Fix visibility and run BM on cloud #18
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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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Mar 23, 2022
### What changes were proposed in this pull request? Currently, Spark DS V2 aggregate push-down doesn't supports project with alias. Refer https://github.com/apache/spark/blob/c91c2e9afec0d5d5bbbd2e155057fe409c5bb928/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/V2ScanRelationPushDown.scala#L96 This PR let it works good with alias. **The first example:** the origin plan show below: ``` Aggregate [DEPT#0], [DEPT#0, sum(mySalary#8) AS total#14] +- Project [DEPT#0, SALARY#2 AS mySalary#8] +- ScanBuilderHolder [DEPT#0, NAME#1, SALARY#2, BONUS#3], RelationV2[DEPT#0, NAME#1, SALARY#2, BONUS#3] test.employee, JDBCScanBuilder(org.apache.spark.sql.test.TestSparkSession77978658,StructType(StructField(DEPT,IntegerType,true),StructField(NAME,StringType,true),StructField(SALARY,DecimalType(20,2),true),StructField(BONUS,DoubleType,true)),org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions5f8da82) ``` If we can complete push down the aggregate, then the plan will be: ``` Project [DEPT#0, SUM(SALARY)#18 AS sum(SALARY#2)#13 AS total#14] +- RelationV2[DEPT#0, SUM(SALARY)#18] test.employee ``` If we can partial push down the aggregate, then the plan will be: ``` Aggregate [DEPT#0], [DEPT#0, sum(cast(SUM(SALARY)#18 as decimal(20,2))) AS total#14] +- RelationV2[DEPT#0, SUM(SALARY)#18] test.employee ``` **The second example:** the origin plan show below: ``` Aggregate [myDept#33], [myDept#33, sum(mySalary#34) AS total#40] +- Project [DEPT#25 AS myDept#33, SALARY#27 AS mySalary#34] +- ScanBuilderHolder [DEPT#25, NAME#26, SALARY#27, BONUS#28], RelationV2[DEPT#25, NAME#26, SALARY#27, BONUS#28] test.employee, JDBCScanBuilder(org.apache.spark.sql.test.TestSparkSession25c4f621,StructType(StructField(DEPT,IntegerType,true),StructField(NAME,StringType,true),StructField(SALARY,DecimalType(20,2),true),StructField(BONUS,DoubleType,true)),org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions345d641e) ``` If we can complete push down the aggregate, then the plan will be: ``` Project [DEPT#25 AS myDept#33, SUM(SALARY)apache#44 AS sum(SALARY#27)apache#39 AS total#40] +- RelationV2[DEPT#25, SUM(SALARY)apache#44] test.employee ``` If we can partial push down the aggregate, then the plan will be: ``` Aggregate [myDept#33], [DEPT#25 AS myDept#33, sum(cast(SUM(SALARY)apache#56 as decimal(20,2))) AS total#52] +- RelationV2[DEPT#25, SUM(SALARY)apache#56] test.employee ``` ### Why are the changes needed? Alias is more useful. ### Does this PR introduce _any_ user-facing change? 'Yes'. Users could see DS V2 aggregate push-down supports project with alias. ### How was this patch tested? New tests. Closes apache#35932 from beliefer/SPARK-38533_new. Authored-by: Jiaan Geng <beliefer@163.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>
MaxGekk
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Mar 24, 2022
### What changes were proposed in this pull request? Currently, Spark DS V2 aggregate push-down doesn't supports project with alias. Refer https://github.com/apache/spark/blob/c91c2e9afec0d5d5bbbd2e155057fe409c5bb928/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/V2ScanRelationPushDown.scala#L96 This PR let it works good with alias. **The first example:** the origin plan show below: ``` Aggregate [DEPT#0], [DEPT#0, sum(mySalary#8) AS total#14] +- Project [DEPT#0, SALARY#2 AS mySalary#8] +- ScanBuilderHolder [DEPT#0, NAME#1, SALARY#2, BONUS#3], RelationV2[DEPT#0, NAME#1, SALARY#2, BONUS#3] test.employee, JDBCScanBuilder(org.apache.spark.sql.test.TestSparkSession77978658,StructType(StructField(DEPT,IntegerType,true),StructField(NAME,StringType,true),StructField(SALARY,DecimalType(20,2),true),StructField(BONUS,DoubleType,true)),org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions5f8da82) ``` If we can complete push down the aggregate, then the plan will be: ``` Project [DEPT#0, SUM(SALARY)#18 AS sum(SALARY#2)#13 AS total#14] +- RelationV2[DEPT#0, SUM(SALARY)#18] test.employee ``` If we can partial push down the aggregate, then the plan will be: ``` Aggregate [DEPT#0], [DEPT#0, sum(cast(SUM(SALARY)#18 as decimal(20,2))) AS total#14] +- RelationV2[DEPT#0, SUM(SALARY)#18] test.employee ``` **The second example:** the origin plan show below: ``` Aggregate [myDept#33], [myDept#33, sum(mySalary#34) AS total#40] +- Project [DEPT#25 AS myDept#33, SALARY#27 AS mySalary#34] +- ScanBuilderHolder [DEPT#25, NAME#26, SALARY#27, BONUS#28], RelationV2[DEPT#25, NAME#26, SALARY#27, BONUS#28] test.employee, JDBCScanBuilder(org.apache.spark.sql.test.TestSparkSession25c4f621,StructType(StructField(DEPT,IntegerType,true),StructField(NAME,StringType,true),StructField(SALARY,DecimalType(20,2),true),StructField(BONUS,DoubleType,true)),org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions345d641e) ``` If we can complete push down the aggregate, then the plan will be: ``` Project [DEPT#25 AS myDept#33, SUM(SALARY)apache#44 AS sum(SALARY#27)apache#39 AS total#40] +- RelationV2[DEPT#25, SUM(SALARY)apache#44] test.employee ``` If we can partial push down the aggregate, then the plan will be: ``` Aggregate [myDept#33], [DEPT#25 AS myDept#33, sum(cast(SUM(SALARY)apache#56 as decimal(20,2))) AS total#52] +- RelationV2[DEPT#25, SUM(SALARY)apache#56] test.employee ``` ### Why are the changes needed? Alias is more useful. ### Does this PR introduce _any_ user-facing change? 'Yes'. Users could see DS V2 aggregate push-down supports project with alias. ### How was this patch tested? New tests. Closes apache#35932 from beliefer/SPARK-38533_new. Authored-by: Jiaan Geng <beliefer@163.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> (cherry picked from commit f327dad) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
MaxGekk
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Oct 21, 2024
### What changes were proposed in this pull request? Restore `scipy` installation in dockerfile ### Why are the changes needed? https://docs.scipy.org/doc/scipy-1.13.1/building/index.html#system-level-dependencies > If you want to use the system Python and pip, you will need: C, C++, and Fortran compilers (typically gcc, g++, and gfortran). ... `scipy` actually depends on `gfortran`, but `apt-get remove --purge -y 'gfortran-11'` broke this dependency. ### Does this PR introduce _any_ user-facing change? no ### How was this patch tested? manually check with the first commit apache@5be0dfa: move `apt-get remove --purge -y 'gfortran-11'` ahead of `scipy` installation, then the installation fails with ``` #18 394.3 Collecting scipy #18 394.4 Downloading scipy-1.13.1.tar.gz (57.2 MB) #18 395.2 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 57.2/57.2 MB 76.7 MB/s eta 0:00:00 #18 401.3 Installing build dependencies: started #18 410.5 Installing build dependencies: finished with status 'done' #18 410.5 Getting requirements to build wheel: started #18 410.7 Getting requirements to build wheel: finished with status 'done' #18 410.7 Installing backend dependencies: started #18 411.8 Installing backend dependencies: finished with status 'done' #18 411.8 Preparing metadata (pyproject.toml): started #18 414.9 Preparing metadata (pyproject.toml): finished with status 'error' #18 414.9 error: subprocess-exited-with-error #18 414.9 #18 414.9 × Preparing metadata (pyproject.toml) did not run successfully. #18 414.9 │ exit code: 1 #18 414.9 ╰─> [42 lines of output] #18 414.9 + meson setup /tmp/pip-install-y77ar9d0/scipy_1e543e0816ed4b26984415533ae9079d /tmp/pip-install-y77ar9d0/scipy_1e543e0816ed4b26984415533ae9079d/.mesonpy-xqfvs4ek -Dbuildtype=release -Db_ndebug=if-release -Db_vscrt=md --native-file=/tmp/pip-install-y77ar9d0/scipy_1e543e0816ed4b26984415533ae9079d/.mesonpy-xqfvs4ek/meson-python-native-file.ini #18 414.9 The Meson build system #18 414.9 Version: 1.5.2 #18 414.9 Source dir: /tmp/pip-install-y77ar9d0/scipy_1e543e0816ed4b26984415533ae9079d #18 414.9 Build dir: /tmp/pip-install-y77ar9d0/scipy_1e543e0816ed4b26984415533ae9079d/.mesonpy-xqfvs4ek #18 414.9 Build type: native build #18 414.9 Project name: scipy #18 414.9 Project version: 1.13.1 #18 414.9 C compiler for the host machine: cc (gcc 11.4.0 "cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0") #18 414.9 C linker for the host machine: cc ld.bfd 2.38 #18 414.9 C++ compiler for the host machine: c++ (gcc 11.4.0 "c++ (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0") #18 414.9 C++ linker for the host machine: c++ ld.bfd 2.38 #18 414.9 Cython compiler for the host machine: cython (cython 3.0.11) #18 414.9 Host machine cpu family: x86_64 #18 414.9 Host machine cpu: x86_64 #18 414.9 Program python found: YES (/usr/local/bin/pypy3) #18 414.9 Run-time dependency python found: YES 3.9 #18 414.9 Program cython found: YES (/tmp/pip-build-env-v_vnvt3h/overlay/bin/cython) #18 414.9 Compiler for C supports arguments -Wno-unused-but-set-variable: YES #18 414.9 Compiler for C supports arguments -Wno-unused-function: YES #18 414.9 Compiler for C supports arguments -Wno-conversion: YES #18 414.9 Compiler for C supports arguments -Wno-misleading-indentation: YES #18 414.9 Library m found: YES #18 414.9 #18 414.9 ../meson.build:78:0: ERROR: Unknown compiler(s): [['gfortran'], ['flang'], ['nvfortran'], ['pgfortran'], ['ifort'], ['ifx'], ['g95']] #18 414.9 The following exception(s) were encountered: #18 414.9 Running `gfortran --version` gave "[Errno 2] No such file or directory: 'gfortran'" #18 414.9 Running `gfortran -V` gave "[Errno 2] No such file or directory: 'gfortran'" #18 414.9 Running `flang --version` gave "[Errno 2] No such file or directory: 'flang'" #18 414.9 Running `flang -V` gave "[Errno 2] No such file or directory: 'flang'" #18 414.9 Running `nvfortran --version` gave "[Errno 2] No such file or directory: 'nvfortran'" #18 414.9 Running `nvfortran -V` gave "[Errno 2] No such file or directory: 'nvfortran'" #18 414.9 Running `pgfortran --version` gave "[Errno 2] No such file or directory: 'pgfortran'" #18 414.9 Running `pgfortran -V` gave "[Errno 2] No such file or directory: 'pgfortran'" #18 414.9 Running `ifort --version` gave "[Errno 2] No such file or directory: 'ifort'" #18 414.9 Running `ifort -V` gave "[Errno 2] No such file or directory: 'ifort'" #18 414.9 Running `ifx --version` gave "[Errno 2] No such file or directory: 'ifx'" #18 414.9 Running `ifx -V` gave "[Errno 2] No such file or directory: 'ifx'" #18 414.9 Running `g95 --version` gave "[Errno 2] No such file or directory: 'g95'" #18 414.9 Running `g95 -V` gave "[Errno 2] No such file or directory: 'g95'" #18 414.9 #18 414.9 A full log can be found at /tmp/pip-install-y77ar9d0/scipy_1e543e0816ed4b26984[4155](https://github.com/zhengruifeng/spark/actions/runs/11357130578/job/31589506939#step:7:4161)33ae9079d/.mesonpy-xqfvs4ek/meson-logs/meson-log.txt #18 414.9 [end of output] ``` see https://github.com/zhengruifeng/spark/actions/runs/11357130578/job/31589506939 ### Was this patch authored or co-authored using generative AI tooling? no Closes apache#48489 from zhengruifeng/infra_scipy. Authored-by: Ruifeng Zheng <ruifengz@apache.org> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
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