spark PythonForeachWriter 源码
spark PythonForeachWriter 代码
文件路径:/sql/core/src/main/scala/org/apache/spark/sql/execution/python/PythonForeachWriter.scala
/*
* 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.python
import java.io.File
import java.util.concurrent.TimeUnit
import java.util.concurrent.locks.ReentrantLock
import org.apache.spark.{SparkEnv, TaskContext}
import org.apache.spark.api.python._
import org.apache.spark.internal.Logging
import org.apache.spark.memory.TaskMemoryManager
import org.apache.spark.sql.ForeachWriter
import org.apache.spark.sql.catalyst.expressions.UnsafeRow
import org.apache.spark.sql.types.StructType
import org.apache.spark.util.{NextIterator, Utils}
class PythonForeachWriter(func: PythonFunction, schema: StructType)
extends ForeachWriter[UnsafeRow] {
private lazy val context = TaskContext.get()
private lazy val buffer = new PythonForeachWriter.UnsafeRowBuffer(
context.taskMemoryManager, new File(Utils.getLocalDir(SparkEnv.get.conf)), schema.fields.length)
private lazy val inputRowIterator = buffer.iterator
private lazy val inputByteIterator = {
EvaluatePython.registerPicklers()
val objIterator = inputRowIterator.map { row => EvaluatePython.toJava(row, schema) }
new SerDeUtil.AutoBatchedPickler(objIterator)
}
private lazy val pythonRunner = {
PythonRunner(func)
}
private lazy val outputIterator =
pythonRunner.compute(inputByteIterator, context.partitionId(), context)
override def open(partitionId: Long, version: Long): Boolean = {
outputIterator // initialize everything
TaskContext.get.addTaskCompletionListener[Unit] { _ => buffer.close() }
true
}
override def process(value: UnsafeRow): Unit = {
buffer.add(value)
}
override def close(errorOrNull: Throwable): Unit = {
buffer.allRowsAdded()
if (outputIterator.hasNext) outputIterator.next() // to throw python exception if there was one
}
}
object PythonForeachWriter {
/**
* A buffer that is designed for the sole purpose of buffering UnsafeRows in PythonForeachWriter.
* It is designed to be used with only 1 writer thread (i.e. JVM task thread) and only 1 reader
* thread (i.e. PythonRunner writing thread that reads from the buffer and writes to the Python
* worker stdin). Adds to the buffer are non-blocking, and reads through the buffer's iterator
* are blocking, that is, it blocks until new data is available or all data has been added.
*
* Internally, it uses a [[HybridRowQueue]] to buffer the rows in a practically unlimited queue
* across memory and local disk. However, HybridRowQueue is designed to be used only with
* EvalPythonExec where the reader is always behind the writer, that is, the reader does not
* try to read n+1 rows if the writer has only written n rows at any point of time. This
* assumption is not true for PythonForeachWriter where rows may be added at a different rate as
* they are consumed by the python worker. Hence, to maintain the invariant of the reader being
* behind the writer while using HybridRowQueue, the buffer does the following
* - Keeps a count of the rows in the HybridRowQueue
* - Blocks the buffer's consuming iterator when the count is 0 so that the reader does not
* try to read more rows than what has been written.
*
* The implementation of the blocking iterator (ReentrantLock, Condition, etc.) has been borrowed
* from that of ArrayBlockingQueue.
*/
class UnsafeRowBuffer(taskMemoryManager: TaskMemoryManager, tempDir: File, numFields: Int)
extends Logging {
private val queue = HybridRowQueue(taskMemoryManager, tempDir, numFields)
private val lock = new ReentrantLock()
private val unblockRemove = lock.newCondition()
// All of these are guarded by `lock`
private var count = 0L
private var allAdded = false
private var exception: Throwable = null
val iterator = new NextIterator[UnsafeRow] {
override protected def getNext(): UnsafeRow = {
val row = remove()
if (row == null) finished = true
row
}
override protected def close(): Unit = { }
}
def add(row: UnsafeRow): Unit = withLock {
assert(queue.add(row), s"Failed to add row to HybridRowQueue while sending data to Python" +
s"[count = $count, allAdded = $allAdded, exception = $exception]")
count += 1
unblockRemove.signal()
logTrace(s"Added $row, $count left")
}
private def remove(): UnsafeRow = withLock {
while (count == 0 && !allAdded && exception == null) {
unblockRemove.await(100, TimeUnit.MILLISECONDS)
}
// If there was any error in the adding thread, then rethrow it in the removing thread
if (exception != null) throw exception
if (count > 0) {
val row = queue.remove()
assert(row != null, "HybridRowQueue.remove() returned null " +
s"[count = $count, allAdded = $allAdded, exception = $exception]")
count -= 1
logTrace(s"Removed $row, $count left")
row
} else {
null
}
}
def allRowsAdded(): Unit = withLock {
allAdded = true
unblockRemove.signal()
}
def close(): Unit = { queue.close() }
private def withLock[T](f: => T): T = {
lock.lockInterruptibly()
try { f } catch {
case e: Throwable =>
if (exception == null) exception = e
throw e
} finally { lock.unlock() }
}
}
}
相关信息
相关文章
spark AggregateInPandasExec 源码
spark ApplyInPandasWithStatePythonRunner 源码
spark ApplyInPandasWithStateWriter 源码
spark AttachDistributedSequenceExec 源码
0
赞
- 所属分类: 前端技术
- 本文标签:
热门推荐
-
2、 - 优质文章
-
3、 gate.io
-
8、 golang
-
9、 openharmony
-
10、 Vue中input框自动聚焦