](#use-failure-store-document-source) section above, failures that occur due to a mapping or indexing issue will be stored as they were after any pipelines had executed. This means that to replay the document into the data stream we will need to make sure to skip any pipelines that have already run. - -:::{tip} -You can greatly simplify this remediation process by writing any ingest pipelines to be idempotent. In that case, any document that has already be processed that passes through a pipeline again would be unchanged. -::: - -:::::{stepper} - -::::{step} Separate out which failures to replay - -Start off by constructing a query that can be used to consistently identify which failures will be remediated. - -```console -POST my-datastream-indexing-example::failures/_search -{ - "query": { - "bool": { - "must_not": [ - { - "exists": { <1> - "field": "error.pipeline" - } - } - ], - "must": [ - { - "match": { <2> - "document.index": "my-datastream-indexing-example" - } - }, - { - "match": { <3> - "error.type": "document_parsing_exception" - } - }, - { - "range": { <4> - "@timestamp": { - "gt": "2025-05-01T00:00:00Z", - "lte": "2025-05-02T00:00:00Z" - } - } - } - ] - } - } -} -``` -1. Require the `error.pipeline` field to not exist. This filters out any ingest pipeline failures, and only returns indexing failures. -2. Filter on the data stream name to remediate documents headed for a specific index. -3. Further narrow which kind of failure you are attempting to remediate. In this example we are targeting a specific type of error. -4. Filter on timestamp to only retrieve failures before a certain point in time. This provides a stable set of documents. - -Take note of the documents that are returned. We can use these to simulate that our remediation logic makes sense -```console-result -{ - "took": 1, - "timed_out": false, - "_shards": { - "total": 1, - "successful": 1, - "skipped": 0, - "failed": 0 - }, - "hits": { - "total": { - "value": 1, - "relation": "eq" - }, - "max_score": 1.5753641, - "hits": [ - { - "_index": ".fs-my-datastream-indexing-example-2025.05.16-000002", - "_id": "_lA-GJcBHLe506UUGL0I", - "_score": 1.5753641, - "_source": { <1> - "@timestamp": "2025-05-02T18:53:31.153Z", - "document": { - "id": "_VA-GJcBHLe506UUFL2i", - "index": "my-datastream-indexing-example", - "source": { - "processed": true, <2> - "data": { - "counter": 37 - } - } - }, - "error": { - "type": "document_parsing_exception", <3> - "message": "[1:40] failed to parse: data stream timestamp field [@timestamp] is missing", - "stack_trace": """o.e.i.m.DocumentParsingException: [1:40] failed to parse: data stream timestamp field [@timestamp] is missing - at o.e.i.m.DocumentParser.wrapInDocumentParsingException(DocumentParser.java:265) - at o.e.i.m.DocumentParser.internalParseDocument(DocumentParser.java:162) - ... 19 more -Caused by: j.l.IllegalArgumentException: data stream timestamp field [@timestamp] is missing - at o.e.i.m.DataStreamTimestampFieldMapper.extractTimestampValue(DataStreamTimestampFieldMapper.java:210) - at o.e.i.m.DataStreamTimestampFieldMapper.postParse(DataStreamTimestampFieldMapper.java:223) - ... 20 more -""" - } - } - } - ] - } -} -``` -1. This document is what we'll use for our simulations. -2. The document was missing a required `@timestamp` field. -3. The document failed with a `document_parsing_exception` because of the missing timestamp. - -:::: - -::::{step} Fix the original problem - -There are a broad set of possible indexing failures. Most of these problems stem from incorrect values for a particular mapping. Sometimes a large number of new fields are dynamically mapped and the maximum number of mapping fields is reached and no more can be added. In our example above, the document being indexed is missing a required timestamp. - -These problems can occur in a number of places: Data sent from a client may be incomplete, ingest pipelines may not be producing the correct result, or the index mapping may need to be updated to account for changes in data. - -Once all clients and pipelines are producing complete and correct documents, and your mappings are correctly configured for your incoming data, proceed with the remediation. - -:::: - -::::{step} Create a pipeline to convert failure documents - -We must convert our failure documents back into their original forms and send them off to be reprocessed. We will create a pipeline to do this. Since the example failure was due to not having a timestamp on the document, we will simply use the timestamp at the time of failure for the document since the original timestamp is missing. This solution assumes that the documents we are remediating were created very closely to when the failure occurred. Your remediation process may need adjustments if this is not applicable for you. - -```console -PUT _ingest/pipeline/my-datastream-remediation-pipeline -{ - "processors": [ - { - "script": { - "lang": "painless", - "source": """ - ctx._index = ctx.document.index; <1> - ctx._routing = ctx.document.routing; - def s = ctx.document.source; <2> - ctx.remove("error"); <3> - ctx.remove("document"); <4> - for (e in s.entrySet()) { <5> - ctx[e.key] = e.value; - }""" - } - } - ] -} -``` -1. Copy the original index name from the failure document over into the document's metadata. If you use custom document routing, copy that over too. -2. Capture the source of the original document. -3. Discard the `error` field since it wont be needed for the remediation. -4. Also discard the `document` field. -5. We extract all the fields from the original document's source back to the root of the document. The `@timestamp` field is not overwritten and thus will be present in the final document. - -:::{important} -Remember that a document that has failed during indexing has already been processed by the ingest processor! It shouldn't need to be processed again unless you made changes to your pipeline to fix the original problem. Make sure that any fixes applied to the ingest pipeline is reflected in the pipeline logic here. -::: - -:::: - -::::{step} Test your pipeline -Before sending data off to be reindexed, be sure to test the remedial pipeline with an example document to make sure it works. Most importantly, make sure the resulting document from the remediation pipeline is shaped how you expect. We can use the [simulate pipeline API](./failure-store.md) for this. - -```console -POST _ingest/pipeline/_simulate -{ - "pipeline": { <1> - "processors": [ - { - "script": { - "lang": "painless", - "source": """ - ctx._index = ctx.document.index; - ctx._routing = ctx.document.routing; - def s = ctx.document.source; - ctx.remove("error"); - ctx.remove("document"); - for (e in s.entrySet()) { - ctx[e.key] = e.value; - }""" - } - } - ] - }, - "docs": [ <2> - { - "_index": ".fs-my-datastream-indexing-example-2025.05.16-000002", - "_id": "_lA-GJcBHLe506UUGL0I", - "_score": 1.5753641, - "_source": { - "@timestamp": "2025-05-02T18:53:31.153Z", - "document": { - "id": "_VA-GJcBHLe506UUFL2i", - "index": "my-datastream-indexing-example", - "source": { - "processed": true, - "data": { - "counter": 37 - } - } - }, - "error": { - "type": "document_parsing_exception", - "message": "[1:40] failed to parse: data stream timestamp field [@timestamp] is missing", - "stack_trace": """o.e.i.m.DocumentParsingException: [1:40] failed to parse: data stream timestamp field [@timestamp] is missing - at o.e.i.m.DocumentParser.wrapInDocumentParsingException(DocumentParser.java:265) - at o.e.i.m.DocumentParser.internalParseDocument(DocumentParser.java:162) - ... 19 more -Caused by: j.l.IllegalArgumentException: data stream timestamp field [@timestamp] is missing - at o.e.i.m.DataStreamTimestampFieldMapper.extractTimestampValue(DataStreamTimestampFieldMapper.java:210) - at o.e.i.m.DataStreamTimestampFieldMapper.postParse(DataStreamTimestampFieldMapper.java:223) - ... 20 more -""" - } - } - } - ] -} -``` -1. The contents of the remediation pipeline written in the previous step. -2. The contents of an example failure document we identified in the previous steps. - -```console-result -{ - "docs": [ - { - "doc": { - "_index": "my-datastream-indexing-example", <1> - "_version": "-3", - "_id": "_lA-GJcBHLe506UUGL0I", - "_source": { <2> - "processed": true, - "@timestamp": "2025-05-28T18:53:31.153Z", <3> - "data": { - "counter": 37 - } - }, - "_ingest": { - "timestamp": "2025-05-28T19:14:50.457560845Z" - } - } - } - ] -} -``` -1. The index has been updated via the script processor. -2. The source should reflect any fixes and match the expected document shape for the final index. -3. In this example case, we find that the failure timestamp has stayed in the source. - -:::: - -::::{step} Reindex the failure documents -Combine the remediation pipeline with the failure store query together in a [reindex operation](./failure-store.md) to replay the failures. - -```console -POST _reindex -{ - "source": { - "index": "my-datastream-indexing-example::failures", <1> - "query": { - "bool": { <2> - "must_not": [ - { - "exists": { - "field": "error.pipeline" - } - } - ], - "must": [ - { - "match": { - "document.index": "my-datastream-indexing-example" - } - }, - { - "match": { - "error.type": "document_parsing_exception" - } - }, - { - "range": { - "@timestamp": { - "gt": "2025-05-01T00:00:00Z", - "lte": "2025-05-28T19:00:00Z" - } - } - } - ] - } - } - }, - "dest": { - "index": "my-datastream-indexing-example", <3> - "op_type": "create", - "pipeline": "my-datastream-remediation-pipeline" <4> - } -} -``` -1. Read from the failure store. -2. Only reindex failure documents that match the ones we are replaying. -3. Set the destination to the data stream the failures originally were sent to. The remediation pipeline above updates the index to be the correct one, but a destination is still required. -4. Replace the pipeline with the remediation pipeline. This will keep any default pipelines from running. - -```console-result -{ - "took": 469, - "timed_out": false, - "total": 1, - "updated": 0, - "created": 1, <1> - "deleted": 0, - "batches": 1, - "version_conflicts": 0, - "noops": 0, - "retries": { - "bulk": 0, - "search": 0 - }, - "throttled_millis": 0, - "requests_per_second": -1, - "throttled_until_millis": 0, - "failures": [] -} -``` -1. The failures have been remediated. - -:::{tip} -Since the failure store is enabled on this data stream, it would be wise to check it for any further failures from the reindexing process. Failures that happen at this point in the process may end up as nested failures in the failure store. Remediating nested failures can quickly become a hassle as the original document gets nested multiple levels deep in the failure document. For this reason, it is suggested to remediate data during a quiet period where no other failures will arise. Furthermore, rolling over the failure store before executing the remediation would allow easier discarding of any new nested failures and only operate on the original failure documents. -::: - -::::{step} Done -:::: - -::::: - -Once any failures have been remediated, you may wish to purge the failures from the failure store to clear up space and to avoid warnings about failed data that has already been replayed. Otherwise, your failures will stay available until the maximum failure store retention should you need to reference them. \ No newline at end of file diff --git a/manage-data/toc.yml b/manage-data/toc.yml index 72cafaace..124bfce49 100644 --- a/manage-data/toc.yml +++ b/manage-data/toc.yml @@ -21,6 +21,8 @@ toc: - file: data-store/data-streams/reindex-tsds.md - file: data-store/data-streams/logs-data-stream.md - file: data-store/data-streams/failure-store.md + children: + - file: data-store/data-streams/failure-store-recipes.md - file: data-store/mapping.md children: - file: data-store/mapping/dynamic-mapping.md From 959bfd8da68557f100697cfe6d336bbd17af4162 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Fri, 30 May 2025 15:40:11 -0400 Subject: [PATCH 26/29] Apply suggestions from code review Co-authored-by: David Kilfoyle <41695641+kilfoyle@users.noreply.github.com> --- .../data-streams/failure-store-recipes.md | 56 +++++++++---------- .../data-store/data-streams/failure-store.md | 2 +- 2 files changed, 29 insertions(+), 29 deletions(-) diff --git a/manage-data/data-store/data-streams/failure-store-recipes.md b/manage-data/data-store/data-streams/failure-store-recipes.md index 1f394336d..b24b3f362 100644 --- a/manage-data/data-store/data-streams/failure-store-recipes.md +++ b/manage-data/data-store/data-streams/failure-store-recipes.md @@ -30,7 +30,7 @@ POST my-datastream-ingest/_doc }, "_seq_no": 2, "_primary_term": 1, - "failure_store": "used" // The document was sent to the failure store + "failure_store": "used" // The document was sent to the failure store. } ``` @@ -66,7 +66,7 @@ GET my-datastream-ingest::failures/_search "index": "my-datastream-ingest", "source": { // When an ingest pipeline fails, the document stored is what was originally sent to the cluster. "important": { - "info": "The rain in Spain falls mainly on the plain" // The important info that we failed to find was originally present on the document. + "info": "The rain in Spain falls mainly on the plain" // The important information that we failed to find was originally present in the document. }, "@timestamp": "2025-04-21T00:00:00Z" } @@ -93,7 +93,7 @@ GET my-datastream-ingest::failures/_search } ``` -Despite not knowing the pipelines beforehand, we have some places to start looking. The `ingest-step-2` pipeline cannot find the `important.info` field despite it being present on the document that was sent to the cluster. If we pull that pipeline definition we find the following: +Despite not knowing the pipelines beforehand, we have some places to start looking. The `ingest-step-2` pipeline cannot find the `important.info` field despite it being present in the document that was sent to the cluster. If we pull that pipeline definition we find the following: ```console GET _ingest/pipeline/ingest-step-2 @@ -126,7 +126,7 @@ GET _ingest/pipeline/ingest-step-1 "processors": [ { "remove": { - "field": "important.info" // A remove processor that is incorrectly getting rid of our important field. + "field": "important.info" // A remove processor that is incorrectly removing our important field. } }, { @@ -143,7 +143,7 @@ We find a remove processor in the first pipeline that is the root cause of the p ## Troubleshooting complicated ingest pipelines [failure-store-recipes-complicated-ingest-troubleshoot] -Ingest processors can be labeled with [tags](./failure-store.md). These tags are user provided information that names or describes the processor's purpose in the pipeline. When documents are redirected to the failure store due to a processor issue, they capture the tag from the processor in which the failure occurred if it exists. Because of this, it is a good practice to tag the processors in your pipeline so that the location of a failure can be identified quickly. +Ingest processors can be labeled with [tags](./failure-store.md). These tags are user-provided information that names or describes the processor's purpose in the pipeline. When documents are redirected to the failure store due to a processor issue, they capture the tag from the processor in which the failure occurred, if it exists. Because of this behavior, it is a good practice to tag the processors in your pipeline so that the location of a failure can be identified quickly. Here we have a needlessly complicated pipeline. It is made up of several set and remove processors. Beneficially, they are all tagged with descriptive names. ```console @@ -194,7 +194,7 @@ PUT _ingest/pipeline/complicated-processor } ``` -We ingest some data and find that it was sent to the failure store +We ingest some data and find that it was sent to the failure store. ```console POST my-datastream-ingest/_doc?pipeline=complicated-processor { @@ -220,7 +220,7 @@ POST my-datastream-ingest/_doc?pipeline=complicated-processor } ``` -Upon checking the failure, we can quickly identify the tagged processor that caused the problem +On checking the failure, we can quickly identify the tagged processor that caused the problem. ```console GET my-datastream-ingest::failures/_search ``` @@ -268,7 +268,7 @@ GET my-datastream-ingest::failures/_search ], "pipeline": "complicated-processor", "processor_type": "set", // Helpful, but which set processor on the pipeline could it be? - "processor_tag": "copy to new counter again" // The tag of the exact processor that it failed on. + "processor_tag": "copy to new counter again" // The tag of the exact processor that the document failed on. } } } @@ -277,11 +277,11 @@ GET my-datastream-ingest::failures/_search } ``` -Without tags in place it would not be as clear where in the pipeline we encountered the problem. Tags provide a unique identifier for a processor that can be quickly referenced in case of an ingest failure. +Without tags in place it would not be as clear where in the pipeline the indexing problem occurred. Tags provide a unique identifier for a processor that can be quickly referenced in case of an ingest failure. ## Alerting on failed ingestion [failure-store-recipes-alerting] -Since failure stores can be searched just like a normal data stream, we can use them as inputs to [alerting rules](./failure-store.md) in Kibana. Here is a simple alerting example to trigger on more than ten failures in the last five minutes for a data stream: +Since failure stores can be searched just like a normal data stream, we can use them as inputs to [alerting rules](./failure-store.md) in Kibana. Here is a simple alerting example that is triggered when more than ten indexing failures have occurred in the last five minutes for a data stream: :::::{stepper} @@ -349,17 +349,17 @@ Care should be taken when replaying data into a data stream from a failure store We recommend a few best practices for remediating failure data. -**Separate your failures beforehand.** As described in the [failure document source](#use-failure-store-document-source) section above, failure documents are structured differently depending on when the document failed during ingestion. We recommend to separate documents by ingest pipeline failures and indexing failures at minimum. Ingest pipeline failures often need to have the original pipeline re-executed, while index failures should skip any pipelines. Further separating failures by index or specific failure type may also be beneficial. +**Separate your failures beforehand.** As described in the [failure document source](#use-failure-store-document-source) section above, failure documents are structured differently depending on when the document failed during ingestion. We recommend to separate documents by ingest pipeline failures and indexing failures at minimum. Ingest pipeline failures often need to have the original pipeline re-run, while index failures should skip any pipelines. Further separating failures by index or specific failure type may also be beneficial. **Perform a failure store rollover.** Consider rolling over the failure store before attempting to remediate failures. This will create a new failure index that will collect any new failures during the remediation process. -**Use an ingest pipeline to convert failure documents back into their original document.** Failure documents store failure information along with the document that failed ingestion. The first step for remediating documents should be to use an ingest pipeline to extract the original source from the failure document and discard any other info on it. +**Use an ingest pipeline to convert failure documents back into their original document.** Failure documents store failure information along with the document that failed ingestion. The first step for remediating documents should be to use an ingest pipeline to extract the original source from the failure document and then discard any other information about the failure. -**Simulate first to avoid repeat failures.** If you must execute a pipeline as part of your remediation process, it is best to simulate the pipeline against the failure first. This will catch any unforeseen issues that may fail the document a second time. Remember, ingest pipeline failures will capture the document before an ingest pipeline was applied to it, which can further complicate remediation when a failure document becomes nested inside a new failure. +**Simulate first to avoid repeat failures.** If you must run a pipeline as part of your remediation process, it is best to simulate the pipeline against the failure first. This will catch any unforeseen issues that may fail the document a second time. Remember, ingest pipeline failures will capture the document before an ingest pipeline is applied to it, which can further complicate remediation when a failure document becomes nested inside a new failure. ### Remediating ingest node failures [failure-store-recipes-remediation-ingest] -Failures that occurred during an ingest processor will be stored as they were before any pipelines were executed. To replay the document into the data stream we will need to rerun any applicable pipelines for the document. +Failures that occurred during ingest processing will be stored as they were before any pipelines were run. To replay the document into the data stream we will need to re-run any applicable pipelines for the document. :::::{stepper} @@ -466,7 +466,7 @@ Take note of the documents that are returned. We can use these to simulate that :::: ::::{step} Fix the original problem -Because ingest pipeline failures need to be reprocessed by their original pipelines, any problems with those pipeline should be fixed before remediating failures. Investigating the pipeline mentioned in the example above shows that there is a processor that expects a field to be present that is not always present. +Because ingest pipeline failures need to be reprocessed by their original pipelines, any problems with those pipelines should be fixed before remediating failures. Investigating the pipeline mentioned in the example above shows that there is a processor that expects a field to be present that is not always present. ```console-result { @@ -500,7 +500,7 @@ PUT _ingest/pipeline/my-datastream-default-pipeline ] } ``` -1. Only conditionally run the processor if the field exists. +1. Conditionally run the processor only if the field exists. :::: @@ -536,7 +536,7 @@ PUT _ingest/pipeline/my-datastream-remediation-pipeline ``` 1. Copy the original index name from the failure document over into the document's metadata. If you use custom document routing, copy that over too. 2. Capture the source of the original document. -3. Discard the `error` field since it wont be needed for the remediation. +3. Discard the `error` field since it won't be needed for the remediation. 4. Also discard the `document` field. 5. We extract all the fields from the original document's source back to the root of the document. 6. Since the pipeline that failed was the default pipeline on `my-datastream-ingest-example`, we will use the `reroute` processor to send any remediated documents to that data stream's default pipeline again to be reprocessed. @@ -632,10 +632,10 @@ POST _ingest/pipeline/_simulate } ``` 1. The index has been updated via the reroute processor. -2. The id has stayed the same. -3. The source should cleanly match what the original document should have been. +2. The document ID has stayed the same. +3. The source should cleanly match the contents of the original document. -Now that the remediation pipeline has been tested, be sure to test the end to end ingestion to verify that no further problems will arise. To do this, we will use the [simulate ingestion API](./failure-store.md) to test multiple pipeline executions. +Now that the remediation pipeline has been tested, be sure to test the end-to-end ingestion to verify that no further problems will arise. To do this, we will use the [simulate ingestion API](./failure-store.md) to test multiple pipeline executions. ```console POST _ingest/_simulate?pipeline=my-datastream-remediation-pipeline <1> @@ -699,7 +699,7 @@ POST _ingest/_simulate?pipeline=my-datastream-remediation-pipeline <1> ] } ``` -1. Set the pipeline to be the remediation pipeline name, otherwise, the default pipeline for the document's index is used. +1. Set the pipeline to be the remediation pipeline name, otherwise the default pipeline for the document's index is used. 2. The contents of the remediation pipeline in previous steps. 3. The contents of the previously identified example failure document. @@ -806,7 +806,7 @@ POST _reindex 1. The failures have been remediated. :::{tip} -Since the failure store is enabled on this data stream, it would be wise to check it for any further failures from the reindexing process. Failures that happen at this point in the process may end up as nested failures in the failure store. Remediating nested failures can quickly become a hassle as the original document gets nested multiple levels deep in the failure document. For this reason, it is suggested to remediate data during a quiet period where no other failures will arise. Furthermore, rolling over the failure store before executing the remediation would allow easier discarding of any new nested failures and only operate on the original failure documents. +Since the failure store is enabled on this data stream, it would be wise to check it for any further failures from the reindexing process. Failures that happen at this point in the process may end up as nested failures in the failure store. Remediating nested failures can quickly become a hassle as the original document gets nested multiple levels deep in the failure document. For this reason, it is suggested to remediate data during a quiet period when no other failures are likely to arise. Furthermore, rolling over the failure store before executing the remediation would allow easier discarding of any new nested failures and only operate on the original failure documents. ::: ::::{step} Done @@ -816,7 +816,7 @@ Since the failure store is enabled on this data stream, it would be wise to chec ### Remediating mapping and shard failures [failure-store-recipes-remediation-mapping] -As described in the [failure document source](#use-failure-store-document-source) section above, failures that occur due to a mapping or indexing issue will be stored as they were after any pipelines had executed. This means that to replay the document into the data stream we will need to make sure to skip any pipelines that have already run. +As described in the previous [failure document source](#use-failure-store-document-source) section, failures that occur due to a mapping or indexing issue will be stored as they were after any pipelines had executed. This means that to replay the document into the data stream we will need to make sure to skip any pipelines that have already run. :::{tip} You can greatly simplify this remediation process by writing any ingest pipelines to be idempotent. In that case, any document that has already be processed that passes through a pipeline again would be unchanged. @@ -869,7 +869,7 @@ POST my-datastream-indexing-example::failures/_search 3. Further narrow which kind of failure you are attempting to remediate. In this example we are targeting a specific type of error. 4. Filter on timestamp to only retrieve failures before a certain point in time. This provides a stable set of documents. -Take note of the documents that are returned. We can use these to simulate that our remediation logic makes sense +Take note of the documents that are returned. We can use these to simulate that our remediation logic makes sense. ```console-result { "took": 1, @@ -930,7 +930,7 @@ Caused by: j.l.IllegalArgumentException: data stream timestamp field [@timestamp ::::{step} Fix the original problem -There are a broad set of possible indexing failures. Most of these problems stem from incorrect values for a particular mapping. Sometimes a large number of new fields are dynamically mapped and the maximum number of mapping fields is reached and no more can be added. In our example above, the document being indexed is missing a required timestamp. +There are a broad set of possible indexing failures. Most of these problems stem from incorrect values for a particular mapping. Sometimes a large number of new fields are dynamically mapped and the maximum number of mapping fields is reached, so no more can be added. In our example above, the document being indexed is missing a required timestamp. These problems can occur in a number of places: Data sent from a client may be incomplete, ingest pipelines may not be producing the correct result, or the index mapping may need to be updated to account for changes in data. @@ -970,7 +970,7 @@ PUT _ingest/pipeline/my-datastream-remediation-pipeline 5. We extract all the fields from the original document's source back to the root of the document. The `@timestamp` field is not overwritten and thus will be present in the final document. :::{important} -Remember that a document that has failed during indexing has already been processed by the ingest processor! It shouldn't need to be processed again unless you made changes to your pipeline to fix the original problem. Make sure that any fixes applied to the ingest pipeline is reflected in the pipeline logic here. +Remember that a document that has failed during indexing has already been processed by the ingest processor! It shouldn't need to be processed again unless you made changes to your pipeline to fix the original problem. Make sure that any fixes applied to the ingest pipeline are reflected in the pipeline logic here. ::: :::: @@ -1115,8 +1115,8 @@ POST _reindex ``` 1. Read from the failure store. 2. Only reindex failure documents that match the ones we are replaying. -3. Set the destination to the data stream the failures originally were sent to. The remediation pipeline above updates the index to be the correct one, but a destination is still required. -4. Replace the pipeline with the remediation pipeline. This will keep any default pipelines from running. +3. Set the destination to the data stream the failures originally were sent to. The remediation pipeline in the example updates the index to be the correct one, but a destination is still required. +4. Replace the original pipeline with the remediation pipeline. This will keep any default pipelines from running. ```console-result { diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index e00f971e5..aff73c00b 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -63,7 +63,7 @@ PUT _data_stream/my-datastream-existing/_options 1. The failure store option will now be enabled. -The failure store redirection can be disabled using this API as well. When the failure store is deactivated, only failed document redirection is halted. Any existing failure data in the data stream will remain until removed by manual deletion or by retention. +The failure store redirection can be disabled using this API as well. When the failure store is deactivated, only failed document redirection is halted. Any existing failure data in the data stream will remain until removed by manual deletion or until the data expires due to reaching its max configured retention. ```console PUT _data_stream/my-datastream-existing/_options From fd8939e1a5cda23a2705c5b292ef4f1e3a9180a1 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Fri, 30 May 2025 15:42:55 -0400 Subject: [PATCH 27/29] typo --- manage-data/data-store/data-streams/failure-store-recipes.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/manage-data/data-store/data-streams/failure-store-recipes.md b/manage-data/data-store/data-streams/failure-store-recipes.md index b24b3f362..b81d44ee5 100644 --- a/manage-data/data-store/data-streams/failure-store-recipes.md +++ b/manage-data/data-store/data-streams/failure-store-recipes.md @@ -4,7 +4,7 @@ When something goes wrong during ingestion it is often not an isolated event. In ## Troubleshooting nested ingest pipelines [failure-store-recipes-nested-ingest-troubleshoot] -When a document fails in an ingest pipeline it can be difficult to figure out exactly what when wrong and where. When these failures are captured by the failure store during this part of the ingestion process, they will contain additional debugging information. Failed documents will note the type of processor and which pipeline was executing when the failure occurred. Failed documents will also contain a pipeline trace which keeps track of any nested pipeline calls that the document was in at time of failure. +When a document fails in an ingest pipeline it can be difficult to figure out exactly what went wrong and where. When these failures are captured by the failure store during this part of the ingestion process, they will contain additional debugging information. Failed documents will note the type of processor and which pipeline was executing when the failure occurred. Failed documents will also contain a pipeline trace which keeps track of any nested pipeline calls that the document was in at time of failure. To demonstrate this, we will follow a failed document through an unfamiliar data stream and ingest pipeline: ```console From ccda059d31b30508edd675340def7cdb1cefeef9 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Mon, 2 Jun 2025 16:56:10 -0400 Subject: [PATCH 28/29] Update links and add privilege info --- .../elasticsearch-privileges.md | 5 +++ .../data-streams/failure-store-recipes.md | 19 ++++++------ .../data-store/data-streams/failure-store.md | 31 +++++++++---------- 3 files changed, 30 insertions(+), 25 deletions(-) diff --git a/deploy-manage/users-roles/cluster-or-deployment-auth/elasticsearch-privileges.md b/deploy-manage/users-roles/cluster-or-deployment-auth/elasticsearch-privileges.md index 33f81e332..de485dfc6 100644 --- a/deploy-manage/users-roles/cluster-or-deployment-auth/elasticsearch-privileges.md +++ b/deploy-manage/users-roles/cluster-or-deployment-auth/elasticsearch-privileges.md @@ -352,6 +352,9 @@ To learn how to assign privileges to a role, refer to [](/deploy-manage/users-ro `manage_data_stream_lifecycle` : All [Data stream lifecycle](../../../manage-data/lifecycle/data-stream.md) operations relating to reading and managing the built-in lifecycle of a data stream. This includes operations such as adding and removing a lifecycle from a data stream. +`manage_failure_store` +: All `monitor` privileges plus index and data stream administration limited to failure stores only. + `manage_follow_index` : All actions that are required to manage the lifecycle of a follower index, which includes creating a follower index, closing it, and converting it to a regular index. This privilege is necessary only on clusters that contain follower indices. @@ -381,6 +384,8 @@ To learn how to assign privileges to a role, refer to [](/deploy-manage/users-ro This privilege is not available in {{serverless-full}}. +`read_failure_store` +: Read-only access to actions performed on a data stream's failure store. Required for access to failure store data (count, explain, get, mget, get indexed scripts, more like this, multi percolate/search/termvector, percolate, scroll, clear_scroll, search, suggest, tv). `view_index_metadata` : Read-only access to index and data stream metadata (aliases, exists, field capabilities, field mappings, get index, get data stream, ilm explain, mappings, search shards, settings, validate query). This privilege is available for use primarily by {{kib}} users. diff --git a/manage-data/data-store/data-streams/failure-store-recipes.md b/manage-data/data-store/data-streams/failure-store-recipes.md index b81d44ee5..497584161 100644 --- a/manage-data/data-store/data-streams/failure-store-recipes.md +++ b/manage-data/data-store/data-streams/failure-store-recipes.md @@ -143,7 +143,7 @@ We find a remove processor in the first pipeline that is the root cause of the p ## Troubleshooting complicated ingest pipelines [failure-store-recipes-complicated-ingest-troubleshoot] -Ingest processors can be labeled with [tags](./failure-store.md). These tags are user-provided information that names or describes the processor's purpose in the pipeline. When documents are redirected to the failure store due to a processor issue, they capture the tag from the processor in which the failure occurred, if it exists. Because of this behavior, it is a good practice to tag the processors in your pipeline so that the location of a failure can be identified quickly. +Ingest processors can be labeled with tags. These tags are user-provided information that names or describes the processor's purpose in the pipeline. When documents are redirected to the failure store due to a processor issue, they capture the tag from the processor in which the failure occurred, if it exists. Because of this behavior, it is a good practice to tag the processors in your pipeline so that the location of a failure can be identified quickly. Here we have a needlessly complicated pipeline. It is made up of several set and remove processors. Beneficially, they are all tagged with descriptive names. ```console @@ -281,7 +281,8 @@ Without tags in place it would not be as clear where in the pipeline the indexin ## Alerting on failed ingestion [failure-store-recipes-alerting] -Since failure stores can be searched just like a normal data stream, we can use them as inputs to [alerting rules](./failure-store.md) in Kibana. Here is a simple alerting example that is triggered when more than ten indexing failures have occurred in the last five minutes for a data stream: +Since failure stores can be searched just like a normal data stream, we can use them as inputs to [alerting rules](../../../explore-analyze/alerts-cases/alerts.md) in +{{kib}}. Here is a simple alerting example that is triggered when more than ten indexing failures have occurred in the last five minutes for a data stream: :::::{stepper} @@ -349,7 +350,7 @@ Care should be taken when replaying data into a data stream from a failure store We recommend a few best practices for remediating failure data. -**Separate your failures beforehand.** As described in the [failure document source](#use-failure-store-document-source) section above, failure documents are structured differently depending on when the document failed during ingestion. We recommend to separate documents by ingest pipeline failures and indexing failures at minimum. Ingest pipeline failures often need to have the original pipeline re-run, while index failures should skip any pipelines. Further separating failures by index or specific failure type may also be beneficial. +**Separate your failures beforehand.** As described in the previous [failure document source](./failure-store.md#use-failure-store-document-source) section, failure documents are structured differently depending on when the document failed during ingestion. We recommend to separate documents by ingest pipeline failures and indexing failures at minimum. Ingest pipeline failures often need to have the original pipeline re-run, while index failures should skip any pipelines. Further separating failures by index or specific failure type may also be beneficial. **Perform a failure store rollover.** Consider rolling over the failure store before attempting to remediate failures. This will create a new failure index that will collect any new failures during the remediation process. @@ -544,7 +545,7 @@ PUT _ingest/pipeline/my-datastream-remediation-pipeline :::: ::::{step} Test your pipelines -Before sending data off to be reindexed, be sure to test the pipelines in question with an example document to make sure they work. First, test to make sure the resulting document from the remediation pipeline is shaped how you expect. We can use the [simulate pipeline API](./failure-store.md) for this. +Before sending data off to be reindexed, be sure to test the pipelines in question with an example document to make sure they work. First, test to make sure the resulting document from the remediation pipeline is shaped how you expect. We can use the [simulate pipeline API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ingest-simulate) for this. ```console POST _ingest/pipeline/_simulate @@ -635,7 +636,7 @@ POST _ingest/pipeline/_simulate 2. The document ID has stayed the same. 3. The source should cleanly match the contents of the original document. -Now that the remediation pipeline has been tested, be sure to test the end-to-end ingestion to verify that no further problems will arise. To do this, we will use the [simulate ingestion API](./failure-store.md) to test multiple pipeline executions. +Now that the remediation pipeline has been tested, be sure to test the end-to-end ingestion to verify that no further problems will arise. To do this, we will use the [simulate ingestion API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-simulate-ingest) to test multiple pipeline executions. ```console POST _ingest/_simulate?pipeline=my-datastream-remediation-pipeline <1> @@ -733,7 +734,7 @@ POST _ingest/_simulate?pipeline=my-datastream-remediation-pipeline <1> :::: ::::{step} Reindex the failure documents -Combine the remediation pipeline with the failure store query together in a [reindex operation](./failure-store.md) to replay the failures. +Combine the remediation pipeline with the failure store query together in a [reindex operation](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-reindex) to replay the failures. ```console POST _reindex @@ -816,7 +817,7 @@ Since the failure store is enabled on this data stream, it would be wise to chec ### Remediating mapping and shard failures [failure-store-recipes-remediation-mapping] -As described in the previous [failure document source](#use-failure-store-document-source) section, failures that occur due to a mapping or indexing issue will be stored as they were after any pipelines had executed. This means that to replay the document into the data stream we will need to make sure to skip any pipelines that have already run. +As described in the previous [failure document source](./failure-store.md#use-failure-store-document-source) section, failures that occur due to a mapping or indexing issue will be stored as they were after any pipelines had executed. This means that to replay the document into the data stream we will need to make sure to skip any pipelines that have already run. :::{tip} You can greatly simplify this remediation process by writing any ingest pipelines to be idempotent. In that case, any document that has already be processed that passes through a pipeline again would be unchanged. @@ -976,7 +977,7 @@ Remember that a document that has failed during indexing has already been proces :::: ::::{step} Test your pipeline -Before sending data off to be reindexed, be sure to test the remedial pipeline with an example document to make sure it works. Most importantly, make sure the resulting document from the remediation pipeline is shaped how you expect. We can use the [simulate pipeline API](./failure-store.md) for this. +Before sending data off to be reindexed, be sure to test the remedial pipeline with an example document to make sure it works. Most importantly, make sure the resulting document from the remediation pipeline is shaped how you expect. We can use the [simulate pipeline API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-ingest-simulate) for this. ```console POST _ingest/pipeline/_simulate @@ -1067,7 +1068,7 @@ Caused by: j.l.IllegalArgumentException: data stream timestamp field [@timestamp :::: ::::{step} Reindex the failure documents -Combine the remediation pipeline with the failure store query together in a [reindex operation](./failure-store.md) to replay the failures. +Combine the remediation pipeline with the failure store query together in a [reindex operation](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-reindex) to replay the failures. ```console POST _reindex diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index aff73c00b..a9f63c84f 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -19,7 +19,7 @@ Each data stream has its own failure store that can be enabled to accept failed You can specify in a data stream's [index template](../templates.md) if it should enable the failure store when it is first created. :::{note} -Unlike the `settings` and `mappings` fields on an [index template](../templates.md) which are repeatedly applied to new data stream write indices on rollover, the `data_stream_options` section of a template is applied to a data stream only once when the data stream is first created. To configure existing data streams, use the put [data stream options API](./failure-store.md). +Unlike the `settings` and `mappings` fields on an [index template](../templates.md) which are repeatedly applied to new data stream write indices on rollover, the `data_stream_options` section of a template is applied to a data stream only once when the data stream is first created. To configure existing data streams, use the put [data stream options API](indices-put-data-stream-options). ::: To enable the failure store on a new data stream, enable it in the `data_stream_options` of the template: @@ -48,8 +48,7 @@ After a matching data stream is created, its failure store will be enabled. ### Set up for existing data streams [set-up-failure-store-existing] Enabling the failure store via [index templates](../templates.md) can only affect data streams that are newly created. Existing data streams that use a template are not affected by changes to the template's `data_stream_options` field. - -To modify an existing data stream's options, use the [put data stream options](./failure-store.md) API: +To modify an existing data stream's options, use the [put data stream options](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-indices-put-data-stream-options) API: ```console PUT _data_stream/my-datastream-existing/_options @@ -78,7 +77,7 @@ PUT _data_stream/my-datastream-existing/_options ### Enable failure store via cluster setting [set-up-failure-store-cluster-setting] -If you have a large number of existing data streams you may want to enable their failure stores in one place. Instead of updating each of their options individually, set `data_streams.failure_store.enabled` to a list of index patterns in the [cluster settings](./failure-store.md). Any data streams that match one of these patterns will operate with their failure store enabled. +If you have a large number of existing data streams you may want to enable their failure stores in one place. Instead of updating each of their options individually, set `data_streams.failure_store.enabled` to a list of index patterns in the [cluster settings](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-cluster-put-settings). Any data streams that match one of these patterns will operate with their failure store enabled. ```console PUT _cluster/settings @@ -90,7 +89,7 @@ PUT _cluster/settings ``` 1. Indices that match `my-datastream-*` or `logs-*` will redirect failures to the failure store unless explicitly disabled. -Matching data streams will ignore this configuration if the failure store is explicitly enabled or disabled in their [data stream options](./failure-store.md). +Matching data streams will ignore this configuration if the failure store is explicitly enabled or disabled in their [data stream options](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-indices-put-data-stream-options). ```console PUT _cluster/settings @@ -121,7 +120,7 @@ Once a failure store is enabled for a data stream it will begin redirecting docu Each data stream's failure store is made up of a list of indices that are dedicated to storing failed documents. These failure indices function much like a data stream's normal backing indices: There is a write index that accepts failed documents, the indices can be rolled over, and they're automatically cleaned up over time subject to a lifecycle policy. Failure indices are lazily created the first time they are needed to store a failed document. -When a document bound for a data stream encounters a problem during its ingestion, the response is annotated with the `failure_store` field which describes how {{es}} responded to that problem. The `failure_store` field is present on both the [bulk](./failure-store.md) and [index](./failure-store.md) API responses when applicable. Clients can use this information to augment their behavior based on the response from {{es}}. +When a document bound for a data stream encounters a problem during its ingestion, the response is annotated with the `failure_store` field which describes how {{es}} responded to that problem. The `failure_store` field is present on both the [bulk](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-bulk) and [index](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-create) API responses when applicable. Clients can use this information to augment their behavior based on the response from {{es}}. Here we have a bulk operation that sends two documents. Both are writing to the `id` field which is mapped as a `long` field type. The first document will be accepted, but the second document would cause a failure because the value `invalid_text` cannot be parsed as a `long`. This second document will be redirected to the failure store: @@ -283,14 +282,14 @@ Once you have accumulated some failures, they can be searched much like a regula :::{warning} Documents redirected to the failure store in the event of a failed ingest pipeline will be stored in their original, unprocessed form. If an ingest pipeline normally redacts sensitive information from a document, then failed documents in their original, unprocessed form may contain sensitive information. -Furthermore, failed documents are likely to be structured differently than normal data in a data stream, and thus are not supported by [document level security](./failure-store.md) or [field level security](./failure-store.md). +Furthermore, failed documents are likely to be structured differently than normal data in a data stream, and thus are not supported by [document level security](../../../deploy-manage/users-roles/cluster-or-deployment-auth/controlling-access-at-document-field-level.md#document-level-security) or [field level security](../../../deploy-manage/users-roles/cluster-or-deployment-auth/controlling-access-at-document-field-level.md#field-level-security). -To limit visibility on potentially sensitive data, users require the [`read_failure_store`](./failure-store.md) index privilege for a data stream in order to search that data stream's failure store data. +To limit visibility on potentially sensitive data, users require the [`read_failure_store`](../../../deploy-manage/users-roles/cluster-or-deployment-auth/elasticsearch-privileges.md#privileges-list-indices) index privilege for a data stream in order to search that data stream's failure store data. ::: Searching a data stream's failure store can be done by making use of the existing search APIs available in {{es}}. -To indicate that the search should be performed on failure store data, use the [index component selector syntax](./failure-store.md) to indicate which part of the data stream to target in the search operation. Appending the `::failures` suffix to the name of the data stream indicates that the operation should be performed against that data stream's failure store instead of its regular backing indices. +To indicate that the search should be performed on failure store data, use the [index component selector syntax](elasticsearch://reference/elasticsearch/rest-apis/api-conventions.md#api-component-selectors) to indicate which part of the data stream to target in the search operation. Appending the `::failures` suffix to the name of the data stream indicates that the operation should be performed against that data stream's failure store instead of its regular backing indices. :::::{tab-set} @@ -431,7 +430,7 @@ Failure documents have a uniform structure that is handled internally by {{es}}. : (`keyword`) The index that the document was being written to when it failed. `document.source` - : (unmapped object) The body of the original document. This field is unmapped and only present in the failure document's source. This prevents mapping conflicts in the failure store when redirecting failed documents. If you need to include fields from the original document's source in your queries, use [runtime fields](./failure-store.md) on the search request. + : (unmapped object) The body of the original document. This field is unmapped and only present in the failure document's source. This prevents mapping conflicts in the failure store when redirecting failed documents. If you need to include fields from the original document's source in your queries, use [runtime fields](../mapping/define-runtime-fields-in-search-request.md) on the search request. `error` : (`object`) Information about the failure that prevented this document from being indexed. @@ -452,7 +451,7 @@ Failure documents have a uniform structure that is handled internally by {{es}}. : (`keyword`, optional array) If the failure occurred in an ingest pipeline, this will contain the list of pipelines that the document had visited up until the failure. `error.processor_tag` - : (`keyword`, optional) If the failure occurred in an ingest processor that is annotated with a [tag](./failure-store.md), the tag contents will be present here. + : (`keyword`, optional) If the failure occurred in an ingest processor that is annotated with a tag, the tag contents will be present here. `error.processor_type` : (`keyword`, optional) If the failure occurred in an ingest processor, this will contain the processor type. (e.g. `script`, `append`, `enrich`, etc.) @@ -699,7 +698,7 @@ Failure data can accumulate in a data stream over time. To help manage this accu ### Failure store rollover [manage-failure-store-rollover] -A data stream treats its failure store much like a secondary set of [backing indices](./failure-store.md). Multiple dedicated hidden indices serve search requests for the failure store, while one index acts as the current write index. You can use the [rollover](./failure-store.md) API to rollover the failure store. Much like the regular indices in a data stream, a new write index will be created in the failure store to accept new failure documents. +A data stream treats its failure store much like a secondary set of [backing indices](../data-streams.md#backing-indices). Multiple dedicated hidden indices serve search requests for the failure store, while one index acts as the current write index. You can use the [rollover](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-indices-rollover) API to rollover the failure store. Much like the regular indices in a data stream, a new write index will be created in the failure store to accept new failure documents. ```console POST my-datastream::failures/_rollover @@ -720,7 +719,7 @@ POST my-datastream::failures/_rollover ### Failure store lifecycle [manage-failure-store-lifecycle] -Failure stores have their retention managed using an internal [data stream lifecycle](./failure-store.md). A thirty day (30d) retention is applied to failure store data. You can view the active lifecycle for a failure store index by calling the [get data stream API](./failure-store.md): +Failure stores have their retention managed using an internal [data stream lifecycle](../../lifecycle/data-stream.md). A thirty day (30d) retention is applied to failure store data. You can view the active lifecycle for a failure store index by calling the [get data stream API](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-indices-get-data-stream): ```console GET _data_stream/my-datastream @@ -783,7 +782,7 @@ GET _data_stream/my-datastream 4. The retention is currently determined by the default. :::{note} -The default retention respects any maximum retention values. If [maximum retention](./failure-store.md) is configured lower than thirty days then the maximum retention will be used as the default value. +The default retention respects any maximum retention values. If [maximum retention](../../lifecycle/data-stream/tutorial-data-stream-retention.md#what-is-retention) is configured lower than thirty days then the maximum retention will be used as the default value. ::: You can update the default retention period for failure stores in your deployment by updating the `data_streams.lifecycle.retention.failures_default` cluster setting. New and existing data streams that have no retention configured on their failure stores will use this value to determine their retention period. @@ -797,7 +796,7 @@ PUT _cluster/settings } ``` -You can also specify the failure store retention period for a data stream on its data stream options. These can be specified via the index template for new data streams, or via the [put data stream options](./failure-store.md) API for existing data streams. +You can also specify the failure store retention period for a data stream on its data stream options. These can be specified via the index template for new data streams, or via the [put data stream options](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-indices-put-data-stream-options) API for existing data streams. ```console PUT _data_stream/my-datastream/_options @@ -815,7 +814,7 @@ PUT _data_stream/my-datastream/_options ### Add and remove from failure store [manage-failure-store-indices] -Failure stores support adding and removing indices from them using the [modify data stream](./failure-store.md) API. +Failure stores support adding and removing indices from them using the [modify data stream](https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-indices-modify-data-stream) API. ```console POST _data_stream/_modify From 86c4c7ecd1165ddc23a57ff81cd0ba477bcb0100 Mon Sep 17 00:00:00 2001 From: James Baiera Date: Mon, 2 Jun 2025 17:24:57 -0400 Subject: [PATCH 29/29] Update example --- manage-data/data-store/data-streams/failure-store.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/manage-data/data-store/data-streams/failure-store.md b/manage-data/data-store/data-streams/failure-store.md index a9f63c84f..cd4abc5a6 100644 --- a/manage-data/data-store/data-streams/failure-store.md +++ b/manage-data/data-store/data-streams/failure-store.md @@ -125,7 +125,7 @@ When a document bound for a data stream encounters a problem during its ingestio Here we have a bulk operation that sends two documents. Both are writing to the `id` field which is mapped as a `long` field type. The first document will be accepted, but the second document would cause a failure because the value `invalid_text` cannot be parsed as a `long`. This second document will be redirected to the failure store: ```console -POST my-datastream/_bulk +POST my-datastream-new/_bulk {"create":{}} {"@timestamp": "2025-05-01T00:00:00Z", "id": 1234} <1> {"create":{}} @@ -141,7 +141,7 @@ POST my-datastream/_bulk "items": [ { "create": { - "_index": ".ds-my-datastream-2025.05.01-000001", <2> + "_index": ".ds-my-datastream-new-2025.05.01-000001", <2> "_id": "YUvQipYB_ZAKuDfZRosB", "_version": 1, "result": "created", @@ -157,7 +157,7 @@ POST my-datastream/_bulk }, { "create": { - "_index": ".fs-my-datastream-2025.05.01-000002", <3> + "_index": ".fs-my-datastream-new-2025.05.01-000002", <3> "_id": "lEu8jZYB_ZAKuDfZNouU", "_version": 1, "result": "created", @@ -186,7 +186,7 @@ If the document was redirected to a data stream's failure store due to a problem ```console-result { - "_index": ".fs-my-datastream-2025.05.01-000002", <1> + "_index": ".fs-my-datastream-new-2025.05.01-000002", <1> "_id": "lEu8jZYB_ZAKuDfZNouU", "_version": 1, "result": "created",