Flink版本: 1.8.0
Scala版本: 2.11
Github地址&＃xff1a;https://github.com/shirukai/flink-examples-debug-state.git
在本地开发带状态的Flink任务时&＃xff0c;经常会遇到这样的问题&＃xff0c;需要验证状态是否生效&＃xff1f;以及重启应用之后&＃xff0c;状态里的数据能否从checkpoint的恢复&＃xff1f;首先要明确的是&＃xff0c;Flink重启时不会自动加载状态&＃xff0c;需要我们手动指定checkpoint路径。笔者从Spark的Structured Streaming转到Flink的时候&＃xff0c;就遇到这样的问题。在Spark中&＃xff0c;我们使用的状态信息会随着程序再次启动时自动被加载出来。所以当时以为Flink状态也会被自动加载&＃xff0c;在开发有状态算子时&＃xff0c;测试重启应用之后&＃xff0c;并没有继续上一次的状态。一开始以为是checkpoint的设置的问题&＃xff0c;调试了好长时间&＃xff0c;发现flink需要手动指定checkpoint路径。本篇文章&＃xff0c;将从搭建项目到编写带状态的任务&＃xff0c;介绍如何在IDEA中调试local模式下带状态的flink任务。
注意&＃xff1a;后期git上的项目名称从debug-flink-state-example改为flink-examples-debug-state
Flink提供了Meven模板&＃xff0c;能够帮助我们快速创建Maven项目。执行如下命令快速创建一个flink项目&＃xff1a;
mvn archetype:generate -DarchetypeGroupId&＃61;org.apache.flink -DarchetypeArtifactId&＃61;flink-quickstart-scala -DarchetypeVersion&＃61;1.8.0 -DgroupId&＃61;flink.examples -DartifactId&＃61;flink-examples-debug-state -Dversion&＃61;1.0 -Dpackage&＃61;flink.debug.state.example -DinteractiveMode&＃61;false
项目创建完成后&＃xff0c;使用IDEA打开项目。
对pom.xml稍微做一下修改。
纠正一下上面这个问题&＃xff0c;flink的两个包作用域都设置为了provided&＃xff0c;在程序执行时汇报类不存在的异常。我们可以注释掉scope作用域&＃xff0c;也可以在Maven里勾选带有flink依赖的Profiles。
这里我们编写一个简单的Flink任务&＃xff0c;实现功能如下
逻辑比较简单&＃xff0c;直接贴代码吧。
package debug.flink.state.exampleimport org.apache.flink.api.common.state.{ValueState, ValueStateDescriptor}
import org.apache.flink.streaming.api.scala.{DataStream, StreamExecutionEnvironment}
import org.apache.flink.api.scala._
import org.apache.flink.configuration.Configuration
import org.apache.flink.streaming.api.functions.KeyedProcessFunction
import org.apache.flink.util.Collector/*** 实时计算事件总个数&＃xff0c;以及value总和** &＃64;author shirukai*/object EventCounterJob {def main(args: Array[String]): Unit &＃61; {// 获取执行环境val env: StreamExecutionEnvironment &＃61; StreamExecutionEnvironment.getExecutionEnvironment// 1. 从socket中接收文本数据val streamText: DataStream[String] &＃61; env.socketTextStream("127.0.0.1", 9000)// 2. 将文本内容按照空格分割转换为事件样例类val events &＃61; streamText.map(s &＃61;> {val tokens &＃61; s.split(" ")Event(tokens(0), tokens(1).toDouble, tokens(2).toLong)})// 3. 按照时间id分区&＃xff0c;然后进行聚合统计val counterResult &＃61; events.keyBy(_.id).process(new EventCounterProcessFunction)// 4. 结果输出到控制台counterResult.print()env.execute("EventCounterJob")}
}/*** 定义事件样例类** &＃64;param id 事件类型id* &＃64;param value 事件值* &＃64;param time 事件时间*/
case class Event(id: String, value: Double, time: Long)/*** 定义事件统计器样例类** &＃64;param id 事件类型id* &＃64;param sum 事件值总和* &＃64;param count 事件个数*/
case class EventCounter(id: String, var sum: Double, var count: Int)/*** 继承KeyedProcessFunction实现事件统计*/
class EventCounterProcessFunction extends KeyedProcessFunction[String, Event, EventCounter] {private var counterState: ValueState[EventCounter] &＃61; _override def open(parameters: Configuration): Unit &＃61; {super.open(parameters)// 从flink上下文中获取状态counterState &＃61; getRuntimeContext.getState(new ValueStateDescriptor[EventCounter]("event-counter", classOf[EventCounter]))}override def processElement(i: Event,context: KeyedProcessFunction[String, Event, EventCounter]#Context,collector: Collector[EventCounter]): Unit &＃61; {// 从状态中获取统计器&＃xff0c;如果统计器不存在给定一个初始值val counter &＃61; Option(counterState.value()).getOrElse(EventCounter(i.id, 0.0, 0))// 统计聚合counter.count &＃43;&＃61; 1counter.sum &＃43;&＃61; i.value// 发送结果到下游collector.collect(counter)// 保存状态counterState.update(counter)}
}
使用nc命令监听9000端口
nl -lk 9000
启动flink任务&＃xff0c;并模拟如下数据发送
event-1 1 1591695864473
event-1 12 1591695864474
event-2 8 1591695864475
event-1 10 1591695864476
event-2 50 1591695864477
event-1 6 1591695864478
效果如下动图所示&＃xff1a;
上一步我们已经编写了一个有状态的简单任务&＃xff0c;但是状态并没有被持久化&＃xff0c;程序重启之后状态会丢失。这时候我们需要给flink任务配置checkpoint。需要简单配置3个地方&＃xff1a;
// 配置checkpoint// 做两个checkpoint的间隔为1秒env.enableCheckpointing(1000)// 表示下 Cancel 时是否需要保留当前的 Checkpoint&＃xff0c;默认 Checkpoint 会在整个作业 Cancel 时被删除。Checkpoint 是作业级别的保存点。env.getCheckpointConfig.enableExternalizedCheckpoints(ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION)// 设置状态后端&＃xff1a;MemoryStateBackend、FsStateBackend、RocksDBStateBackend&＃xff0c;这里设置基于文件的状态后端env.setStateBackend(new FsStateBackend("file:///tmp/checkpoints/event-counter"))
启动程序&＃xff0c;同样模拟数据发送。
这次先发送前三条数据
event-1 1 1591695864473
event-1 12 1591695864474
event-2 8 1591695864475
从以上动图中的日志可以看出&＃xff0c;flink每隔一秒都会在做checkpoint。
15:59:32,989 INFO org.apache.flink.runtime.checkpoint.CheckpointCoordinator - Triggering checkpoint 102 &＃64; 1592035172989 for job 0c3d201188fc9953cb65498adb4954f4.
15:59:32,997 INFO org.apache.flink.runtime.checkpoint.CheckpointCoordinator - Completed checkpoint 102 for job 0c3d201188fc9953cb65498adb4954f4 (21340 bytes in 7 ms).
15:59:33,990 INFO org.apache.flink.runtime.checkpoint.CheckpointCoordinator - Triggering checkpoint 103 &＃64; 1592035173989 for job 0c3d201188fc9953cb65498adb4954f4.
15:59:34,001 INFO org.apache.flink.runtime.checkpoint.CheckpointCoordinator - Completed checkpoint 103 for job 0c3d201188fc9953cb65498adb4954f4 (21340 bytes in 11 ms).
15:59:34,989 INFO org.apache.flink.runtime.checkpoint.CheckpointCoordinator - Triggering checkpoint 104 &＃64; 1592035174989 for job 0c3d201188fc9953cb65498adb4954f4.
15:59:35,006 INFO org.apache.flink.runtime.checkpoint.CheckpointCoordinator - Completed checkpoint 104 for job 0c3d201188fc9953cb65498adb4954f4 (21340 bytes in 15 ms).
查看checkpoint 的目录&＃xff0c;发现有checkpoint生成。
ls /tmp/checkpoints/event-counter
这里简单说明一下checkpoint目录&＃xff0c;程序每次启动都会在指定的目录下&＃xff08;如/tmp/checkpoints/event-counter&＃xff09;根据id生成一个目录&＃xff0c;该目录会包含三个目录chk-*、shared、taskowned&＃xff0c;每秒做的状态会报存在chk-*目录下&＃xff0c;整体目录结构如下所示&＃xff1a;
/tmp/checkpoints
└── event-counter└── 0c3d201188fc9953cb65498adb4954f4├── chk-104│ ├── 01f2561f-ca48-4699-bbea-40fc849b2b0f│ ├── 021a7b75-f034-4da3-ad0c-e9801a8f1141│ ├── 17fcf354-c212-43ec-8e7c-99e37a7653c9│ ├── 33af50a1-e2cb-4364-a723-4c182c5fdb47│ ├── 3fa88dc7-ea81-4735-83ba-3d4630b7b8ac│ ├── 792068d4-2f89-4d21-aa27-88ef61c7fa99│ ├── 793d349b-8029-4cb6-b522-22445ec19bae│ ├── _metadata│ ├── acd28b9b-a0cb-4880-9564-9b9fe3c29200│ ├── c7cbb990-917a-400d-9838-1ac28c92ea10│ ├── e202ca66-5f9e-4858-bf15-02ca17a4e2b1│ ├── e7370373-c4be-4c7c-b6df-d959127b31a3│ └── eb619830-b102-4449-a29c-59d82b6bfbfe├── shared└── taskowned
重启程序之后再发送后三条数据
event-1 10 1591695864476
event-2 50 1591695864477
event-1 6 1591695864478
按照预期&＃xff0c;当我们发送event-1 10 1591695864476这条数据时&＃xff0c;我们得到的结果应该是EventCounter(event-1,11.5,3)&＃xff0c;但实际上得到的是EventCounter(event-1,10.0,1)&＃xff0c;很明显之前的状态丢失了&＃xff0c;原因在文章开头已经说过&＃xff0c;这是由于flink并不会自动加载之前的状态&＃xff0c;需要我们手动指定checkpoint&＃xff0c;如果使用命令行提交任务的话&＃xff0c;可以使用-s参数指定savepoint的目录&＃xff0c;那么如果在IDEA里开发测试时如何指定呢&＃xff1f;下一章会介绍通过魔改源码的方式&＃xff0c;实现checkpoint的加载。
4 魔改LocalStreamEnvironment首先讲一下思路&＃xff0c;当执行env.execute(“EventCounterJob”)时&＃xff0c;程序会根据不同的执行环境选择不同的StreamExecutionEnvironment&＃xff0c;flink里有两种执行环境&＃xff1a;LocalStreamEnvironment和RemoteStreamEnvironment&＃xff0c;当我们在IDEA直接运行时&＃xff0c;使用的是LocalStreamEnvironment。通过查看RemoteStreamEnvironment的源码可以发现&＃xff0c;它最终在构造JobGraph的时候&＃xff0c;会将SavepointRestoreSettings的配置通过JobGraph的setSavepointRestoreSettings方法传入到JobGraph中。而在LocalStreamEnvironment中构造的JobGraph没有传入SavepointRestoreSettings的配置&＃xff0c;这里我们需要通过修改源码&＃xff0c;给JobGraph添加SavepointRestoreSettings配置。
RemoteStreamEnvironment的源码位置&＃xff1a;org.apache.flink.streaming.api.environment.RemoteStreamEnvironment。LocalStreamEnvironment的源码位置&＃xff1a;org.apache.flink.streaming.api.environment.LocalStreamEnvironment&＃xff0c;它的execute()实现源码如下&＃xff1a;
public JobExecutionResult execute(String jobName) throws Exception {// transform the streaming program into a JobGraphStreamGraph streamGraph &＃61; getStreamGraph();streamGraph.setJobName(jobName);JobGraph jobGraph &＃61; streamGraph.getJobGraph();jobGraph.setAllowQueuedScheduling(true);Configuration configuration &＃61; new Configuration();configuration.addAll(jobGraph.getJobConfiguration());configuration.setString(TaskManagerOptions.MANAGED_MEMORY_SIZE, "0");// add (and override) the settings with what the user definedconfiguration.addAll(this.configuration);if (!configuration.contains(RestOptions.BIND_PORT)) {configuration.setString(RestOptions.BIND_PORT, "0");}int numSlotsPerTaskManager &＃61; configuration.getInteger(TaskManagerOptions.NUM_TASK_SLOTS, jobGraph.getMaximumParallelism());MiniClusterConfiguration cfg &＃61; new MiniClusterConfiguration.Builder().setConfiguration(configuration).setNumSlotsPerTaskManager(numSlotsPerTaskManager).build();if (LOG.isInfoEnabled()) {LOG.info("Running job on local embedded Flink mini cluster");}MiniCluster miniCluster &＃61; new MiniCluster(cfg);try {miniCluster.start();configuration.setInteger(RestOptions.PORT, miniCluster.getRestAddress().get().getPort());return miniCluster.executeJobBlocking(jobGraph);}finally {transformations.clear();miniCluster.close();}}
这段代码的大体逻辑是这样的&＃xff1a;
我们可以在提交JobGraph给MiniCluster之前&＃xff0c;将SavepointRestoreSettings动态设置给JobGraph&＃xff0c;从而实现加载指定savepoint的目的。
When this environment is instantiated, it uses a default parallelism of {&＃64;code 1}. The default* parallelism can be set via {&＃64;link #setParallelism(int)}.*//** 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.flink.streaming.api.environment;import org.apache.flink.annotation.Public;
import org.apache.flink.api.common.InvalidProgramException;
import org.apache.flink.api.common.JobExecutionResult;
import org.apache.flink.api.java.ExecutionEnvironment;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.configuration.RestOptions;
import org.apache.flink.configuration.TaskManagerOptions;
import org.apache.flink.runtime.jobgraph.JobGraph;
import org.apache.flink.runtime.jobgraph.SavepointRestoreSettings;
import org.apache.flink.runtime.minicluster.MiniCluster;
import org.apache.flink.runtime.minicluster.MiniClusterConfiguration;
import org.apache.flink.streaming.api.graph.StreamGraph;import org.slf4j.Logger;
import org.slf4j.LoggerFactory;import javax.annotation.Nonnull;
import java.util.Map;/*** The LocalStreamEnvironment is a StreamExecutionEnvironment that runs the program locally,* multi-threaded, in the JVM where the environment is instantiated. It spawns an embedded* Flink cluster in the background and executes the program on that cluster.**
&＃64;Public
public class LocalStreamEnvironment extends StreamExecutionEnvironment {private static final Logger LOG &＃61; LoggerFactory.getLogger(LocalStreamEnvironment.class);private final Configuration configuration;private static final String LAST_CHECKPOINT &＃61; "last-checkpoint";/*** Creates a new mini cluster stream environment that uses the default configuration.*/public LocalStreamEnvironment() {this(new Configuration());}/*** Creates a new mini cluster stream environment that configures its local executor with the given configuration.** &＃64;param configuration The configuration used to configure the local executor.*/public LocalStreamEnvironment(&＃64;Nonnull Configuration configuration) {if (!ExecutionEnvironment.areExplicitEnvironmentsAllowed()) {throw new InvalidProgramException("The LocalStreamEnvironment cannot be used when submitting a program through a client, " &＃43;"or running in a TestEnvironment context.");}this.configuration &＃61; configuration;setParallelism(1);}protected Configuration getConfiguration() {return configuration;}/*** Executes the JobGraph of the on a mini cluster of CLusterUtil with a user* specified name.** &＃64;param jobName name of the job* &＃64;return The result of the job execution, containing elapsed time and accumulators.*/&＃64;Overridepublic JobExecutionResult execute(String jobName) throws Exception {// transform the streaming program into a JobGraphStreamGraph streamGraph &＃61; getStreamGraph();streamGraph.setJobName(jobName);JobGraph jobGraph &＃61; streamGraph.getJobGraph();jobGraph.setAllowQueuedScheduling(true);// ##############################################################################// 获取全局Job参数Map
}
上面魔改的代码部分思路是&＃xff1a;从Job的全局参数中拿到最后一个checkpoint的路径&＃xff0c;这个路径是我们传入进来的。然后通过jobGraph.setSavepointRestoreSettings(SavepointRestoreSettings.forPath(checkpointPath));设置到JobGraph中。
最后&＃xff0c;需要修改主程序&＃xff0c;让其自动获取最后一个checkpoint路径&＃xff0c;然后传入给Job全局参数&＃xff0c;添加代码如下&＃xff1a;
var params: ParameterTool &＃61; ParameterTool.fromArgs(args)val checkPointDirPath &＃61; params.get("checkpoint-dir")// 获取最后一个checkpoint文件夹val checkpointDirs &＃61; new io.Directory(new File(checkPointDirPath)).listif (checkpointDirs.nonEmpty) {val lastCheckpointDir &＃61; checkpointDirs.maxBy(_.lastModified)val checkpoints &＃61; new Directory(lastCheckpointDir.jfile).list.filter(_.name.startsWith("chk-"))if (checkpoints.nonEmpty) {val lastCheckpoint &＃61; checkpoints.maxBy(_.lastModified).pathval newArgs &＃61; Array("--last-checkpoint", "file://" &＃43; lastCheckpoint)// 重新载入配置params &＃61; ParameterTool.fromArgs(args &＃43;&＃43; newArgs)}}env.getConfig.setGlobalJobParameters(params)// ################################省略代码……// 设置状态后端&＃xff1a;MemoryStateBackend、FsStateBackend、RocksDBStateBackend&＃xff0c;这里设置基于文件的状态后端env.setStateBackend(new FsStateBackend("file://"&＃43;checkPointDirPath))
测试之前&＃xff0c;先清除已有checkpoint
rm -rf /tmp/checkpoints/event-counter
命令行执行nc -lk 9000
启动程序&＃xff0c;指定参数–checkpoint-dir /tmp/checkpoints/event-counter
先发送三条数据
event-1 1 1591695864473
event-1 12 1591695864474
event-2 8 1591695864475
重启应用
再发送三条数据
event-1 1 1591695864473
event-1 12 1591695864474
event-2 8 1591695864475
经过魔改后的LocalStreamEnvironment&＃xff0c;能够在程序启动时&＃xff0c;自动的从指定的checkpoint目录获取最近一次的提交任务的最新的checkpoint&＃xff0c;然后指定给JobGraph&＃xff0c;使我们的程序能够加载到之前的状态。这种方式只是为了在本地验证状态的可用性&＃xff0c;方便我们对状态进行调试&＃xff0c;有这种需求的同学&＃xff0c;不妨试一下&＃xff0c;代码已经提交到github上了&＃xff0c;另外有更好的方法&＃xff0c;可以一起交流。
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