The client implement for SkyWalking BanyanDB in Java.
Create a BanyanDBClient with the server's several addresses and then use connect() to establish a connection.
// use `default` group
BanyanDBClient client = new BanyanDBClient("banyandb.svc:17912", "10.0.12.9:17912");
// to send any request, a connection to the server must be estabilished
client.connect();These addresses are either IP addresses or DNS names.
The client will try to connect to the server in a round-robin manner. The client will periodically refresh the server
addresses. The refresh interval can be configured by refreshInterval option.
Besides, you may pass a customized options while building a BanyanDBClient. Supported
options are listed below,
| Option | Description | Default |
|---|---|---|
| maxInboundMessageSize | Max inbound message size | 1024 * 1024 * 50 (~50MB) |
| deadline | Threshold of gRPC blocking query, unit is second | 30 (seconds) |
| refreshInterval | Refresh interval for the gRPC channel, unit is second | 30 (seconds) |
| resolveDNSInterval | DNS resolve interval, unit is second | 30 (minutes) |
| forceReconnectionThreshold | Threshold of force gRPC reconnection if network issue is encountered | 1 |
| forceTLS | Force use TLS for gRPC | false |
| sslTrustCAPath | SSL: Trusted CA Path | |
| sslCertChainPath | SSL: Cert Chain Path, BanyanDB server not support mTLS yet | |
| sslKeyPath | SSL: Cert Key Path, BanyanDB server not support mTLS yet | |
| username | Basic Auth: username of BanyanDB server | |
| password | Basic Auth: password of BanyanDB server |
// build a group sw_record for Stream with 2 shards and ttl equals to 3 days
Group g = Group.newBuilder().setMetadata(Metadata.newBuilder().setName("sw_record"))
.setCatalog(Catalog.CATALOG_STREAM)
.setResourceOpts(ResourceOpts.newBuilder()
.setShardNum(2)
.setSegmentInterval(
IntervalRule.newBuilder()
.setUnit(
IntervalRule.Unit.UNIT_DAY)
.setNum(
1))
.setTtl(
IntervalRule.newBuilder()
.setUnit(
IntervalRule.Unit.UNIT_DAY)
.setNum(
3)))
.build();
client.define(g);Then we may define a stream with customized configurations.
Here illustrates how to use the lifecycle stages feature for hot-warm-cold data architecture:
// build a group sw_record for Stream with hot-warm-cold lifecycle stages
Group g = Group.newBuilder().setMetadata(Metadata.newBuilder().setName("sw_record"))
.setCatalog(Catalog.CATALOG_STREAM)
.setResourceOpts(ResourceOpts.newBuilder()
// Hot configuration
.setShardNum(3)
// Default segment interval (will be overridden by stages if defined)
.setSegmentInterval(
IntervalRule.newBuilder()
.setUnit(IntervalRule.Unit.UNIT_DAY)
.setNum(1))
// Default TTL (will be overridden by stages if defined)
.setTtl(
IntervalRule.newBuilder()
.setUnit(IntervalRule.Unit.UNIT_DAY)
.setNum(3))
// Define lifecycle stages (hot → warm → cold)
.addStages(LifecycleStage.newBuilder()
.setName("warm")
.setShardNum(2) // Fewer shards
.setSegmentInterval(IntervalRule.newBuilder()
.setUnit(IntervalRule.Unit.UNIT_DAY)
.setNum(1))
.setTtl(IntervalRule.newBuilder()
.setUnit(IntervalRule.Unit.UNIT_DAY)
.setNum(7)) // Keep in warm for 7 days
.setNodeSelector("hdd-nodes") // Store on cheaper HDD nodes
.build())
.addStages(LifecycleStage.newBuilder()
.setName("cold")
.setShardNum(1) // Minimal shards for archived data
.setSegmentInterval(IntervalRule.newBuilder()
.setUnit(IntervalRule.Unit.UNIT_DAY)
.setNum(7)) // Larger segments for cold data
.setTtl(IntervalRule.newBuilder()
.setUnit(IntervalRule.Unit.UNIT_DAY)
.setNum(30)) // Keep in cold for 30 more days
.setNodeSelector("archive-nodes") // Store on archive nodes
.setClose(true) // Close segments that are no longer live
.build()))
.build();
client.define(g);This configuration creates a hot-warm-cold architecture where:
- Hot stage: Data is stored on fast SSD nodes with many shards for 1 day, optimized for high query performance
- Warm stage: Data moves to HDD nodes with fewer shards for 7 days, balanced between performance and cost
- Cold stage: Data finally moves to archive nodes with minimal shards for 30 days, optimized for storage efficiency
Data automatically flows through these stages according to the defined TTLs. The total retention of data is 38 days (1+7+30).
// build a stream trace with above group
Stream s = Stream.newBuilder()
.setMetadata(Metadata.newBuilder()
.setGroup("sw_record")
.setName("trace"))
.setEntity(Entity.newBuilder().addAllTagNames(
Arrays.asList("service_id", "service_instance_id", "is_error")))
.addTagFamilies(TagFamilySpec.newBuilder()
.setName("data")
.addTags(TagSpec.newBuilder()
.setName("data_binary")
.setType(TagType.TAG_TYPE_DATA_BINARY)))
.addTagFamilies(TagFamilySpec.newBuilder()
.setName("searchable")
.addTags(TagSpec.newBuilder()
.setName("trace_id")
.setType(TagType.TAG_TYPE_STRING))
.addTags(TagSpec.newBuilder()
.setName("is_error")
.setType(TagType.TAG_TYPE_INT))
.addTags(TagSpec.newBuilder()
.setName("service_id")
.setType(TagType.TAG_TYPE_STRING)
.setIndexedOnly(true)))
.build();
client.define(s);IndexRule.Builder ir = IndexRule.newBuilder()
.setMetadata(Metadata.newBuilder()
.setGroup("sw_record")
.setName("trace_id"))
.addTags("trace_id")
.setType(IndexRule.Type.TYPE_INVERTED)
.setAnalyzer("simple");
client.define(ir.build());IndexRuleBinding.Builder irb = IndexRuleBinding.newBuilder()
.setMetadata(BanyandbCommon.Metadata.newBuilder()
.setGroup("sw_record")
.setName("trace_binding"))
.setSubject(BanyandbDatabase.Subject.newBuilder()
.setCatalog(
BanyandbCommon.Catalog.CATALOG_STREAM)
.setName("trace"))
.addAllRules(
Arrays.asList("trace_id"))
.setBeginAt(TimeUtils.buildTimestamp(ZonedDateTime.of(2024, 1, 1, 0, 0, 0, 0, ZoneOffset.UTC)))
.setExpireAt(TimeUtils.buildTimestamp(DEFAULT_EXPIRE_AT));
client.define(irb.build());For the last line in the code block, a simple API (i.e. BanyanDBClient.define(Stream)) is used to define the schema of Stream.
The same works for Measure which will be demonstrated later.
Measure can also be defined directly with BanyanDBClient,
// build a group sw_metrics for Measure with 2 shards and ttl equals to 7 days
Group g = Group.newBuilder().setMetadata(Metadata.newBuilder().setName("sw_metric"))
.setCatalog(Catalog.CATALOG_MEASURE)
.setResourceOpts(ResourceOpts.newBuilder()
.setShardNum(2)
.setSegmentInterval(
IntervalRule.newBuilder()
.setUnit(
IntervalRule.Unit.UNIT_DAY)
.setNum(
1))
.setTtl(
IntervalRule.newBuilder()
.setUnit(
IntervalRule.Unit.UNIT_DAY)
.setNum(
7)))
.build();
client.define(g);// create a new measure schema with an additional interval
// the interval is used to specify how frequently to send a data point
Measure m = Measure.newBuilder()
.setMetadata(Metadata.newBuilder()
.setGroup("sw_metric")
.setName("service_cpm_minute"))
.setInterval(Duration.ofMinutes(1).format())
.setEntity(Entity.newBuilder().addTagNames("entity_id"))
.setShardingKey(ShardingKey.newBuilder().addTagNames("service_id"))
.addTagFamilies(
TagFamilySpec.newBuilder()
.setName("default")
.addTags(
TagSpec.newBuilder()
.setName("entity_id")
.setType(
TagType.TAG_TYPE_STRING))
.addTags(
TagSpec.newBuilder()
.setName("service_id")
.setType(
TagType.TAG_TYPE_STRING))
.addTags(
TagSpec.newBuilder()
.setName("scope")
.setType(
TagType.TAG_TYPE_STRING)))
.addFields(
FieldSpec.newBuilder()
.setName("total")
.setFieldType(
FieldType.FIELD_TYPE_INT)
.setCompressionMethod(
CompressionMethod.COMPRESSION_METHOD_ZSTD)
.setEncodingMethod(
EncodingMethod.ENCODING_METHOD_GORILLA))
.addFields(
FieldSpec.newBuilder()
.setName("value")
.setFieldType(
FieldType.FIELD_TYPE_INT)
.setCompressionMethod(
CompressionMethod.COMPRESSION_METHOD_ZSTD)
.setEncodingMethod(
EncodingMethod.ENCODING_METHOD_GORILLA))
.build();
// define a measure, as we've mentioned above
client.define(m);If you want to create an index_mode Measure:
Measure m = Measure.newBuilder()
.setMetadata(Metadata.newBuilder()
.setGroup("sw_metric")
.setName("service_traffic"))
.setEntity(Entity.newBuilder().addTagNames("service_id"))
.setIndexMode(true)
.addTagFamilies(
TagFamilySpec.newBuilder()
.setName("default")
.addTags(
TagSpec.newBuilder()
.setName("service_id")
.setType(
TagType.TAG_TYPE_STRING))
.addTags(
TagSpec.newBuilder()
.setName("layer")
.setType(
TagType.TAG_TYPE_STRING)))
.build();
// define a "index_mode" measure, as we've mentioned above
client.define(m);Trace is a first-class model for storing tracing data (binary span payloads plus searchable tags). You can define it directly via BanyanDBClient.
// build a group sw_trace for Trace with 2 shards and ttl equals to 7 days
Group g = Group.newBuilder().setMetadata(Metadata.newBuilder().setName("sw_trace"))
.setCatalog(Catalog.CATALOG_TRACE)
.setResourceOpts(ResourceOpts.newBuilder()
.setShardNum(2)
.setSegmentInterval(
IntervalRule.newBuilder()
.setUnit(
IntervalRule.Unit.UNIT_DAY)
.setNum(
1))
.setTtl(
IntervalRule.newBuilder()
.setUnit(
IntervalRule.Unit.UNIT_DAY)
.setNum(
7)))
.build();
client.define(g);// Create a Trace schema with tag definitions and required identifiers
BanyandbDatabase.Trace trace = BanyandbDatabase.Trace.newBuilder()
.setMetadata(Metadata.newBuilder()
.setGroup("sw_trace")
.setName("trace_data"))
// Define searchable tags (order matters when writing)
.addTags(BanyandbDatabase.TraceTagSpec.newBuilder()
.setName("trace_id")
.setType(TagType.TAG_TYPE_STRING))
.addTags(BanyandbDatabase.TraceTagSpec.newBuilder()
.setName("span_id")
.setType(TagType.TAG_TYPE_STRING))
.addTags(BanyandbDatabase.TraceTagSpec.newBuilder()
.setName("service_name")
.setType(TagType.TAG_TYPE_STRING))
.addTags(BanyandbDatabase.TraceTagSpec.newBuilder()
.setName("start_time")
.setType(TagType.TAG_TYPE_TIMESTAMP))
// Mandatory identifiers
.setTraceIdTagName("trace_id")
.setTimestampTagName("start_time")
.build();
client.define(trace);// Index start_time for range queries and ordering
IndexRule ir = IndexRule.newBuilder()
.setMetadata(Metadata.newBuilder()
.setGroup("sw_trace")
.setName("start_time"))
.addTags("start_time")
.setType(IndexRule.Type.TYPE_TREE)
.build();
client.define(ir);
// Bind the index rule to the Trace schema
IndexRuleBinding irb = IndexRuleBinding.newBuilder()
.setMetadata(Metadata.newBuilder()
.setGroup("sw_trace")
.setName("trace_binding"))
.setSubject(BanyandbDatabase.Subject.newBuilder()
.setCatalog(Catalog.CATALOG_TRACE)
.setName("trace_data"))
.addAllRules(Arrays.asList("start_time"))
.setBeginAt(TimeUtils.buildTimestamp(ZonedDateTime.of(2024, 1, 1, 0, 0, 0, 0, ZoneOffset.UTC)))
.setExpireAt(TimeUtils.buildTimestamp(DEFAULT_EXPIRE_AT))
.build();
client.define(irb);Note: Group lifecycle stages (hot/warm/cold) also apply to the Trace catalog.
// Define property schema
BanyandbDatabase.Property propertyDef =
BanyandbDatabase.Property.newBuilder()
.setMetadata(Metadata.newBuilder()
.setGroup("default")
.setName("ui_template"))
.addTags(
TagSpec.newBuilder()
.setName("name")
.setType(
TagType.TAG_TYPE_STRING))
.build();
client.define(propertyDef);For more APIs usage, refer to test cases and API docs.
Construct a StreamQuery instance with given time-range and other conditions.
Note: time-range is left-inclusive and right-exclusive.
For example,
// [begin, end) = [ now - 15min, now )
Instant end = Instant.now();
Instant begin = end.minus(15, ChronoUnit.MINUTES);
// with stream schema, group=default, name=sw
StreamQuery query = new StreamQuery(Lists.newArrayList("sw_record"), "trace",
new TimestampRange(begin.toEpochMilli(), end.toEpochMilli()),
// projection tags which are indexed
ImmutableSet.of("state", "start_time", "duration", "trace_id"));
// search for all states
query.and(PairQueryCondition.StringQueryCondition.eq("searchable", "trace_id" , "1a60e0846817447eac4cd498eefd3743.1.17261060724190003"));
// set order by condition
query.setOrderBy(new AbstractQuery.OrderBy(AbstractQuery.Sort.DESC));
// set projection for un-indexed tags
query.setDataProjections(ImmutableSet.of("data_binary"));
// send the query request
client.query(query);After response is returned, elements can be fetched,
StreamQueryResponse resp = client.query(query);
List<RowEntity> entities = resp.getElements();Every item RowEntity in the list contains elementId, timestamp and tag families requested.
The StreamQueryResponse, RowEntity, TagFamily and Tag (i.e. TagAndValue) forms a hierarchical structure, where
the order of the tag families and containing tags, i.e. indexes of these objects in the List, follow the order specified
in the projection condition we've used in the request.
If you want to trace the query, you can use query.enableTrace() to get the trace spans.
// enable trace
query.enableTrace();
// send the query request
client.query(query);After response is returned, trace can be extracted,
// send the query request
StreamQueryResponse resp = client.queryStreams(query);
Trace t = resp.getTrace();For Measure, it is similar to the Stream,
// [begin, end) = [ now - 15min, now )
Instant end = Instant.now();
Instant begin = end.minus(15, ChronoUnit.MINUTES);
// with stream schema, group=sw_metrics, name=service_instance_cpm_day
MeasureQuery query = new MeasureQuery(Lists.newArrayList("sw_metrics"), "service_instance_cpm_day",
new TimestampRange(begin.toEpochMilli(), end.toEpochMilli()),
ImmutableSet.of("id", "scope", "service_id"),
ImmutableSet.of("total"));
// query max "total" with group by tag "service_id"
query.maxBy("total", ImmutableSet.of("service_id"));
// use conditions
query.and(PairQueryCondition.StringQueryCondition.eq("default", "service_id", "abc"));
// send the query request
client.query(query);After response is returned, dataPoints can be extracted,
MeasureQueryResponse resp = client.query(query);
List<DataPoint> dataPointList = resp.getDataPoints();Measure API supports TopN/BottomN search. The results or (grouped-)results are
ordered by the given field,
MeasureQuery query = new MeasureQuery("sw_metrics", "service_instance_cpm_day",
new TimestampRange(begin.toEpochMilli(), end.toEpochMilli()),
ImmutableSet.of("id", "scope", "service_id"),
ImmutableSet.of("total"));
query.topN(5, "total"); // bottomNBesides, limit and offset are used to support pagination. Tag-based sort can also
be done to the final results,
query.limit(5);
query.offset(1);
query.orderBy("service_id", Sort.DESC);If you want to trace the query, you can use query.enableTrace() to get the trace spans.
// enable trace
query.enableTrace();
// send the query request
client.query(query);After response is returned, trace can be extracted,
// send the query request
MeasureQueryResponse resp = client.query(query);
Trace trace = resp.getTrace();Construct a TraceQuery to search traces by tags and/or time range. The query returns a list of traces, each containing spans with binary content.
// Optional time range
Instant end = Instant.now();
Instant begin = end.minus(15, ChronoUnit.MINUTES);
// Create a query for trace schema: group=sw_trace, name=trace_data
TraceQuery query = new TraceQuery(Lists.newArrayList("sw_trace"), "trace_data",
// with or without time range
new TimestampRange(begin.toEpochMilli(), end.toEpochMilli()),
// project on tags you want to sort/filter by
ImmutableSet.of("start_time", "service_name"));
// Filter by trace_id
query.and(PairQueryCondition.StringQueryCondition.eq("trace_id", "trace-query-test-12345"));
// Order and paginate
query.setOrderBy(new AbstractQuery.OrderBy("start_time", AbstractQuery.Sort.DESC));
query.setLimit(10);
query.setOffset(0);
// Optionally restrict to lifecycle stages
query.stages(ImmutableSet.of("warm", "cold"));
// Enable query execution tracing (diagnostics)
query.enableTrace();
// Execute
TraceQueryResponse resp = client.query(query);
// Traces (data) with spans (binary payload)
resp.getTraces().forEach(t -> {
// t.getSpansList() -> spans; t.getTraceId() if defined in server response
});
// Execution trace (diagnostics), if enabled
String executionTrace = resp.getTraceResult();Tip: StreamQueryResponse.getTrace() and MeasureQueryResponse.getTrace() return the execution trace of the query, not the Trace data model. Use TraceQuery/TraceQueryResponse to fetch tracing data.
Query properties:
BanyandbProperty.QueryRequest queryRequest = new PropertyQuery(Lists.newArrayList("default"), "ui_template", ImmutableSet.of("name")).build();
BanyandbProperty.QueryResponse queryResponse = client.query(queryRequest);Query properties based on ID:
BanyandbProperty.QueryRequest queryRequest = new PropertyQuery(Lists.newArrayList("default"), "ui_template", ImmutableSet.of("name")).id("dashboard-1").build();
BanyandbProperty.QueryResponse queryResponse = client.query(queryRequest);Query properties based on tags:
PropertyQuery pQuery = new PropertyQuery(Lists.newArrayList("default"), "ui_template", ImmutableSet.of("name"));
pQuery.criteria(PairQueryCondition.StringQueryCondition.eq("name", "foo"));
BanyandbProperty.QueryResponse resp = client.query(pQuery.build());Both StreamQuery and MeausreQuery support the criteria flag to filter data.
criteria supports logical expressions and binary condition operations.
The expression (a=1 and b = 2) or (a=4 and b=5) could use below operations to support.
query.criteria(Or.create(
And.create(
PairQueryCondition.LongQueryCondition.eq("a", 1L),
PairQueryCondition.LongQueryCondition.eq("b", 1L)),
And.create(
PairQueryCondition.LongQueryCondition.eq("a", 4L),
PairQueryCondition.LongQueryCondition.eq("b", 5L)
)
));The execution order of conditions is from the inside to outside. The deepest condition will get executed first.
The client also provides syntactic sugar for using and or or methods.
The criteria method has a higher priority, overwriting these sugar methods.
Caveat: Sugar methods CAN NOT handle nested query.
criteriais the canonical method to take such tasks as above example shows.
When filtering data matches all the conditions, the query can append several and:
query.and(PairQueryCondition.LongQueryCondition.eq("state", 1L))
.and(PairQueryCondition.StringQueryCondition.eq("service_id", serviceId))
.and(PairQueryCondition.StringQueryCondition.eq("service_instance_id", serviceInstanceId))
.and(PairQueryCondition.StringQueryCondition.match("endpoint_id", endpointId))
.and(PairQueryCondition.LongQueryCondition.ge("duration", minDuration))
.and(PairQueryCondition.LongQueryCondition.le("duration", maxDuration))When gathering all data matches any of the conditions, the query can combine a series of or:
segmentIds.forEach(id -> query.or(PairQueryCondition.LongQueryCondition.eq("segment_id", id)))Since grpc bidi streaming is used for write protocol, build a StreamBulkWriteProcessor which would handle back-pressure for you.
Adjust maxBulkSize, flushInterval, concurrency and timeout of the consumer in different scenarios to meet requirements.
// build a StreamBulkWriteProcessor from client
StreamBulkWriteProcessor streamBulkWriteProcessor = client.buildStreamWriteProcessor(maxBulkSize, flushInterval, concurrency, timeout);The StreamBulkWriteProcessor is thread-safe and thus can be used across threads.
We highly recommend you to reuse it.
The procedure of constructing StreamWrite entity must comply with the schema of the stream, e.g.
the order of tags must exactly be the same with that defined in the schema.
And the non-existing tags must be fulfilled (with NullValue) instead of compacting all non-null tag values.
StreamWrite streamWrite = client.createStreamWrite("default", "sw", segmentId, now.toEpochMilli())
.tag("data_binary", Value.binaryTagValue(byteData))
.tag("trace_id", Value.stringTagValue(traceId)) // 0
.tag("state", Value.longTagValue(state)) // 1
.tag("service_id", Value.stringTagValue(serviceId)) // 2
.tag("service_instance_id", Value.stringTagValue(serviceInstanceId)) // 3
.tag("endpoint_id", Value.stringTagValue(endpointId)) // 4
.tag("duration", Value.longTagValue(latency)) // 5
.tag("http.method", Value.stringTagValue(null)) // 6
.tag("status_code", Value.stringTagValue(httpStatusCode)) // 7
.tag("db.type", Value.stringTagValue(dbType)) // 8
.tag("db.instance", Value.stringTagValue(dbInstance)) // 9
.tag("mq.broker", Value.stringTagValue(broker)) // 10
.tag("mq.topic", Value.stringTagValue(topic)) // 11
.tag("mq.queue", Value.stringTagValue(queue)); // 12
CompletableFuture<Void> f = streamBulkWriteProcessor.add(streamWrite);
f.get(10, TimeUnit.SECONDS);The writing procedure for Measure is similar to the above described process and leverages the bidirectional streaming of gRPC,
// build a MeasureBulkWriteProcessor from client
MeasureBulkWriteProcessor measureBulkWriteProcessor = client.buildMeasureWriteProcessor(maxBulkSize, flushInterval, concurrency, timeout);A BulkWriteProcessor is created by calling buildMeasureWriteProcessor. Then build the MeasureWrite object and send with bulk processor,
Instant now = Instant.now();
MeasureWrite measureWrite = client.createMeasureWrite("sw_metric", "service_cpm_minute", now.toEpochMilli());
measureWrite.tag("id", TagAndValue.stringTagValue("1"))
.tag("entity_id", TagAndValue.stringTagValue("entity_1"))
.field("total", TagAndValue.longFieldValue(100))
.field("value", TagAndValue.longFieldValue(1));
CompletableFuture<Void> f = measureBulkWriteProcessor.add(measureWrite);
f.get(10, TimeUnit.SECONDS);Trace writes use gRPC bidirectional streaming as well. Build a TraceBulkWriteProcessor, then create TraceWrite objects carrying tags and a binary span payload.
// build a TraceBulkWriteProcessor from client
TraceBulkWriteProcessor traceBulkWriteProcessor = client.buildTraceWriteProcessor(maxBulkSize, flushInterval, concurrency, timeout);
// Create and send a trace write
TraceWrite traceWrite = client.createTraceWrite("sw_trace", "trace_data")
// tag order must follow schema definition
.tag("trace_id", Value.stringTagValue("trace-query-test-12345"))
.tag("span_id", Value.stringTagValue("span-1"))
.tag("service_name", Value.stringTagValue("order-test-service"))
.tag("start_time", Value.timestampTagValue(Instant.now().toEpochMilli()))
// binary span payload (your tracing format bytes)
.span("span-bytes".getBytes())
// optional write version
.version(1L);
CompletableFuture<Void> f = traceBulkWriteProcessor.add(traceWrite);
f.get(10, TimeUnit.SECONDS);Unlike Stream and Measure, Property is a single write operation. The Property object is created and sent to the server.
// Apply a property (create or update)
Property property = Property.newBuilder()
.setMetadata(
BanyandbCommon.Metadata.newBuilder()
.setGroup("default")
.setName("sw").build())
.setId("dashboard-1")
.addTags(Tag.newBuilder().setKey("name").setValue(
TagValue.newBuilder().setStr(Str.newBuilder().setValue("hello"))))
.build();
ApplyResponse response = client.apply(property);You can also apply with a specific strategy:
// Apply with merge strategy
ApplyResponse response = client.apply(property, Strategy.STRATEGY_MERGE);Stream, Measure, and Trace are deleted by the TTL mechanism. Set TTL when defining the group schema.
Delete a property:
// Delete a property
DeleteResponse deleteResponse = client.deleteProperty("default", "ui_template", "dashboard-1");./mvnw clean package
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configurations through Help->Edit Custom Properties..:
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