diff --git a/output/openapi/elasticsearch-openapi.json b/output/openapi/elasticsearch-openapi.json index 93e723fa1a..e93b0c9f48 100644 --- a/output/openapi/elasticsearch-openapi.json +++ b/output/openapi/elasticsearch-openapi.json @@ -38506,7 +38506,7 @@ "snapshot" ], "summary": "Analyze a snapshot repository", - "description": "Analyze the performance characteristics and any incorrect behaviour found in a repository.\n\nThe response exposes implementation details of the analysis which may change from version to version.\nThe response body format is therefore not considered stable and may be different in newer versions.\n\nThere are a large number of third-party storage systems available, not all of which are suitable for use as a snapshot repository by Elasticsearch.\nSome storage systems behave incorrectly, or perform poorly, especially when accessed concurrently by multiple clients as the nodes of an Elasticsearch cluster do. This API performs a collection of read and write operations on your repository which are designed to detect incorrect behaviour and to measure the performance characteristics of your storage system.\n\nThe default values for the parameters are deliberately low to reduce the impact of running an analysis inadvertently and to provide a sensible starting point for your investigations.\nRun your first analysis with the default parameter values to check for simple problems.\nIf successful, run a sequence of increasingly large analyses until you encounter a failure or you reach a `blob_count` of at least `2000`, a `max_blob_size` of at least `2gb`, a `max_total_data_size` of at least `1tb`, and a `register_operation_count` of at least `100`.\nAlways specify a generous timeout, possibly `1h` or longer, to allow time for each analysis to run to completion.\nPerform the analyses using a multi-node cluster of a similar size to your production cluster so that it can detect any problems that only arise when the repository is accessed by many nodes at once.\n\nIf the analysis fails, Elasticsearch detected that your repository behaved unexpectedly.\nThis usually means you are using a third-party storage system with an incorrect or incompatible implementation of the API it claims to support.\nIf so, this storage system is not suitable for use as a snapshot repository.\nYou will need to work with the supplier of your storage system to address the incompatibilities that Elasticsearch detects.\n\nIf the analysis is successful, the API returns details of the testing process, optionally including how long each operation took.\nYou can use this information to determine the performance of your storage system.\nIf any operation fails or returns an incorrect result, the API returns an error.\nIf the API returns an error, it may not have removed all the data it wrote to the repository.\nThe error will indicate the location of any leftover data and this path is also recorded in the Elasticsearch logs.\nYou should verify that this location has been cleaned up correctly.\nIf there is still leftover data at the specified location, you should manually remove it.\n\nIf the connection from your client to Elasticsearch is closed while the client is waiting for the result of the analysis, the test is cancelled.\nSome clients are configured to close their connection if no response is received within a certain timeout.\nAn analysis takes a long time to complete so you might need to relax any such client-side timeouts.\nOn cancellation the analysis attempts to clean up the data it was writing, but it may not be able to remove it all.\nThe path to the leftover data is recorded in the Elasticsearch logs.\nYou should verify that this location has been cleaned up correctly.\nIf there is still leftover data at the specified location, you should manually remove it.\n\nIf the analysis is successful then it detected no incorrect behaviour, but this does not mean that correct behaviour is guaranteed.\nThe analysis attempts to detect common bugs but it does not offer 100% coverage.\nAdditionally, it does not test the following:\n\n* Your repository must perform durable writes. Once a blob has been written it must remain in place until it is deleted, even after a power loss or similar disaster.\n* Your repository must not suffer from silent data corruption. Once a blob has been written, its contents must remain unchanged until it is deliberately modified or deleted.\n* Your repository must behave correctly even if connectivity from the cluster is disrupted. Reads and writes may fail in this case, but they must not return incorrect results.\n\nIMPORTANT: An analysis writes a substantial amount of data to your repository and then reads it back again.\nThis consumes bandwidth on the network between the cluster and the repository, and storage space and I/O bandwidth on the repository itself.\nYou must ensure this load does not affect other users of these systems.\nAnalyses respect the repository settings `max_snapshot_bytes_per_sec` and `max_restore_bytes_per_sec` if available and the cluster setting `indices.recovery.max_bytes_per_sec` which you can use to limit the bandwidth they consume.\n\nNOTE: This API is intended for exploratory use by humans. You should expect the request parameters and the response format to vary in future versions.\n\nNOTE: Different versions of Elasticsearch may perform different checks for repository compatibility, with newer versions typically being stricter than older ones.\nA storage system that passes repository analysis with one version of Elasticsearch may fail with a different version.\nThis indicates it behaves incorrectly in ways that the former version did not detect.\nYou must work with the supplier of your storage system to address the incompatibilities detected by the repository analysis API in any version of Elasticsearch.\n\nNOTE: This API may not work correctly in a mixed-version cluster.\n\n*Implementation details*\n\nNOTE: This section of documentation describes how the repository analysis API works in this version of Elasticsearch, but you should expect the implementation to vary between versions. The request parameters and response format depend on details of the implementation so may also be different in newer versions.\n\nThe analysis comprises a number of blob-level tasks, as set by the `blob_count` parameter and a number of compare-and-exchange operations on linearizable registers, as set by the `register_operation_count` parameter.\nThese tasks are distributed over the data and master-eligible nodes in the cluster for execution.\n\nFor most blob-level tasks, the executing node first writes a blob to the repository and then instructs some of the other nodes in the cluster to attempt to read the data it just wrote.\nThe size of the blob is chosen randomly, according to the `max_blob_size` and `max_total_data_size` parameters.\nIf any of these reads fails then the repository does not implement the necessary read-after-write semantics that Elasticsearch requires.\n\nFor some blob-level tasks, the executing node will instruct some of its peers to attempt to read the data before the writing process completes.\nThese reads are permitted to fail, but must not return partial data.\nIf any read returns partial data then the repository does not implement the necessary atomicity semantics that Elasticsearch requires.\n\nFor some blob-level tasks, the executing node will overwrite the blob while its peers are reading it.\nIn this case the data read may come from either the original or the overwritten blob, but the read operation must not return partial data or a mix of data from the two blobs.\nIf any of these reads returns partial data or a mix of the two blobs then the repository does not implement the necessary atomicity semantics that Elasticsearch requires for overwrites.\n\nThe executing node will use a variety of different methods to write the blob.\nFor instance, where applicable, it will use both single-part and multi-part uploads.\nSimilarly, the reading nodes will use a variety of different methods to read the data back again.\nFor instance they may read the entire blob from start to end or may read only a subset of the data.\n\nFor some blob-level tasks, the executing node will cancel the write before it is complete.\nIn this case, it still instructs some of the other nodes in the cluster to attempt to read the blob but all of these reads must fail to find the blob.\n\nLinearizable registers are special blobs that Elasticsearch manipulates using an atomic compare-and-exchange operation.\nThis operation ensures correct and strongly-consistent behavior even when the blob is accessed by multiple nodes at the same time.\nThe detailed implementation of the compare-and-exchange operation on linearizable registers varies by repository type.\nRepository analysis verifies that that uncontended compare-and-exchange operations on a linearizable register blob always succeed.\nRepository analysis also verifies that contended operations either succeed or report the contention but do not return incorrect results.\nIf an operation fails due to contention, Elasticsearch retries the operation until it succeeds.\nMost of the compare-and-exchange operations performed by repository analysis atomically increment a counter which is represented as an 8-byte blob.\nSome operations also verify the behavior on small blobs with sizes other than 8 bytes.", + "description": "Performs operations on a snapshot repository in order to check for incorrect behaviour.\n\nThere are a large number of third-party storage systems available, not all of which are suitable for use as a snapshot repository by Elasticsearch.\nSome storage systems behave incorrectly, or perform poorly, especially when accessed concurrently by multiple clients as the nodes of an Elasticsearch cluster do.\nThis API performs a collection of read and write operations on your repository which are designed to detect incorrect behaviour and to measure the performance characteristics of your storage system.\n\nThe default values for the parameters are deliberately low to reduce the impact of running an analysis inadvertently and to provide a sensible starting point for your investigations.\nRun your first analysis with the default parameter values to check for simple problems.\nSome repositories may behave correctly when lightly loaded but incorrectly under production-like workloads.\nIf the first analysis is successful, run a sequence of increasingly large analyses until you encounter a failure or you reach a `blob_count` of at least `2000`, a `max_blob_size` of at least `2gb`, a `max_total_data_size` of at least `1tb`, and a `register_operation_count` of at least `100`.\nAlways specify a generous timeout, possibly `1h` or longer, to allow time for each analysis to run to completion.\nSome repositories may behave correctly when accessed by a small number of Elasticsearch nodes but incorrectly when accessed concurrently by a production-scale cluster.\nPerform the analyses using a multi-node cluster of a similar size to your production cluster so that it can detect any problems that only arise when the repository is accessed by many nodes at once.\n\nIf the analysis fails, Elasticsearch detected that your repository behaved unexpectedly.\nThis usually means you are using a third-party storage system with an incorrect or incompatible implementation of the API it claims to support.\nIf so, this storage system is not suitable for use as a snapshot repository.\nRepository analysis triggers conditions that occur only rarely when taking snapshots in a production system.\nSnapshotting to unsuitable storage may appear to work correctly most of the time despite repository analysis failures.\nHowever your snapshot data is at risk if you store it in a snapshot repository that does not reliably pass repository analysis.\nYou can demonstrate that the analysis failure is due to an incompatible storage implementation by verifying that Elasticsearch does not detect the same problem when analysing the reference implementation of the storage protocol you are using.\nFor instance, if you are using storage that offers an API which the supplier claims to be compatible with AWS S3, verify that repositories in AWS S3 do not fail repository analysis.\nThis allows you to demonstrate to your storage supplier that a repository analysis failure must only be caused by an incompatibility with AWS S3 and cannot be attributed to a problem in Elasticsearch.\nPlease do not report Elasticsearch issues involving third-party storage systems unless you can demonstrate that the same issue exists when analysing a repository that uses the reference implementation of the same storage protocol.\nYou will need to work with the supplier of your storage system to address the incompatibilities that Elasticsearch detects.\n\nIf the analysis is successful, the API returns details of the testing process, optionally including how long each operation took.\nYou can use this information to determine the performance of your storage system.\nIf any operation fails or returns an incorrect result, the API returns an error.\nIf the API returns an error, it may not have removed all the data it wrote to the repository.\nThe error will indicate the location of any leftover data and this path is also recorded in the Elasticsearch logs.\nYou should verify that this location has been cleaned up correctly.\nIf there is still leftover data at the specified location, you should manually remove it.\n\nIf the connection from your client to Elasticsearch is closed while the client is waiting for the result of the analysis, the test is cancelled.\nSome clients are configured to close their connection if no response is received within a certain timeout.\nAn analysis takes a long time to complete so you might need to relax any such client-side timeouts.\nOn cancellation the analysis attempts to clean up the data it was writing, but it may not be able to remove it all.\nThe path to the leftover data is recorded in the Elasticsearch logs.\nYou should verify that this location has been cleaned up correctly.\nIf there is still leftover data at the specified location, you should manually remove it.\n\nIf the analysis is successful then it detected no incorrect behaviour, but this does not mean that correct behaviour is guaranteed.\nThe analysis attempts to detect common bugs but it does not offer 100% coverage.\nAdditionally, it does not test the following:\n\n* Your repository must perform durable writes. Once a blob has been written it must remain in place until it is deleted, even after a power loss or similar disaster.\n* Your repository must not suffer from silent data corruption. Once a blob has been written, its contents must remain unchanged until it is deliberately modified or deleted.\n* Your repository must behave correctly even if connectivity from the cluster is disrupted. Reads and writes may fail in this case, but they must not return incorrect results.\n\nIMPORTANT: An analysis writes a substantial amount of data to your repository and then reads it back again.\nThis consumes bandwidth on the network between the cluster and the repository, and storage space and I/O bandwidth on the repository itself.\nYou must ensure this load does not affect other users of these systems.\nAnalyses respect the repository settings `max_snapshot_bytes_per_sec` and `max_restore_bytes_per_sec` if available and the cluster setting `indices.recovery.max_bytes_per_sec` which you can use to limit the bandwidth they consume.\n\nNOTE: This API is intended for exploratory use by humans.\nYou should expect the request parameters and the response format to vary in future versions.\nThe response exposes immplementation details of the analysis which may change from version to version.\n\nNOTE: Different versions of Elasticsearch may perform different checks for repository compatibility, with newer versions typically being stricter than older ones.\nA storage system that passes repository analysis with one version of Elasticsearch may fail with a different version.\nThis indicates it behaves incorrectly in ways that the former version did not detect.\nYou must work with the supplier of your storage system to address the incompatibilities detected by the repository analysis API in any version of Elasticsearch.\n\nNOTE: This API may not work correctly in a mixed-version cluster.\n\n*Implementation details*\n\nNOTE: This section of documentation describes how the repository analysis API works in this version of Elasticsearch, but you should expect the implementation to vary between versions.\nThe request parameters and response format depend on details of the implementation so may also be different in newer versions.\n\nThe analysis comprises a number of blob-level tasks, as set by the `blob_count` parameter and a number of compare-and-exchange operations on linearizable registers, as set by the `register_operation_count` parameter.\nThese tasks are distributed over the data and master-eligible nodes in the cluster for execution.\n\nFor most blob-level tasks, the executing node first writes a blob to the repository and then instructs some of the other nodes in the cluster to attempt to read the data it just wrote.\nThe size of the blob is chosen randomly, according to the `max_blob_size` and `max_total_data_size` parameters.\nIf any of these reads fails then the repository does not implement the necessary read-after-write semantics that Elasticsearch requires.\n\nFor some blob-level tasks, the executing node will instruct some of its peers to attempt to read the data before the writing process completes.\nThese reads are permitted to fail, but must not return partial data.\nIf any read returns partial data then the repository does not implement the necessary atomicity semantics that Elasticsearch requires.\n\nFor some blob-level tasks, the executing node will overwrite the blob while its peers are reading it.\nIn this case the data read may come from either the original or the overwritten blob, but the read operation must not return partial data or a mix of data from the two blobs.\nIf any of these reads returns partial data or a mix of the two blobs then the repository does not implement the necessary atomicity semantics that Elasticsearch requires for overwrites.\n\nThe executing node will use a variety of different methods to write the blob.\nFor instance, where applicable, it will use both single-part and multi-part uploads.\nSimilarly, the reading nodes will use a variety of different methods to read the data back again.\nFor instance they may read the entire blob from start to end or may read only a subset of the data.\n\nFor some blob-level tasks, the executing node will cancel the write before it is complete.\nIn this case, it still instructs some of the other nodes in the cluster to attempt to read the blob but all of these reads must fail to find the blob.\n\nLinearizable registers are special blobs that Elasticsearch manipulates using an atomic compare-and-exchange operation.\nThis operation ensures correct and strongly-consistent behavior even when the blob is accessed by multiple nodes at the same time.\nThe detailed implementation of the compare-and-exchange operation on linearizable registers varies by repository type.\nRepository analysis verifies that that uncontended compare-and-exchange operations on a linearizable register blob always succeed.\nRepository analysis also verifies that contended operations either succeed or report the contention but do not return incorrect results.\nIf an operation fails due to contention, Elasticsearch retries the operation until it succeeds.\nMost of the compare-and-exchange operations performed by repository analysis atomically increment a counter which is represented as an 8-byte blob.\nSome operations also verify the behavior on small blobs with sizes other than 8 bytes.", "operationId": "snapshot-repository-analyze", "parameters": [ { diff --git a/output/schema/schema.json b/output/schema/schema.json index 97eb6a6aa4..b7a4242e0d 100644 --- a/output/schema/schema.json +++ b/output/schema/schema.json @@ -21162,7 +21162,7 @@ "visibility": "public" } }, - "description": "Analyze a snapshot repository.\nAnalyze the performance characteristics and any incorrect behaviour found in a repository.\n\nThe response exposes implementation details of the analysis which may change from version to version.\nThe response body format is therefore not considered stable and may be different in newer versions.\n\nThere are a large number of third-party storage systems available, not all of which are suitable for use as a snapshot repository by Elasticsearch.\nSome storage systems behave incorrectly, or perform poorly, especially when accessed concurrently by multiple clients as the nodes of an Elasticsearch cluster do. This API performs a collection of read and write operations on your repository which are designed to detect incorrect behaviour and to measure the performance characteristics of your storage system.\n\nThe default values for the parameters are deliberately low to reduce the impact of running an analysis inadvertently and to provide a sensible starting point for your investigations.\nRun your first analysis with the default parameter values to check for simple problems.\nIf successful, run a sequence of increasingly large analyses until you encounter a failure or you reach a `blob_count` of at least `2000`, a `max_blob_size` of at least `2gb`, a `max_total_data_size` of at least `1tb`, and a `register_operation_count` of at least `100`.\nAlways specify a generous timeout, possibly `1h` or longer, to allow time for each analysis to run to completion.\nPerform the analyses using a multi-node cluster of a similar size to your production cluster so that it can detect any problems that only arise when the repository is accessed by many nodes at once.\n\nIf the analysis fails, Elasticsearch detected that your repository behaved unexpectedly.\nThis usually means you are using a third-party storage system with an incorrect or incompatible implementation of the API it claims to support.\nIf so, this storage system is not suitable for use as a snapshot repository.\nYou will need to work with the supplier of your storage system to address the incompatibilities that Elasticsearch detects.\n\nIf the analysis is successful, the API returns details of the testing process, optionally including how long each operation took.\nYou can use this information to determine the performance of your storage system.\nIf any operation fails or returns an incorrect result, the API returns an error.\nIf the API returns an error, it may not have removed all the data it wrote to the repository.\nThe error will indicate the location of any leftover data and this path is also recorded in the Elasticsearch logs.\nYou should verify that this location has been cleaned up correctly.\nIf there is still leftover data at the specified location, you should manually remove it.\n\nIf the connection from your client to Elasticsearch is closed while the client is waiting for the result of the analysis, the test is cancelled.\nSome clients are configured to close their connection if no response is received within a certain timeout.\nAn analysis takes a long time to complete so you might need to relax any such client-side timeouts.\nOn cancellation the analysis attempts to clean up the data it was writing, but it may not be able to remove it all.\nThe path to the leftover data is recorded in the Elasticsearch logs.\nYou should verify that this location has been cleaned up correctly.\nIf there is still leftover data at the specified location, you should manually remove it.\n\nIf the analysis is successful then it detected no incorrect behaviour, but this does not mean that correct behaviour is guaranteed.\nThe analysis attempts to detect common bugs but it does not offer 100% coverage.\nAdditionally, it does not test the following:\n\n* Your repository must perform durable writes. Once a blob has been written it must remain in place until it is deleted, even after a power loss or similar disaster.\n* Your repository must not suffer from silent data corruption. Once a blob has been written, its contents must remain unchanged until it is deliberately modified or deleted.\n* Your repository must behave correctly even if connectivity from the cluster is disrupted. Reads and writes may fail in this case, but they must not return incorrect results.\n\nIMPORTANT: An analysis writes a substantial amount of data to your repository and then reads it back again.\nThis consumes bandwidth on the network between the cluster and the repository, and storage space and I/O bandwidth on the repository itself.\nYou must ensure this load does not affect other users of these systems.\nAnalyses respect the repository settings `max_snapshot_bytes_per_sec` and `max_restore_bytes_per_sec` if available and the cluster setting `indices.recovery.max_bytes_per_sec` which you can use to limit the bandwidth they consume.\n\nNOTE: This API is intended for exploratory use by humans. You should expect the request parameters and the response format to vary in future versions.\n\nNOTE: Different versions of Elasticsearch may perform different checks for repository compatibility, with newer versions typically being stricter than older ones.\nA storage system that passes repository analysis with one version of Elasticsearch may fail with a different version.\nThis indicates it behaves incorrectly in ways that the former version did not detect.\nYou must work with the supplier of your storage system to address the incompatibilities detected by the repository analysis API in any version of Elasticsearch.\n\nNOTE: This API may not work correctly in a mixed-version cluster.\n\n*Implementation details*\n\nNOTE: This section of documentation describes how the repository analysis API works in this version of Elasticsearch, but you should expect the implementation to vary between versions. The request parameters and response format depend on details of the implementation so may also be different in newer versions.\n\nThe analysis comprises a number of blob-level tasks, as set by the `blob_count` parameter and a number of compare-and-exchange operations on linearizable registers, as set by the `register_operation_count` parameter.\nThese tasks are distributed over the data and master-eligible nodes in the cluster for execution.\n\nFor most blob-level tasks, the executing node first writes a blob to the repository and then instructs some of the other nodes in the cluster to attempt to read the data it just wrote.\nThe size of the blob is chosen randomly, according to the `max_blob_size` and `max_total_data_size` parameters.\nIf any of these reads fails then the repository does not implement the necessary read-after-write semantics that Elasticsearch requires.\n\nFor some blob-level tasks, the executing node will instruct some of its peers to attempt to read the data before the writing process completes.\nThese reads are permitted to fail, but must not return partial data.\nIf any read returns partial data then the repository does not implement the necessary atomicity semantics that Elasticsearch requires.\n\nFor some blob-level tasks, the executing node will overwrite the blob while its peers are reading it.\nIn this case the data read may come from either the original or the overwritten blob, but the read operation must not return partial data or a mix of data from the two blobs.\nIf any of these reads returns partial data or a mix of the two blobs then the repository does not implement the necessary atomicity semantics that Elasticsearch requires for overwrites.\n\nThe executing node will use a variety of different methods to write the blob.\nFor instance, where applicable, it will use both single-part and multi-part uploads.\nSimilarly, the reading nodes will use a variety of different methods to read the data back again.\nFor instance they may read the entire blob from start to end or may read only a subset of the data.\n\nFor some blob-level tasks, the executing node will cancel the write before it is complete.\nIn this case, it still instructs some of the other nodes in the cluster to attempt to read the blob but all of these reads must fail to find the blob.\n\nLinearizable registers are special blobs that Elasticsearch manipulates using an atomic compare-and-exchange operation.\nThis operation ensures correct and strongly-consistent behavior even when the blob is accessed by multiple nodes at the same time.\nThe detailed implementation of the compare-and-exchange operation on linearizable registers varies by repository type.\nRepository analysis verifies that that uncontended compare-and-exchange operations on a linearizable register blob always succeed.\nRepository analysis also verifies that contended operations either succeed or report the contention but do not return incorrect results.\nIf an operation fails due to contention, Elasticsearch retries the operation until it succeeds.\nMost of the compare-and-exchange operations performed by repository analysis atomically increment a counter which is represented as an 8-byte blob.\nSome operations also verify the behavior on small blobs with sizes other than 8 bytes.", + "description": "Analyze a snapshot repository.\n\nPerforms operations on a snapshot repository in order to check for incorrect behaviour.\n\nThere are a large number of third-party storage systems available, not all of which are suitable for use as a snapshot repository by Elasticsearch.\nSome storage systems behave incorrectly, or perform poorly, especially when accessed concurrently by multiple clients as the nodes of an Elasticsearch cluster do.\nThis API performs a collection of read and write operations on your repository which are designed to detect incorrect behaviour and to measure the performance characteristics of your storage system.\n\nThe default values for the parameters are deliberately low to reduce the impact of running an analysis inadvertently and to provide a sensible starting point for your investigations.\nRun your first analysis with the default parameter values to check for simple problems.\nSome repositories may behave correctly when lightly loaded but incorrectly under production-like workloads.\nIf the first analysis is successful, run a sequence of increasingly large analyses until you encounter a failure or you reach a `blob_count` of at least `2000`, a `max_blob_size` of at least `2gb`, a `max_total_data_size` of at least `1tb`, and a `register_operation_count` of at least `100`.\nAlways specify a generous timeout, possibly `1h` or longer, to allow time for each analysis to run to completion.\nSome repositories may behave correctly when accessed by a small number of Elasticsearch nodes but incorrectly when accessed concurrently by a production-scale cluster.\nPerform the analyses using a multi-node cluster of a similar size to your production cluster so that it can detect any problems that only arise when the repository is accessed by many nodes at once.\n\nIf the analysis fails, Elasticsearch detected that your repository behaved unexpectedly.\nThis usually means you are using a third-party storage system with an incorrect or incompatible implementation of the API it claims to support.\nIf so, this storage system is not suitable for use as a snapshot repository.\nRepository analysis triggers conditions that occur only rarely when taking snapshots in a production system.\nSnapshotting to unsuitable storage may appear to work correctly most of the time despite repository analysis failures.\nHowever your snapshot data is at risk if you store it in a snapshot repository that does not reliably pass repository analysis.\nYou can demonstrate that the analysis failure is due to an incompatible storage implementation by verifying that Elasticsearch does not detect the same problem when analysing the reference implementation of the storage protocol you are using.\nFor instance, if you are using storage that offers an API which the supplier claims to be compatible with AWS S3, verify that repositories in AWS S3 do not fail repository analysis.\nThis allows you to demonstrate to your storage supplier that a repository analysis failure must only be caused by an incompatibility with AWS S3 and cannot be attributed to a problem in Elasticsearch.\nPlease do not report Elasticsearch issues involving third-party storage systems unless you can demonstrate that the same issue exists when analysing a repository that uses the reference implementation of the same storage protocol.\nYou will need to work with the supplier of your storage system to address the incompatibilities that Elasticsearch detects.\n\nIf the analysis is successful, the API returns details of the testing process, optionally including how long each operation took.\nYou can use this information to determine the performance of your storage system.\nIf any operation fails or returns an incorrect result, the API returns an error.\nIf the API returns an error, it may not have removed all the data it wrote to the repository.\nThe error will indicate the location of any leftover data and this path is also recorded in the Elasticsearch logs.\nYou should verify that this location has been cleaned up correctly.\nIf there is still leftover data at the specified location, you should manually remove it.\n\nIf the connection from your client to Elasticsearch is closed while the client is waiting for the result of the analysis, the test is cancelled.\nSome clients are configured to close their connection if no response is received within a certain timeout.\nAn analysis takes a long time to complete so you might need to relax any such client-side timeouts.\nOn cancellation the analysis attempts to clean up the data it was writing, but it may not be able to remove it all.\nThe path to the leftover data is recorded in the Elasticsearch logs.\nYou should verify that this location has been cleaned up correctly.\nIf there is still leftover data at the specified location, you should manually remove it.\n\nIf the analysis is successful then it detected no incorrect behaviour, but this does not mean that correct behaviour is guaranteed.\nThe analysis attempts to detect common bugs but it does not offer 100% coverage.\nAdditionally, it does not test the following:\n\n* Your repository must perform durable writes. Once a blob has been written it must remain in place until it is deleted, even after a power loss or similar disaster.\n* Your repository must not suffer from silent data corruption. Once a blob has been written, its contents must remain unchanged until it is deliberately modified or deleted.\n* Your repository must behave correctly even if connectivity from the cluster is disrupted. Reads and writes may fail in this case, but they must not return incorrect results.\n\nIMPORTANT: An analysis writes a substantial amount of data to your repository and then reads it back again.\nThis consumes bandwidth on the network between the cluster and the repository, and storage space and I/O bandwidth on the repository itself.\nYou must ensure this load does not affect other users of these systems.\nAnalyses respect the repository settings `max_snapshot_bytes_per_sec` and `max_restore_bytes_per_sec` if available and the cluster setting `indices.recovery.max_bytes_per_sec` which you can use to limit the bandwidth they consume.\n\nNOTE: This API is intended for exploratory use by humans.\nYou should expect the request parameters and the response format to vary in future versions.\nThe response exposes immplementation details of the analysis which may change from version to version.\n\nNOTE: Different versions of Elasticsearch may perform different checks for repository compatibility, with newer versions typically being stricter than older ones.\nA storage system that passes repository analysis with one version of Elasticsearch may fail with a different version.\nThis indicates it behaves incorrectly in ways that the former version did not detect.\nYou must work with the supplier of your storage system to address the incompatibilities detected by the repository analysis API in any version of Elasticsearch.\n\nNOTE: This API may not work correctly in a mixed-version cluster.\n\n*Implementation details*\n\nNOTE: This section of documentation describes how the repository analysis API works in this version of Elasticsearch, but you should expect the implementation to vary between versions.\nThe request parameters and response format depend on details of the implementation so may also be different in newer versions.\n\nThe analysis comprises a number of blob-level tasks, as set by the `blob_count` parameter and a number of compare-and-exchange operations on linearizable registers, as set by the `register_operation_count` parameter.\nThese tasks are distributed over the data and master-eligible nodes in the cluster for execution.\n\nFor most blob-level tasks, the executing node first writes a blob to the repository and then instructs some of the other nodes in the cluster to attempt to read the data it just wrote.\nThe size of the blob is chosen randomly, according to the `max_blob_size` and `max_total_data_size` parameters.\nIf any of these reads fails then the repository does not implement the necessary read-after-write semantics that Elasticsearch requires.\n\nFor some blob-level tasks, the executing node will instruct some of its peers to attempt to read the data before the writing process completes.\nThese reads are permitted to fail, but must not return partial data.\nIf any read returns partial data then the repository does not implement the necessary atomicity semantics that Elasticsearch requires.\n\nFor some blob-level tasks, the executing node will overwrite the blob while its peers are reading it.\nIn this case the data read may come from either the original or the overwritten blob, but the read operation must not return partial data or a mix of data from the two blobs.\nIf any of these reads returns partial data or a mix of the two blobs then the repository does not implement the necessary atomicity semantics that Elasticsearch requires for overwrites.\n\nThe executing node will use a variety of different methods to write the blob.\nFor instance, where applicable, it will use both single-part and multi-part uploads.\nSimilarly, the reading nodes will use a variety of different methods to read the data back again.\nFor instance they may read the entire blob from start to end or may read only a subset of the data.\n\nFor some blob-level tasks, the executing node will cancel the write before it is complete.\nIn this case, it still instructs some of the other nodes in the cluster to attempt to read the blob but all of these reads must fail to find the blob.\n\nLinearizable registers are special blobs that Elasticsearch manipulates using an atomic compare-and-exchange operation.\nThis operation ensures correct and strongly-consistent behavior even when the blob is accessed by multiple nodes at the same time.\nThe detailed implementation of the compare-and-exchange operation on linearizable registers varies by repository type.\nRepository analysis verifies that that uncontended compare-and-exchange operations on a linearizable register blob always succeed.\nRepository analysis also verifies that contended operations either succeed or report the contention but do not return incorrect results.\nIf an operation fails due to contention, Elasticsearch retries the operation until it succeeds.\nMost of the compare-and-exchange operations performed by repository analysis atomically increment a counter which is represented as an 8-byte blob.\nSome operations also verify the behavior on small blobs with sizes other than 8 bytes.", "docId": "analyze-repository", "docUrl": "https://www.elastic.co/docs/api/doc/elasticsearch/operation/operation-snapshot-repository-analyze", "name": "snapshot.repository_analyze", @@ -227218,7 +227218,7 @@ "body": { "kind": "no_body" }, - "description": "Analyze a snapshot repository.\nAnalyze the performance characteristics and any incorrect behaviour found in a repository.\n\nThe response exposes implementation details of the analysis which may change from version to version.\nThe response body format is therefore not considered stable and may be different in newer versions.\n\nThere are a large number of third-party storage systems available, not all of which are suitable for use as a snapshot repository by Elasticsearch.\nSome storage systems behave incorrectly, or perform poorly, especially when accessed concurrently by multiple clients as the nodes of an Elasticsearch cluster do. This API performs a collection of read and write operations on your repository which are designed to detect incorrect behaviour and to measure the performance characteristics of your storage system.\n\nThe default values for the parameters are deliberately low to reduce the impact of running an analysis inadvertently and to provide a sensible starting point for your investigations.\nRun your first analysis with the default parameter values to check for simple problems.\nIf successful, run a sequence of increasingly large analyses until you encounter a failure or you reach a `blob_count` of at least `2000`, a `max_blob_size` of at least `2gb`, a `max_total_data_size` of at least `1tb`, and a `register_operation_count` of at least `100`.\nAlways specify a generous timeout, possibly `1h` or longer, to allow time for each analysis to run to completion.\nPerform the analyses using a multi-node cluster of a similar size to your production cluster so that it can detect any problems that only arise when the repository is accessed by many nodes at once.\n\nIf the analysis fails, Elasticsearch detected that your repository behaved unexpectedly.\nThis usually means you are using a third-party storage system with an incorrect or incompatible implementation of the API it claims to support.\nIf so, this storage system is not suitable for use as a snapshot repository.\nYou will need to work with the supplier of your storage system to address the incompatibilities that Elasticsearch detects.\n\nIf the analysis is successful, the API returns details of the testing process, optionally including how long each operation took.\nYou can use this information to determine the performance of your storage system.\nIf any operation fails or returns an incorrect result, the API returns an error.\nIf the API returns an error, it may not have removed all the data it wrote to the repository.\nThe error will indicate the location of any leftover data and this path is also recorded in the Elasticsearch logs.\nYou should verify that this location has been cleaned up correctly.\nIf there is still leftover data at the specified location, you should manually remove it.\n\nIf the connection from your client to Elasticsearch is closed while the client is waiting for the result of the analysis, the test is cancelled.\nSome clients are configured to close their connection if no response is received within a certain timeout.\nAn analysis takes a long time to complete so you might need to relax any such client-side timeouts.\nOn cancellation the analysis attempts to clean up the data it was writing, but it may not be able to remove it all.\nThe path to the leftover data is recorded in the Elasticsearch logs.\nYou should verify that this location has been cleaned up correctly.\nIf there is still leftover data at the specified location, you should manually remove it.\n\nIf the analysis is successful then it detected no incorrect behaviour, but this does not mean that correct behaviour is guaranteed.\nThe analysis attempts to detect common bugs but it does not offer 100% coverage.\nAdditionally, it does not test the following:\n\n* Your repository must perform durable writes. Once a blob has been written it must remain in place until it is deleted, even after a power loss or similar disaster.\n* Your repository must not suffer from silent data corruption. Once a blob has been written, its contents must remain unchanged until it is deliberately modified or deleted.\n* Your repository must behave correctly even if connectivity from the cluster is disrupted. Reads and writes may fail in this case, but they must not return incorrect results.\n\nIMPORTANT: An analysis writes a substantial amount of data to your repository and then reads it back again.\nThis consumes bandwidth on the network between the cluster and the repository, and storage space and I/O bandwidth on the repository itself.\nYou must ensure this load does not affect other users of these systems.\nAnalyses respect the repository settings `max_snapshot_bytes_per_sec` and `max_restore_bytes_per_sec` if available and the cluster setting `indices.recovery.max_bytes_per_sec` which you can use to limit the bandwidth they consume.\n\nNOTE: This API is intended for exploratory use by humans. You should expect the request parameters and the response format to vary in future versions.\n\nNOTE: Different versions of Elasticsearch may perform different checks for repository compatibility, with newer versions typically being stricter than older ones.\nA storage system that passes repository analysis with one version of Elasticsearch may fail with a different version.\nThis indicates it behaves incorrectly in ways that the former version did not detect.\nYou must work with the supplier of your storage system to address the incompatibilities detected by the repository analysis API in any version of Elasticsearch.\n\nNOTE: This API may not work correctly in a mixed-version cluster.\n\n*Implementation details*\n\nNOTE: This section of documentation describes how the repository analysis API works in this version of Elasticsearch, but you should expect the implementation to vary between versions. The request parameters and response format depend on details of the implementation so may also be different in newer versions.\n\nThe analysis comprises a number of blob-level tasks, as set by the `blob_count` parameter and a number of compare-and-exchange operations on linearizable registers, as set by the `register_operation_count` parameter.\nThese tasks are distributed over the data and master-eligible nodes in the cluster for execution.\n\nFor most blob-level tasks, the executing node first writes a blob to the repository and then instructs some of the other nodes in the cluster to attempt to read the data it just wrote.\nThe size of the blob is chosen randomly, according to the `max_blob_size` and `max_total_data_size` parameters.\nIf any of these reads fails then the repository does not implement the necessary read-after-write semantics that Elasticsearch requires.\n\nFor some blob-level tasks, the executing node will instruct some of its peers to attempt to read the data before the writing process completes.\nThese reads are permitted to fail, but must not return partial data.\nIf any read returns partial data then the repository does not implement the necessary atomicity semantics that Elasticsearch requires.\n\nFor some blob-level tasks, the executing node will overwrite the blob while its peers are reading it.\nIn this case the data read may come from either the original or the overwritten blob, but the read operation must not return partial data or a mix of data from the two blobs.\nIf any of these reads returns partial data or a mix of the two blobs then the repository does not implement the necessary atomicity semantics that Elasticsearch requires for overwrites.\n\nThe executing node will use a variety of different methods to write the blob.\nFor instance, where applicable, it will use both single-part and multi-part uploads.\nSimilarly, the reading nodes will use a variety of different methods to read the data back again.\nFor instance they may read the entire blob from start to end or may read only a subset of the data.\n\nFor some blob-level tasks, the executing node will cancel the write before it is complete.\nIn this case, it still instructs some of the other nodes in the cluster to attempt to read the blob but all of these reads must fail to find the blob.\n\nLinearizable registers are special blobs that Elasticsearch manipulates using an atomic compare-and-exchange operation.\nThis operation ensures correct and strongly-consistent behavior even when the blob is accessed by multiple nodes at the same time.\nThe detailed implementation of the compare-and-exchange operation on linearizable registers varies by repository type.\nRepository analysis verifies that that uncontended compare-and-exchange operations on a linearizable register blob always succeed.\nRepository analysis also verifies that contended operations either succeed or report the contention but do not return incorrect results.\nIf an operation fails due to contention, Elasticsearch retries the operation until it succeeds.\nMost of the compare-and-exchange operations performed by repository analysis atomically increment a counter which is represented as an 8-byte blob.\nSome operations also verify the behavior on small blobs with sizes other than 8 bytes.", + "description": "Analyze a snapshot repository.\n\nPerforms operations on a snapshot repository in order to check for incorrect behaviour.\n\nThere are a large number of third-party storage systems available, not all of which are suitable for use as a snapshot repository by Elasticsearch.\nSome storage systems behave incorrectly, or perform poorly, especially when accessed concurrently by multiple clients as the nodes of an Elasticsearch cluster do.\nThis API performs a collection of read and write operations on your repository which are designed to detect incorrect behaviour and to measure the performance characteristics of your storage system.\n\nThe default values for the parameters are deliberately low to reduce the impact of running an analysis inadvertently and to provide a sensible starting point for your investigations.\nRun your first analysis with the default parameter values to check for simple problems.\nSome repositories may behave correctly when lightly loaded but incorrectly under production-like workloads.\nIf the first analysis is successful, run a sequence of increasingly large analyses until you encounter a failure or you reach a `blob_count` of at least `2000`, a `max_blob_size` of at least `2gb`, a `max_total_data_size` of at least `1tb`, and a `register_operation_count` of at least `100`.\nAlways specify a generous timeout, possibly `1h` or longer, to allow time for each analysis to run to completion.\nSome repositories may behave correctly when accessed by a small number of Elasticsearch nodes but incorrectly when accessed concurrently by a production-scale cluster.\nPerform the analyses using a multi-node cluster of a similar size to your production cluster so that it can detect any problems that only arise when the repository is accessed by many nodes at once.\n\nIf the analysis fails, Elasticsearch detected that your repository behaved unexpectedly.\nThis usually means you are using a third-party storage system with an incorrect or incompatible implementation of the API it claims to support.\nIf so, this storage system is not suitable for use as a snapshot repository.\nRepository analysis triggers conditions that occur only rarely when taking snapshots in a production system.\nSnapshotting to unsuitable storage may appear to work correctly most of the time despite repository analysis failures.\nHowever your snapshot data is at risk if you store it in a snapshot repository that does not reliably pass repository analysis.\nYou can demonstrate that the analysis failure is due to an incompatible storage implementation by verifying that Elasticsearch does not detect the same problem when analysing the reference implementation of the storage protocol you are using.\nFor instance, if you are using storage that offers an API which the supplier claims to be compatible with AWS S3, verify that repositories in AWS S3 do not fail repository analysis.\nThis allows you to demonstrate to your storage supplier that a repository analysis failure must only be caused by an incompatibility with AWS S3 and cannot be attributed to a problem in Elasticsearch.\nPlease do not report Elasticsearch issues involving third-party storage systems unless you can demonstrate that the same issue exists when analysing a repository that uses the reference implementation of the same storage protocol.\nYou will need to work with the supplier of your storage system to address the incompatibilities that Elasticsearch detects.\n\nIf the analysis is successful, the API returns details of the testing process, optionally including how long each operation took.\nYou can use this information to determine the performance of your storage system.\nIf any operation fails or returns an incorrect result, the API returns an error.\nIf the API returns an error, it may not have removed all the data it wrote to the repository.\nThe error will indicate the location of any leftover data and this path is also recorded in the Elasticsearch logs.\nYou should verify that this location has been cleaned up correctly.\nIf there is still leftover data at the specified location, you should manually remove it.\n\nIf the connection from your client to Elasticsearch is closed while the client is waiting for the result of the analysis, the test is cancelled.\nSome clients are configured to close their connection if no response is received within a certain timeout.\nAn analysis takes a long time to complete so you might need to relax any such client-side timeouts.\nOn cancellation the analysis attempts to clean up the data it was writing, but it may not be able to remove it all.\nThe path to the leftover data is recorded in the Elasticsearch logs.\nYou should verify that this location has been cleaned up correctly.\nIf there is still leftover data at the specified location, you should manually remove it.\n\nIf the analysis is successful then it detected no incorrect behaviour, but this does not mean that correct behaviour is guaranteed.\nThe analysis attempts to detect common bugs but it does not offer 100% coverage.\nAdditionally, it does not test the following:\n\n* Your repository must perform durable writes. Once a blob has been written it must remain in place until it is deleted, even after a power loss or similar disaster.\n* Your repository must not suffer from silent data corruption. Once a blob has been written, its contents must remain unchanged until it is deliberately modified or deleted.\n* Your repository must behave correctly even if connectivity from the cluster is disrupted. Reads and writes may fail in this case, but they must not return incorrect results.\n\nIMPORTANT: An analysis writes a substantial amount of data to your repository and then reads it back again.\nThis consumes bandwidth on the network between the cluster and the repository, and storage space and I/O bandwidth on the repository itself.\nYou must ensure this load does not affect other users of these systems.\nAnalyses respect the repository settings `max_snapshot_bytes_per_sec` and `max_restore_bytes_per_sec` if available and the cluster setting `indices.recovery.max_bytes_per_sec` which you can use to limit the bandwidth they consume.\n\nNOTE: This API is intended for exploratory use by humans.\nYou should expect the request parameters and the response format to vary in future versions.\nThe response exposes immplementation details of the analysis which may change from version to version.\n\nNOTE: Different versions of Elasticsearch may perform different checks for repository compatibility, with newer versions typically being stricter than older ones.\nA storage system that passes repository analysis with one version of Elasticsearch may fail with a different version.\nThis indicates it behaves incorrectly in ways that the former version did not detect.\nYou must work with the supplier of your storage system to address the incompatibilities detected by the repository analysis API in any version of Elasticsearch.\n\nNOTE: This API may not work correctly in a mixed-version cluster.\n\n*Implementation details*\n\nNOTE: This section of documentation describes how the repository analysis API works in this version of Elasticsearch, but you should expect the implementation to vary between versions.\nThe request parameters and response format depend on details of the implementation so may also be different in newer versions.\n\nThe analysis comprises a number of blob-level tasks, as set by the `blob_count` parameter and a number of compare-and-exchange operations on linearizable registers, as set by the `register_operation_count` parameter.\nThese tasks are distributed over the data and master-eligible nodes in the cluster for execution.\n\nFor most blob-level tasks, the executing node first writes a blob to the repository and then instructs some of the other nodes in the cluster to attempt to read the data it just wrote.\nThe size of the blob is chosen randomly, according to the `max_blob_size` and `max_total_data_size` parameters.\nIf any of these reads fails then the repository does not implement the necessary read-after-write semantics that Elasticsearch requires.\n\nFor some blob-level tasks, the executing node will instruct some of its peers to attempt to read the data before the writing process completes.\nThese reads are permitted to fail, but must not return partial data.\nIf any read returns partial data then the repository does not implement the necessary atomicity semantics that Elasticsearch requires.\n\nFor some blob-level tasks, the executing node will overwrite the blob while its peers are reading it.\nIn this case the data read may come from either the original or the overwritten blob, but the read operation must not return partial data or a mix of data from the two blobs.\nIf any of these reads returns partial data or a mix of the two blobs then the repository does not implement the necessary atomicity semantics that Elasticsearch requires for overwrites.\n\nThe executing node will use a variety of different methods to write the blob.\nFor instance, where applicable, it will use both single-part and multi-part uploads.\nSimilarly, the reading nodes will use a variety of different methods to read the data back again.\nFor instance they may read the entire blob from start to end or may read only a subset of the data.\n\nFor some blob-level tasks, the executing node will cancel the write before it is complete.\nIn this case, it still instructs some of the other nodes in the cluster to attempt to read the blob but all of these reads must fail to find the blob.\n\nLinearizable registers are special blobs that Elasticsearch manipulates using an atomic compare-and-exchange operation.\nThis operation ensures correct and strongly-consistent behavior even when the blob is accessed by multiple nodes at the same time.\nThe detailed implementation of the compare-and-exchange operation on linearizable registers varies by repository type.\nRepository analysis verifies that that uncontended compare-and-exchange operations on a linearizable register blob always succeed.\nRepository analysis also verifies that contended operations either succeed or report the contention but do not return incorrect results.\nIf an operation fails due to contention, Elasticsearch retries the operation until it succeeds.\nMost of the compare-and-exchange operations performed by repository analysis atomically increment a counter which is represented as an 8-byte blob.\nSome operations also verify the behavior on small blobs with sizes other than 8 bytes.", "inherits": { "type": { "name": "RequestBase", @@ -227401,7 +227401,7 @@ } } ], - "specLocation": "snapshot/repository_analyze/SnapshotAnalyzeRepositoryRequest.ts#L25-L202" + "specLocation": "snapshot/repository_analyze/SnapshotAnalyzeRepositoryRequest.ts#L25-L213" }, { "kind": "response", diff --git a/specification/snapshot/repository_analyze/SnapshotAnalyzeRepositoryRequest.ts b/specification/snapshot/repository_analyze/SnapshotAnalyzeRepositoryRequest.ts index 993be44e35..2837c3e596 100644 --- a/specification/snapshot/repository_analyze/SnapshotAnalyzeRepositoryRequest.ts +++ b/specification/snapshot/repository_analyze/SnapshotAnalyzeRepositoryRequest.ts @@ -24,23 +24,31 @@ import { Duration } from '@_types/Time' /** * Analyze a snapshot repository. - * Analyze the performance characteristics and any incorrect behaviour found in a repository. * - * The response exposes implementation details of the analysis which may change from version to version. - * The response body format is therefore not considered stable and may be different in newer versions. + * Performs operations on a snapshot repository in order to check for incorrect behaviour. * * There are a large number of third-party storage systems available, not all of which are suitable for use as a snapshot repository by Elasticsearch. - * Some storage systems behave incorrectly, or perform poorly, especially when accessed concurrently by multiple clients as the nodes of an Elasticsearch cluster do. This API performs a collection of read and write operations on your repository which are designed to detect incorrect behaviour and to measure the performance characteristics of your storage system. + * Some storage systems behave incorrectly, or perform poorly, especially when accessed concurrently by multiple clients as the nodes of an Elasticsearch cluster do. + * This API performs a collection of read and write operations on your repository which are designed to detect incorrect behaviour and to measure the performance characteristics of your storage system. * * The default values for the parameters are deliberately low to reduce the impact of running an analysis inadvertently and to provide a sensible starting point for your investigations. * Run your first analysis with the default parameter values to check for simple problems. - * If successful, run a sequence of increasingly large analyses until you encounter a failure or you reach a `blob_count` of at least `2000`, a `max_blob_size` of at least `2gb`, a `max_total_data_size` of at least `1tb`, and a `register_operation_count` of at least `100`. + * Some repositories may behave correctly when lightly loaded but incorrectly under production-like workloads. + * If the first analysis is successful, run a sequence of increasingly large analyses until you encounter a failure or you reach a `blob_count` of at least `2000`, a `max_blob_size` of at least `2gb`, a `max_total_data_size` of at least `1tb`, and a `register_operation_count` of at least `100`. * Always specify a generous timeout, possibly `1h` or longer, to allow time for each analysis to run to completion. + * Some repositories may behave correctly when accessed by a small number of Elasticsearch nodes but incorrectly when accessed concurrently by a production-scale cluster. * Perform the analyses using a multi-node cluster of a similar size to your production cluster so that it can detect any problems that only arise when the repository is accessed by many nodes at once. * * If the analysis fails, Elasticsearch detected that your repository behaved unexpectedly. * This usually means you are using a third-party storage system with an incorrect or incompatible implementation of the API it claims to support. * If so, this storage system is not suitable for use as a snapshot repository. + * Repository analysis triggers conditions that occur only rarely when taking snapshots in a production system. + * Snapshotting to unsuitable storage may appear to work correctly most of the time despite repository analysis failures. + * However your snapshot data is at risk if you store it in a snapshot repository that does not reliably pass repository analysis. + * You can demonstrate that the analysis failure is due to an incompatible storage implementation by verifying that Elasticsearch does not detect the same problem when analysing the reference implementation of the storage protocol you are using. + * For instance, if you are using storage that offers an API which the supplier claims to be compatible with AWS S3, verify that repositories in AWS S3 do not fail repository analysis. + * This allows you to demonstrate to your storage supplier that a repository analysis failure must only be caused by an incompatibility with AWS S3 and cannot be attributed to a problem in Elasticsearch. + * Please do not report Elasticsearch issues involving third-party storage systems unless you can demonstrate that the same issue exists when analysing a repository that uses the reference implementation of the same storage protocol. * You will need to work with the supplier of your storage system to address the incompatibilities that Elasticsearch detects. * * If the analysis is successful, the API returns details of the testing process, optionally including how long each operation took. @@ -72,7 +80,9 @@ import { Duration } from '@_types/Time' * You must ensure this load does not affect other users of these systems. * Analyses respect the repository settings `max_snapshot_bytes_per_sec` and `max_restore_bytes_per_sec` if available and the cluster setting `indices.recovery.max_bytes_per_sec` which you can use to limit the bandwidth they consume. * - * NOTE: This API is intended for exploratory use by humans. You should expect the request parameters and the response format to vary in future versions. + * NOTE: This API is intended for exploratory use by humans. + * You should expect the request parameters and the response format to vary in future versions. + * The response exposes immplementation details of the analysis which may change from version to version. * * NOTE: Different versions of Elasticsearch may perform different checks for repository compatibility, with newer versions typically being stricter than older ones. * A storage system that passes repository analysis with one version of Elasticsearch may fail with a different version. @@ -83,7 +93,8 @@ import { Duration } from '@_types/Time' * * *Implementation details* * - * NOTE: This section of documentation describes how the repository analysis API works in this version of Elasticsearch, but you should expect the implementation to vary between versions. The request parameters and response format depend on details of the implementation so may also be different in newer versions. + * NOTE: This section of documentation describes how the repository analysis API works in this version of Elasticsearch, but you should expect the implementation to vary between versions. + * The request parameters and response format depend on details of the implementation so may also be different in newer versions. * * The analysis comprises a number of blob-level tasks, as set by the `blob_count` parameter and a number of compare-and-exchange operations on linearizable registers, as set by the `register_operation_count` parameter. * These tasks are distributed over the data and master-eligible nodes in the cluster for execution.