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[SPARK-27453] Pass partitionBy as options in DataFrameWriter #24365

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[SPARK-27453] Pass partitionBy as options in DataFrameWriter #24365

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61a4c96
pass partitionBy as options
liwensun Apr 12, 2019
c8cdd01
address comments
liwensun Apr 15, 2019
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9 changes: 9 additions & 0 deletions sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
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Expand Up @@ -1687,6 +1687,15 @@ object SQLConf {
.booleanConf
.createWithDefault(false)

val LEGACY_PASS_PARTITION_BY_AS_OPTIONS =
buildConf("spark.sql.legacy.sources.write.passPartitionByAsOptions")
.internal()
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@attilapiros attilapiros Apr 16, 2019

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Are you sure this must be internal?

I see some of the previous ones with LEGACY_ prefix are internal but before them there are a few externals: LEGACY_REPLACE_DATABRICKS_SPARK_AVRO_ENABLED and LEGACY_SIZE_OF_NULL. So what is the reason defining this as internal?

I assume if we expect the users to set it it then it must be external.
What is your (and the others) opinion?

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@zsxwing zsxwing Apr 16, 2019

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We don't want to document this flag for users. This is just in case we break some existing production workloads. For other users, they don't need to know anything about this flag.

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@attilapiros attilapiros Apr 16, 2019

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Ok, I see. Thanks for the explanation.

.doc("Whether to pass the partitionBy columns as options in DataFrameWriter. " +
"Data source V1 now silently drops partitionBy columns for non-file-format sources; " +
"turning the flag on provides a way for these sources to see these partitionBy columns.")
.booleanConf
.createWithDefault(false)

val NAME_NON_STRUCT_GROUPING_KEY_AS_VALUE =
buildConf("spark.sql.legacy.dataset.nameNonStructGroupingKeyAsValue")
.internal()
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11 changes: 10 additions & 1 deletion sql/core/src/main/scala/org/apache/spark/sql/DataFrameWriter.scala
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Expand Up @@ -29,8 +29,9 @@ import org.apache.spark.sql.catalyst.expressions.Literal
import org.apache.spark.sql.catalyst.plans.logical.{AppendData, InsertIntoTable, LogicalPlan, OverwriteByExpression}
import org.apache.spark.sql.execution.SQLExecution
import org.apache.spark.sql.execution.command.DDLUtils
import org.apache.spark.sql.execution.datasources.{CreateTable, DataSource, LogicalRelation}
import org.apache.spark.sql.execution.datasources.{CreateTable, DataSource, DataSourceUtils, LogicalRelation}
import org.apache.spark.sql.execution.datasources.v2.{DataSourceV2Relation, DataSourceV2Utils, FileDataSourceV2, WriteToDataSourceV2}
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.sources.BaseRelation
import org.apache.spark.sql.sources.v2._
import org.apache.spark.sql.sources.v2.TableCapability._
Expand Down Expand Up @@ -313,6 +314,14 @@ final class DataFrameWriter[T] private[sql](ds: Dataset[T]) {
}

private def saveToV1Source(): Unit = {
if (SparkSession.active.sessionState.conf.getConf(
SQLConf.LEGACY_PASS_PARTITION_BY_AS_OPTIONS)) {
partitioningColumns.foreach { columns =>
extraOptions += (DataSourceUtils.PARTITIONING_COLUMNS_KEY ->
DataSourceUtils.encodePartitioningColumns(columns))
}
}

// Code path for data source v1.
runCommand(df.sparkSession, "save") {
DataSource(
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20 changes: 20 additions & 0 deletions sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/DataSourceUtils.scala
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Expand Up @@ -18,12 +18,32 @@
package org.apache.spark.sql.execution.datasources

import org.apache.hadoop.fs.Path
import org.json4s.NoTypeHints
import org.json4s.jackson.Serialization

import org.apache.spark.sql.AnalysisException
import org.apache.spark.sql.types._


object DataSourceUtils {
/**
* The key to use for storing partitionBy columns as options.
*/
val PARTITIONING_COLUMNS_KEY = "__partition_columns"

/**
* Utility methods for converting partitionBy columns to options and back.
*/
private implicit val formats = Serialization.formats(NoTypeHints)

def encodePartitioningColumns(columns: Seq[String]): String = {
Serialization.write(columns)
}

def decodePartitioningColumns(str: String): Seq[String] = {
Serialization.read[Seq[String]](str)
}

/**
* Verify if the schema is supported in datasource. This verification should be done
* in a driver side.
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19 changes: 19 additions & 0 deletions sql/core/src/test/scala/org/apache/spark/sql/test/DataFrameReaderWriterSuite.scala
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Expand Up @@ -38,6 +38,7 @@ import org.apache.spark.internal.io.HadoopMapReduceCommitProtocol
import org.apache.spark.scheduler.{SparkListener, SparkListenerJobStart}
import org.apache.spark.sql._
import org.apache.spark.sql.catalyst.TableIdentifier
import org.apache.spark.sql.execution.datasources.DataSourceUtils
import org.apache.spark.sql.execution.datasources.parquet.SpecificParquetRecordReaderBase
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.sources._
Expand Down Expand Up @@ -219,6 +220,24 @@ class DataFrameReaderWriterSuite extends QueryTest with SharedSQLContext with Be
assert(LastOptions.parameters("opt3") == "3")
}

test("pass partitionBy as options") {
Seq(true, false).foreach { flag =>
withSQLConf(SQLConf.LEGACY_PASS_PARTITION_BY_AS_OPTIONS.key -> s"$flag") {
Seq(1).toDF.write
.format("org.apache.spark.sql.test")
.partitionBy("col1", "col2")
.save()

if (flag) {
val partColumns = LastOptions.parameters(DataSourceUtils.PARTITIONING_COLUMNS_KEY)
assert(DataSourceUtils.decodePartitioningColumns(partColumns) === Seq("col1", "col2"))
} else {
assert(!LastOptions.parameters.contains(DataSourceUtils.PARTITIONING_COLUMNS_KEY))
}
}
}
}

test("save mode") {
val df = spark.read
.format("org.apache.spark.sql.test")
Expand Down