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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
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
这里的STARTSTOP=
flink-daemon.sh脚本
核心分为以下三个代码块
# Start/stop a Flink daemon.
USAGE="Usage: flink-daemon.sh (start|stop|stop-all) (taskexecutor|zookeeper|historyserver|standalonesession|standalonejob) [args]"
STARTSTOP=$1
DAEMON=$2
ARGS=("${@:3}") # get remaining arguments as array
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=
1 ， 其 实 就 是 S T A R T S T O P = s t a r t ， 所 以 这 里 会 走 f l i n k − d a e m o n . s h 脚 本 这 条 线 。 ¨ K 1 2 K 核 心 分 为 以 下 三 个 代 码 块 ¨ G 2 G 从 脚 本 的 使 用 定 义 来 看 f l i n k − d a e m o n . s h 可 以 启 动 t a s k e x e c u t o r ∣ z o o k e e p e r ∣ h i s t o r y s e r v e r ∣ s t a n d a l o n e s e s s i o n ∣ s t a n d a l o n e j o b 这 里 的 D A E M O N =
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
根据
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
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);
}
}
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);
}
});
}
}
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()));
}
}
-
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。
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