hdh-spark-job - Details for Query 1075

Submitted Time: 2026/10/23 08:12:42
Duration: 55 ms
Succeeded Jobs: 1085 1086

Show the Stage ID and Task ID that corresponds to the max metric

digraph G { 0 [labelType="html" label=" AdaptiveSparkPlan "]; subgraph cluster1 { isCluster="true"; label="WholeStageCodegen (4)\n \nduration: 0 ms"; 2 [labelType="html" label="HashAggregate time in aggregation build: 0 ms number of output rows: 1"]; } 3 [labelType="html" label="Exchange shuffle records written: 2 local merged chunks fetched: 0 shuffle write time total (min, med, max (stageId: taskId)) 0 ms (0 ms, 0 ms, 0 ms (stage 1466.0: task 1319)) remote merged bytes read: 0.0 B local merged blocks fetched: 0 corrupt merged block chunks: 0 remote merged reqs duration: 0 ms remote merged blocks fetched: 0 records read: 2 local bytes read: 118.0 B fetch wait time: 0 ms remote bytes read: 0.0 B merged fetch fallback count: 0 local blocks read: 2 remote merged chunks fetched: 0 remote blocks read: 0 data size total (min, med, max (stageId: taskId)) 32.0 B (16.0 B, 16.0 B, 16.0 B (stage 1466.0: task 1319)) local merged bytes read: 0.0 B number of partitions: 1 remote reqs duration: 0 ms remote bytes read to disk: 0.0 B shuffle bytes written total (min, med, max (stageId: taskId)) 118.0 B (59.0 B, 59.0 B, 59.0 B (stage 1466.0: task 1319))"]; subgraph cluster4 { isCluster="true"; label="WholeStageCodegen (3)\n \nduration: total (min, med, max (stageId: taskId))\n2 ms (1 ms, 1 ms, 1 ms (stage 1466.0: task 1319))"; 5 [labelType="html" label="HashAggregate time in aggregation build total (min, med, max (stageId: taskId)) 2 ms (1 ms, 1 ms, 1 ms (stage 1466.0: task 1319)) number of output rows: 2"]; } 6 [labelType="html" label=" Union "]; subgraph cluster7 { isCluster="true"; label="WholeStageCodegen (1)\n \nduration: 1 ms"; 8 [labelType="html" label="Scan JDBCRelation((SELECT \"jn_numjou\", \"jn_action\", \"jn_typjou\", \"jn_table\", \"jn_datjou\", \"jn_heujou\", \"jn_codper\", \"jn_numdos\", \"jn_numdem\", \"jn_numexa\", \"jn_numdrv\", \"jn_numcr\", \"jn_numfus\", \"jn_comment\", \"jn_codsit\", \"jn_xmldata\", \"jn_prg_code\", \"jn_terminal\", \"jn_osuser\", \"jn_ip_address\", \"jn_flenv\", \"jn_datenv\", \"jn_nument\", \"jn_numstu\", \"jn_numser\", \"jn_numima\", \"jn_numrb\", \"jn_dursec\" FROM \"xplore\".\"h_journal\") as x) [numPartitions=1] number of output rows: 8,765"]; } subgraph cluster9 { isCluster="true"; label="WholeStageCodegen (2)\n \nduration: 1 ms"; 10 [labelType="html" label="Scan JDBCRelation((SELECT \"jn_numjou\", \"jn_action\", \"jn_typjou\", \"jn_table\", \"jn_datjou\", \"jn_heujou\", \"jn_codper\", \"jn_numdos\", \"jn_numdem\", \"jn_numexa\", \"jn_numdrv\", \"jn_numcr\", \"jn_numfus\", \"jn_comment\", \"jn_codsit\", \"jn_xmldata\", \"jn_prg_code\", \"jn_terminal\", \"jn_osuser\", \"jn_ip_address\", \"jn_flenv\", \"jn_datenv\", \"jn_nument\", \"jn_numstu\", \"jn_numser\", \"jn_numima\", \"jn_numrb\", \"jn_dursec\" FROM \"xplore2\".\"h_journal\") as x) [numPartitions=1] number of output rows: 8,765"]; } 2->0; 3->2; 5->3; 6->5; 8->6; 10->6; }

AdaptiveSparkPlan isFinalPlan=true

HashAggregate(keys=[], functions=[count(1)])

WholeStageCodegen (4)

Exchange SinglePartition, ENSURE_REQUIREMENTS, [plan_id=23351]

HashAggregate(keys=[], functions=[partial_count(1)])

WholeStageCodegen (3)

Union

Scan JDBCRelation((SELECT "jn_numjou", "jn_action", "jn_typjou", "jn_table", "jn_datjou", "jn_heujou", "jn_codper", "jn_numdos", "jn_numdem", "jn_numexa", "jn_numdrv", "jn_numcr", "jn_numfus", "jn_comment", "jn_codsit", "jn_xmldata", "jn_prg_code", "jn_terminal", "jn_osuser", "jn_ip_address", "jn_flenv", "jn_datenv", "jn_nument", "jn_numstu", "jn_numser", "jn_numima", "jn_numrb", "jn_dursec" FROM "xplore"."h_journal") as x) [numPartitions=1] [] PushedFilters: [], ReadSchema: struct<>

WholeStageCodegen (1)

Scan JDBCRelation((SELECT "jn_numjou", "jn_action", "jn_typjou", "jn_table", "jn_datjou", "jn_heujou", "jn_codper", "jn_numdos", "jn_numdem", "jn_numexa", "jn_numdrv", "jn_numcr", "jn_numfus", "jn_comment", "jn_codsit", "jn_xmldata", "jn_prg_code", "jn_terminal", "jn_osuser", "jn_ip_address", "jn_flenv", "jn_datenv", "jn_nument", "jn_numstu", "jn_numser", "jn_numima", "jn_numrb", "jn_dursec" FROM "xplore2"."h_journal") as x) [numPartitions=1] [] PushedFilters: [], ReadSchema: struct<>

WholeStageCodegen (2)

Details

== Physical Plan ==
AdaptiveSparkPlan (12)
+- == Final Plan ==
   * HashAggregate (7)
   +- ShuffleQueryStage (6), Statistics(sizeInBytes=32.0 B, rowCount=2)
      +- Exchange (5)
         +- * HashAggregate (4)
            +- Union (3)
               :- * Scan JDBCRelation((SELECT "jn_numjou", "jn_action", "jn_typjou", "jn_table", "jn_datjou", "jn_heujou", "jn_codper", "jn_numdos", "jn_numdem", "jn_numexa", "jn_numdrv", "jn_numcr", "jn_numfus", "jn_comment", "jn_codsit", "jn_xmldata", "jn_prg_code", "jn_terminal", "jn_osuser", "jn_ip_address", "jn_flenv", "jn_datenv", "jn_nument", "jn_numstu", "jn_numser", "jn_numima", "jn_numrb", "jn_dursec" FROM "xplore"."h_journal") as x) [numPartitions=1]  (1)
               +- * Scan JDBCRelation((SELECT "jn_numjou", "jn_action", "jn_typjou", "jn_table", "jn_datjou", "jn_heujou", "jn_codper", "jn_numdos", "jn_numdem", "jn_numexa", "jn_numdrv", "jn_numcr", "jn_numfus", "jn_comment", "jn_codsit", "jn_xmldata", "jn_prg_code", "jn_terminal", "jn_osuser", "jn_ip_address", "jn_flenv", "jn_datenv", "jn_nument", "jn_numstu", "jn_numser", "jn_numima", "jn_numrb", "jn_dursec" FROM "xplore2"."h_journal") as x) [numPartitions=1]  (2)
+- == Initial Plan ==
   HashAggregate (11)
   +- Exchange (10)
      +- HashAggregate (9)
         +- Union (8)
            :- Scan JDBCRelation((SELECT "jn_numjou", "jn_action", "jn_typjou", "jn_table", "jn_datjou", "jn_heujou", "jn_codper", "jn_numdos", "jn_numdem", "jn_numexa", "jn_numdrv", "jn_numcr", "jn_numfus", "jn_comment", "jn_codsit", "jn_xmldata", "jn_prg_code", "jn_terminal", "jn_osuser", "jn_ip_address", "jn_flenv", "jn_datenv", "jn_nument", "jn_numstu", "jn_numser", "jn_numima", "jn_numrb", "jn_dursec" FROM "xplore"."h_journal") as x) [numPartitions=1]  (1)
            +- Scan JDBCRelation((SELECT "jn_numjou", "jn_action", "jn_typjou", "jn_table", "jn_datjou", "jn_heujou", "jn_codper", "jn_numdos", "jn_numdem", "jn_numexa", "jn_numdrv", "jn_numcr", "jn_numfus", "jn_comment", "jn_codsit", "jn_xmldata", "jn_prg_code", "jn_terminal", "jn_osuser", "jn_ip_address", "jn_flenv", "jn_datenv", "jn_nument", "jn_numstu", "jn_numser", "jn_numima", "jn_numrb", "jn_dursec" FROM "xplore2"."h_journal") as x) [numPartitions=1]  (2)


(1) Scan JDBCRelation((SELECT "jn_numjou", "jn_action", "jn_typjou", "jn_table", "jn_datjou", "jn_heujou", "jn_codper", "jn_numdos", "jn_numdem", "jn_numexa", "jn_numdrv", "jn_numcr", "jn_numfus", "jn_comment", "jn_codsit", "jn_xmldata", "jn_prg_code", "jn_terminal", "jn_osuser", "jn_ip_address", "jn_flenv", "jn_datenv", "jn_nument", "jn_numstu", "jn_numser", "jn_numima", "jn_numrb", "jn_dursec" FROM "xplore"."h_journal") as x) [numPartitions=1]  [codegen id : 1]
Output: []
ReadSchema: struct<>

(2) Scan JDBCRelation((SELECT "jn_numjou", "jn_action", "jn_typjou", "jn_table", "jn_datjou", "jn_heujou", "jn_codper", "jn_numdos", "jn_numdem", "jn_numexa", "jn_numdrv", "jn_numcr", "jn_numfus", "jn_comment", "jn_codsit", "jn_xmldata", "jn_prg_code", "jn_terminal", "jn_osuser", "jn_ip_address", "jn_flenv", "jn_datenv", "jn_nument", "jn_numstu", "jn_numser", "jn_numima", "jn_numrb", "jn_dursec" FROM "xplore2"."h_journal") as x) [numPartitions=1]  [codegen id : 2]
Output: []
ReadSchema: struct<>

(3) Union

(4) HashAggregate [codegen id : 3]
Input: []
Keys: []
Functions [1]: [partial_count(1)]
Aggregate Attributes [1]: [count#328893L]
Results [1]: [count#328894L]

(5) Exchange
Input [1]: [count#328894L]
Arguments: SinglePartition, ENSURE_REQUIREMENTS, [plan_id=23351]

(6) ShuffleQueryStage
Output [1]: [count#328894L]
Arguments: 0

(7) HashAggregate [codegen id : 4]
Input [1]: [count#328894L]
Keys: []
Functions [1]: [count(1)]
Aggregate Attributes [1]: [count(1)#328861L]
Results [1]: [count(1)#328861L AS count#328862L]

(8) Union

(9) HashAggregate
Input: []
Keys: []
Functions [1]: [partial_count(1)]
Aggregate Attributes [1]: [count#328893L]
Results [1]: [count#328894L]

(10) Exchange
Input [1]: [count#328894L]
Arguments: SinglePartition, ENSURE_REQUIREMENTS, [plan_id=23337]

(11) HashAggregate
Input [1]: [count#328894L]
Keys: []
Functions [1]: [count(1)]
Aggregate Attributes [1]: [count(1)#328861L]
Results [1]: [count(1)#328861L AS count#328862L]

(12) AdaptiveSparkPlan
Output [1]: [count#328862L]
Arguments: isFinalPlan=true

SQL / DataFrame Properties

Name	Value
spark.sql.optimizer.nestedPredicatePushdown.supportedFileSources	parquet,orc,geoparquet