Flink源码解析 | 从Example出发：理解Flink启动流程

从《Apache Flink本地部署》这篇文章中可以看到，我们启动集群都是通过脚本start-cluster.sh开始执行。
我们的源码解析之路就从flink的bash脚本入手。

start-cluster.sh
bin=`dirname "$0"`
bin=`cd "$bin"; pwd`

. "$bin"/config.sh

# Start the JobManager instance(s)
shopt -s nocasematch
if [[ $HIGH_AVAILABILITY == "zookeeper" ]]; then
# HA Mode
readMasters

echo "Starting HA cluster with ${#MASTERS[@]} masters."

for ((i=0;i<${#MASTERS[@]};++i)); do
master=${MASTERS[i]}
webuiport=${WEBUIPORTS[i]}

if [ ${MASTERS_ALL_LOCALHOST} = true ] ; then
"${FLINK_BIN_DIR}"/jobmanager.sh start "${master}" "${webuiport}"
else
ssh -n $FLINK_SSH_OPTS $master -- "nohup /bin/bash -l \"${FLINK_BIN_DIR}/jobmanager.sh\" start ${master} ${webuiport} &"
fi
done

else
echo "Starting cluster."

# Start single JobManager on this machine
"$FLINK_BIN_DIR"/jobmanager.sh start
fi
shopt -u nocasematch

# Start TaskManager instance(s)
TMSlaves start
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从代码上看，不管是启动HA模式，还是非HA模式，脚本都会调用jobmanager.sh

###jobmanager.sh

STARTSTOP=$1
...
ENTRYPOINT=standalonesession
...
if [[ $STARTSTOP == "start-foreground" ]]; then
exec "${FLINK_BIN_DIR}"/flink-console.sh $ENTRYPOINT "${args[@]}"
else
"${FLINK_BIN_DIR}"/flink-daemon.sh $STARTSTOP $ENTRYPOINT "${args[@]}"
fi
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这里的STARTSTOP=$1，其实就是STARTSTOP=start，所以这里会走flink-daemon.sh脚本这条线。

###flink-daemon.sh脚本
核心分为以下三个代码块

STARTSTOP=$1
DAEMON=$2
ARGS=("${@:3}") # get remaining arguments as array

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从脚本的使用定义来看flink-daemon.sh可以启动taskexecutor|zookeeper|historyserver|standalonesession|standalonejob
这里的DAEMON=$2从上一个脚本传递的值可以的值DAEMON=standalonesession

case $DAEMON in
(taskexecutor)
CLASS_TO_RUN=org.apache.flink.runtime.taskexecutor.TaskManagerRunner
;;

(zookeeper)
CLASS_TO_RUN=org.apache.flink.runtime.zookeeper.FlinkZooKeeperQuorumPeer
;;

(historyserver)
CLASS_TO_RUN=org.apache.flink.runtime.webmonitor.history.HistoryServer
;;

(standalonesession)
CLASS_TO_RUN=org.apache.flink.runtime.entrypoint.StandaloneSessionClusterEntrypoint
;;

(standalonejob)
CLASS_TO_RUN=org.apache.flink.container.entrypoint.StandaloneJobClusterEntryPoint
;;

(*)
echo "Unknown daemon '${DAEMON}'. $USAGE."
exit 1
;;
esac
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根据$DAEMON来指定所需要运行的类名称，这里是
CLASS_TO_RUN=org.apache.flink.runtime.entrypoint.StandaloneSessionClust

case $STARTSTOP in

(start)
# Rotate log files
rotateLogFilesWithPrefix "$FLINK_LOG_DIR" "$FLINK_LOG_PREFIX"

# Print a warning if daemons are already running on host
if [ -f "$pid" ]; then
active=()
while IFS='' read -r p || [[ -n "$p" ]]; do
kill -0 $p >/dev/null 2>&1
if [ $? -eq 0 ]; then
active+=($p)
fi
done < "${pid}"

count="${#active[@]}"

if [ ${count} -gt 0 ]; then
echo "[INFO] $count instance(s) of $DAEMON are already running on $HOSTNAME."
fi
fi

# Evaluate user options for local variable expansion
FLINK_ENV_JAVA_OPTS=$(eval echo ${FLINK_ENV_JAVA_OPTS})

echo "Starting $DAEMON daemon on host $HOSTNAME."
$JAVA_RUN $JVM_ARGS ${FLINK_ENV_JAVA_OPTS} "${log_setting[@]}" -classpath "`manglePathList "$FLINK_TM_CLASSPATH:$INTERNAL_HADOOP_CLASSPATHS"`" ${CLASS_TO_RUN} "${ARGS[@]}" > "$out" 200<&- 2>&1 < /dev/null &

mypid=$!

# Add to pid file if successful start
if [[ ${mypid} =~ ${IS_NUMBER} ]] && kill -0 $mypid > /dev/null 2>&1 ; then
echo $mypid >> "$pid"
else
echo "Error starting $DAEMON daemon."
exit 1
fi
;;
...
esac
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case $STARTSTOP in程序块中包含三个功能：start、stop、stop-all。
我们这里通过start功能进行启动

StandaloneSessionClusterEntrypoint.java
public class StandaloneSessionClusterEntrypoint extends SessionClusterEntrypoint {

public StandaloneSessionClusterEntrypoint(Configuration configuration) {
super(configuration);
}

@Override
protected DispatcherResourceManagerComponentFactory<?> createDispatcherResourceManagerComponentFactory(Configuration configuration) {
return new SessionDispatcherResourceManagerComponentFactory(StandaloneResourceManagerFactory.INSTANCE);
}

public static void main(String[] args) {
// startup checks and logging
EnvironmentInformation.logEnvironmentInfo(LOG, StandaloneSessionClusterEntrypoint.class.getSimpleName(), args);
SignalHandler.register(LOG);
JvmShutdownSafeguard.installAsShutdownHook(LOG);

EntrypointClusterConfiguration entrypointClusterConfiguration = null;
final CommandLineParser<EntrypointClusterConfiguration> commandLineParser = new CommandLineParser<>(new EntrypointClusterConfigurationParserFactory());

try {
entrypointClusterConfiguration = commandLineParser.parse(args);
} catch (FlinkParseException e) {
LOG.error("Could not parse command line arguments {}.", args, e);
commandLineParser.printHelp(StandaloneSessionClusterEntrypoint.class.getSimpleName());
System.exit(1);
}

Configuration configuration = loadConfiguration(entrypointClusterConfiguration);

StandaloneSessionClusterEntrypoint entrypoint = new StandaloneSessionClusterEntrypoint(configuration);

ClusterEntrypoint.runClusterEntrypoint(entrypoint);
}
}

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StandaloneSessionClusterEntrypoint的main方法的执行流程如下：

通过commandLineParser对象解析参数信息
loadConfiguration加载配置
通过配置实例化StandaloneSessionClusterEntrypoint对象
最终通过ClusterEntrypoint的runClusterEntrypoint方法运行StandaloneSessionClusterEntrypoint实例
ClusterEntrypoint.java
private void runCluster(Configuration configuration) throws Exception {
synchronized (lock) {
initializeServices(configuration);

// write host information into configuration
configuration.setString(JobManagerOptions.ADDRESS, commonRpcService.getAddress());
configuration.setInteger(JobManagerOptions.PORT, commonRpcService.getPort());

final DispatcherResourceManagerComponentFactory<?> dispatcherResourceManagerComponentFactory = createDispatcherResourceManagerComponentFactory(configuration);

clusterComponent = dispatcherResourceManagerComponentFactory.create(
configuration,
commonRpcService,
haServices,
blobServer,
heartbeatServices,
metricRegistry,
archivedExecutionGraphStore,
new AkkaQueryServiceRetriever(
metricQueryServiceActorSystem,
Time.milliseconds(configuration.getLong(WebOptions.TIMEOUT))),
this);

clusterComponent.getShutDownFuture().whenComplete(
(ApplicationStatus applicationStatus, Throwable throwable) -> {
if (throwable != null) {
shutDownAsync(
ApplicationStatus.UNKNOWN,
ExceptionUtils.stringifyException(throwable),
false);
} else {
// This is the general shutdown path. If a separate more specific shutdown was
// already triggered, this will do nothing
shutDownAsync(
applicationStatus,
null,
true);
}
});
}
}
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runCluster方法中流程如下：

protected void initializeServices(Configuration configuration) throws Exception {

LOG.info("Initializing cluster services.");

synchronized (lock) {
final String bindAddress = configuration.getString(JobManagerOptions.ADDRESS);
final String portRange = getRPCPortRange(configuration);

commonRpcService = createRpcService(configuration, bindAddress, portRange);

// update the configuration used to create the high availability services
configuration.setString(JobManagerOptions.ADDRESS, commonRpcService.getAddress());
configuration.setInteger(JobManagerOptions.PORT, commonRpcService.getPort());

haServices = createHaServices(configuration, commonRpcService.getExecutor());
blobServer = new BlobServer(configuration, haServices.createBlobStore());
blobServer.start();
heartbeatServices = createHeartbeatServices(configuration);
metricRegistry = createMetricRegistry(configuration);

// TODO: This is a temporary hack until we have ported the MetricQueryService to the new RpcEndpoint
// Start actor system for metric query service on any available port
metricQueryServiceActorSystem = MetricUtils.startMetricsActorSystem(configuration, bindAddress, LOG);
metricRegistry.startQueryService(metricQueryServiceActorSystem, null);

archivedExecutionGraphStore = createSerializableExecutionGraphStore(configuration, commonRpcService.getScheduledExecutor());

transientBlobCache = new TransientBlobCache(
configuration,
new InetSocketAddress(
commonRpcService.getAddress(),
blobServer.getPort()));
}
}
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initializeServices(configuration); 初始化服务
创建RPC服务
创建HA服务
创建blob服务
创建心跳服务
创建metrice注册
创建ActorSystem
创建ArchivedExecutionGraphStore
创建TransientBlobCache
创建dispatcherResourceManagerComponentFactory对象
dispatcherResourceManagerComponentFactory的create方法参数中我们可以看到很多服务相关的信息：
configuration,
commonRpcService,
haServices,
blobServer,
heartbeatServices,
metricRegistry,
archivedExecutionGraphStore,
AkkaQueryServiceRetriever
这块主要是Flink UI上所要展示的内容相关信息。

总结
到这里我们就了解了Apache Flink是如何通过start-cluster.sh脚本执行到最后的程序运行启动的全流程。从下一篇文章开始会根据ScoketWindowWorkCount这个示例，开始讲解任务运行后相关的一些内容。
————————————————
版权声明：本文为CSDN博主「Jonathan-Wei」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/WeiJonathan/article/details/84979831

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