[GitHub] spark issue #21863: [SPARK-18874][SQL][FOLLOW-UP] Improvement type mismatche...
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[GitHub] spark issue #21822: [SPARK-24865] Remove AnalysisBarrier
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[GitHub] spark issue #21822: [SPARK-24865] Remove AnalysisBarrier
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[GitHub] spark issue #21822: [SPARK-24865] Remove AnalysisBarrier
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[GitHub] spark pull request #21863: [SPARK-18874][SQL][FOLLOW-UP] Improvement type mi...
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[GitHub] spark issue #21869: [SPARK-24891][FOLLOWUP][HOT-FIX][2.3] Fix the Compilatio...
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[GitHub] spark issue #21834: [SPARK-22814][SQL] Support Date/Timestamp in a JDBC part...
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[GitHub] spark issue #21869: [SPARK-24891][FOLLOWUP][HOT-FIX][2.3] Fix the Compilatio...
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[GitHub] spark issue #21834: [SPARK-22814][SQL] Support Date/Timestamp in a JDBC part...
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[GitHub] spark issue #21866: [SPARK-24768][FollowUp][SQL]Avro migration followup: cha...
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[GitHub] spark issue #21803: [SPARK-24849][SPARK-24911][SQL] Converting a value of St...
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[GitHub] spark issue #21850: [SPARK-24892] [SQL] Simplify `CaseWhen` to `If` when the...
Github user gatorsmile commented on the issue: https://github.com/apache/spark/pull/21850 Personally, I do not think we need this extra case. > If primitive has more opportunities for further optimization. Could you explain more? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #21870: Branch 2.3
GitHub user lovezeropython opened a pull request: https://github.com/apache/spark/pull/21870 Branch 2.3 EOFError # ConnectionResetError: [Errno 54] Connection reset by peer (Please fill in changes proposed in this fix) ``` /pyspark.zip/pyspark/worker.py", line 255, in main if read_int(infile) == SpecialLengths.END_OF_STREAM: File "/Users/songhao/apps/spark-2.3.1-bin-hadoop2.7/python/lib/pyspark.zip/pyspark/serializers.py", line 683, in read_int length = stream.read(4) ConnectionResetError: [Errno 54] Connection reset by peerenter code here ```  You can merge this pull request into a Git repository by running: $ git pull https://github.com/apache/spark branch-2.3 Alternatively you can review and apply these changes as the patch at: https://github.com/apache/spark/pull/21870.patch To close this pull request, make a commit to your master/trunk branch with (at least) the following in the commit message: This closes #21870 commit 3737c3d32bb92e73cadaf3b1b9759d9be00b288d Author: gatorsmile Date: 2018-02-13T06:05:13Z [SPARK-20090][FOLLOW-UP] Revert the deprecation of `names` in PySpark ## What changes were proposed in this pull request? Deprecating the field `name` in PySpark is not expected. This PR is to revert the change. ## How was this patch tested? N/A Author: gatorsmile Closes #20595 from gatorsmile/removeDeprecate. (cherry picked from commit 407f67249639709c40c46917700ed6dd736daa7d) Signed-off-by: hyukjinkwon commit 1c81c0c626f115fbfe121ad6f6367b695e9f3b5f Author: guoxiaolong Date: 2018-02-13T12:23:10Z [SPARK-23384][WEB-UI] When it has no incomplete(completed) applications found, the last updated time is not formatted and client local time zone is not show in history server web ui. ## What changes were proposed in this pull request? When it has no incomplete(completed) applications found, the last updated time is not formatted and client local time zone is not show in history server web ui. It is a bug. fix before:  fix after:  ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Please review http://spark.apache.org/contributing.html before opening a pull request. Author: guoxiaolong Closes #20573 from guoxiaolongzte/SPARK-23384. (cherry picked from commit 300c40f50ab4258d697f06a814d1491dc875c847) Signed-off-by: Sean Owen commit dbb1b399b6cf8372a3659c472f380142146b1248 Author: huangtengfei Date: 2018-02-13T15:59:21Z [SPARK-23053][CORE] taskBinarySerialization and task partitions calculate in DagScheduler.submitMissingTasks should keep the same RDD checkpoint status ## What changes were proposed in this pull request? When we run concurrent jobs using the same rdd which is marked to do checkpoint. If one job has finished running the job, and start the process of RDD.doCheckpoint, while another job is submitted, then submitStage and submitMissingTasks will be called. In [submitMissingTasks](https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala#L961), will serialize taskBinaryBytes and calculate task partitions which are both affected by the status of checkpoint, if the former is calculated before doCheckpoint finished, while the latter is calculated after doCheckpoint finished, when run task, rdd.compute will be called, for some rdds with particular partition type such as [UnionRDD](https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rdd/UnionRDD.scala) who will do partition type cast, will get a ClassCastException because the part params is actually a CheckpointRDDPartition. This error occurs because rdd.doCheckpoint occurs in the same thread that called sc.runJob, while the task serialization occurs in the DAGSchedulers event loop. ## How was this patch tested? the exist uts and also add a test case in DAGScheduerSuite to show the exception case. Author: huangtengfei Closes #20244 from ivoson/branch-taskpart-mistype. (cherry picked from commit 091a000d27f324de8c5c527880854ecfcf5de9a4) Signed-off-by: Imran Rashid commit ab01ba718c7752b564e801a1ea546aedc2055dc0 Author: Bogdan Raducanu Date: 2018-02-13T17:49:52Z [SPARK-23316][SQL]
[GitHub] spark issue #21870: Branch 2.3
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[GitHub] spark issue #21860: [SPARK-24901][SQL]Merge the codegen of RegularHashMap an...
Github user heary-cao commented on the issue: https://github.com/apache/spark/pull/21860 @kiszk, I have add a ignore test case to verifies the newly added code generation. can you help me to review it if you have some time. thanks. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21870: Branch 2.3
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[GitHub] spark issue #21870: Branch 2.3
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[GitHub] spark pull request #21857: [SPARK-21274][SQL] Implement EXCEPT ALL clause.
Github user ueshin commented on a diff in the pull request:
https://github.com/apache/spark/pull/21857#discussion_r204977789
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala
---
@@ -1275,6 +1276,64 @@ object ReplaceExceptWithAntiJoin extends
Rule[LogicalPlan] {
}
}
+/**
+ * Replaces logical [[ExceptAll]] operator using a combination of Union,
Aggregate
+ * and Generate operator.
+ *
+ * Input Query :
+ * {{{
+ *SELECT c1 FROM ut1 EXCEPT ALL SELECT c1 FROM ut2
+ * }}}
+ *
+ * Rewritten Query:
+ * {{{
+ * SELECT c1
+ * FROM (
+ * SELECT replicate_rows(sum_val, c1) AS (sum_val, c1)
+ * FROM (
+ * SELECT c1, cnt, sum_val
--- End diff --
nit: there is no `cnt`?
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[GitHub] spark pull request #19691: [SPARK-14922][SPARK-17732][SQL]ALTER TABLE DROP P...
Github user DazhuangSu commented on a diff in the pull request:
https://github.com/apache/spark/pull/19691#discussion_r205013855
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/command/ddl.scala ---
@@ -510,40 +511,86 @@ case class AlterTableRenamePartitionCommand(
*
* The syntax of this command is:
* {{{
- * ALTER TABLE table DROP [IF EXISTS] PARTITION spec1[, PARTITION spec2,
...] [PURGE];
+ * ALTER TABLE table DROP [IF EXISTS] PARTITION (spec1, expr1)
+ * [, PARTITION (spec2, expr2), ...] [PURGE];
* }}}
*/
case class AlterTableDropPartitionCommand(
tableName: TableIdentifier,
-specs: Seq[TablePartitionSpec],
+partitions: Seq[(TablePartitionSpec, Seq[Expression])],
ifExists: Boolean,
purge: Boolean,
retainData: Boolean)
- extends RunnableCommand {
+ extends RunnableCommand with PredicateHelper {
override def run(sparkSession: SparkSession): Seq[Row] = {
val catalog = sparkSession.sessionState.catalog
val table = catalog.getTableMetadata(tableName)
+val resolver = sparkSession.sessionState.conf.resolver
DDLUtils.verifyAlterTableType(catalog, table, isView = false)
DDLUtils.verifyPartitionProviderIsHive(sparkSession, table, "ALTER
TABLE DROP PARTITION")
-val normalizedSpecs = specs.map { spec =>
- PartitioningUtils.normalizePartitionSpec(
-spec,
-table.partitionColumnNames,
-table.identifier.quotedString,
-sparkSession.sessionState.conf.resolver)
+val toDrop = partitions.flatMap { partition =>
+ if (partition._1.isEmpty && !partition._2.isEmpty) {
+// There are only expressions in this drop condition.
+extractFromPartitionFilter(partition._2, catalog, table, resolver)
+ } else if (!partition._1.isEmpty && partition._2.isEmpty) {
+// There are only partitionSpecs in this drop condition.
+extractFromPartitionSpec(partition._1, table, resolver)
+ } else if (!partition._1.isEmpty && !partition._2.isEmpty) {
+// This drop condition has both partitionSpecs and expressions.
+extractFromPartitionFilter(partition._2, catalog, table,
resolver).intersect(
--- End diff --
@mgaido91 I understand your point, yes it would be inefficient. I will work
on this soon
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[GitHub] spark issue #21869: [SPARK-24891][FOLLOWUP][HOT-FIX][2.3] Fix the Compilatio...
Github user ueshin commented on the issue: https://github.com/apache/spark/pull/21869 LGTM, pending Jenkins. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #21320: [SPARK-4502][SQL] Parquet nested column pruning -...
Github user mallman commented on a diff in the pull request:
https://github.com/apache/spark/pull/21320#discussion_r205020970
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetReadSupport.scala
---
@@ -71,9 +80,22 @@ private[parquet] class ParquetReadSupport(val convertTz:
Option[TimeZone])
StructType.fromString(schemaString)
}
-val parquetRequestedSchema =
+val clippedParquetSchema =
ParquetReadSupport.clipParquetSchema(context.getFileSchema,
catalystRequestedSchema)
+val parquetRequestedSchema = if (parquetMrCompatibility) {
+ // Parquet-mr will throw an exception if we try to read a superset
of the file's schema.
--- End diff --
This change is not part of this PR.
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[GitHub] spark pull request #21320: [SPARK-4502][SQL] Parquet nested column pruning -...
Github user mallman commented on a diff in the pull request:
https://github.com/apache/spark/pull/21320#discussion_r205021140
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaPruningSuite.scala
---
@@ -0,0 +1,156 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.parquet
+
+import java.io.File
+
+import org.apache.spark.sql.{QueryTest, Row}
+import org.apache.spark.sql.execution.FileSchemaPruningTest
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.test.SharedSQLContext
+
+class ParquetSchemaPruningSuite
+extends QueryTest
+with ParquetTest
+with FileSchemaPruningTest
+with SharedSQLContext {
--- End diff --
No comment.
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[GitHub] spark pull request #21320: [SPARK-4502][SQL] Parquet nested column pruning -...
Github user mallman commented on a diff in the pull request:
https://github.com/apache/spark/pull/21320#discussion_r205021282
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/ProjectionOverSchema.scala
---
@@ -0,0 +1,62 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.planning
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types._
+
+/**
+ * A Scala extractor that projects an expression over a given schema. Data
types,
+ * field indexes and field counts of complex type extractors and attributes
+ * are adjusted to fit the schema. All other expressions are left as-is.
This
+ * class is motivated by columnar nested schema pruning.
+ */
+case class ProjectionOverSchema(schema: StructType) {
--- End diff --
Okay. So...
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[GitHub] spark pull request #21320: [SPARK-4502][SQL] Parquet nested column pruning -...
Github user mallman commented on a diff in the pull request:
https://github.com/apache/spark/pull/21320#discussion_r205021469
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaPruning.scala
---
@@ -0,0 +1,153 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.parquet
+
+import org.apache.spark.sql.catalyst.expressions.{And, Attribute,
Expression, NamedExpression}
+import org.apache.spark.sql.catalyst.planning.{PhysicalOperation,
ProjectionOverSchema, SelectedField}
+import org.apache.spark.sql.catalyst.plans.logical.{Filter, LogicalPlan,
Project}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.execution.datasources.{HadoopFsRelation,
LogicalRelation}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.{ArrayType, DataType, MapType,
StructField, StructType}
+
+/**
+ * Prunes unnecessary Parquet columns given a [[PhysicalOperation]] over a
+ * [[ParquetRelation]]. By "Parquet column", we mean a column as defined
in the
+ * Parquet format. In Spark SQL, a root-level Parquet column corresponds
to a
+ * SQL column, and a nested Parquet column corresponds to a
[[StructField]].
+ */
+private[sql] object ParquetSchemaPruning extends Rule[LogicalPlan] {
+ override def apply(plan: LogicalPlan): LogicalPlan =
+if (SQLConf.get.nestedSchemaPruningEnabled) {
+ apply0(plan)
+} else {
+ plan
+}
+
+ private def apply0(plan: LogicalPlan): LogicalPlan =
+plan transformDown {
+ case op @ PhysicalOperation(projects, filters,
+ l @ LogicalRelation(hadoopFsRelation @ HadoopFsRelation(_, _,
+dataSchema, _, parquetFormat: ParquetFileFormat, _), _, _, _))
=>
+val projectionRootFields = projects.flatMap(getRootFields)
+val filterRootFields = filters.flatMap(getRootFields)
+val requestedRootFields = (projectionRootFields ++
filterRootFields).distinct
+
+// If [[requestedRootFields]] includes a nested field, continue.
Otherwise,
+// return [[op]]
+if (requestedRootFields.exists { case RootField(_, derivedFromAtt)
=> !derivedFromAtt }) {
+ val prunedSchema = requestedRootFields
+.map { case RootField(field, _) => StructType(Array(field)) }
+.reduceLeft(_ merge _)
+ val dataSchemaFieldNames = dataSchema.fieldNames.toSet
+ val prunedDataSchema =
+StructType(prunedSchema.filter(f =>
dataSchemaFieldNames.contains(f.name)))
+
+ // If the data schema is different from the pruned data schema,
continue. Otherwise,
+ // return [[op]]. We effect this comparison by counting the
number of "leaf" fields in
+ // each schemata, assuming the fields in [[prunedDataSchema]]
are a subset of the fields
+ // in [[dataSchema]].
+ if (countLeaves(dataSchema) > countLeaves(prunedDataSchema)) {
+val prunedParquetRelation =
+ hadoopFsRelation.copy(dataSchema =
prunedDataSchema)(hadoopFsRelation.sparkSession)
+
+// We need to replace the expression ids of the pruned
relation output attributes
+// with the expression ids of the original relation output
attributes so that
+// references to the original relation's output are not broken
+val outputIdMap = l.output.map(att => (att.name,
att.exprId)).toMap
+val prunedRelationOutput =
+ prunedParquetRelation
+.schema
+.toAttributes
+.map {
+ case att if outputIdMap.contains(att.name) =>
+att.withExprId(outputIdMap(att.name))
+ case att => att
+}
+val prunedRelation =
+ l.copy(relation = prunedParquetRe
[GitHub] spark pull request #21320: [SPARK-4502][SQL] Parquet nested column pruning -...
Github user mallman commented on a diff in the pull request: https://github.com/apache/spark/pull/21320#discussion_r205021712 --- Diff: sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/planning/SelectedFieldSuite.scala --- @@ -0,0 +1,388 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + *http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.sql.catalyst.planning + +import org.scalatest.BeforeAndAfterAll +import org.scalatest.exceptions.TestFailedException + +import org.apache.spark.SparkFunSuite +import org.apache.spark.sql.catalyst.dsl.plans._ +import org.apache.spark.sql.catalyst.expressions.NamedExpression +import org.apache.spark.sql.catalyst.parser.CatalystSqlParser +import org.apache.spark.sql.catalyst.plans.logical.LocalRelation +import org.apache.spark.sql.types._ + +// scalastyle:off line.size.limit --- End diff -- No comment. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #21320: [SPARK-4502][SQL] Parquet nested column pruning -...
Github user mallman commented on a diff in the pull request:
https://github.com/apache/spark/pull/21320#discussion_r205022799
--- Diff:
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaPruningSuite.scala
---
@@ -0,0 +1,156 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.parquet
+
+import java.io.File
+
+import org.apache.spark.sql.{QueryTest, Row}
+import org.apache.spark.sql.execution.FileSchemaPruningTest
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.test.SharedSQLContext
+
+class ParquetSchemaPruningSuite
+extends QueryTest
+with ParquetTest
+with FileSchemaPruningTest
+with SharedSQLContext {
+ case class FullName(first: String, middle: String, last: String)
+ case class Contact(name: FullName, address: String, pets: Int, friends:
Array[FullName] = Array(),
+relatives: Map[String, FullName] = Map())
+
+ val contacts =
+Contact(FullName("Jane", "X.", "Doe"), "123 Main Street", 1) ::
+Contact(FullName("John", "Y.", "Doe"), "321 Wall Street", 3) :: Nil
+
+ case class Name(first: String, last: String)
+ case class BriefContact(name: Name, address: String)
+
+ val briefContacts =
+BriefContact(Name("Janet", "Jones"), "567 Maple Drive") ::
+BriefContact(Name("Jim", "Jones"), "6242 Ash Street") :: Nil
+
+ case class ContactWithDataPartitionColumn(name: FullName, address:
String, pets: Int,
+friends: Array[FullName] = Array(), relatives: Map[String, FullName] =
Map(), p: Int)
+
+ case class BriefContactWithDataPartitionColumn(name: Name, address:
String, p: Int)
+
+ val contactsWithDataPartitionColumn =
+contacts.map { case Contact(name, address, pets, friends, relatives) =>
+ ContactWithDataPartitionColumn(name, address, pets, friends,
relatives, 1) }
+ val briefContactsWithDataPartitionColumn =
+briefContacts.map { case BriefContact(name: Name, address: String) =>
+ BriefContactWithDataPartitionColumn(name, address, 2) }
+
+ testSchemaPruning("select a single complex field") {
+val query = sql("select name.middle from contacts")
+checkScanSchemata(query, "struct>")
+checkAnswer(query, Row("X.") :: Row("Y.") :: Row(null) :: Row(null) ::
Nil)
+ }
+
+ testSchemaPruning("select a single complex field and the partition
column") {
+val query = sql("select name.middle, p from contacts")
+checkScanSchemata(query, "struct>")
+checkAnswer(query, Row("X.", 1) :: Row("Y.", 1) :: Row(null, 2) ::
Row(null, 2) :: Nil)
+ }
+
+ ignore("partial schema intersection - select missing subfield") {
+val query = sql("select name.middle, address from contacts where p=2")
+checkScanSchemata(query,
"struct,address:string>")
+checkAnswer(query,
+ Row(null, "567 Maple Drive") ::
+ Row(null, "6242 Ash Street") :: Nil)
+ }
+
+ testSchemaPruning("no unnecessary schema pruning") {
+val query =
+ sql("select name.last, name.middle, name.first, relatives[''].last,
" +
+"relatives[''].middle, relatives[''].first, friends[0].last,
friends[0].middle, " +
+"friends[0].first, pets, address from contacts where p=2")
+// We've selected every field in the schema. Therefore, no schema
pruning should be performed.
+// We check this by asserting that the scanned schema of the query is
identical to the schema
+// of the contacts relation, even though the fields are selected in
different orders.
+checkScanSchemata(query,
+
"struct,address:string,pets:int,"
+
+ "friends:array>," +
+
"relatives:map>>")
+checkAnswer(query,
+ Row("Jones", null, "Janet", null, null, null, null, null, null,
null, "567 Maple Drive") ::
+ Row("Jones", null, "Jim", null, null, null, null, null, null, null,
"6242 Ash
[GitHub] spark pull request #21320: [SPARK-4502][SQL] Parquet nested column pruning -...
Github user mallman commented on a diff in the pull request:
https://github.com/apache/spark/pull/21320#discussion_r205022895
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaPruning.scala
---
@@ -0,0 +1,153 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.execution.datasources.parquet
+
+import org.apache.spark.sql.catalyst.expressions.{And, Attribute,
Expression, NamedExpression}
+import org.apache.spark.sql.catalyst.planning.{PhysicalOperation,
ProjectionOverSchema, SelectedField}
+import org.apache.spark.sql.catalyst.plans.logical.{Filter, LogicalPlan,
Project}
+import org.apache.spark.sql.catalyst.rules.Rule
+import org.apache.spark.sql.execution.datasources.{HadoopFsRelation,
LogicalRelation}
+import org.apache.spark.sql.internal.SQLConf
+import org.apache.spark.sql.types.{ArrayType, DataType, MapType,
StructField, StructType}
+
+/**
+ * Prunes unnecessary Parquet columns given a [[PhysicalOperation]] over a
+ * [[ParquetRelation]]. By "Parquet column", we mean a column as defined
in the
+ * Parquet format. In Spark SQL, a root-level Parquet column corresponds
to a
+ * SQL column, and a nested Parquet column corresponds to a
[[StructField]].
+ */
+private[sql] object ParquetSchemaPruning extends Rule[LogicalPlan] {
+ override def apply(plan: LogicalPlan): LogicalPlan =
+if (SQLConf.get.nestedSchemaPruningEnabled) {
+ apply0(plan)
+} else {
+ plan
+}
+
+ private def apply0(plan: LogicalPlan): LogicalPlan =
+plan transformDown {
+ case op @ PhysicalOperation(projects, filters,
+ l @ LogicalRelation(hadoopFsRelation @ HadoopFsRelation(_, _,
+dataSchema, _, parquetFormat: ParquetFileFormat, _), _, _, _))
=>
+val projectionRootFields = projects.flatMap(getRootFields)
+val filterRootFields = filters.flatMap(getRootFields)
+val requestedRootFields = (projectionRootFields ++
filterRootFields).distinct
+
+// If [[requestedRootFields]] includes a nested field, continue.
Otherwise,
+// return [[op]]
+if (requestedRootFields.exists { case RootField(_, derivedFromAtt)
=> !derivedFromAtt }) {
+ val prunedSchema = requestedRootFields
+.map { case RootField(field, _) => StructType(Array(field)) }
+.reduceLeft(_ merge _)
+ val dataSchemaFieldNames = dataSchema.fieldNames.toSet
+ val prunedDataSchema =
+StructType(prunedSchema.filter(f =>
dataSchemaFieldNames.contains(f.name)))
+
+ // If the data schema is different from the pruned data schema,
continue. Otherwise,
+ // return [[op]]. We effect this comparison by counting the
number of "leaf" fields in
+ // each schemata, assuming the fields in [[prunedDataSchema]]
are a subset of the fields
+ // in [[dataSchema]].
+ if (countLeaves(dataSchema) > countLeaves(prunedDataSchema)) {
--- End diff --
No comment.
---
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[GitHub] spark pull request #21850: [SPARK-24892] [SQL] Simplify `CaseWhen` to `If` w...
Github user ueshin commented on a diff in the pull request:
https://github.com/apache/spark/pull/21850#discussion_r205022884
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/expressions.scala
---
@@ -414,6 +414,16 @@ object SimplifyConditionals extends Rule[LogicalPlan]
with PredicateHelper {
// these branches can be pruned away
val (h, t) = branches.span(_._1 != TrueLiteral)
CaseWhen( h :+ t.head, None)
+
+ case CaseWhen(branches, elseValue) if branches.length == 1 =>
+// Using pattern matching like `CaseWhen((cond, branchValue) ::
Nil, elseValue)` will not
+// work since the implementation of `branches` can be
`ArrayBuffer`. A full test is in
--- End diff --
How about:
```scala
case CaseWhen(Seq((cond, trueValue)), elseValue) =>
```
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[GitHub] spark issue #21139: [SPARK-24066][SQL]Add a window exchange rule to eliminat...
Github user heary-cao commented on the issue: https://github.com/apache/spark/pull/21139 @hvanhovell, can you help review it again if you have some time, thanks. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #21320: [SPARK-4502][SQL] Parquet nested column pruning -...
Github user mallman commented on a diff in the pull request:
https://github.com/apache/spark/pull/21320#discussion_r205022974
--- Diff:
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/planning/SelectedFieldSuite.scala
---
@@ -0,0 +1,388 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.planning
+
+import org.scalatest.BeforeAndAfterAll
+import org.scalatest.exceptions.TestFailedException
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.sql.catalyst.dsl.plans._
+import org.apache.spark.sql.catalyst.expressions.NamedExpression
+import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
+import org.apache.spark.sql.catalyst.plans.logical.LocalRelation
+import org.apache.spark.sql.types._
+
+// scalastyle:off line.size.limit
+class SelectedFieldSuite extends SparkFunSuite with BeforeAndAfterAll {
+ // The test schema as a tree string, i.e. `schema.treeString`
+ // root
+ // |-- col1: string (nullable = false)
+ // |-- col2: struct (nullable = true)
+ // ||-- field1: integer (nullable = true)
+ // ||-- field2: array (nullable = true)
+ // |||-- element: integer (containsNull = false)
+ // ||-- field3: array (nullable = false)
+ // |||-- element: struct (containsNull = true)
+ // ||||-- subfield1: integer (nullable = true)
+ // ||||-- subfield2: integer (nullable = true)
+ // ||||-- subfield3: array (nullable = true)
+ // |||||-- element: integer (containsNull = true)
+ // ||-- field4: map (nullable = true)
+ // |||-- key: string
+ // |||-- value: struct (valueContainsNull = false)
+ // ||||-- subfield1: integer (nullable = true)
+ // ||||-- subfield2: array (nullable = true)
+ // |||||-- element: integer (containsNull = false)
+ // ||-- field5: array (nullable = false)
+ // |||-- element: struct (containsNull = true)
+ // ||||-- subfield1: struct (nullable = false)
+ // |||||-- subsubfield1: integer (nullable = true)
+ // |||||-- subsubfield2: integer (nullable = true)
+ // ||||-- subfield2: struct (nullable = true)
+ // |||||-- subsubfield1: struct (nullable = true)
+ // ||||||-- subsubsubfield1: string (nullable =
true)
+ // |||||-- subsubfield2: integer (nullable = true)
+ // ||-- field6: struct (nullable = true)
+ // |||-- subfield1: string (nullable = false)
+ // |||-- subfield2: string (nullable = true)
+ // ||-- field7: struct (nullable = true)
+ // |||-- subfield1: struct (nullable = true)
+ // ||||-- subsubfield1: integer (nullable = true)
+ // ||||-- subsubfield2: integer (nullable = true)
+ // ||-- field8: map (nullable = true)
+ // |||-- key: string
+ // |||-- value: array (valueContainsNull = false)
+ // ||||-- element: struct (containsNull = true)
+ // |||||-- subfield1: integer (nullable = true)
+ // |||||-- subfield2: array (nullable = true)
+ // ||||||-- element: integer (containsNull = false)
+ // ||-- field9: map (nullable = true)
+ // |||-- key: string
+ // |||-- value: integer (valueContainsNull = false)
+ // |-- col3: array (nullable = false)
+ // ||-- element: struct (containsNull = false)
+ // |||-- field1: struct (nullable = true)
+ // ||||-- subfield1: integer (nullable = false)
+ // ||||-- subfield2: integer (nullable = true)
+ // |||-- field2: map (nullable = true)
+ // ||||-- key: string
+ // ||||-- value: integer (valueContainsN
[GitHub] spark issue #21320: [SPARK-4502][SQL] Parquet nested column pruning - founda...
Github user mallman commented on the issue: https://github.com/apache/spark/pull/21320 > Regarding #21320 (comment), can you at least set this enable by default and see if some existing tests are broken or not? I have no intention to at this point, no. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #21650: [SPARK-24624][SQL][PYTHON] Support mixture of Pyt...
Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/21650#discussion_r205025311
--- Diff: python/pyspark/sql/tests.py ---
@@ -5060,6 +5049,147 @@ def test_type_annotation(self):
df = self.spark.range(1).select(pandas_udf(f=_locals['noop'],
returnType='bigint')('id'))
self.assertEqual(df.first()[0], 0)
+def test_mixed_udf(self):
+import pandas as pd
+from pyspark.sql.functions import col, udf, pandas_udf
+
+df = self.spark.range(0, 1).toDF('v')
+
+# Test mixture of multiple UDFs and Pandas UDFs
+
+@udf('int')
+def f1(x):
+assert type(x) == int
+return x + 1
+
+@pandas_udf('int')
+def f2(x):
+assert type(x) == pd.Series
+return x + 10
+
+@udf('int')
+def f3(x):
+assert type(x) == int
+return x + 100
+
+@pandas_udf('int')
+def f4(x):
+assert type(x) == pd.Series
+return x + 1000
+
+# Test mixed udfs in a single projection
+df1 = df \
+.withColumn('f1', f1(col('v'))) \
+.withColumn('f2', f2(col('v'))) \
+.withColumn('f3', f3(col('v'))) \
+.withColumn('f4', f4(col('v'))) \
+.withColumn('f2_f1', f2(col('f1'))) \
+.withColumn('f3_f1', f3(col('f1'))) \
+.withColumn('f4_f1', f4(col('f1'))) \
+.withColumn('f3_f2', f3(col('f2'))) \
+.withColumn('f4_f2', f4(col('f2'))) \
+.withColumn('f4_f3', f4(col('f3'))) \
+.withColumn('f3_f2_f1', f3(col('f2_f1'))) \
+.withColumn('f4_f2_f1', f4(col('f2_f1'))) \
+.withColumn('f4_f3_f1', f4(col('f3_f1'))) \
+.withColumn('f4_f3_f2', f4(col('f3_f2'))) \
+.withColumn('f4_f3_f2_f1', f4(col('f3_f2_f1')))
+
+# Test mixed udfs in a single expression
+df2 = df \
+.withColumn('f1', f1(col('v'))) \
+.withColumn('f2', f2(col('v'))) \
+.withColumn('f3', f3(col('v'))) \
+.withColumn('f4', f4(col('v'))) \
+.withColumn('f2_f1', f2(f1(col('v' \
+.withColumn('f3_f1', f3(f1(col('v' \
+.withColumn('f4_f1', f4(f1(col('v' \
+.withColumn('f3_f2', f3(f2(col('v' \
+.withColumn('f4_f2', f4(f2(col('v' \
+.withColumn('f4_f3', f4(f3(col('v' \
+.withColumn('f3_f2_f1', f3(f2(f1(col('v') \
+.withColumn('f4_f2_f1', f4(f2(f1(col('v') \
+.withColumn('f4_f3_f1', f4(f3(f1(col('v') \
+.withColumn('f4_f3_f2', f4(f3(f2(col('v') \
+.withColumn('f4_f3_f2_f1', f4(f3(f2(f1(col('v'))
+
+# expected result
+df3 = df \
+.withColumn('f1', df['v'] + 1) \
+.withColumn('f2', df['v'] + 10) \
+.withColumn('f3', df['v'] + 100) \
+.withColumn('f4', df['v'] + 1000) \
+.withColumn('f2_f1', df['v'] + 11) \
+.withColumn('f3_f1', df['v'] + 101) \
+.withColumn('f4_f1', df['v'] + 1001) \
+.withColumn('f3_f2', df['v'] + 110) \
+.withColumn('f4_f2', df['v'] + 1010) \
+.withColumn('f4_f3', df['v'] + 1100) \
+.withColumn('f3_f2_f1', df['v'] + 111) \
+.withColumn('f4_f2_f1', df['v'] + 1011) \
+.withColumn('f4_f3_f1', df['v'] + 1101) \
+.withColumn('f4_f3_f2', df['v'] + 1110) \
+.withColumn('f4_f3_f2_f1', df['v'] + )
+
+self.assertEquals(df3.collect(), df1.collect())
+self.assertEquals(df3.collect(), df2.collect())
+
+def test_mixed_udf_and_sql(self):
+import pandas as pd
+from pyspark.sql.functions import udf, pandas_udf
+
+df = self.spark.range(0, 1).toDF('v')
+
+# Test mixture of UDFs, Pandas UDFs and SQL expression.
+
+@udf('int')
+def f1(x):
+assert type(x) == int
+return x + 1
+
+def f2(x):
+return x + 10
+
+@pandas_udf('int')
+def f3(x):
+assert type(x) == pd.Series
+return x + 100
+
+df1 = df.withColumn('f1', f1(df['v'])) \
+.withColumn('f2', f2(df['v'])) \
+.withColumn('f3', f3(df['v'])) \
+.withColumn('f1_f2', f1(f2(df['v']))) \
+.withColumn('f1_f3', f1(f3(df['v']))) \
+.w
[GitHub] spark pull request #21650: [SPARK-24624][SQL][PYTHON] Support mixture of Pyt...
Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/21650#discussion_r205024958
--- Diff: python/pyspark/sql/tests.py ---
@@ -5060,6 +5049,147 @@ def test_type_annotation(self):
df = self.spark.range(1).select(pandas_udf(f=_locals['noop'],
returnType='bigint')('id'))
self.assertEqual(df.first()[0], 0)
+def test_mixed_udf(self):
+import pandas as pd
+from pyspark.sql.functions import col, udf, pandas_udf
+
+df = self.spark.range(0, 1).toDF('v')
+
+# Test mixture of multiple UDFs and Pandas UDFs
+
+@udf('int')
+def f1(x):
+assert type(x) == int
+return x + 1
+
+@pandas_udf('int')
+def f2(x):
+assert type(x) == pd.Series
+return x + 10
+
+@udf('int')
+def f3(x):
+assert type(x) == int
+return x + 100
+
+@pandas_udf('int')
+def f4(x):
+assert type(x) == pd.Series
+return x + 1000
+
+# Test mixed udfs in a single projection
+df1 = df \
+.withColumn('f1', f1(col('v'))) \
+.withColumn('f2', f2(col('v'))) \
+.withColumn('f3', f3(col('v'))) \
+.withColumn('f4', f4(col('v'))) \
+.withColumn('f2_f1', f2(col('f1'))) \
+.withColumn('f3_f1', f3(col('f1'))) \
--- End diff --
This looks testing udf + udf
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[GitHub] spark pull request #21650: [SPARK-24624][SQL][PYTHON] Support mixture of Pyt...
Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/21650#discussion_r205025755
--- Diff: python/pyspark/sql/tests.py ---
@@ -5487,6 +5617,22 @@ def dummy_pandas_udf(df):
F.col('temp0.key') ==
F.col('temp1.key'))
self.assertEquals(res.count(), 5)
+def test_mixed_scalar_udfs_followed_by_grouby_apply(self):
+# Test Pandas UDF and scalar Python UDF followed by groupby apply
+from pyspark.sql.functions import udf, pandas_udf, PandasUDFType
+import pandas as pd
--- End diff --
not a big deal at all really .. but I would swap the import order
(thridparty, pyspark)
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[GitHub] spark pull request #21650: [SPARK-24624][SQL][PYTHON] Support mixture of Pyt...
Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/21650#discussion_r205030035
--- Diff: python/pyspark/sql/tests.py ---
@@ -5060,6 +5049,147 @@ def test_type_annotation(self):
df = self.spark.range(1).select(pandas_udf(f=_locals['noop'],
returnType='bigint')('id'))
self.assertEqual(df.first()[0], 0)
+def test_mixed_udf(self):
+import pandas as pd
+from pyspark.sql.functions import col, udf, pandas_udf
+
+df = self.spark.range(0, 1).toDF('v')
+
+# Test mixture of multiple UDFs and Pandas UDFs
+
+@udf('int')
+def f1(x):
+assert type(x) == int
+return x + 1
+
+@pandas_udf('int')
+def f2(x):
+assert type(x) == pd.Series
+return x + 10
+
+@udf('int')
+def f3(x):
+assert type(x) == int
+return x + 100
+
+@pandas_udf('int')
+def f4(x):
+assert type(x) == pd.Series
+return x + 1000
+
+# Test mixed udfs in a single projection
+df1 = df \
+.withColumn('f1', f1(col('v'))) \
+.withColumn('f2', f2(col('v'))) \
+.withColumn('f3', f3(col('v'))) \
+.withColumn('f4', f4(col('v'))) \
+.withColumn('f2_f1', f2(col('f1'))) \
+.withColumn('f3_f1', f3(col('f1'))) \
+.withColumn('f4_f1', f4(col('f1'))) \
+.withColumn('f3_f2', f3(col('f2'))) \
+.withColumn('f4_f2', f4(col('f2'))) \
+.withColumn('f4_f3', f4(col('f3'))) \
+.withColumn('f3_f2_f1', f3(col('f2_f1'))) \
+.withColumn('f4_f2_f1', f4(col('f2_f1'))) \
+.withColumn('f4_f3_f1', f4(col('f3_f1'))) \
+.withColumn('f4_f3_f2', f4(col('f3_f2'))) \
+.withColumn('f4_f3_f2_f1', f4(col('f3_f2_f1')))
+
+# Test mixed udfs in a single expression
+df2 = df \
+.withColumn('f1', f1(col('v'))) \
+.withColumn('f2', f2(col('v'))) \
+.withColumn('f3', f3(col('v'))) \
+.withColumn('f4', f4(col('v'))) \
+.withColumn('f2_f1', f2(f1(col('v' \
+.withColumn('f3_f1', f3(f1(col('v' \
+.withColumn('f4_f1', f4(f1(col('v' \
+.withColumn('f3_f2', f3(f2(col('v' \
+.withColumn('f4_f2', f4(f2(col('v' \
+.withColumn('f4_f3', f4(f3(col('v' \
+.withColumn('f3_f2_f1', f3(f2(f1(col('v') \
+.withColumn('f4_f2_f1', f4(f2(f1(col('v') \
+.withColumn('f4_f3_f1', f4(f3(f1(col('v') \
+.withColumn('f4_f3_f2', f4(f3(f2(col('v') \
+.withColumn('f4_f3_f2_f1', f4(f3(f2(f1(col('v'))
+
+# expected result
+df3 = df \
+.withColumn('f1', df['v'] + 1) \
+.withColumn('f2', df['v'] + 10) \
+.withColumn('f3', df['v'] + 100) \
+.withColumn('f4', df['v'] + 1000) \
+.withColumn('f2_f1', df['v'] + 11) \
+.withColumn('f3_f1', df['v'] + 101) \
+.withColumn('f4_f1', df['v'] + 1001) \
+.withColumn('f3_f2', df['v'] + 110) \
+.withColumn('f4_f2', df['v'] + 1010) \
+.withColumn('f4_f3', df['v'] + 1100) \
+.withColumn('f3_f2_f1', df['v'] + 111) \
+.withColumn('f4_f2_f1', df['v'] + 1011) \
+.withColumn('f4_f3_f1', df['v'] + 1101) \
+.withColumn('f4_f3_f2', df['v'] + 1110) \
+.withColumn('f4_f3_f2_f1', df['v'] + )
+
+self.assertEquals(df3.collect(), df1.collect())
+self.assertEquals(df3.collect(), df2.collect())
+
+def test_mixed_udf_and_sql(self):
+import pandas as pd
+from pyspark.sql.functions import udf, pandas_udf
+
+df = self.spark.range(0, 1).toDF('v')
+
+# Test mixture of UDFs, Pandas UDFs and SQL expression.
+
+@udf('int')
+def f1(x):
+assert type(x) == int
+return x + 1
+
+def f2(x):
--- End diff --
Ah, I see why it looks confusing. Can we add an assert here too (check if
it's a column)?
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[GitHub] spark issue #21320: [SPARK-4502][SQL] Parquet nested column pruning - founda...
Github user mallman commented on the issue: https://github.com/apache/spark/pull/21320 > gentle ping @mallman since the code freeze is close Outside of my primary occupation, my top priority on this PR right now is investigating https://github.com/apache/spark/pull/21320#issuecomment-396498487. I don't think I'm going to get a test file from the OP, so I'm going to try to reproduce the issue on my own. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #20949: [SPARK-19018][SQL] Add support for custom encoding on cs...
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[GitHub] spark issue #21863: [SPARK-18874][SQL][FOLLOW-UP] Improvement type mismatche...
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[GitHub] spark pull request #21871: [SPARK-24916][SQL] Fix type coercion for IN expre...
GitHub user wangyum opened a pull request: https://github.com/apache/spark/pull/21871 [SPARK-24916][SQL] Fix type coercion for IN expression with subquery ## What changes were proposed in this pull request? The below SQL will throw `AnalysisException`. but it can success on Spark 2.1.x. This pr fix this issue. ```sql CREATE TEMPORARY VIEW t4 AS SELECT * FROM VALUES (CAST(1 AS DOUBLE), CAST(2 AS STRING), CAST(3 AS STRING)) AS t1(t4a, t4b, t4c); CREATE TEMPORARY VIEW t5 AS SELECT * FROM VALUES (CAST(1 AS DECIMAL(18, 0)), CAST(2 AS STRING), CAST(3 AS BIGINT)) AS t1(t5a, t5b, t5c); SELECT * FROM t4 WHERE (t4a, t4b, t4c) IN (SELECT t5a, t5b, t5c FROM t5); ``` ## How was this patch tested? unit tests You can merge this pull request into a Git repository by running: $ git pull https://github.com/wangyum/spark SPARK-24916 Alternatively you can review and apply these changes as the patch at: https://github.com/apache/spark/pull/21871.patch To close this pull request, make a commit to your master/trunk branch with (at least) the following in the commit message: This closes #21871 commit d60185b159ffc1a0d74a8c5bfba0c11ceac4241b Author: Yuming Wang Date: 2018-07-25T07:33:45Z default: findCommonTypeForBinaryComparison(l.dataType, r.dataType, conf).orElse(findTightestCommonType(l.dataType, r.dataType)) commit 8a118b5bdf63a7f4b0f0033c0783aa220c9c1eb1 Author: Yuming Wang Date: 2018-07-25T07:37:03Z findCommonTypeForBinaryComparison(l.dataType, r.dataType, conf).orElse(findWiderTypeForTwo(l.dataType, r.dataType)) commit c306810f0a0e701e6b46434db75bbd9813c7337c Author: Yuming Wang Date: 2018-07-25T07:39:40Z findWiderTypeForTwo(l.dataType, r.dataType) commit daa120e15153c77c17a7966df7b727fcea4bb02b Author: Yuming Wang Date: 2018-07-25T07:42:46Z findCommonTypeForBinaryComparison(l.dataType, r.dataType, conf).orElse(findWiderTypeWithoutStringPromotionForTwo(l.dataType, r.dataType)) commit c84ba4d9823a50953f560e110638a9d4e094b17a Author: Yuming Wang Date: 2018-07-25T07:45:43Z findWiderTypeWithoutStringPromotionForTwo(l.dataType, r.dataType) commit bc41b99b7548a22db1ed278fda1c741fd08b78ef Author: Yuming Wang Date: 2018-07-25T08:03:26Z findCommonTypeForBinaryComparison(l.dataType, r.dataType, conf).orElse(findTightestCommonType(l.dataType, r.dataType)).orElse(findWiderTypeForDecimal(l.dataType, r.dataType)) commit 8ef142f78c22b980fe60d836c56d7d18d221a958 Author: Yuming Wang Date: 2018-07-25T09:27:51Z Fix type coercion for IN expression with subquery --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21871: [SPARK-24916][SQL] Fix type coercion for IN expression w...
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[GitHub] spark issue #21403: [SPARK-24341][SQL] Support only IN subqueries with the s...
Github user mgaido91 commented on the issue: https://github.com/apache/spark/pull/21403 @maryannxue that is feasible too and indeed it was the original implementation I did, I switched to this approach according to [this discussion](https://github.com/apache/spark/pull/21403#discussion_r198826199): the goal was to avoid to change the `In` signature and the parsing logic in the many places. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #19691: [SPARK-14922][SPARK-17732][SQL]ALTER TABLE DROP P...
Github user mgaido91 commented on a diff in the pull request:
https://github.com/apache/spark/pull/19691#discussion_r205047473
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/command/ddl.scala ---
@@ -510,40 +511,86 @@ case class AlterTableRenamePartitionCommand(
*
* The syntax of this command is:
* {{{
- * ALTER TABLE table DROP [IF EXISTS] PARTITION spec1[, PARTITION spec2,
...] [PURGE];
+ * ALTER TABLE table DROP [IF EXISTS] PARTITION (spec1, expr1)
+ * [, PARTITION (spec2, expr2), ...] [PURGE];
* }}}
*/
case class AlterTableDropPartitionCommand(
tableName: TableIdentifier,
-specs: Seq[TablePartitionSpec],
+partitions: Seq[(TablePartitionSpec, Seq[Expression])],
ifExists: Boolean,
purge: Boolean,
retainData: Boolean)
- extends RunnableCommand {
+ extends RunnableCommand with PredicateHelper {
override def run(sparkSession: SparkSession): Seq[Row] = {
val catalog = sparkSession.sessionState.catalog
val table = catalog.getTableMetadata(tableName)
+val resolver = sparkSession.sessionState.conf.resolver
DDLUtils.verifyAlterTableType(catalog, table, isView = false)
DDLUtils.verifyPartitionProviderIsHive(sparkSession, table, "ALTER
TABLE DROP PARTITION")
-val normalizedSpecs = specs.map { spec =>
- PartitioningUtils.normalizePartitionSpec(
-spec,
-table.partitionColumnNames,
-table.identifier.quotedString,
-sparkSession.sessionState.conf.resolver)
+val toDrop = partitions.flatMap { partition =>
+ if (partition._1.isEmpty && !partition._2.isEmpty) {
+// There are only expressions in this drop condition.
+extractFromPartitionFilter(partition._2, catalog, table, resolver)
+ } else if (!partition._1.isEmpty && partition._2.isEmpty) {
+// There are only partitionSpecs in this drop condition.
+extractFromPartitionSpec(partition._1, table, resolver)
+ } else if (!partition._1.isEmpty && !partition._2.isEmpty) {
+// This drop condition has both partitionSpecs and expressions.
+extractFromPartitionFilter(partition._2, catalog, table,
resolver).intersect(
--- End diff --
thank you @DazhuangSu
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[GitHub] spark issue #21871: [SPARK-24916][SQL] Fix type coercion for IN expression w...
Github user mgaido91 commented on the issue: https://github.com/apache/spark/pull/21871 I think this is basically the same of what I proposed in https://github.com/apache/spark/pull/19635. Unfortunately, that PR got a bit stuck... --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #21858: [SPARK-24899][SQL][DOC] Add example of monotonica...
Github user jaceklaskowski commented on a diff in the pull request:
https://github.com/apache/spark/pull/21858#discussion_r205058875
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/functions.scala ---
@@ -1150,16 +1150,48 @@ object functions {
/**
* A column expression that generates monotonically increasing 64-bit
integers.
*
- * The generated ID is guaranteed to be monotonically increasing and
unique, but not consecutive.
+ * The generated IDs are guaranteed to be monotonically increasing and
unique, but not
+ * consecutive (unless all rows are in the same single partition which
you rarely want due to
+ * the volume of the data).
* The current implementation puts the partition ID in the upper 31
bits, and the record number
* within each partition in the lower 33 bits. The assumption is that
the data frame has
* less than 1 billion partitions, and each partition has less than 8
billion records.
*
- * As an example, consider a `DataFrame` with two partitions, each with
3 records.
- * This expression would return the following IDs:
- *
* {{{
- * 0, 1, 2, 8589934592 (1L << 33), 8589934593, 8589934594.
+ * // Create a dataset with four partitions, each with two rows.
+ * val q = spark.range(start = 0, end = 8, step = 1, numPartitions = 4)
+ *
+ * // Make sure that every partition has the same number of rows
+ * q.mapPartitions(rows => Iterator(rows.size)).foreachPartition(rows =>
assert(rows.next == 2))
+ * q.select(monotonically_increasing_id).show
--- End diff --
I thought about explaining the "internals" of the operator through a more
involved example and actually thought about removing the line 1166 (but
forgot). I think the following lines make for a very in-depth explanation and
use other operators in use.
In other words, I'm in favour of removing the line 1166 and leaving the
others with no changes. Possible?
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[GitHub] spark issue #21803: [SPARK-24849][SPARK-24911][SQL] Converting a value of St...
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[GitHub] spark issue #21869: [SPARK-24891][FOLLOWUP][HOT-FIX][2.3] Fix the Compilatio...
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[GitHub] spark pull request #21650: [SPARK-24624][SQL][PYTHON] Support mixture of Pyt...
Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/21650#discussion_r205061160
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/python/ExtractPythonUDFs.scala
---
@@ -94,36 +95,59 @@ object ExtractPythonUDFFromAggregate extends
Rule[LogicalPlan] {
*/
object ExtractPythonUDFs extends Rule[SparkPlan] with PredicateHelper {
- private def hasPythonUDF(e: Expression): Boolean = {
+ private def hasScalarPythonUDF(e: Expression): Boolean = {
e.find(PythonUDF.isScalarPythonUDF).isDefined
}
- private def canEvaluateInPython(e: PythonUDF): Boolean = {
-e.children match {
- // single PythonUDF child could be chained and evaluated in Python
- case Seq(u: PythonUDF) => canEvaluateInPython(u)
- // Python UDF can't be evaluated directly in JVM
- case children => !children.exists(hasPythonUDF)
+ private def canEvaluateInPython(e: PythonUDF, evalType: Int): Boolean = {
+if (e.evalType != evalType) {
--- End diff --
Can we rename this function or write a comment since Scalar both Vectorized
UDF and normal UDF can be evaluated in Python each but it returns `false` in
this case?
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[GitHub] spark issue #21650: [SPARK-24624][SQL][PYTHON] Support mixture of Python UDF...
Github user HyukjinKwon commented on the issue: https://github.com/apache/spark/pull/21650 I'm okay with https://github.com/apache/spark/pull/21650#issuecomment-407506043's way too but should be really simplified. Either way LGTM. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #21858: [SPARK-24899][SQL][DOC] Add example of monotonica...
Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/21858#discussion_r205062686
--- Diff: sql/core/src/main/scala/org/apache/spark/sql/functions.scala ---
@@ -1150,16 +1150,48 @@ object functions {
/**
* A column expression that generates monotonically increasing 64-bit
integers.
*
- * The generated ID is guaranteed to be monotonically increasing and
unique, but not consecutive.
+ * The generated IDs are guaranteed to be monotonically increasing and
unique, but not
+ * consecutive (unless all rows are in the same single partition which
you rarely want due to
+ * the volume of the data).
* The current implementation puts the partition ID in the upper 31
bits, and the record number
* within each partition in the lower 33 bits. The assumption is that
the data frame has
* less than 1 billion partitions, and each partition has less than 8
billion records.
*
- * As an example, consider a `DataFrame` with two partitions, each with
3 records.
- * This expression would return the following IDs:
- *
* {{{
- * 0, 1, 2, 8589934592 (1L << 33), 8589934593, 8589934594.
+ * // Create a dataset with four partitions, each with two rows.
+ * val q = spark.range(start = 0, end = 8, step = 1, numPartitions = 4)
+ *
+ * // Make sure that every partition has the same number of rows
+ * q.mapPartitions(rows => Iterator(rows.size)).foreachPartition(rows =>
assert(rows.next == 2))
+ * q.select(monotonically_increasing_id).show
--- End diff --
I know you're exploring the internals but .. to be honest I was wondering
if users are usually interested in such in-deep explanation since I guess most
of them wouldn't care about the details.
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[GitHub] spark issue #21320: [SPARK-4502][SQL] Parquet nested column pruning - founda...
Github user HyukjinKwon commented on the issue: https://github.com/apache/spark/pull/21320 > I have no intention to at this point, no. Yup, but I guess we should do when we are about to be complete to avoid breaking things by switching this feature on. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark pull request #21320: [SPARK-4502][SQL] Parquet nested column pruning -...
Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/21320#discussion_r205063684
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/ProjectionOverSchema.scala
---
@@ -0,0 +1,62 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.sql.catalyst.planning
+
+import org.apache.spark.sql.catalyst.expressions._
+import org.apache.spark.sql.types._
+
+/**
+ * A Scala extractor that projects an expression over a given schema. Data
types,
+ * field indexes and field counts of complex type extractors and attributes
+ * are adjusted to fit the schema. All other expressions are left as-is.
This
+ * class is motivated by columnar nested schema pruning.
+ */
+case class ProjectionOverSchema(schema: StructType) {
--- End diff --
can we move it to `catalyst`?
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[GitHub] spark issue #21320: [SPARK-4502][SQL] Parquet nested column pruning - founda...
Github user HyukjinKwon commented on the issue: https://github.com/apache/spark/pull/21320 Few comments like https://github.com/apache/spark/pull/21320#discussion_r203933307 are not minor or nits. I leave hard -1 if they are not addressed. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21789: [SPARK-24829][STS]In Spark Thrift Server, CAST AS FLOAT ...
Github user mgaido91 commented on the issue: https://github.com/apache/spark/pull/21789 LGTM, I checked and the same hack is done also in Hive. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21866: [SPARK-24768][FollowUp][SQL]Avro migration followup: cha...
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[GitHub] spark issue #21866: [SPARK-24768][FollowUp][SQL]Avro migration followup: cha...
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[GitHub] spark pull request #21871: [SPARK-24916][SQL] Fix type coercion for IN expre...
Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/21871#discussion_r205068186
--- Diff:
sql/core/src/test/resources/sql-tests/inputs/typeCoercion/native/inConversion.sql
---
@@ -328,3 +328,159 @@ SELECT cast('2017-12-12 09:30:00' as date) in
(cast('2017-12-12 09:30:00' as dat
SELECT cast('2017-12-12 09:30:00' as date) in (cast('2017-12-12 09:30:00'
as date), cast(1 as boolean)) FROM t;
SELECT cast('2017-12-12 09:30:00' as date) in (cast('2017-12-12 09:30:00'
as date), cast('2017-12-11 09:30:00.0' as timestamp)) FROM t;
SELECT cast('2017-12-12 09:30:00' as date) in (cast('2017-12-12 09:30:00'
as date), cast('2017-12-11 09:30:00' as date)) FROM t;
+
+SELECT * FROM t WHERE (cast(1 as tinyint)) IN (SELECT cast(1 as tinyint)
FROM t);
--- End diff --
Do we really need to test all the combinations? We need most of such logics
should be tested in `findWiderTypeWithoutStringPromotionForTwo` and we could
have just few end to end tests.
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[GitHub] spark issue #21870: Branch 2.3
Github user HyukjinKwon commented on the issue: https://github.com/apache/spark/pull/21870 @lovezeropython, we usually file an issue in JIRA (please see https://spark.apache.org/contributing.html) or ask a question to mailing list (please see https://spark.apache.org/community.html). Mind closing this PR please? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21869: [SPARK-24891][FOLLOWUP][HOT-FIX][2.3] Fix the Compilatio...
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[GitHub] spark issue #21866: [SPARK-24768][FollowUp][SQL]Avro migration followup: cha...
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[GitHub] spark issue #21871: [SPARK-24916][SQL] Fix type coercion for IN expression w...
Github user wangyum commented on the issue: https://github.com/apache/spark/pull/21871 Oh. It turns out that @dilipbiswal is talking about that PR. I didn't find it in your recent PR. Letâs wait if the test can pass. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #21866: [SPARK-24768][FollowUp][SQL]Avro migration followup: cha...
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[GitHub] spark issue #21866: [SPARK-24768][FollowUp][SQL]Avro migration followup: cha...
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[GitHub] spark issue #21834: [SPARK-22814][SQL] Support Date/Timestamp in a JDBC part...
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[GitHub] spark pull request #21221: [SPARK-23429][CORE] Add executor memory metrics t...
Github user edwinalu commented on a diff in the pull request:
https://github.com/apache/spark/pull/21221#discussion_r205095575
--- Diff:
core/src/test/scala/org/apache/spark/scheduler/EventLoggingListenerSuite.scala
---
@@ -251,6 +261,215 @@ class EventLoggingListenerSuite extends SparkFunSuite
with LocalSparkContext wit
}
}
+ /**
+ * Test stage executor metrics logging functionality. This checks that
peak
+ * values from SparkListenerExecutorMetricsUpdate events during a stage
are
+ * logged in a StageExecutorMetrics event for each executor at stage
completion.
+ */
+ private def testStageExecutorMetricsEventLogging() {
+val conf = getLoggingConf(testDirPath, None)
+val logName = "stageExecutorMetrics-test"
+val eventLogger = new EventLoggingListener(logName, None,
testDirPath.toUri(), conf)
+val listenerBus = new LiveListenerBus(conf)
+
+// expected StageExecutorMetrics, for the given stage id and executor
id
+val expectedMetricsEvents: Map[(Int, String),
SparkListenerStageExecutorMetrics] =
+ Map(
+((0, "1"),
+ new SparkListenerStageExecutorMetrics("1", 0, 0,
+ Array(5000L, 50L, 50L, 20L, 50L, 10L, 100L, 30L, 70L, 20L))),
+((0, "2"),
+ new SparkListenerStageExecutorMetrics("2", 0, 0,
+ Array(7000L, 70L, 50L, 20L, 10L, 10L, 50L, 30L, 80L, 40L))),
+((1, "1"),
+ new SparkListenerStageExecutorMetrics("1", 1, 0,
+ Array(7000L, 70L, 50L, 30L, 60L, 30L, 80L, 55L, 50L, 0L))),
+((1, "2"),
+ new SparkListenerStageExecutorMetrics("2", 1, 0,
+ Array(7000L, 70L, 50L, 40L, 10L, 30L, 50L, 60L, 40L, 40L
+
+// Events to post.
+val events = Array(
+ SparkListenerApplicationStart("executionMetrics", None,
+1L, "update", None),
+ createExecutorAddedEvent(1),
+ createExecutorAddedEvent(2),
+ createStageSubmittedEvent(0),
+ // receive 3 metric updates from each executor with just stage 0
running,
+ // with different peak updates for each executor
+ createExecutorMetricsUpdateEvent(1,
+ Array(4000L, 50L, 20L, 0L, 40L, 0L, 60L, 0L, 70L, 20L)),
+ createExecutorMetricsUpdateEvent(2,
+ Array(1500L, 50L, 20L, 0L, 0L, 0L, 20L, 0L, 70L, 0L)),
+ // exec 1: new stage 0 peaks for metrics at indexes: 2, 4, 6
+ createExecutorMetricsUpdateEvent(1,
+ Array(4000L, 50L, 50L, 0L, 50L, 0L, 100L, 0L, 70L, 20L)),
+ // exec 2: new stage 0 peaks for metrics at indexes: 0, 4, 6
+ createExecutorMetricsUpdateEvent(2,
+ Array(2000L, 50L, 10L, 0L, 10L, 0L, 30L, 0L, 70L, 0L)),
+ // exec 1: new stage 0 peaks for metrics at indexes: 5, 7
+ createExecutorMetricsUpdateEvent(1,
+ Array(2000L, 40L, 50L, 0L, 40L, 10L, 90L, 10L, 50L, 0L)),
+ // exec 2: new stage 0 peaks for metrics at indexes: 0, 5, 6, 7, 8
+ createExecutorMetricsUpdateEvent(2,
+ Array(3500L, 50L, 15L, 0L, 10L, 10L, 35L, 10L, 80L, 0L)),
+ // now start stage 1, one more metric update for each executor, and
new
+ // peaks for some stage 1 metrics (as listed), initialize stage 1
peaks
+ createStageSubmittedEvent(1),
+ // exec 1: new stage 0 peaks for metrics at indexes: 0, 3, 7
--- End diff --
Stage 0 is still running, and these are new peaks for that stage. It is
also initializing all the stage 1 metric values, since these are the first
executor metrics seen for stage 1 (I'll add this to the comments).
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[GitHub] spark pull request #21758: [SPARK-24795][CORE] Implement barrier execution m...
Github user jiangxb1987 commented on a diff in the pull request:
https://github.com/apache/spark/pull/21758#discussion_r205096607
--- Diff: core/src/main/scala/org/apache/spark/rdd/RDDBarrier.scala ---
@@ -0,0 +1,52 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ *http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.rdd
+
+import scala.reflect.ClassTag
+
+import org.apache.spark.BarrierTaskContext
+import org.apache.spark.TaskContext
+import org.apache.spark.annotation.{Experimental, Since}
+
+/** Represents an RDD barrier, which forces Spark to launch tasks of this
stage together. */
+class RDDBarrier[T: ClassTag](rdd: RDD[T]) {
+
+ /**
+ * :: Experimental ::
+ * Maps partitions together with a provided BarrierTaskContext.
+ *
+ * `preservesPartitioning` indicates whether the input function
preserves the partitioner, which
+ * should be `false` unless `rdd` is a pair RDD and the input function
doesn't modify the keys.
+ */
+ @Experimental
+ @Since("2.4.0")
+ def mapPartitions[S: ClassTag](
--- End diff --
`RDDBarrier` is actually expected to be used like a builder, we shall
provide more options for the barrier stage in the future, eg. config a timeout
of a barrier stage.
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[GitHub] spark pull request #21872: [WIP] merge upstream
GitHub user onursatici opened a pull request: https://github.com/apache/spark/pull/21872 [WIP] merge upstream ## What changes were proposed in this pull request? (Please fill in changes proposed in this fix) ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Please review http://spark.apache.org/contributing.html before opening a pull request. You can merge this pull request into a Git repository by running: $ git pull https://github.com/palantir/spark os/merge-upstream Alternatively you can review and apply these changes as the patch at: https://github.com/apache/spark/pull/21872.patch To close this pull request, make a commit to your master/trunk branch with (at least) the following in the commit message: This closes #21872 commit a4697f7d4d5cbe31934f04c788c58c3fd598e0d5 Author: Robert Kruszewski Date: 2018-03-12T15:08:13Z v2 commit 48ecf0b688d00cbf57495dec9c338d479d8f55f3 Author: Robert Kruszewski Date: 2018-03-12T15:11:26Z more changes commit e7429d45e086fa82f5c214a74255232aedb727da Author: Dan Sanduleac Date: 2018-03-12T15:18:00Z Propagate hadoop exclusions to additional hadoop deps commit adeb886d94c0a018382f49340962500c1d3498c2 Author: Robert Kruszewski Date: 2018-03-12T16:21:17Z R tests commit 50e7e92cb29fd914d32b002dc96303360427aff4 Author: Robert Kruszewski Date: 2018-03-12T16:25:56Z nesting commit 2d3ef4a5199b401697d415520cf8aa9cd4a65862 Author: Dan Sanduleac Date: 2018-03-12T16:28:05Z Undo mistake commit 62deec487f2c48680eb86148abc5d818da696a26 Author: Robert Kruszewski Date: 2018-03-12T17:04:47Z maybe commit 9a4cf19c0070ce7d9bf7dfbde7f6646bb0fe2f1a Author: Dan Sanduleac Date: 2018-03-12T17:38:49Z Try to add better logging for test_conda commit 1b7a75835d3abcd7ba55504b56e269a3f061969a Author: Dan Sanduleac Date: 2018-03-12T17:39:39Z Comment out saving the build-sbt-* cache commit a10bdff90cae0fc593d9275dca6193b22e3a2c51 Author: Robert Kruszewski Date: 2018-03-12T18:06:59Z move r tests reporting commit 0ece2d6dfa4a21c2bc05de5b545ba52d5193234a Author: Dan Sanduleac Date: 2018-03-12T18:19:32Z Remove old cache restore commit 570e0e6536fd34a22059e4758ef38144f12d3413 Author: Dan Sanduleac Date: 2018-03-12T19:31:40Z Limit python parallelism in an attempt to reduce flakes commit 020adda18d642d27b3dd43b25260fb37ff6d4692 Author: Dan Sanduleac Date: 2018-03-12T20:24:04Z Make run_python_tests verbose so circle doesn't time it out commit e970d355f66bbe6df3da45213ddf06b797364d30 Author: Dan Sanduleac Date: 2018-03-12T21:25:38Z Use circleci machinery to split test classes by timings commit 87d3173a13c57d0f99bb1710c97e78db224e2ca2 Author: Dan Sanduleac Date: 2018-03-12T22:00:53Z Install unishark into python image commit 7eaafedb755740d9019eda20f6796c203b84e6c7 Author: Dan Sanduleac Date: 2018-03-12T22:01:06Z Run python tests using unishark commit ae3f14e11d7de4095174f09401e222659cf9e0ef Author: Dan Sanduleac Date: 2018-03-12T22:08:45Z don't expect every project to have an entry in circleTestsForProject commit 6e5b03c79a710159e883bcfcff42bad4cdd125b2 Author: Dan Sanduleac Date: 2018-03-13T12:03:45Z Also resolve the oldDeps project before saving ivy-dependency-cache commit 45cec9d9e3f97848acea84631824a22d5cd59d0f Author: Dan Sanduleac Date: 2018-03-13T12:32:47Z Revert "Also resolve the oldDeps project before saving ivy-dependency-cache" This reverts commit 6e5b03c79a710159e883bcfcff42bad4cdd125b2. commit 6c7809cec248bb778ad54117db85868222c38a3f Author: Dan Sanduleac Date: 2018-03-13T13:43:32Z Pipe test output for python/run-tests.py commit 487796d07e24cfa4534a243cbb7451e3397b7137 Author: Dan Sanduleac Date: 2018-03-13T16:00:09Z Try to set respectSessionTimeZone to fix python tests commit bb2076d1b80820fc9280af27c9a63c8ca2bbefb4 Author: Dan Sanduleac Date: 2018-03-13T18:46:46Z Revert "Try to set respectSessionTimeZone to fix python tests" This reverts commit 487796d commit 52cbda2e67cf37346f0ca7f90a2f5a8659447d1d Author: Dan Sanduleac Date: 2018-03-13T18:50:28Z Skip bad arrow tests for now - https://github.com/palantir/spark/issues/328 commit 51858bf187c53aefb123b4f56b7e63b63c544771 Author: Dan Sanduleac Date: 2018-03-13T19:04:27Z Make python tests output xml results in PYSPARK_PYTHON subdir commit 2620ab3fbd3d013a1070a5bd6b1c4250d5e4 Author: Dan Sanduleac Date: 2018-03-13T19:05:15Z Try to use unishark instead of xmlrunner in pyspark.streaming.tests too commit 77eaa80b4f8de046b20f14456bb7c9b5cb223016 Author: Dan Sanduleac Date: 2018-03-13T19:20:25Z Only the basename of PYSPARK_PYTHON please... commit 19abd29d2806842ccb1
[GitHub] spark pull request #21872: [WIP] merge upstream
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[GitHub] spark pull request #21758: [SPARK-24795][CORE] Implement barrier execution m...
Github user jiangxb1987 commented on a diff in the pull request:
https://github.com/apache/spark/pull/21758#discussion_r205100534
--- Diff: core/src/main/scala/org/apache/spark/rdd/RDD.scala ---
@@ -1647,6 +1647,14 @@ abstract class RDD[T: ClassTag](
}
}
+ /**
+ * :: Experimental ::
+ * Indicates that Spark must launch the tasks together for the current
stage.
+ */
+ @Experimental
+ @Since("2.4.0")
+ def barrier(): RDDBarrier[T] = withScope(new RDDBarrier[T](this))
--- End diff --
Em, thanks for raising this question. IMO we indeed require users be aware
of how many tasks they may launch for a barrier stage, and tasks may exchange
internal data between each other in the middle, so users really care about the
task numbers. I agree it shall be very useful to enable specify the number of
tasks in a barrier stage, maybe we can have `RDDBarrier.coalesce(numPartitions:
Int)` to enforce the number of tasks to be launched together in a barrier stage.
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[GitHub] spark pull request #21758: [SPARK-24795][CORE] Implement barrier execution m...
Github user jiangxb1987 commented on a diff in the pull request:
https://github.com/apache/spark/pull/21758#discussion_r205102656
--- Diff:
core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala ---
@@ -359,20 +366,55 @@ private[spark] class TaskSchedulerImpl(
// of locality levels so that it gets a chance to launch local tasks
on all of them.
// NOTE: the preferredLocality order: PROCESS_LOCAL, NODE_LOCAL,
NO_PREF, RACK_LOCAL, ANY
for (taskSet <- sortedTaskSets) {
- var launchedAnyTask = false
- var launchedTaskAtCurrentMaxLocality = false
- for (currentMaxLocality <- taskSet.myLocalityLevels) {
-do {
- launchedTaskAtCurrentMaxLocality = resourceOfferSingleTaskSet(
-taskSet, currentMaxLocality, shuffledOffers, availableCpus,
tasks)
- launchedAnyTask |= launchedTaskAtCurrentMaxLocality
-} while (launchedTaskAtCurrentMaxLocality)
- }
- if (!launchedAnyTask) {
-taskSet.abortIfCompletelyBlacklisted(hostToExecutors)
+ // Skip the barrier taskSet if the available slots are less than the
number of pending tasks.
+ if (taskSet.isBarrier && availableSlots < taskSet.numTasks) {
--- End diff --
As listed in the design doc, support running barrier stage with dynamic
resource allocation is Non-Goal of this task. However, we do plan to better
integrate this feature with dynamic resource allocation, hopefully we can get
to work on this before Spark 3.0.
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[GitHub] spark issue #21871: [SPARK-24916][SQL] Fix type coercion for IN expression w...
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