[
https://issues.apache.org/jira/browse/SPARK-35252?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
lynn updated SPARK-35252:
-------------------------
Description:
The codes of "MyPartitionReaderFactory" :
{code:scala}
// Implemention Class
package com.lynn.spark.sql.v2
import org.apache.spark.internal.Logging
import org.apache.spark.sql.catalyst.InternalRow
import
com.lynn.spark.sql.v2.MyPartitionReaderFactory.{MY_VECTORIZED_READER_BATCH_SIZE,
MY_VECTORIZED_READER_ENABLED}
import org.apache.spark.sql.connector.read.{InputPartition, PartitionReader,
PartitionReaderFactory}
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.vectorized.ColumnarBatch
import org.apache.spark.sql.internal.SQLConf.buildConf
case class MyPartitionReaderFactory(sqlConf: SQLConf,
dataSchema: StructType,
readSchema: StructType)
extends PartitionReaderFactory with Logging {
val enableVectorized = sqlConf.getConf(MY_VECTORIZED_READER_ENABLED, false)
val batchSize = sqlConf.getConf(MY_VECTORIZED_READER_BATCH_SIZE, 4096)
override def createReader(partition: InputPartition):
PartitionReader[InternalRow] = {
MyRowReader(batchSize, dataSchema, readSchema)
}
override def createColumnarReader(partition: InputPartition):
PartitionReader[ColumnarBatch] = {
if(!supportColumnarReads(partition))
throw new UnsupportedOperationException("Cannot create columnar reader.")
MyColumnReader(batchSize, dataSchema, readSchema)
}
override def supportColumnarReads(partition: InputPartition) =
enableVectorized
}
object MyPartitionReaderFactory {
val MY_VECTORIZED_READER_ENABLED =
buildConf("spark.sql.my.enableVectorizedReader")
.doc("Enables vectorized my source scan.")
.version("1.0.0")
.booleanConf
.createWithDefault(false)
val MY_VECTORIZED_READER_BATCH_SIZE =
buildConf("spark.sql.my.columnarReaderBatchSize")
.doc("The number of rows to include in a my source vectorized reader
batch. The number should " +
"be carefully chosen to minimize overhead and avoid OOMs in reading
data.")
.version("1.0.0")
.intConf
.createWithDefault(4096)
}
{code}
The driver construct a RDD instance(DataSourceRDD), the sqlConf parameter pass
to the MyPartitionReaderFactory is not null.
But when the executor deserialize the RDD, the sqlConf parameter is null.
The codes as follows:
{code:scala}
// RunTask.scala
override def runTask(context: TaskContext): U = {
// Deserialize the RDD and the func using the broadcast variables.
val threadMXBean = ManagementFactory.getThreadMXBean
val deserializeStartTimeNs = System.nanoTime()
val deserializeStartCpuTime = if
(threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime
} else 0L
val ser = SparkEnv.get.closureSerializer.newInstance()
val (rdd, func) = ser.deserialize[(RDD[T], (TaskContext, Iterator[T]) =>
U)](
ByteBuffer.wrap(taskBinary.value),
Thread.currentThread.getContextClassLoader)
_executorDeserializeTimeNs = System.nanoTime() - deserializeStartTimeNs
_executorDeserializeCpuTime = if
(threadMXBean.isCurrentThreadCpuTimeSupported) {
threadMXBean.getCurrentThreadCpuTime - deserializeStartCpuTime
} else 0L
func(context, rdd.iterator(partition, context))
}
{code}
was:
The codes of "MyPartitionReaderFactory" :
{code:java}
// Implemention Class
package com.lynn.spark.sql.v2
import org.apache.spark.internal.Logging
import org.apache.spark.sql.catalyst.InternalRow
import
com.lynn.spark.sql.v2.MyPartitionReaderFactory.{MY_VECTORIZED_READER_BATCH_SIZE,
MY_VECTORIZED_READER_ENABLED}
import org.apache.spark.sql.connector.read.{InputPartition, PartitionReader,
PartitionReaderFactory}
import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.vectorized.ColumnarBatch
import org.apache.spark.sql.internal.SQLConf.buildConf
case class MyPartitionReaderFactory(sqlConf: SQLConf,
dataSchema: StructType,
readSchema: StructType)
extends PartitionReaderFactory with Logging {
val enableVectorized = sqlConf.getConf(MY_VECTORIZED_READER_ENABLED, false)
val batchSize = sqlConf.getConf(MY_VECTORIZED_READER_BATCH_SIZE, 4096)
override def createReader(partition: InputPartition):
PartitionReader[InternalRow] = {
MyRowReader(batchSize, dataSchema, readSchema)
}
override def createColumnarReader(partition: InputPartition):
PartitionReader[ColumnarBatch] = {
if(!supportColumnarReads(partition))
throw new UnsupportedOperationException("Cannot create columnar reader.")
MyColumnReader(batchSize, dataSchema, readSchema)
}
override def supportColumnarReads(partition: InputPartition) =
enableVectorized
}
object MyPartitionReaderFactory {
val MY_VECTORIZED_READER_ENABLED =
buildConf("spark.sql.my.enableVectorizedReader")
.doc("Enables vectorized my source scan.")
.version("1.0.0")
.booleanConf
.createWithDefault(false)
val MY_VECTORIZED_READER_BATCH_SIZE =
buildConf("spark.sql.my.columnarReaderBatchSize")
.doc("The number of rows to include in a my source vectorized reader
batch. The number should " +
"be carefully chosen to minimize overhead and avoid OOMs in reading
data.")
.version("1.0.0")
.intConf
.createWithDefault(4096)
}
{code}
The driver construct a RDD instance, the sqlConf parameter pass to the
MyPartitionReaderFactory is not null.
But when the executor deserialize the RDD, the sqlConf parameter is null.
> PartitionReaderFactory's Implemention Class of DataSourceV2 sqlConf parameter
> is null
> -------------------------------------------------------------------------------------
>
> Key: SPARK-35252
> URL: https://issues.apache.org/jira/browse/SPARK-35252
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.0.2
> Reporter: lynn
> Priority: Major
> Attachments: spark-sqlconf-isnull.png, znbase-sqlconf-isnull.png
>
>
> The codes of "MyPartitionReaderFactory" :
> {code:scala}
> // Implemention Class
> package com.lynn.spark.sql.v2
> import org.apache.spark.internal.Logging
> import org.apache.spark.sql.catalyst.InternalRow
> import
> com.lynn.spark.sql.v2.MyPartitionReaderFactory.{MY_VECTORIZED_READER_BATCH_SIZE,
> MY_VECTORIZED_READER_ENABLED}
> import org.apache.spark.sql.connector.read.{InputPartition, PartitionReader,
> PartitionReaderFactory}
> import org.apache.spark.sql.internal.SQLConf
> import org.apache.spark.sql.types.StructType
> import org.apache.spark.sql.vectorized.ColumnarBatch
> import org.apache.spark.sql.internal.SQLConf.buildConf
> case class MyPartitionReaderFactory(sqlConf: SQLConf,
> dataSchema: StructType,
> readSchema: StructType)
> extends PartitionReaderFactory with Logging {
> val enableVectorized = sqlConf.getConf(MY_VECTORIZED_READER_ENABLED, false)
> val batchSize = sqlConf.getConf(MY_VECTORIZED_READER_BATCH_SIZE, 4096)
> override def createReader(partition: InputPartition):
> PartitionReader[InternalRow] = {
> MyRowReader(batchSize, dataSchema, readSchema)
> }
> override def createColumnarReader(partition: InputPartition):
> PartitionReader[ColumnarBatch] = {
> if(!supportColumnarReads(partition))
> throw new UnsupportedOperationException("Cannot create columnar
> reader.")
> MyColumnReader(batchSize, dataSchema, readSchema)
> }
> override def supportColumnarReads(partition: InputPartition) =
> enableVectorized
> }
> object MyPartitionReaderFactory {
> val MY_VECTORIZED_READER_ENABLED =
> buildConf("spark.sql.my.enableVectorizedReader")
> .doc("Enables vectorized my source scan.")
> .version("1.0.0")
> .booleanConf
> .createWithDefault(false)
> val MY_VECTORIZED_READER_BATCH_SIZE =
> buildConf("spark.sql.my.columnarReaderBatchSize")
> .doc("The number of rows to include in a my source vectorized reader
> batch. The number should " +
> "be carefully chosen to minimize overhead and avoid OOMs in reading
> data.")
> .version("1.0.0")
> .intConf
> .createWithDefault(4096)
> }
> {code}
> The driver construct a RDD instance(DataSourceRDD), the sqlConf parameter
> pass to the MyPartitionReaderFactory is not null.
> But when the executor deserialize the RDD, the sqlConf parameter is null.
> The codes as follows:
> {code:scala}
> // RunTask.scala
> override def runTask(context: TaskContext): U = {
> // Deserialize the RDD and the func using the broadcast variables.
> val threadMXBean = ManagementFactory.getThreadMXBean
> val deserializeStartTimeNs = System.nanoTime()
> val deserializeStartCpuTime = if
> (threadMXBean.isCurrentThreadCpuTimeSupported) {
> threadMXBean.getCurrentThreadCpuTime
> } else 0L
> val ser = SparkEnv.get.closureSerializer.newInstance()
> val (rdd, func) = ser.deserialize[(RDD[T], (TaskContext, Iterator[T]) =>
> U)](
> ByteBuffer.wrap(taskBinary.value),
> Thread.currentThread.getContextClassLoader)
> _executorDeserializeTimeNs = System.nanoTime() - deserializeStartTimeNs
> _executorDeserializeCpuTime = if
> (threadMXBean.isCurrentThreadCpuTimeSupported) {
> threadMXBean.getCurrentThreadCpuTime - deserializeStartCpuTime
> } else 0L
> func(context, rdd.iterator(partition, context))
> }
> {code}
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