GitHub Actions test #3 #20

HyukjinKwon · 2020-08-12T10:08:28Z

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…Hub Actions This PR proposes to report the failed and succeeded tests in GitHub Actions in order to improve the development velocity by leveraging [ScaCap/action-surefire-report](https://github.com/ScaCap/action-surefire-report). See the example below: ![Screen Shot 2020-08-13 at 8 17 52 PM](https://user-images.githubusercontent.com/6477701/90128649-28f7f280-dda2-11ea-9211-e98e34332f6b.png) Note that we cannot just use [ScaCap/action-surefire-report](https://github.com/ScaCap/action-surefire-report) in Apache Spark because PRs are from the forked repository, and GitHub secrets are unavailable for the security reason. This plugin and all similar plugins require to have the GitHub token that has the write access in order to post test results but it is unavailable in PRs. To work around this limitation, I took this approach: 1. In workflow A, run the tests and upload the JUnit XML test results. GitHub provides to upload and download some files. 2. GitHub introduced new event type [`workflow_run`](https://github.blog/2020-08-03-github-actions-improvements-for-fork-and-pull-request-workflows/) 10 days ago. By leveraging this, it triggers another workflow B. 3. Workflow B is in the main repo instead of fork repo, and has the write access the plugin needs. In workflow B, it downloads the artifact uploaded from workflow A (from the forked repository). 4. Workflow B generates the test reports to port from JUnit xml files. 5. Workflow B looks up the PR and posts the test reports. The `workflow_run` event is very new feature, and looks not so many GitHub Actions plugins support. In order to make this working with [ScaCap/action-surefire-report](https://github.com/ScaCap/action-surefire-report), I had to fork two GitHub Actions plugins to use: - [ScaCap/action-surefire-report](https://github.com/ScaCap/action-surefire-report) to have this custom fix: HyukjinKwon/action-surefire-report@c96094c It added `commit` argument to specify the commit to post the test reports. With `workflow_run`, it can access, in workflow B, to the commit from workflow A. - [dawidd6/action-download-artifact](https://github.com/dawidd6/action-download-artifact) to have this custom fix: HyukjinKwon/action-download-artifact@750b71a It added the support of downloading all artifacts from workflow A, in workflow B. By default, it only supports to specify the name of artifact. Note that I was not able to use the official [actions/download-artifact](https://github.com/actions/download-artifact) because: - It does not support to download artifacts between different workflows, see also actions/download-artifact#3. Once this issue is resolved, we can switch it back to [actions/download-artifact](https://github.com/actions/download-artifact). I plan to make a pull request for both repositories so we don't have to rely on forks. Currently, it's difficult to check the failed tests. You should scroll down long logs from GitHub Actions logs. No, dev-only. Manually tested at: #17, #18, #19, #20, and master branch of my forked repository. Closes apache#29333 from HyukjinKwon/SPARK-32357-fix. Lead-authored-by: Hyukjin Kwon <gurwls223@apache.org> Co-authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>

…ries if AQE is enabled ### What changes were proposed in this pull request? This PR fixes an issue that when AQE is enabled, EXPLAIN FORMATTED doesn't show the plan for subqueries. ```scala val df = spark.range(1, 100) df.createTempView("df") spark.sql("SELECT (SELECT min(id) AS v FROM df)").explain("FORMATTED") == Physical Plan == AdaptiveSparkPlan (3) +- Project (2) +- Scan OneRowRelation (1) (1) Scan OneRowRelation Output: [] Arguments: ParallelCollectionRDD[0] at explain at <console>:24, OneRowRelation, UnknownPartitioning(0) (2) Project Output [1]: [Subquery subquery#3, [id=#20] AS scalarsubquery()#5L] Input: [] (3) AdaptiveSparkPlan Output [1]: [scalarsubquery()#5L] Arguments: isFinalPlan=false ``` After this change, the plan for the subquerie is shown. ```scala == Physical Plan == * Project (2) +- * Scan OneRowRelation (1) (1) Scan OneRowRelation [codegen id : 1] Output: [] Arguments: ParallelCollectionRDD[0] at explain at <console>:24, OneRowRelation, UnknownPartitioning(0) (2) Project [codegen id : 1] Output [1]: [Subquery scalar-subquery#3, [id=#24] AS scalarsubquery()#5L] Input: [] ===== Subqueries ===== Subquery:1 Hosting operator id = 2 Hosting Expression = Subquery scalar-subquery#3, [id=#24] * HashAggregate (6) +- Exchange (5) +- * HashAggregate (4) +- * Range (3) (3) Range [codegen id : 1] Output [1]: [id#0L] Arguments: Range (1, 100, step=1, splits=Some(12)) (4) HashAggregate [codegen id : 1] Input [1]: [id#0L] Keys: [] Functions [1]: [partial_min(id#0L)] Aggregate Attributes [1]: [min#7L] Results [1]: [min#8L] (5) Exchange Input [1]: [min#8L] Arguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#20] (6) HashAggregate [codegen id : 2] Input [1]: [min#8L] Keys: [] Functions [1]: [min(id#0L)] Aggregate Attributes [1]: [min(id#0L)#4L] Results [1]: [min(id#0L)#4L AS v#2L] ``` ### Why are the changes needed? For better debuggability. ### Does this PR introduce _any_ user-facing change? Yes. Users can see the formatted plan for subqueries. ### How was this patch tested? New test. Closes apache#30855 from sarutak/fix-aqe-explain. Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>

…subquery code ### What changes were proposed in this pull request? This PR fixes an issue that `EXPLAIN CODEGEN` and `BenchmarkQueryTest` don't show the corresponding code for subqueries. The following example is about `EXPLAIN CODEGEN`. ``` spark.conf.set("spark.sql.adaptive.enabled", "false") val df = spark.range(1, 100) df.createTempView("df") spark.sql("SELECT (SELECT min(id) AS v FROM df)").explain("CODEGEN") scala> spark.sql("SELECT (SELECT min(id) AS v FROM df)").explain("CODEGEN") Found 1 WholeStageCodegen subtrees. == Subtree 1 / 1 (maxMethodCodeSize:55; maxConstantPoolSize:97(0.15% used); numInnerClasses:0) == *(1) Project [Subquery scalar-subquery#3, [id=#24] AS scalarsubquery()#5L] : +- Subquery scalar-subquery#3, [id=#24] : +- *(2) HashAggregate(keys=[], functions=[min(id#0L)], output=[v#2L]) : +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#20] : +- *(1) HashAggregate(keys=[], functions=[partial_min(id#0L)], output=[min#8L]) : +- *(1) Range (1, 100, step=1, splits=12) +- *(1) Scan OneRowRelation[] Generated code: /* 001 */ public Object generate(Object[] references) { /* 002 */ return new GeneratedIteratorForCodegenStage1(references); /* 003 */ } /* 004 */ /* 005 */ // codegenStageId=1 /* 006 */ final class GeneratedIteratorForCodegenStage1 extends org.apache.spark.sql.execution.BufferedRowIterator { /* 007 */ private Object[] references; /* 008 */ private scala.collection.Iterator[] inputs; /* 009 */ private scala.collection.Iterator rdd_input_0; /* 010 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] project_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[1]; /* 011 */ /* 012 */ public GeneratedIteratorForCodegenStage1(Object[] references) { /* 013 */ this.references = references; /* 014 */ } /* 015 */ /* 016 */ public void init(int index, scala.collection.Iterator[] inputs) { /* 017 */ partitionIndex = index; /* 018 */ this.inputs = inputs; /* 019 */ rdd_input_0 = inputs[0]; /* 020 */ project_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0); /* 021 */ /* 022 */ } /* 023 */ /* 024 */ private void project_doConsume_0() throws java.io.IOException { /* 025 */ // common sub-expressions /* 026 */ /* 027 */ project_mutableStateArray_0[0].reset(); /* 028 */ /* 029 */ if (false) { /* 030 */ project_mutableStateArray_0[0].setNullAt(0); /* 031 */ } else { /* 032 */ project_mutableStateArray_0[0].write(0, 1L); /* 033 */ } /* 034 */ append((project_mutableStateArray_0[0].getRow())); /* 035 */ /* 036 */ } /* 037 */ /* 038 */ protected void processNext() throws java.io.IOException { /* 039 */ while ( rdd_input_0.hasNext()) { /* 040 */ InternalRow rdd_row_0 = (InternalRow) rdd_input_0.next(); /* 041 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1); /* 042 */ project_doConsume_0(); /* 043 */ if (shouldStop()) return; /* 044 */ } /* 045 */ } /* 046 */ /* 047 */ } ``` After this change, the corresponding code for subqueries are shown. ``` Found 3 WholeStageCodegen subtrees. == Subtree 1 / 3 (maxMethodCodeSize:282; maxConstantPoolSize:206(0.31% used); numInnerClasses:0) == *(1) HashAggregate(keys=[], functions=[partial_min(id#0L)], output=[min#8L]) +- *(1) Range (1, 100, step=1, splits=12) Generated code: /* 001 */ public Object generate(Object[] references) { /* 002 */ return new GeneratedIteratorForCodegenStage1(references); /* 003 */ } /* 004 */ /* 005 */ // codegenStageId=1 /* 006 */ final class GeneratedIteratorForCodegenStage1 extends org.apache.spark.sql.execution.BufferedRowIterator { /* 007 */ private Object[] references; /* 008 */ private scala.collection.Iterator[] inputs; /* 009 */ private boolean agg_initAgg_0; /* 010 */ private boolean agg_bufIsNull_0; /* 011 */ private long agg_bufValue_0; /* 012 */ private boolean range_initRange_0; /* 013 */ private long range_nextIndex_0; /* 014 */ private TaskContext range_taskContext_0; /* 015 */ private InputMetrics range_inputMetrics_0; /* 016 */ private long range_batchEnd_0; /* 017 */ private long range_numElementsTodo_0; /* 018 */ private boolean agg_agg_isNull_2_0; /* 019 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] range_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[3]; /* 020 */ /* 021 */ public GeneratedIteratorForCodegenStage1(Object[] references) { /* 022 */ this.references = references; /* 023 */ } /* 024 */ /* 025 */ public void init(int index, scala.collection.Iterator[] inputs) { /* 026 */ partitionIndex = index; /* 027 */ this.inputs = inputs; /* 028 */ /* 029 */ range_taskContext_0 = TaskContext.get(); /* 030 */ range_inputMetrics_0 = range_taskContext_0.taskMetrics().inputMetrics(); /* 031 */ range_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0); /* 032 */ range_mutableStateArray_0[1] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0); /* 033 */ range_mutableStateArray_0[2] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0); /* 034 */ /* 035 */ } /* 036 */ /* 037 */ private void agg_doAggregateWithoutKey_0() throws java.io.IOException { /* 038 */ // initialize aggregation buffer /* 039 */ agg_bufIsNull_0 = true; /* 040 */ agg_bufValue_0 = -1L; /* 041 */ /* 042 */ // initialize Range /* 043 */ if (!range_initRange_0) { /* 044 */ range_initRange_0 = true; /* 045 */ initRange(partitionIndex); /* 046 */ } /* 047 */ /* 048 */ while (true) { /* 049 */ if (range_nextIndex_0 == range_batchEnd_0) { /* 050 */ long range_nextBatchTodo_0; /* 051 */ if (range_numElementsTodo_0 > 1000L) { /* 052 */ range_nextBatchTodo_0 = 1000L; /* 053 */ range_numElementsTodo_0 -= 1000L; /* 054 */ } else { /* 055 */ range_nextBatchTodo_0 = range_numElementsTodo_0; /* 056 */ range_numElementsTodo_0 = 0; /* 057 */ if (range_nextBatchTodo_0 == 0) break; /* 058 */ } /* 059 */ range_batchEnd_0 += range_nextBatchTodo_0 * 1L; /* 060 */ } /* 061 */ /* 062 */ int range_localEnd_0 = (int)((range_batchEnd_0 - range_nextIndex_0) / 1L); /* 063 */ for (int range_localIdx_0 = 0; range_localIdx_0 < range_localEnd_0; range_localIdx_0++) { /* 064 */ long range_value_0 = ((long)range_localIdx_0 * 1L) + range_nextIndex_0; /* 065 */ /* 066 */ agg_doConsume_0(range_value_0); /* 067 */ /* 068 */ // shouldStop check is eliminated /* 069 */ } /* 070 */ range_nextIndex_0 = range_batchEnd_0; /* 071 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(range_localEnd_0); /* 072 */ range_inputMetrics_0.incRecordsRead(range_localEnd_0); /* 073 */ range_taskContext_0.killTaskIfInterrupted(); /* 074 */ } /* 075 */ /* 076 */ } /* 077 */ /* 078 */ private void initRange(int idx) { /* 079 */ java.math.BigInteger index = java.math.BigInteger.valueOf(idx); /* 080 */ java.math.BigInteger numSlice = java.math.BigInteger.valueOf(12L); /* 081 */ java.math.BigInteger numElement = java.math.BigInteger.valueOf(99L); /* 082 */ java.math.BigInteger step = java.math.BigInteger.valueOf(1L); /* 083 */ java.math.BigInteger start = java.math.BigInteger.valueOf(1L); /* 084 */ long partitionEnd; /* 085 */ /* 086 */ java.math.BigInteger st = index.multiply(numElement).divide(numSlice).multiply(step).add(start); /* 087 */ if (st.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) { /* 088 */ range_nextIndex_0 = Long.MAX_VALUE; /* 089 */ } else if (st.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) { /* 090 */ range_nextIndex_0 = Long.MIN_VALUE; /* 091 */ } else { /* 092 */ range_nextIndex_0 = st.longValue(); /* 093 */ } /* 094 */ range_batchEnd_0 = range_nextIndex_0; /* 095 */ /* 096 */ java.math.BigInteger end = index.add(java.math.BigInteger.ONE).multiply(numElement).divide(numSlice) /* 097 */ .multiply(step).add(start); /* 098 */ if (end.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) { /* 099 */ partitionEnd = Long.MAX_VALUE; /* 100 */ } else if (end.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) { /* 101 */ partitionEnd = Long.MIN_VALUE; /* 102 */ } else { /* 103 */ partitionEnd = end.longValue(); /* 104 */ } /* 105 */ /* 106 */ java.math.BigInteger startToEnd = java.math.BigInteger.valueOf(partitionEnd).subtract( /* 107 */ java.math.BigInteger.valueOf(range_nextIndex_0)); /* 108 */ range_numElementsTodo_0 = startToEnd.divide(step).longValue(); /* 109 */ if (range_numElementsTodo_0 < 0) { /* 110 */ range_numElementsTodo_0 = 0; /* 111 */ } else if (startToEnd.remainder(step).compareTo(java.math.BigInteger.valueOf(0L)) != 0) { /* 112 */ range_numElementsTodo_0++; /* 113 */ } /* 114 */ } /* 115 */ /* 116 */ private void agg_doConsume_0(long agg_expr_0_0) throws java.io.IOException { /* 117 */ // do aggregate /* 118 */ // common sub-expressions /* 119 */ /* 120 */ // evaluate aggregate functions and update aggregation buffers /* 121 */ /* 122 */ agg_agg_isNull_2_0 = true; /* 123 */ long agg_value_2 = -1L; /* 124 */ /* 125 */ if (!agg_bufIsNull_0 && (agg_agg_isNull_2_0 || /* 126 */ agg_value_2 > agg_bufValue_0)) { /* 127 */ agg_agg_isNull_2_0 = false; /* 128 */ agg_value_2 = agg_bufValue_0; /* 129 */ } /* 130 */ /* 131 */ if (!false && (agg_agg_isNull_2_0 || /* 132 */ agg_value_2 > agg_expr_0_0)) { /* 133 */ agg_agg_isNull_2_0 = false; /* 134 */ agg_value_2 = agg_expr_0_0; /* 135 */ } /* 136 */ /* 137 */ agg_bufIsNull_0 = agg_agg_isNull_2_0; /* 138 */ agg_bufValue_0 = agg_value_2; /* 139 */ /* 140 */ } /* 141 */ /* 142 */ protected void processNext() throws java.io.IOException { /* 143 */ while (!agg_initAgg_0) { /* 144 */ agg_initAgg_0 = true; /* 145 */ long agg_beforeAgg_0 = System.nanoTime(); /* 146 */ agg_doAggregateWithoutKey_0(); /* 147 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[2] /* aggTime */).add((System.nanoTime() - agg_beforeAgg_0) / 1000000); /* 148 */ /* 149 */ // output the result /* 150 */ /* 151 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[1] /* numOutputRows */).add(1); /* 152 */ range_mutableStateArray_0[2].reset(); /* 153 */ /* 154 */ range_mutableStateArray_0[2].zeroOutNullBytes(); /* 155 */ /* 156 */ if (agg_bufIsNull_0) { /* 157 */ range_mutableStateArray_0[2].setNullAt(0); /* 158 */ } else { /* 159 */ range_mutableStateArray_0[2].write(0, agg_bufValue_0); /* 160 */ } /* 161 */ append((range_mutableStateArray_0[2].getRow())); /* 162 */ } /* 163 */ } /* 164 */ /* 165 */ } ``` ### Why are the changes needed? For better debuggability. ### Does this PR introduce _any_ user-facing change? Yes. After this change, users can see subquery code by `EXPLAIN CODEGEN`. ### How was this patch tested? New test. Closes apache#30859 from sarutak/explain-codegen-subqueries. Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>

…ries if AQE is enabled ### What changes were proposed in this pull request? This PR fixes an issue that when AQE is enabled, EXPLAIN FORMATTED doesn't show the plan for subqueries. ```scala val df = spark.range(1, 100) df.createTempView("df") spark.sql("SELECT (SELECT min(id) AS v FROM df)").explain("FORMATTED") == Physical Plan == AdaptiveSparkPlan (3) +- Project (2) +- Scan OneRowRelation (1) (1) Scan OneRowRelation Output: [] Arguments: ParallelCollectionRDD[0] at explain at <console>:24, OneRowRelation, UnknownPartitioning(0) (2) Project Output [1]: [Subquery subquery#3, [id=#20] AS scalarsubquery()#5L] Input: [] (3) AdaptiveSparkPlan Output [1]: [scalarsubquery()#5L] Arguments: isFinalPlan=false ``` After this change, the plan for the subquerie is shown. ```scala == Physical Plan == * Project (2) +- * Scan OneRowRelation (1) (1) Scan OneRowRelation [codegen id : 1] Output: [] Arguments: ParallelCollectionRDD[0] at explain at <console>:24, OneRowRelation, UnknownPartitioning(0) (2) Project [codegen id : 1] Output [1]: [Subquery scalar-subquery#3, [id=#24] AS scalarsubquery()#5L] Input: [] ===== Subqueries ===== Subquery:1 Hosting operator id = 2 Hosting Expression = Subquery scalar-subquery#3, [id=#24] * HashAggregate (6) +- Exchange (5) +- * HashAggregate (4) +- * Range (3) (3) Range [codegen id : 1] Output [1]: [id#0L] Arguments: Range (1, 100, step=1, splits=Some(12)) (4) HashAggregate [codegen id : 1] Input [1]: [id#0L] Keys: [] Functions [1]: [partial_min(id#0L)] Aggregate Attributes [1]: [min#7L] Results [1]: [min#8L] (5) Exchange Input [1]: [min#8L] Arguments: SinglePartition, ENSURE_REQUIREMENTS, [id=#20] (6) HashAggregate [codegen id : 2] Input [1]: [min#8L] Keys: [] Functions [1]: [min(id#0L)] Aggregate Attributes [1]: [min(id#0L)#4L] Results [1]: [min(id#0L)#4L AS v#2L] ``` ### Why are the changes needed? For better debuggability. ### Does this PR introduce _any_ user-facing change? Yes. Users can see the formatted plan for subqueries. ### How was this patch tested? New test. Closes apache#30855 from sarutak/fix-aqe-explain. Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com> Signed-off-by: Dongjoon Hyun <dongjoon@apache.org> (cherry picked from commit 70da86a) Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>

…subquery code ### What changes were proposed in this pull request? This PR fixes an issue that `EXPLAIN CODEGEN` and `BenchmarkQueryTest` don't show the corresponding code for subqueries. The following example is about `EXPLAIN CODEGEN`. ``` spark.conf.set("spark.sql.adaptive.enabled", "false") val df = spark.range(1, 100) df.createTempView("df") spark.sql("SELECT (SELECT min(id) AS v FROM df)").explain("CODEGEN") scala> spark.sql("SELECT (SELECT min(id) AS v FROM df)").explain("CODEGEN") Found 1 WholeStageCodegen subtrees. == Subtree 1 / 1 (maxMethodCodeSize:55; maxConstantPoolSize:97(0.15% used); numInnerClasses:0) == *(1) Project [Subquery scalar-subquery#3, [id=#24] AS scalarsubquery()#5L] : +- Subquery scalar-subquery#3, [id=#24] : +- *(2) HashAggregate(keys=[], functions=[min(id#0L)], output=[v#2L]) : +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#20] : +- *(1) HashAggregate(keys=[], functions=[partial_min(id#0L)], output=[min#8L]) : +- *(1) Range (1, 100, step=1, splits=12) +- *(1) Scan OneRowRelation[] Generated code: /* 001 */ public Object generate(Object[] references) { /* 002 */ return new GeneratedIteratorForCodegenStage1(references); /* 003 */ } /* 004 */ /* 005 */ // codegenStageId=1 /* 006 */ final class GeneratedIteratorForCodegenStage1 extends org.apache.spark.sql.execution.BufferedRowIterator { /* 007 */ private Object[] references; /* 008 */ private scala.collection.Iterator[] inputs; /* 009 */ private scala.collection.Iterator rdd_input_0; /* 010 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] project_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[1]; /* 011 */ /* 012 */ public GeneratedIteratorForCodegenStage1(Object[] references) { /* 013 */ this.references = references; /* 014 */ } /* 015 */ /* 016 */ public void init(int index, scala.collection.Iterator[] inputs) { /* 017 */ partitionIndex = index; /* 018 */ this.inputs = inputs; /* 019 */ rdd_input_0 = inputs[0]; /* 020 */ project_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0); /* 021 */ /* 022 */ } /* 023 */ /* 024 */ private void project_doConsume_0() throws java.io.IOException { /* 025 */ // common sub-expressions /* 026 */ /* 027 */ project_mutableStateArray_0[0].reset(); /* 028 */ /* 029 */ if (false) { /* 030 */ project_mutableStateArray_0[0].setNullAt(0); /* 031 */ } else { /* 032 */ project_mutableStateArray_0[0].write(0, 1L); /* 033 */ } /* 034 */ append((project_mutableStateArray_0[0].getRow())); /* 035 */ /* 036 */ } /* 037 */ /* 038 */ protected void processNext() throws java.io.IOException { /* 039 */ while ( rdd_input_0.hasNext()) { /* 040 */ InternalRow rdd_row_0 = (InternalRow) rdd_input_0.next(); /* 041 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(1); /* 042 */ project_doConsume_0(); /* 043 */ if (shouldStop()) return; /* 044 */ } /* 045 */ } /* 046 */ /* 047 */ } ``` After this change, the corresponding code for subqueries are shown. ``` Found 3 WholeStageCodegen subtrees. == Subtree 1 / 3 (maxMethodCodeSize:282; maxConstantPoolSize:206(0.31% used); numInnerClasses:0) == *(1) HashAggregate(keys=[], functions=[partial_min(id#0L)], output=[min#8L]) +- *(1) Range (1, 100, step=1, splits=12) Generated code: /* 001 */ public Object generate(Object[] references) { /* 002 */ return new GeneratedIteratorForCodegenStage1(references); /* 003 */ } /* 004 */ /* 005 */ // codegenStageId=1 /* 006 */ final class GeneratedIteratorForCodegenStage1 extends org.apache.spark.sql.execution.BufferedRowIterator { /* 007 */ private Object[] references; /* 008 */ private scala.collection.Iterator[] inputs; /* 009 */ private boolean agg_initAgg_0; /* 010 */ private boolean agg_bufIsNull_0; /* 011 */ private long agg_bufValue_0; /* 012 */ private boolean range_initRange_0; /* 013 */ private long range_nextIndex_0; /* 014 */ private TaskContext range_taskContext_0; /* 015 */ private InputMetrics range_inputMetrics_0; /* 016 */ private long range_batchEnd_0; /* 017 */ private long range_numElementsTodo_0; /* 018 */ private boolean agg_agg_isNull_2_0; /* 019 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[] range_mutableStateArray_0 = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter[3]; /* 020 */ /* 021 */ public GeneratedIteratorForCodegenStage1(Object[] references) { /* 022 */ this.references = references; /* 023 */ } /* 024 */ /* 025 */ public void init(int index, scala.collection.Iterator[] inputs) { /* 026 */ partitionIndex = index; /* 027 */ this.inputs = inputs; /* 028 */ /* 029 */ range_taskContext_0 = TaskContext.get(); /* 030 */ range_inputMetrics_0 = range_taskContext_0.taskMetrics().inputMetrics(); /* 031 */ range_mutableStateArray_0[0] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0); /* 032 */ range_mutableStateArray_0[1] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0); /* 033 */ range_mutableStateArray_0[2] = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(1, 0); /* 034 */ /* 035 */ } /* 036 */ /* 037 */ private void agg_doAggregateWithoutKey_0() throws java.io.IOException { /* 038 */ // initialize aggregation buffer /* 039 */ agg_bufIsNull_0 = true; /* 040 */ agg_bufValue_0 = -1L; /* 041 */ /* 042 */ // initialize Range /* 043 */ if (!range_initRange_0) { /* 044 */ range_initRange_0 = true; /* 045 */ initRange(partitionIndex); /* 046 */ } /* 047 */ /* 048 */ while (true) { /* 049 */ if (range_nextIndex_0 == range_batchEnd_0) { /* 050 */ long range_nextBatchTodo_0; /* 051 */ if (range_numElementsTodo_0 > 1000L) { /* 052 */ range_nextBatchTodo_0 = 1000L; /* 053 */ range_numElementsTodo_0 -= 1000L; /* 054 */ } else { /* 055 */ range_nextBatchTodo_0 = range_numElementsTodo_0; /* 056 */ range_numElementsTodo_0 = 0; /* 057 */ if (range_nextBatchTodo_0 == 0) break; /* 058 */ } /* 059 */ range_batchEnd_0 += range_nextBatchTodo_0 * 1L; /* 060 */ } /* 061 */ /* 062 */ int range_localEnd_0 = (int)((range_batchEnd_0 - range_nextIndex_0) / 1L); /* 063 */ for (int range_localIdx_0 = 0; range_localIdx_0 < range_localEnd_0; range_localIdx_0++) { /* 064 */ long range_value_0 = ((long)range_localIdx_0 * 1L) + range_nextIndex_0; /* 065 */ /* 066 */ agg_doConsume_0(range_value_0); /* 067 */ /* 068 */ // shouldStop check is eliminated /* 069 */ } /* 070 */ range_nextIndex_0 = range_batchEnd_0; /* 071 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[0] /* numOutputRows */).add(range_localEnd_0); /* 072 */ range_inputMetrics_0.incRecordsRead(range_localEnd_0); /* 073 */ range_taskContext_0.killTaskIfInterrupted(); /* 074 */ } /* 075 */ /* 076 */ } /* 077 */ /* 078 */ private void initRange(int idx) { /* 079 */ java.math.BigInteger index = java.math.BigInteger.valueOf(idx); /* 080 */ java.math.BigInteger numSlice = java.math.BigInteger.valueOf(12L); /* 081 */ java.math.BigInteger numElement = java.math.BigInteger.valueOf(99L); /* 082 */ java.math.BigInteger step = java.math.BigInteger.valueOf(1L); /* 083 */ java.math.BigInteger start = java.math.BigInteger.valueOf(1L); /* 084 */ long partitionEnd; /* 085 */ /* 086 */ java.math.BigInteger st = index.multiply(numElement).divide(numSlice).multiply(step).add(start); /* 087 */ if (st.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) { /* 088 */ range_nextIndex_0 = Long.MAX_VALUE; /* 089 */ } else if (st.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) { /* 090 */ range_nextIndex_0 = Long.MIN_VALUE; /* 091 */ } else { /* 092 */ range_nextIndex_0 = st.longValue(); /* 093 */ } /* 094 */ range_batchEnd_0 = range_nextIndex_0; /* 095 */ /* 096 */ java.math.BigInteger end = index.add(java.math.BigInteger.ONE).multiply(numElement).divide(numSlice) /* 097 */ .multiply(step).add(start); /* 098 */ if (end.compareTo(java.math.BigInteger.valueOf(Long.MAX_VALUE)) > 0) { /* 099 */ partitionEnd = Long.MAX_VALUE; /* 100 */ } else if (end.compareTo(java.math.BigInteger.valueOf(Long.MIN_VALUE)) < 0) { /* 101 */ partitionEnd = Long.MIN_VALUE; /* 102 */ } else { /* 103 */ partitionEnd = end.longValue(); /* 104 */ } /* 105 */ /* 106 */ java.math.BigInteger startToEnd = java.math.BigInteger.valueOf(partitionEnd).subtract( /* 107 */ java.math.BigInteger.valueOf(range_nextIndex_0)); /* 108 */ range_numElementsTodo_0 = startToEnd.divide(step).longValue(); /* 109 */ if (range_numElementsTodo_0 < 0) { /* 110 */ range_numElementsTodo_0 = 0; /* 111 */ } else if (startToEnd.remainder(step).compareTo(java.math.BigInteger.valueOf(0L)) != 0) { /* 112 */ range_numElementsTodo_0++; /* 113 */ } /* 114 */ } /* 115 */ /* 116 */ private void agg_doConsume_0(long agg_expr_0_0) throws java.io.IOException { /* 117 */ // do aggregate /* 118 */ // common sub-expressions /* 119 */ /* 120 */ // evaluate aggregate functions and update aggregation buffers /* 121 */ /* 122 */ agg_agg_isNull_2_0 = true; /* 123 */ long agg_value_2 = -1L; /* 124 */ /* 125 */ if (!agg_bufIsNull_0 && (agg_agg_isNull_2_0 || /* 126 */ agg_value_2 > agg_bufValue_0)) { /* 127 */ agg_agg_isNull_2_0 = false; /* 128 */ agg_value_2 = agg_bufValue_0; /* 129 */ } /* 130 */ /* 131 */ if (!false && (agg_agg_isNull_2_0 || /* 132 */ agg_value_2 > agg_expr_0_0)) { /* 133 */ agg_agg_isNull_2_0 = false; /* 134 */ agg_value_2 = agg_expr_0_0; /* 135 */ } /* 136 */ /* 137 */ agg_bufIsNull_0 = agg_agg_isNull_2_0; /* 138 */ agg_bufValue_0 = agg_value_2; /* 139 */ /* 140 */ } /* 141 */ /* 142 */ protected void processNext() throws java.io.IOException { /* 143 */ while (!agg_initAgg_0) { /* 144 */ agg_initAgg_0 = true; /* 145 */ long agg_beforeAgg_0 = System.nanoTime(); /* 146 */ agg_doAggregateWithoutKey_0(); /* 147 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[2] /* aggTime */).add((System.nanoTime() - agg_beforeAgg_0) / 1000000); /* 148 */ /* 149 */ // output the result /* 150 */ /* 151 */ ((org.apache.spark.sql.execution.metric.SQLMetric) references[1] /* numOutputRows */).add(1); /* 152 */ range_mutableStateArray_0[2].reset(); /* 153 */ /* 154 */ range_mutableStateArray_0[2].zeroOutNullBytes(); /* 155 */ /* 156 */ if (agg_bufIsNull_0) { /* 157 */ range_mutableStateArray_0[2].setNullAt(0); /* 158 */ } else { /* 159 */ range_mutableStateArray_0[2].write(0, agg_bufValue_0); /* 160 */ } /* 161 */ append((range_mutableStateArray_0[2].getRow())); /* 162 */ } /* 163 */ } /* 164 */ /* 165 */ } ``` ### Why are the changes needed? For better debuggability. ### Does this PR introduce _any_ user-facing change? Yes. After this change, users can see subquery code by `EXPLAIN CODEGEN`. ### How was this patch tested? New test. Closes apache#30859 from sarutak/explain-codegen-subqueries. Authored-by: Kousuke Saruta <sarutak@oss.nttdata.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com> (cherry picked from commit f4e1069) Signed-off-by: Dongjoon Hyun <dhyun@apple.com>

### What changes were proposed in this pull request? AQE can not reuse subquery if it is pushed into `InMemoryTableScan`. There are two issues: - `ReuseAdaptiveSubquery` can not support reuse subquery if two subquery have the same exprId - `InMemoryTableScan` miss apply `ReuseAdaptiveSubquery` when wrap `TableCacheQueryStageExec` For example: ``` Seq(1).toDF("c1").cache().createOrReplaceTempView("t1") Seq(2).toDF("c2").createOrReplaceTempView("t2") spark.sql("SELECT * FROM t1 WHERE c1 < (SELECT c2 FROM t2)") ``` There are two `subquery#27` but have no `ReusedSubquery` ``` AdaptiveSparkPlan isFinalPlan=true +- == Final Plan == *(1) Filter (c1#14 < Subquery subquery#27, [id=#20]) : +- Subquery subquery#27, [id=#20] : +- AdaptiveSparkPlan isFinalPlan=true : +- LocalTableScan [c2#25] +- TableCacheQueryStage 0 +- InMemoryTableScan [c1#14], [(c1#14 < Subquery subquery#27, [id=#20])] :- InMemoryRelation [c1#14], StorageLevel(disk, memory, deserialized, 1 replicas) : +- LocalTableScan [c1#14] +- Subquery subquery#27, [id=#20] +- AdaptiveSparkPlan isFinalPlan=true +- LocalTableScan [c2#25] ``` ### Why are the changes needed? Improve the coverage of reuse subquery. Note that, it is not a real perf issue because the subquery has been already reused (the same Java object). This pr just makes the plan clearer about subquery reuse. ### Does this PR introduce _any_ user-facing change? no ### How was this patch tested? add test Closes apache#41046 from ulysses-you/aqe-subquery. Authored-by: ulysses-you <ulyssesyou18@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>

…te` for Java 21 ### What changes were proposed in this pull request? SPARK-44507(apache#42130) updated `try_arithmetic.sql.out` and `numeric.sql.out`, SPARK-44868(apache#42534) updated `datetime-formatting.sql.out`, but these PRs didn’t pay attention to the test health on Java 21. So this PR has regenerated the golden files `try_arithmetic.sql.out.java21`, `numeric.sql.out.java21`, and `datetime-formatting.sql.out.java21` of `SQLQueryTestSuite` so that `SQLQueryTestSuite` can be tested with Java 21. ### Why are the changes needed? Restore `SQLQueryTestSuite` to be tested with Java 21. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? - Pass GitHub Actions - Manual checked: ``` java -version openjdk version "21-ea" 2023-09-19 OpenJDK Runtime Environment Zulu21+69-CA (build 21-ea+28) OpenJDK 64-Bit Server VM Zulu21+69-CA (build 21-ea+28, mixed mode, sharing) ``` ``` SPARK_GENERATE_GOLDEN_FILES=0 build/sbt "sql/testOnly org.apache.spark.sql.SQLQueryTestSuite" ``` **Before** ``` ... [info] - datetime-formatting.sql *** FAILED *** (316 milliseconds) [info] datetime-formatting.sql [info] Array("-- Automatically generated by SQLQueryTestSuite [info] ", "create temporary view v as select col from values [info] (timestamp '1582-06-01 11:33:33.123UTC+080000'), [info] (timestamp '1970-01-01 00:00:00.000Europe/Paris'), [info] (timestamp '1970-12-31 23:59:59.999Asia/Srednekolymsk'), [info] (timestamp '1996-04-01 00:33:33.123Australia/Darwin'), [info] (timestamp '2018-11-17 13:33:33.123Z'), [info] (timestamp '2020-01-01 01:33:33.123Asia/Shanghai'), [info] (timestamp '2100-01-01 01:33:33.123America/Los_Angeles') t(col) [info] ", "struct<> [info] ", " [info] [info] [info] ", "select col, date_format(col, 'G GG GGG GGGG') from v [info] ", "struct<col:timestamp,date_format(col, G GG GGG GGGG):string> [info] ", "1582-05-31 19:40:35.123 AD AD AD Anno Domini [info] 1969-12-31 15:00:00 AD AD AD Anno Domini [info] 1970-12-31 04:59:59.999 AD AD AD Anno Domini [info] 1996-03-31 07:03:33.123 AD AD AD Anno Domini [info] 2018-11-17 05:33:33.123 AD AD AD Anno Domini [info] 2019-12-31 09:33:33.123 AD AD AD Anno Domini [info] 2100-01-01 01:33:33.123 AD AD AD Anno Domini [info] [info] [info] ", "select col, date_format(col, 'y yy yyy yyyy yyyyy yyyyyy') from v [info] ", "struct<col:timestamp,date_format(col, y yy yyy yyyy yyyyy yyyyyy):string> [info] ", "1582-05-31 19:40:35.123 1582 82 1582 1582 01582 001582 [info] 1969-12-31 15:00:00 1969 69 1969 1969 01969 001969 [info] 1970-12-31 04:59:59.999 1970 70 1970 1970 01970 001970 [info] 1996-03-31 07:03:33.123 1996 96 1996 1996 01996 001996 [info] 2018-11-17 05:33:33.123 2018 18 2018 2018 02018 002018 [info] 2019-12-31 09:33:33.123 2019 19 2019 2019 02019 002019 [info] 2100-01-01 01:33:33.123 2100 00 2100 2100 02100 002100 [info] ... [info] - postgreSQL/numeric.sql *** FAILED *** (35 seconds, 848 milliseconds) [info] postgreSQL/numeric.sql [info] Expected "...rg.apache.spark.sql.[]AnalysisException [info] { [info] ...", but got "...rg.apache.spark.sql.[catalyst.Extended]AnalysisException [info] { [info] ..." Result did not match for query apache#544 [info] SELECT '' AS to_number_2, to_number('-34,338,492.654,878', '99G999G999D999G999') (SQLQueryTestSuite.scala:876) [info] org.scalatest.exceptions.TestFailedException: ... [info] - try_arithmetic.sql *** FAILED *** (314 milliseconds) [info] try_arithmetic.sql [info] Expected "...rg.apache.spark.sql.[]AnalysisException [info] { [info] ...", but got "...rg.apache.spark.sql.[catalyst.Extended]AnalysisException [info] { [info] ..." Result did not match for query #20 [info] SELECT try_add(interval 2 year, interval 2 second) (SQLQueryTestSuite.scala:876) [info] org.scalatest.exceptions.TestFailedException: ``` **After** ``` [info] Run completed in 9 minutes, 10 seconds. [info] Total number of tests run: 572 [info] Suites: completed 1, aborted 0 [info] Tests: succeeded 572, failed 0, canceled 0, ignored 59, pending 0 [info] All tests passed. ``` ### Was this patch authored or co-authored using generative AI tooling? No Closes apache#42580 from LuciferYang/SPARK-44888. Authored-by: yangjie01 <yangjie01@baidu.com> Signed-off-by: yangjie01 <yangjie01@baidu.com>

### What changes were proposed in this pull request? This PR aims to enforce to install `six` to Python 3.10 because `Python 3.10` is missing `six` and causes `Pandas` detection failures in CIs. - https://github.com/apache/spark/actions/runs/8525063765/job/23373974516 - Note that `pandas` is visible in the installed package list, but it fails when PySpark detects it due to the missing `six`. ``` $ docker run -it --rm ghcr.io/apache/apache-spark-ci-image:master-8345361470 python3.9 -m pip freeze | grep six six==1.16.0 $ docker run -it --rm ghcr.io/apache/apache-spark-ci-image:master-8345361470 python3.10 -m pip freeze | grep six $ docker run -it --rm ghcr.io/apache/apache-spark-ci-image:master-8345361470 python3.11 -m pip freeze | grep six six==1.16.0 $ docker run -it --rm ghcr.io/apache/apache-spark-ci-image:master-8345361470 python3.12 -m pip freeze | grep six six==1.16.0 ``` - CI failure message example. - https://github.com/apache/spark/actions/runs/8525063765/job/23373974096 ``` Starting test(python3.10): pyspark.ml.tests.connect.test_connect_classification (temp output: /__w/spark/spark/python/target/370eb2c4-12f2-411f-96d1-f617f5d59528/python3.10__pyspark.ml.tests.connect.test_connect_classification__v6itdsxy.log) Traceback (most recent call last): File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main return _run_code(code, main_globals, None, File "/usr/lib/python3.10/runpy.py", line 86, in _run_code exec(code, run_globals) File "/__w/spark/spark/python/pyspark/ml/tests/connect/test_connect_classification.py", line 37, in <module> class ClassificationTestsOnConnect(ClassificationTestsMixin, unittest.TestCase): NameError: name 'ClassificationTestsMixin' is not defined ``` ### Why are the changes needed? Since Python 3.10 is the default Python version of Ubuntu OS, the behavior is different. ``` RUN python3.10 -m pip install numpy pyarrow>=15.0.0 six==1.16.0 ... ... #20 0.766 Requirement already satisfied: six==1.16.0 in /usr/lib/python3/dist-packages (1.16.0) ``` ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Check the docker image built by this PR. - https://github.com/dongjoon-hyun/spark/actions/runs/8533625657/job/23376659246 ``` $ docker pull --platform amd64 ghcr.io/dongjoon-hyun/apache-spark-ci-image:master-8533625657 $ docker run -it --rm ghcr.io/dongjoon-hyun/apache-spark-ci-image:master-8533625657 python3.10 -m pip freeze | grep six six==1.16.0 ``` Run tests on new docker image. ``` $ docker run -it --rm -v $PWD:/spark ghcr.io/dongjoon-hyun/apache-spark-ci-image:master-8533625657 rootb7f5f56892b0:/# cd /spark rootb7f5f56892b0:/spark# python/run-tests --modules=pyspark-mllib,pyspark-ml,pyspark-ml-connect --parallelism=1 --python-executables=python3.10 Running PySpark tests. Output is in /spark/python/unit-tests.log Will test against the following Python executables: ['python3.10'] Will test the following Python modules: ['pyspark-mllib', 'pyspark-ml', 'pyspark-ml-connect'] python3.10 python_implementation is CPython python3.10 version is: Python 3.10.12 Starting test(python3.10): pyspark.ml.tests.connect.test_connect_classification (temp output: /spark/python/target/675eccdc-3c4b-4146-a58b-030302bdc6d7/python3.10__pyspark.ml.tests.connect.test_connect_classification__9habp0rh.log) Finished test(python3.10): pyspark.ml.tests.connect.test_connect_classification (159s) Starting test(python3.10): pyspark.ml.tests.connect.test_connect_evaluation (temp output: /spark/python/target/fbac93ba-c72d-40e4-acfe-f3ac01b4932a/python3.10__pyspark.ml.tests.connect.test_connect_evaluation__js11z0ux.log) Finished test(python3.10): pyspark.ml.tests.connect.test_connect_evaluation (36s) Starting test(python3.10): pyspark.ml.tests.connect.test_connect_feature (temp output: /spark/python/target/fdb8828e-4241-4e78-a7d6-b2a4beb3cfc1/python3.10__pyspark.ml.tests.connect.test_connect_feature__et5gr30f.log) Finished test(python3.10): pyspark.ml.tests.connect.test_connect_feature (30s) Starting test(python3.10): pyspark.ml.tests.connect.test_connect_function (temp output: /spark/python/target/e365e62f-a09b-483d-9101-fe9dfc0801f2/python3.10__pyspark.ml.tests.connect.test_connect_function__5e288azs.log) Finished test(python3.10): pyspark.ml.tests.connect.test_connect_function (24s) Starting test(python3.10): pyspark.ml.tests.connect.test_connect_pipeline (temp output: /spark/python/target/bdc167be-6d6e-4704-b840-cf5d23c4b21e/python3.10__pyspark.ml.tests.connect.test_connect_pipeline__63blw3o2.log) ... ``` ### Was this patch authored or co-authored using generative AI tooling? No. Closes apache#45832 from dongjoon-hyun/SPARK-47452-2. Authored-by: Dongjoon Hyun <dhyun@apple.com> Signed-off-by: Dongjoon Hyun <dhyun@apple.com>

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[SPARK-32357][INFRA] Publish failed and succeeded test reports in GitHub Actions apache/spark#29333

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Publish failed and succeeded test reports in GitHub Actions

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