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elasticsearchmachine opened this issue Mar 18, 2025 · 2 comments
Open

[CI] EsqlSpecIT class failing #125170

elasticsearchmachine opened this issue Mar 18, 2025 · 2 comments
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@jimczi
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:Analytics/ES|QL AKA ESQL medium-risk An open issue or test failure that is a medium risk to future releases Team:Analytics Meta label for analytical engine team (ESQL/Aggs/Geo) >test-failure Triaged test failures from CI

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@elasticsearchmachine
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elasticsearchmachine commented Mar 18, 2025 •
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Build Scans:

Reproduction Line:

./gradlew ":x-pack:plugin:esql:qa:server:multi-node:javaRestTest" --tests "org.elasticsearch.xpack.esql.qa.multi_node.EsqlSpecIT" -Dtests.method="test {match-function.TestMultiValuedFieldWithConjunction SYNC}" -Dtests.seed=2FC72A5116028313 -Dtests.locale=ca-AD -Dtests.timezone=America/Rio_Branco -Druntime.java=24

Applicable branches:
8.19

Reproduces locally?:
N/A

Failure History:
See dashboard

Failure Message:

java.net.ConnectException: Connection refused

Issue Reasons:

  • [8.19] 10 failures in class org.elasticsearch.xpack.esql.qa.multi_node.EsqlSpecIT (1.3% fail rate in 752 executions)
  • [8.19] 7 failures in pipeline elasticsearch-periodic-platform-support (33.3% fail rate in 21 executions)
  • [8.19] 2 failures in pipeline elasticsearch-periodic (9.5% fail rate in 21 executions)

Note:
This issue was created using new test triage automation. Please report issues or feedback to es-delivery.
@elasticsearchmachine elasticsearchmachine added :Analytics/ES|QL AKA ESQL >test-failure Triaged test failures from CI Team:Analytics Meta label for analytical engine team (ESQL/Aggs/Geo) needs:risk Requires assignment of a risk label (low, medium, blocker) labels Mar 18, 2025
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Pinging @elastic/es-analytical-engine (Team:Analytics)

@dnhatn
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dnhatn commented Mar 18, 2025

@ioanatia The semantic_text issue just happened again.

[2025-03-18T22:18:49,193][ERROR][o.e.b.ElasticsearchUncaughtExceptionHandler] [test-cluster-0] fatal error in thread [elasticsearch[test-cluster-0][generic][T#12]], exiting java.lang.AssertionError: java.lang.AssertionError: seqNo [0] was processed twice in generation [2], with different data. prvOp [Index{id='1', seqNo=0, primaryTerm=1, version=1, autoGeneratedIdTimestamp=-1} source: {st_logs=2024-12-23T12:15:00.000Z 1.2.3.4 [email protected] 4553, st_double=5.20128E11, st_geopoint=POINT(42.97109630194 14.7552534413725), _inference_fields={st_logs={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_logs=[{start_offset=0, end_offset=57, embeddings={feature_0=9.994094E7, feature_1=9.994094E7, feature_2=9.994094E7, feature_3=9.994095E7, feature_4=9.994095E7}}]}}}, st_geopoint={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_geopoint=[{start_offset=0, end_offset=38, embeddings={feature_0=4.8845428E7, feature_1=4.8845428E7, feature_2=4.8845428E7, feature_3=4.884543E7, feature_4=4.884543E7}}]}}}, st_double={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_double=[{start_offset=0, end_offset=10, embeddings={feature_0=1.02139264E9, feature_1=1.02139264E9, feature_2=1.02139264E9, feature_3=1.02139264E9, feature_4=1.02139264E9}}]}}}, st_integer={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_integer=[{start_offset=0, end_offset=2, embeddings={feature_0=1602.0, feature_1=1603.0, feature_2=1604.0, feature_3=1605.0, feature_4=1606.0}}]}}}, st_geoshape={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_geoshape=[{start_offset=0, end_offset=45, embeddings={feature_0=2.9025277E8, feature_1=2.9025277E8, feature_2=2.9025277E8, feature_3=2.9025277E8, feature_4=2.9025277E8}}]}}}, st_unicode={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_unicode=[{start_offset=0, end_offset=5, embeddings={feature_0=2.0297848E9, feature_1=2.0297848E9, feature_2=2.0297848E9, feature_3=2.0297848E9, feature_4=2.0297848E9}}]}}}, st_datetime={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_datetime=[{start_offset=0, end_offset=24, embeddings={feature_0=1.7914711E9, feature_1=1.7914711E9, feature_2=1.7914711E9, feature_3=1.7914711E9, feature_4=1.7914711E9}}]}}}, st_unsigned_long={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_unsigned_long=[{start_offset=0, end_offset=10, embeddings={feature_0=3.597711E8, feature_1=3.597711E8, feature_2=3.597711E8, feature_3=3.597711E8, feature_4=3.597711E8}}]}}}, st_bool={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_bool=[{start_offset=0, end_offset=5, embeddings={feature_0=9.719632E7, feature_1=9.719633E7, feature_2=9.719633E7, feature_3=9.719633E7, feature_4=9.719633E7}}]}}}, st_ip={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_ip=[{start_offset=0, end_offset=7, embeddings={feature_0=1.9016198E9, feature_1=1.9016198E9, feature_2=1.9016198E9, feature_3=1.9016198E9, feature_4=1.9016198E9}}]}}}, st_base64={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_base64=[{start_offset=0, end_offset=12, embeddings={feature_0=1.4194486E9, feature_1=1.4194486E9, feature_2=1.4194486E9, feature_3=1.4194486E9, feature_4=1.4194486E9}}]}}}, st_multi_value={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_multi_value=[{start_offset=0, end_offset=12, embeddings={feature_0=1.457005E8, feature_1=1.457005E8, feature_2=1.4570051E8, feature_3=1.4570051E8, feature_4=1.4570051E8}}, {start_offset=13, end_offset=35, embeddings={feature_0=8.689444E8, feature_1=8.689444E8, feature_2=8.689444E8, feature_3=8.689444E8, feature_4=8.689444E8}}, {start_offset=36, end_offset=56, embeddings={feature_0=1.5083834E9, feature_1=1.5083834E9, feature_2=1.5083834E9, feature_3=1.5083834E9, feature_4=1.5083834E9}}]}}}, st_long={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_long=[{start_offset=0, end_offset=10, embeddings={feature_0=3.597711E8, feature_1=3.597711E8, feature_2=3.597711E8, feature_3=3.597711E8, feature_4=3.597711E8}}]}}}, semantic_text_field={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={semantic_text_field=[{start_offset=0, end_offset=21, embeddings={feature_0=2.9864544E8, feature_1=2.9864544E8, feature_2=2.9864544E8, feature_3=2.9864544E8, feature_4=2.9864544E8}}]}}}, st_cartesian_point={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_cartesian_point=[{start_offset=0, end_offset=23, embeddings={feature_0=2.032471E9, feature_1=2.032471E9, feature_2=2.032471E9, feature_3=2.032471E9, feature_4=2.032471E9}}]}}}, st_version={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_version=[{start_offset=0, end_offset=5, embeddings={feature_0=4.6672444E7, feature_1=4.6672444E7, feature_2=4.6672444E7, feature_3=4.667245E7, feature_4=4.667245E7}}]}}}}, st_integer=23, st_geoshape=POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10)), description=some description1, st_datetime=1953-09-02T00:00:00.000Z, st_unicode=你吃饭了吗, st_unsigned_long=2147483648, st_bool=false, st_ip=1.1.1.1, st_base64=ZWxhc3RpYw==, st_multi_value=[Hello there!, This is a random value, for testing purposes], st_long=2147483648, host=host1, semantic_text_field=live long and prosper, st_cartesian_point=POINT(4297.11 -1475.53), language_name=English, value=1001, st_version=1.2.3}], newOp [Index{id='1', seqNo=0, primaryTerm=1, version=1, autoGeneratedIdTimestamp=-1} source: {st_logs=2024-12-23T12:15:00.000Z 1.2.3.4 [email protected] 4553, st_double=5.20128E11, st_geopoint=POINT(42.97109630194 14.7552534413725), st_integer=23, st_geoshape=POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10)), description=some description1, st_datetime=1953-09-02T00:00:00.000Z, st_unicode=你吃饭了吗, st_unsigned_long=2147483648, st_bool=false, st_ip=1.1.1.1, st_base64=ZWxhc3RpYw==, st_multi_value=[Hello there!, This is a random value, for testing purposes], st_long=2147483648, host=host1, semantic_text_field=live long and prosper, st_cartesian_point=POINT(4297.11 -1475.53), language_name=English, value=1001, st_version=1.2.3, _inference_fields={st_logs={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_logs=[{start_offset=0, end_offset=57, embeddings={feature_0=9.987686E7, feature_1=9.987686E7, feature_2=9.987686E7, feature_3=9.987686E7, feature_4=9.987686E7}}]}}}, st_geopoint={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_geopoint=[{start_offset=0, end_offset=38, embeddings={feature_0=4.8758784E7, feature_1=4.8758784E7, feature_2=4.8758784E7, feature_3=4.8758784E7, feature_4=4.8758784E7}}]}}}, st_double={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_double=[{start_offset=0, end_offset=10, embeddings={feature_0=1.021313E9, feature_1=1.021313E9, feature_2=1.021313E9, feature_3=1.021313E9, feature_4=1.021313E9}}]}}}, st_integer={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_integer=[{start_offset=0, end_offset=2, embeddings={feature_0=1600.0, feature_1=1600.0, feature_2=1604.0, feature_3=1604.0, feature_4=1604.0}}]}}}, st_geoshape={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_geoshape=[{start_offset=0, end_offset=45, embeddings={feature_0=2.8940698E8, feature_1=2.8940698E8, feature_2=2.8940698E8, feature_3=2.8940698E8, feature_4=2.8940698E8}}]}}}, st_unicode={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_unicode=[{start_offset=0, end_offset=5, embeddings={feature_0=2.0258488E9, feature_1=2.0258488E9, feature_2=2.0258488E9, feature_3=2.0258488E9, feature_4=2.0258488E9}}]}}}, st_datetime={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_datetime=[{start_offset=0, end_offset=24, embeddings={feature_0=1.7909678E9, feature_1=1.7909678E9, feature_2=1.7909678E9, feature_3=1.7909678E9, feature_4=1.7909678E9}}]}}}, st_unsigned_long={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_unsigned_long=[{start_offset=0, end_offset=10, embeddings={feature_0=3.5966157E8, feature_1=3.5966157E8, feature_2=3.5966157E8, feature_3=3.5966157E8, feature_4=3.5966157E8}}]}}}, st_bool={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_bool=[{start_offset=0, end_offset=5, embeddings={feature_0=9.699328E7, feature_1=9.699328E7, feature_2=9.699328E7, feature_3=9.699328E7, feature_4=9.699328E7}}]}}}, st_ip={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_ip=[{start_offset=0, end_offset=7, embeddings={feature_0=1.9000197E9, feature_1=1.9000197E9, feature_2=1.9000197E9, feature_3=1.9000197E9, feature_4=1.9000197E9}}]}}}, st_base64={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_base64=[{start_offset=0, end_offset=12, embeddings={feature_0=1.4176748E9, feature_1=1.4176748E9, feature_2=1.4176748E9, feature_3=1.4176748E9, feature_4=1.4176748E9}}]}}}, st_multi_value={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_multi_value=[{start_offset=0, end_offset=12, embeddings={feature_0=1.4522778E8, feature_1=1.4522778E8, feature_2=1.4522778E8, feature_3=1.4522778E8, feature_4=1.4522778E8}}, {start_offset=13, end_offset=35, embeddings={feature_0=8.682209E8, feature_1=8.682209E8, feature_2=8.682209E8, feature_3=8.682209E8, feature_4=8.682209E8}}, {start_offset=36, end_offset=56, embeddings={feature_0=1.5057551E9, feature_1=1.5057551E9, feature_2=1.5057551E9, feature_3=1.5057551E9, feature_4=1.5057551E9}}]}}}, st_long={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_long=[{start_offset=0, end_offset=10, embeddings={feature_0=3.5966157E8, feature_1=3.5966157E8, feature_2=3.5966157E8, feature_3=3.5966157E8, feature_4=3.5966157E8}}]}}}, semantic_text_field={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={semantic_text_field=[{start_offset=0, end_offset=21, embeddings={feature_0=2.977956E8, feature_1=2.977956E8, feature_2=2.977956E8, feature_3=2.977956E8, feature_4=2.977956E8}}]}}}, st_cartesian_point={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_cartesian_point=[{start_offset=0, end_offset=23, embeddings={feature_0=2.0300431E9, feature_1=2.0300431E9, feature_2=2.0300431E9, feature_3=2.0300431E9, feature_4=2.0300431E9}}]}}}, st_version={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_version=[{start_offset=0, end_offset=5, embeddings={feature_0=4.666163E7, feature_1=4.666163E7, feature_2=4.666163E7, feature_3=4.666163E7, feature_4=4.666163E7}}]}}}}}]
	at [email protected]/org.elasticsearch.index.translog.TranslogWriter.assertNoSeqNumberConflict(TranslogWriter.java:307)
	at [email protected]/org.elasticsearch.index.translog.TranslogWriter.add(TranslogWriter.java:265)
	at [email protected]/org.elasticsearch.index.translog.Translog.add(Translog.java:633)
	at [email protected]/org.elasticsearch.index.engine.InternalEngine.index(InternalEngine.java:1229)
	at [email protected]/org.elasticsearch.index.shard.IndexShard.index(IndexShard.java:1089)
	at [email protected]/org.elasticsearch.index.shard.IndexShard.applyIndexOperation(IndexShard.java:1015)
	at [email protected]/org.elasticsearch.index.shard.IndexShard.applyTranslogOperation(IndexShard.java:2031)
	at [email protected]/org.elasticsearch.index.shard.IndexShard.applyTranslogOperation(IndexShard.java:2018)
	at [email protected]/org.elasticsearch.indices.recovery.RecoveryTarget.lambda$indexTranslogOperations$4(RecoveryTarget.java:454)
	at [email protected]/org.elasticsearch.action.ActionListener.completeWith(ActionListener.java:356)
	at [email protected]/org.elasticsearch.indices.recovery.RecoveryTarget.indexTranslogOperations(RecoveryTarget.java:429)
	at [email protected]/org.elasticsearch.indices.recovery.PeerRecoveryTargetService$TranslogOperationsRequestHandler.performTranslogOps(PeerRecoveryTargetService.java:649)
	at [email protected]/org.elasticsearch.indices.recovery.PeerRecoveryTargetService$TranslogOperationsRequestHandler.handleRequest(PeerRecoveryTargetService.java:596)
	at [email protected]/org.elasticsearch.indices.recovery.PeerRecoveryTargetService$TranslogOperationsRequestHandler.handleRequest(PeerRecoveryTargetService.java:588)
	at [email protected]/org.elasticsearch.indices.recovery.PeerRecoveryTargetService$RecoveryRequestHandler.messageReceived(PeerRecoveryTargetService.java:682)
	at [email protected]/org.elasticsearch.indices.recovery.PeerRecoveryTargetService$RecoveryRequestHandler.messageReceived(PeerRecoveryTargetService.java:669)
	at [email protected]/org.elasticsearch.transport.RequestHandlerRegistry.processMessageReceived(RequestHandlerRegistry.java:90)
	at [email protected]/org.elasticsearch.transport.InboundHandler.doHandleRequest(InboundHandler.java:289)
	at [email protected]/org.elasticsearch.transport.InboundHandler$1.doRun(InboundHandler.java:302)
	at [email protected]/org.elasticsearch.common.util.concurrent.ThreadContext$ContextPreservingAbstractRunnable.doRun(ThreadContext.java:1044)
	at [email protected]/org.elasticsearch.common.util.concurrent.AbstractRunnable.run(AbstractRunnable.java:27)
	at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1095)
	at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:619)
	at java.base/java.lang.Thread.run(Thread.java:1447)
Caused by: java.lang.AssertionError: seqNo [0] was processed twice in generation [2], with different data. prvOp [Index{id='1', seqNo=0, primaryTerm=1, version=1, autoGeneratedIdTimestamp=-1} source: {st_logs=2024-12-23T12:15:00.000Z 1.2.3.4 [email protected] 4553, st_double=5.20128E11, st_geopoint=POINT(42.97109630194 14.7552534413725), _inference_fields={st_logs={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_logs=[{start_offset=0, end_offset=57, embeddings={feature_0=9.994094E7, feature_1=9.994094E7, feature_2=9.994094E7, feature_3=9.994095E7, feature_4=9.994095E7}}]}}}, st_geopoint={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_geopoint=[{start_offset=0, end_offset=38, embeddings={feature_0=4.8845428E7, feature_1=4.8845428E7, feature_2=4.8845428E7, feature_3=4.884543E7, feature_4=4.884543E7}}]}}}, st_double={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_double=[{start_offset=0, end_offset=10, embeddings={feature_0=1.02139264E9, feature_1=1.02139264E9, feature_2=1.02139264E9, feature_3=1.02139264E9, feature_4=1.02139264E9}}]}}}, st_integer={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_integer=[{start_offset=0, end_offset=2, embeddings={feature_0=1602.0, feature_1=1603.0, feature_2=1604.0, feature_3=1605.0, feature_4=1606.0}}]}}}, st_geoshape={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_geoshape=[{start_offset=0, end_offset=45, embeddings={feature_0=2.9025277E8, feature_1=2.9025277E8, feature_2=2.9025277E8, feature_3=2.9025277E8, feature_4=2.9025277E8}}]}}}, st_unicode={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_unicode=[{start_offset=0, end_offset=5, embeddings={feature_0=2.0297848E9, feature_1=2.0297848E9, feature_2=2.0297848E9, feature_3=2.0297848E9, feature_4=2.0297848E9}}]}}}, st_datetime={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_datetime=[{start_offset=0, end_offset=24, embeddings={feature_0=1.7914711E9, feature_1=1.7914711E9, feature_2=1.7914711E9, feature_3=1.7914711E9, feature_4=1.7914711E9}}]}}}, st_unsigned_long={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_unsigned_long=[{start_offset=0, end_offset=10, embeddings={feature_0=3.597711E8, feature_1=3.597711E8, feature_2=3.597711E8, feature_3=3.597711E8, feature_4=3.597711E8}}]}}}, st_bool={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_bool=[{start_offset=0, end_offset=5, embeddings={feature_0=9.719632E7, feature_1=9.719633E7, feature_2=9.719633E7, feature_3=9.719633E7, feature_4=9.719633E7}}]}}}, st_ip={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_ip=[{start_offset=0, end_offset=7, embeddings={feature_0=1.9016198E9, feature_1=1.9016198E9, feature_2=1.9016198E9, feature_3=1.9016198E9, feature_4=1.9016198E9}}]}}}, st_base64={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_base64=[{start_offset=0, end_offset=12, embeddings={feature_0=1.4194486E9, feature_1=1.4194486E9, feature_2=1.4194486E9, feature_3=1.4194486E9, feature_4=1.4194486E9}}]}}}, st_multi_value={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_multi_value=[{start_offset=0, end_offset=12, embeddings={feature_0=1.457005E8, feature_1=1.457005E8, feature_2=1.4570051E8, feature_3=1.4570051E8, feature_4=1.4570051E8}}, {start_offset=13, end_offset=35, embeddings={feature_0=8.689444E8, feature_1=8.689444E8, feature_2=8.689444E8, feature_3=8.689444E8, feature_4=8.689444E8}}, {start_offset=36, end_offset=56, embeddings={feature_0=1.5083834E9, feature_1=1.5083834E9, feature_2=1.5083834E9, feature_3=1.5083834E9, feature_4=1.5083834E9}}]}}}, st_long={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_long=[{start_offset=0, end_offset=10, embeddings={feature_0=3.597711E8, feature_1=3.597711E8, feature_2=3.597711E8, feature_3=3.597711E8, feature_4=3.597711E8}}]}}}, semantic_text_field={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={semantic_text_field=[{start_offset=0, end_offset=21, embeddings={feature_0=2.9864544E8, feature_1=2.9864544E8, feature_2=2.9864544E8, feature_3=2.9864544E8, feature_4=2.9864544E8}}]}}}, st_cartesian_point={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_cartesian_point=[{start_offset=0, end_offset=23, embeddings={feature_0=2.032471E9, feature_1=2.032471E9, feature_2=2.032471E9, feature_3=2.032471E9, feature_4=2.032471E9}}]}}}, st_version={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_version=[{start_offset=0, end_offset=5, embeddings={feature_0=4.6672444E7, feature_1=4.6672444E7, feature_2=4.6672444E7, feature_3=4.667245E7, feature_4=4.667245E7}}]}}}}, st_integer=23, st_geoshape=POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10)), description=some description1, st_datetime=1953-09-02T00:00:00.000Z, st_unicode=你吃饭了吗, st_unsigned_long=2147483648, st_bool=false, st_ip=1.1.1.1, st_base64=ZWxhc3RpYw==, st_multi_value=[Hello there!, This is a random value, for testing purposes], st_long=2147483648, host=host1, semantic_text_field=live long and prosper, st_cartesian_point=POINT(4297.11 -1475.53), language_name=English, value=1001, st_version=1.2.3}], newOp [Index{id='1', seqNo=0, primaryTerm=1, version=1, autoGeneratedIdTimestamp=-1} source: {st_logs=2024-12-23T12:15:00.000Z 1.2.3.4 [email protected] 4553, st_double=5.20128E11, st_geopoint=POINT(42.97109630194 14.7552534413725), st_integer=23, st_geoshape=POLYGON ((30 10, 40 40, 20 40, 10 20, 30 10)), description=some description1, st_datetime=1953-09-02T00:00:00.000Z, st_unicode=你吃饭了吗, st_unsigned_long=2147483648, st_bool=false, st_ip=1.1.1.1, st_base64=ZWxhc3RpYw==, st_multi_value=[Hello there!, This is a random value, for testing purposes], st_long=2147483648, host=host1, semantic_text_field=live long and prosper, st_cartesian_point=POINT(4297.11 -1475.53), language_name=English, value=1001, st_version=1.2.3, _inference_fields={st_logs={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_logs=[{start_offset=0, end_offset=57, embeddings={feature_0=9.987686E7, feature_1=9.987686E7, feature_2=9.987686E7, feature_3=9.987686E7, feature_4=9.987686E7}}]}}}, st_geopoint={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_geopoint=[{start_offset=0, end_offset=38, embeddings={feature_0=4.8758784E7, feature_1=4.8758784E7, feature_2=4.8758784E7, feature_3=4.8758784E7, feature_4=4.8758784E7}}]}}}, st_double={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_double=[{start_offset=0, end_offset=10, embeddings={feature_0=1.021313E9, feature_1=1.021313E9, feature_2=1.021313E9, feature_3=1.021313E9, feature_4=1.021313E9}}]}}}, st_integer={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_integer=[{start_offset=0, end_offset=2, embeddings={feature_0=1600.0, feature_1=1600.0, feature_2=1604.0, feature_3=1604.0, feature_4=1604.0}}]}}}, st_geoshape={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_geoshape=[{start_offset=0, end_offset=45, embeddings={feature_0=2.8940698E8, feature_1=2.8940698E8, feature_2=2.8940698E8, feature_3=2.8940698E8, feature_4=2.8940698E8}}]}}}, st_unicode={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_unicode=[{start_offset=0, end_offset=5, embeddings={feature_0=2.0258488E9, feature_1=2.0258488E9, feature_2=2.0258488E9, feature_3=2.0258488E9, feature_4=2.0258488E9}}]}}}, st_datetime={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_datetime=[{start_offset=0, end_offset=24, embeddings={feature_0=1.7909678E9, feature_1=1.7909678E9, feature_2=1.7909678E9, feature_3=1.7909678E9, feature_4=1.7909678E9}}]}}}, st_unsigned_long={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_unsigned_long=[{start_offset=0, end_offset=10, embeddings={feature_0=3.5966157E8, feature_1=3.5966157E8, feature_2=3.5966157E8, feature_3=3.5966157E8, feature_4=3.5966157E8}}]}}}, st_bool={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_bool=[{start_offset=0, end_offset=5, embeddings={feature_0=9.699328E7, feature_1=9.699328E7, feature_2=9.699328E7, feature_3=9.699328E7, feature_4=9.699328E7}}]}}}, st_ip={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_ip=[{start_offset=0, end_offset=7, embeddings={feature_0=1.9000197E9, feature_1=1.9000197E9, feature_2=1.9000197E9, feature_3=1.9000197E9, feature_4=1.9000197E9}}]}}}, st_base64={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_base64=[{start_offset=0, end_offset=12, embeddings={feature_0=1.4176748E9, feature_1=1.4176748E9, feature_2=1.4176748E9, feature_3=1.4176748E9, feature_4=1.4176748E9}}]}}}, st_multi_value={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_multi_value=[{start_offset=0, end_offset=12, embeddings={feature_0=1.4522778E8, feature_1=1.4522778E8, feature_2=1.4522778E8, feature_3=1.4522778E8, feature_4=1.4522778E8}}, {start_offset=13, end_offset=35, embeddings={feature_0=8.682209E8, feature_1=8.682209E8, feature_2=8.682209E8, feature_3=8.682209E8, feature_4=8.682209E8}}, {start_offset=36, end_offset=56, embeddings={feature_0=1.5057551E9, feature_1=1.5057551E9, feature_2=1.5057551E9, feature_3=1.5057551E9, feature_4=1.5057551E9}}]}}}, st_long={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_long=[{start_offset=0, end_offset=10, embeddings={feature_0=3.5966157E8, feature_1=3.5966157E8, feature_2=3.5966157E8, feature_3=3.5966157E8, feature_4=3.5966157E8}}]}}}, semantic_text_field={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={semantic_text_field=[{start_offset=0, end_offset=21, embeddings={feature_0=2.977956E8, feature_1=2.977956E8, feature_2=2.977956E8, feature_3=2.977956E8, feature_4=2.977956E8}}]}}}, st_cartesian_point={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_cartesian_point=[{start_offset=0, end_offset=23, embeddings={feature_0=2.0300431E9, feature_1=2.0300431E9, feature_2=2.0300431E9, feature_3=2.0300431E9, feature_4=2.0300431E9}}]}}}, st_version={inference={inference_id=test_sparse_inference, model_settings={task_type=sparse_embedding}, chunks={st_version=[{start_offset=0, end_offset=5, embeddings={feature_0=4.666163E7, feature_1=4.666163E7, feature_2=4.666163E7, feature_3=4.666163E7, feature_4=4.666163E7}}]}}}}}]
	... 24 more
Caused by: java.lang.RuntimeException: stack capture previous op
	at [email protected]/org.elasticsearch.index.translog.TranslogWriter.assertNoSeqNumberConflict(TranslogWriter.java:313)
	at [email protected]/org.elasticsearch.index.translog.TranslogWriter.add(TranslogWriter.java:265)
	at [email protected]/org.elasticsearch.index.translog.Translog.add(Translog.java:633)
	at [email protected]/org.elasticsearch.index.engine.InternalEngine.index(InternalEngine.java:1229)
	at [email protected]/org.elasticsearch.index.shard.IndexShard.index(IndexShard.java:1089)
	at [email protected]/org.elasticsearch.index.shard.IndexShard.applyIndexOperation(IndexShard.java:1015)
	at [email protected]/org.elasticsearch.index.shard.IndexShard.applyIndexOperationOnReplica(IndexShard.java:956)
	at [email protected]/org.elasticsearch.action.bulk.TransportShardBulkAction.performOpOnReplica(TransportShardBulkAction.java:713)
	at [email protected]/org.elasticsearch.action.bulk.TransportShardBulkAction.performOnReplica(TransportShardBulkAction.java:690)
	at [email protected]/org.elasticsearch.action.bulk.TransportShardBulkAction.lambda$dispatchedShardOperationOnReplica$7(TransportShardBulkAction.java:646)
	at [email protected]/org.elasticsearch.action.ActionListener.completeWith(ActionListener.java:356)
	at [email protected]/org.elasticsearch.action.bulk.TransportShardBulkAction.dispatchedShardOperationOnReplica(TransportShardBulkAction.java:644)
	at [email protected]/org.elasticsearch.action.bulk.TransportShardBulkAction.dispatchedShardOperationOnReplica(TransportShardBulkAction.java:83)
	at [email protected]/org.elasticsearch.action.support.replication.TransportWriteAction$2.doRun(TransportWriteAction.java:248)
	at [email protected]/org.elasticsearch.common.util.concurrent.AbstractRunnable.run(AbstractRunnable.java:27)
	at [email protected]/org.elasticsearch.common.util.concurrent.TimedRunnable.doRun(TimedRunnable.java:34)
	... 5 more

@dnhatn dnhatn added medium-risk An open issue or test failure that is a medium risk to future releases and removed needs:risk Requires assignment of a risk label (low, medium, blocker) labels Mar 18, 2025
@jimczi jimczi assigned jimczi and unassigned ioanatia Mar 19, 2025
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:Analytics/ES|QL AKA ESQL medium-risk An open issue or test failure that is a medium risk to future releases Team:Analytics Meta label for analytical engine team (ESQL/Aggs/Geo) >test-failure Triaged test failures from CI
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