fix #15
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fix #15
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MaxGekk
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Sep 4, 2019
## What changes were proposed in this pull request? This PR aims at improving the way physical plans are explained in spark. Currently, the explain output for physical plan may look very cluttered and each operator's string representation can be very wide and wraps around in the display making it little hard to follow. This especially happens when explaining a query 1) Operating on wide tables 2) Has complex expressions etc. This PR attempts to split the output into two sections. In the header section, we display the basic operator tree with a number associated with each operator. In this section, we strictly control what we output for each operator. In the footer section, each operator is verbosely displayed. Based on the feedback from Maryann, the uncorrelated subqueries (SubqueryExecs) are not included in the main plan. They are printed separately after the main plan and can be correlated by the originating expression id from its parent plan. To illustrate, here is a simple plan displayed in old vs new way. Example query1 : ``` EXPLAIN SELECT key, Max(val) FROM explain_temp1 WHERE key > 0 GROUP BY key HAVING max(val) > 0 ``` Old : ``` *(2) Project [key#2, max(val)#15] +- *(2) Filter (isnotnull(max(val#3)#18) AND (max(val#3)#18 > 0)) +- *(2) HashAggregate(keys=[key#2], functions=[max(val#3)], output=[key#2, max(val)#15, max(val#3)#18]) +- Exchange hashpartitioning(key#2, 200) +- *(1) HashAggregate(keys=[key#2], functions=[partial_max(val#3)], output=[key#2, max#21]) +- *(1) Project [key#2, val#3] +- *(1) Filter (isnotnull(key#2) AND (key#2 > 0)) +- *(1) FileScan parquet default.explain_temp1[key#2,val#3] Batched: true, DataFilters: [isnotnull(key#2), (key#2 > 0)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp1], PartitionFilters: [], PushedFilters: [IsNotNull(key), GreaterThan(key,0)], ReadSchema: struct<key:int,val:int> ``` New : ``` Project (8) +- Filter (7) +- HashAggregate (6) +- Exchange (5) +- HashAggregate (4) +- Project (3) +- Filter (2) +- Scan parquet default.explain_temp1 (1) (1) Scan parquet default.explain_temp1 [codegen id : 1] Output: [key#2, val#3] (2) Filter [codegen id : 1] Input : [key#2, val#3] Condition : (isnotnull(key#2) AND (key#2 > 0)) (3) Project [codegen id : 1] Output : [key#2, val#3] Input : [key#2, val#3] (4) HashAggregate [codegen id : 1] Input: [key#2, val#3] (5) Exchange Input: [key#2, max#11] (6) HashAggregate [codegen id : 2] Input: [key#2, max#11] (7) Filter [codegen id : 2] Input : [key#2, max(val)#5, max(val#3)#8] Condition : (isnotnull(max(val#3)#8) AND (max(val#3)#8 > 0)) (8) Project [codegen id : 2] Output : [key#2, max(val)#5] Input : [key#2, max(val)#5, max(val#3)#8] ``` Example Query2 (subquery): ``` SELECT * FROM explain_temp1 WHERE KEY = (SELECT Max(KEY) FROM explain_temp2 WHERE KEY = (SELECT Max(KEY) FROM explain_temp3 WHERE val > 0) AND val = 2) AND val > 3 ``` Old: ``` *(1) Project [key#2, val#3] +- *(1) Filter (((isnotnull(KEY#2) AND isnotnull(val#3)) AND (KEY#2 = Subquery scalar-subquery#39)) AND (val#3 > 3)) : +- Subquery scalar-subquery#39 : +- *(2) HashAggregate(keys=[], functions=[max(KEY#26)], output=[max(KEY)apache#45]) : +- Exchange SinglePartition : +- *(1) HashAggregate(keys=[], functions=[partial_max(KEY#26)], output=[max#47]) : +- *(1) Project [key#26] : +- *(1) Filter (((isnotnull(KEY#26) AND isnotnull(val#27)) AND (KEY#26 = Subquery scalar-subquery#38)) AND (val#27 = 2)) : : +- Subquery scalar-subquery#38 : : +- *(2) HashAggregate(keys=[], functions=[max(KEY#28)], output=[max(KEY)apache#43]) : : +- Exchange SinglePartition : : +- *(1) HashAggregate(keys=[], functions=[partial_max(KEY#28)], output=[max#49]) : : +- *(1) Project [key#28] : : +- *(1) Filter (isnotnull(val#29) AND (val#29 > 0)) : : +- *(1) FileScan parquet default.explain_temp3[key#28,val#29] Batched: true, DataFilters: [isnotnull(val#29), (val#29 > 0)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp3], PartitionFilters: [], PushedFilters: [IsNotNull(val), GreaterThan(val,0)], ReadSchema: struct<key:int,val:int> : +- *(1) FileScan parquet default.explain_temp2[key#26,val#27] Batched: true, DataFilters: [isnotnull(key#26), isnotnull(val#27), (val#27 = 2)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp2], PartitionFilters: [], PushedFilters: [IsNotNull(key), IsNotNull(val), EqualTo(val,2)], ReadSchema: struct<key:int,val:int> +- *(1) FileScan parquet default.explain_temp1[key#2,val#3] Batched: true, DataFilters: [isnotnull(key#2), isnotnull(val#3), (val#3 > 3)], Format: Parquet, Location: InMemoryFileIndex[file:/user/hive/warehouse/explain_temp1], PartitionFilters: [], PushedFilters: [IsNotNull(key), IsNotNull(val), GreaterThan(val,3)], ReadSchema: struct<key:int,val:int> ``` New: ``` Project (3) +- Filter (2) +- Scan parquet default.explain_temp1 (1) (1) Scan parquet default.explain_temp1 [codegen id : 1] Output: [key#2, val#3] (2) Filter [codegen id : 1] Input : [key#2, val#3] Condition : (((isnotnull(KEY#2) AND isnotnull(val#3)) AND (KEY#2 = Subquery scalar-subquery#23)) AND (val#3 > 3)) (3) Project [codegen id : 1] Output : [key#2, val#3] Input : [key#2, val#3] ===== Subqueries ===== Subquery:1 Hosting operator id = 2 Hosting Expression = Subquery scalar-subquery#23 HashAggregate (9) +- Exchange (8) +- HashAggregate (7) +- Project (6) +- Filter (5) +- Scan parquet default.explain_temp2 (4) (4) Scan parquet default.explain_temp2 [codegen id : 1] Output: [key#26, val#27] (5) Filter [codegen id : 1] Input : [key#26, val#27] Condition : (((isnotnull(KEY#26) AND isnotnull(val#27)) AND (KEY#26 = Subquery scalar-subquery#22)) AND (val#27 = 2)) (6) Project [codegen id : 1] Output : [key#26] Input : [key#26, val#27] (7) HashAggregate [codegen id : 1] Input: [key#26] (8) Exchange Input: [max#35] (9) HashAggregate [codegen id : 2] Input: [max#35] Subquery:2 Hosting operator id = 5 Hosting Expression = Subquery scalar-subquery#22 HashAggregate (15) +- Exchange (14) +- HashAggregate (13) +- Project (12) +- Filter (11) +- Scan parquet default.explain_temp3 (10) (10) Scan parquet default.explain_temp3 [codegen id : 1] Output: [key#28, val#29] (11) Filter [codegen id : 1] Input : [key#28, val#29] Condition : (isnotnull(val#29) AND (val#29 > 0)) (12) Project [codegen id : 1] Output : [key#28] Input : [key#28, val#29] (13) HashAggregate [codegen id : 1] Input: [key#28] (14) Exchange Input: [max#37] (15) HashAggregate [codegen id : 2] Input: [max#37] ``` Note: I opened this PR as a WIP to start getting feedback. I will be on vacation starting tomorrow would not be able to immediately incorporate the feedback. I will start to work on them as soon as i can. Also, currently this PR provides a basic infrastructure for explain enhancement. The details about individual operators will be implemented in follow-up prs ## How was this patch tested? Added a new test `explain.sql` that tests basic scenarios. Need to add more tests. Closes apache#24759 from dilipbiswal/explain_feature. Authored-by: Dilip Biswal <dbiswal@us.ibm.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>
MaxGekk
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Apr 22, 2023
…onnect ### What changes were proposed in this pull request? Implement Arrow-optimized Python UDFs in Spark Connect. Please see apache#39384 for motivation and performance improvements of Arrow-optimized Python UDFs. ### Why are the changes needed? Parity with vanilla PySpark. ### Does this PR introduce _any_ user-facing change? Yes. In Spark Connect Python Client, users can: 1. Set `useArrow` parameter True to enable Arrow optimization for a specific Python UDF. ```sh >>> df = spark.range(2) >>> df.select(udf(lambda x : x + 1, useArrow=True)('id')).show() +------------+ |<lambda>(id)| +------------+ | 1| | 2| +------------+ # ArrowEvalPython indicates Arrow optimization >>> df.select(udf(lambda x : x + 1, useArrow=True)('id')).explain() == Physical Plan == *(2) Project [pythonUDF0#18 AS <lambda>(id)#16] +- ArrowEvalPython [<lambda>(id#14L)#15], [pythonUDF0#18], 200 +- *(1) Range (0, 2, step=1, splits=1) ``` 2. Enable `spark.sql.execution.pythonUDF.arrow.enabled` Spark Conf to make all Python UDFs Arrow-optimized. ```sh >>> spark.conf.set("spark.sql.execution.pythonUDF.arrow.enabled", True) >>> df.select(udf(lambda x : x + 1)('id')).show() +------------+ |<lambda>(id)| +------------+ | 1| | 2| +------------+ # ArrowEvalPython indicates Arrow optimization >>> df.select(udf(lambda x : x + 1)('id')).explain() == Physical Plan == *(2) Project [pythonUDF0#30 AS <lambda>(id)#28] +- ArrowEvalPython [<lambda>(id#26L)#27], [pythonUDF0#30], 200 +- *(1) Range (0, 2, step=1, splits=1) ``` ### How was this patch tested? Parity unit tests. Closes apache#40725 from xinrong-meng/connect_arrow_py_udf. Authored-by: Xinrong Meng <xinrong@apache.org> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
MaxGekk
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Jul 3, 2025
…pressions in `buildAggExprList` ### What changes were proposed in this pull request? Trim aliases before matching Sort/Having/Filter expressions with semantically equal expression from the Aggregate below in `buildAggExprList` ### Why are the changes needed? For a query like: ``` SELECT course, year, GROUPING(course) FROM courseSales GROUP BY CUBE(course, year) ORDER BY GROUPING(course) ``` Plan after `ResolveReferences` and before `ResolveAggregateFunctions` looks like: ``` !Sort [cast((shiftright(tempresolvedcolumn(spark_grouping_id#18L, spark_grouping_id, false), 1) & 1) as tinyint) AS grouping(course)#22 ASC NULLS FIRST], true +- Aggregate [course#19, year#20, spark_grouping_id#18L], [course#19, year#20, cast((shiftright(spark_grouping_id#18L, 1) & 1) as tinyint) AS grouping(course)#21 AS grouping(course)#15] .... ``` Because aggregate list has `Alias(Alias(cast((shiftright(spark_grouping_id#18L, 1) & 1) as tinyint))` expression from `SortOrder` won't get matched as semantically equal and it will result in adding an unnecessary `Project`. By stripping inner aliases from aggregate list (that are going to get removed anyways in `CleanupAliases`) we can match `SortOrder` expression and resolve it as `grouping(course)#15` ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Existing tests ### Was this patch authored or co-authored using generative AI tooling? No Closes apache#51339 from mihailotim-db/mihailotim-db/fix_inner_aliases_semi_structured. Authored-by: Mihailo Timotic <mihailo.timotic@databricks.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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