SP1 - Details for Query 253

Submitted Time: 2026/08/27 10:17:27
Duration: 0.1 s
Succeeded Jobs: 294 296

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

digraph G { 0 [labelType="html" label=" AdaptiveSparkPlan "]; 1 [labelType="html" label=" TakeOrderedAndProject "]; subgraph cluster2 { isCluster="true"; label="WholeStageCodegen (2)\n \nduration: 4 ms"; 3 [labelType="html" label="HashAggregate spill size: 0.0 B time in aggregation build: 0 ms peak memory: 256.0 KiB number of output rows: 60 number of sort fallback tasks: 0 avg hash probes per key: 0"]; } 4 [labelType="html" label="AQEShuffleRead number of partitions: 1 partition data size: 1027.0 B number of coalesced partitions: 1"]; 5 [labelType="html" label="Exchange shuffle records written: 60 local merged chunks fetched: 0 shuffle write time: 1 ms 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: 60 local bytes read: 978.0 B fetch wait time: 0 ms remote bytes read: 0.0 B merged fetch fallback count: 0 local blocks read: 1 remote merged chunks fetched: 0 remote blocks read: 0 data size: 1440.0 B local merged bytes read: 0.0 B number of partitions: 10 remote reqs duration: 0 ms remote bytes read to disk: 0.0 B shuffle bytes written: 978.0 B"]; subgraph cluster6 { isCluster="true"; label="WholeStageCodegen (1)\n \nduration: 5 ms"; 7 [labelType="html" label="HashAggregate spill size: 0.0 B time in aggregation build: 1 ms peak memory: 256.0 KiB number of output rows: 60 number of sort fallback tasks: 0 avg hash probes per key: 0"]; 8 [labelType="html" label=" Project "]; 9 [labelType="html" label="Filter number of output rows: 60"]; 10 [labelType="html" label="Scan JDBCRelation((SELECT \"usager_id\", \"etablissement_id\", \"age\", \"sexe\", \"type_accompagnement\", \"date_entree\" FROM \"seraphin\".\"usagers\") as x) [numPartitions=1] number of output rows: 60"]; } 1->0; 3->1; 4->3; 5->4; 7->5; 8->7; 9->8; 10->9; }

AdaptiveSparkPlan isFinalPlan=true

TakeOrderedAndProject(limit=100, orderBy=[COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257 ASC NULLS FIRST], output=[COL_B8E09662_9644_46A3_945B_685463F545D3#47289])

HashAggregate(keys=[COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257], functions=[])

WholeStageCodegen (2)

AQEShuffleRead coalesced

Exchange hashpartitioning(COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257, 10), ENSURE_REQUIREMENTS, [plan_id=9314]

HashAggregate(keys=[COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257], functions=[])

Project [usager_id#47083 AS COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]

Filter CASE WHEN isnull(Contains(str_remove_accent(upper(str_remove_accent(usager_id#47083))), )) THEN false ELSE Contains(str_remove_accent(upper(str_remove_accent(usager_id#47083))), ) END

Scan JDBCRelation((SELECT "usager_id", "etablissement_id", "age", "sexe", "type_accompagnement", "date_entree" FROM "seraphin"."usagers") as x) [numPartitions=1] [usager_id#47083] PushedFilters: [], ReadSchema: struct<usager_id:string>

WholeStageCodegen (1)

Details

== Physical Plan ==
AdaptiveSparkPlan (13)
+- == Final Plan ==
   TakeOrderedAndProject (9)
   +- * HashAggregate (8)
      +- AQEShuffleRead (7)
         +- ShuffleQueryStage (6), Statistics(sizeInBytes=1440.0 B, rowCount=60)
            +- Exchange (5)
               +- * HashAggregate (4)
                  +- * Project (3)
                     +- * Filter (2)
                        +- * Scan JDBCRelation((SELECT "usager_id", "etablissement_id", "age", "sexe", "type_accompagnement", "date_entree" FROM "seraphin"."usagers") as x) [numPartitions=1]  (1)
+- == Initial Plan ==
   TakeOrderedAndProject (12)
   +- HashAggregate (11)
      +- Exchange (10)
         +- HashAggregate (4)
            +- Project (3)
               +- Filter (2)
                  +- Scan JDBCRelation((SELECT "usager_id", "etablissement_id", "age", "sexe", "type_accompagnement", "date_entree" FROM "seraphin"."usagers") as x) [numPartitions=1]  (1)


(1) Scan JDBCRelation((SELECT "usager_id", "etablissement_id", "age", "sexe", "type_accompagnement", "date_entree" FROM "seraphin"."usagers") as x) [numPartitions=1]  [codegen id : 1]
Output [1]: [usager_id#47083]
ReadSchema: struct<usager_id:string>

(2) Filter [codegen id : 1]
Input [1]: [usager_id#47083]
Condition : CASE WHEN isnull(Contains(str_remove_accent(upper(str_remove_accent(usager_id#47083))), )) THEN false ELSE Contains(str_remove_accent(upper(str_remove_accent(usager_id#47083))), ) END

(3) Project [codegen id : 1]
Output [1]: [usager_id#47083 AS COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]
Input [1]: [usager_id#47083]

(4) HashAggregate [codegen id : 1]
Input [1]: [COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]
Keys [1]: [COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]
Functions: []
Aggregate Attributes: []
Results [1]: [COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]

(5) Exchange
Input [1]: [COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]
Arguments: hashpartitioning(COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257, 10), ENSURE_REQUIREMENTS, [plan_id=9314]

(6) ShuffleQueryStage
Output [1]: [COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]
Arguments: 0

(7) AQEShuffleRead
Input [1]: [COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]
Arguments: coalesced

(8) HashAggregate [codegen id : 2]
Input [1]: [COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]
Keys [1]: [COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]
Functions: []
Aggregate Attributes: []
Results [2]: [COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257 AS COL_B8E09662_9644_46A3_945B_685463F545D3#47289, COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]

(9) TakeOrderedAndProject
Input [2]: [COL_B8E09662_9644_46A3_945B_685463F545D3#47289, COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]
Arguments: 100, [COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257 ASC NULLS FIRST], [COL_B8E09662_9644_46A3_945B_685463F545D3#47289]

(10) Exchange
Input [1]: [COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]
Arguments: hashpartitioning(COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257, 10), ENSURE_REQUIREMENTS, [plan_id=9301]

(11) HashAggregate
Input [1]: [COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]
Keys [1]: [COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]
Functions: []
Aggregate Attributes: []
Results [2]: [COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257 AS COL_B8E09662_9644_46A3_945B_685463F545D3#47289, COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]

(12) TakeOrderedAndProject
Input [2]: [COL_B8E09662_9644_46A3_945B_685463F545D3#47289, COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257]
Arguments: 100, [COL_2FEEECAA_7C4C_4C6A_A294_7759C6845B25#47257 ASC NULLS FIRST], [COL_B8E09662_9644_46A3_945B_685463F545D3#47289]

(13) AdaptiveSparkPlan
Output [1]: [COL_B8E09662_9644_46A3_945B_685463F545D3#47289]
Arguments: isFinalPlan=true

SQL / DataFrame Properties

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