org.apache.spark.sql.ForeachWriter.scala Maven / Gradle / Ivy
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* 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
/**
* The abstract class for writing custom logic to process data generated by a query.
* This is often used to write the output of a streaming query to arbitrary storage systems.
* Any implementation of this base class will be used by Spark in the following way.
*
*
* - A single instance of this class is responsible of all the data generated by a single task
* in a query. In other words, one instance is responsible for processing one partition of the
* data generated in a distributed manner.
*
*
- Any implementation of this class must be serializable because each task will get a fresh
* serialized-deserialized copy of the provided object. Hence, it is strongly recommended that
* any initialization for writing data (e.g. opening a connection or starting a transaction)
* is done after the `open(...)` method has been called, which signifies that the task is
* ready to generate data.
*
*
- The lifecycle of the methods are as follows.
*
*
* For each partition with `partitionId`:
* For each batch/epoch of streaming data (if its streaming query) with `epochId`:
* Method `open(partitionId, epochId)` is called.
* If `open` returns true:
* For each row in the partition and batch/epoch, method `process(row)` is called.
* Method `close(errorOrNull)` is called with error (if any) seen while processing rows.
*
*
*
*
* Important points to note:
*
* - Spark doesn't guarantee same output for (partitionId, epochId), so deduplication
* cannot be achieved with (partitionId, epochId). e.g. source provides different number of
* partitions for some reason, Spark optimization changes number of partitions, etc.
* Refer SPARK-28650 for more details. If you need deduplication on output, try out
* `foreachBatch` instead.
*
*
- The `close()` method will be called if `open()` method returns successfully (irrespective
* of the return value), except if the JVM crashes in the middle.
*
*
* Scala example:
* {{{
* datasetOfString.writeStream.foreach(new ForeachWriter[String] {
*
* def open(partitionId: Long, version: Long): Boolean = {
* // open connection
* }
*
* def process(record: String) = {
* // write string to connection
* }
*
* def close(errorOrNull: Throwable): Unit = {
* // close the connection
* }
* })
* }}}
*
* Java example:
* {{{
* datasetOfString.writeStream().foreach(new ForeachWriter() {
*
* @Override
* public boolean open(long partitionId, long version) {
* // open connection
* }
*
* @Override
* public void process(String value) {
* // write string to connection
* }
*
* @Override
* public void close(Throwable errorOrNull) {
* // close the connection
* }
* });
* }}}
*
* @since 2.0.0
*/
abstract class ForeachWriter[T] extends Serializable {
// TODO: Move this to org.apache.spark.sql.util or consolidate this with batch API.
/**
* Called when starting to process one partition of new data in the executor. See the class
* docs for more information on how to use the `partitionId` and `epochId`.
*
* @param partitionId the partition id.
* @param epochId a unique id for data deduplication.
* @return `true` if the corresponding partition and version id should be processed. `false`
* indicates the partition should be skipped.
*/
def open(partitionId: Long, epochId: Long): Boolean
/**
* Called to process the data in the executor side. This method will be called only if `open`
* returns `true`.
*/
def process(value: T): Unit
/**
* Called when stopping to process one partition of new data in the executor side. This is
* guaranteed to be called either `open` returns `true` or `false`. However,
* `close` won't be called in the following cases:
*
*
* - JVM crashes without throwing a `Throwable`
* - `open` throws a `Throwable`.
*
*
* @param errorOrNull the error thrown during processing data or null if there was no error.
*/
def close(errorOrNull: Throwable): Unit
}