flink - accumulator
读accumlator
JobManager
在job finish的时候会汇总accumulator的值,
newJobStatus match {
case JobStatus.FINISHED =>
try {
val accumulatorResults = executionGraph.getAccumulatorsSerialized()
val result = new SerializedJobExecutionResult(
jobID,
jobInfo.duration,
accumulatorResults)
jobInfo.client ! decorateMessage(JobResultSuccess(result))
}
在client请求accumulation时,
public Map<String, Object> getAccumulators(JobID jobID, ClassLoader loader) throws Exception {
ActorGateway jobManagerGateway = getJobManagerGateway();
Future<Object> response;
try {
response = jobManagerGateway.ask(new RequestAccumulatorResults(jobID), timeout);
} catch (Exception e) {
throw new Exception("Failed to query the job manager gateway for accumulators.", e);
}
消息传到job manager
case message: AccumulatorMessage => handleAccumulatorMessage(message)
private def handleAccumulatorMessage(message: AccumulatorMessage): Unit = {
message match {
case RequestAccumulatorResults(jobID) =>
try {
currentJobs.get(jobID) match {
case Some((graph, jobInfo)) =>
val accumulatorValues = graph.getAccumulatorsSerialized()
sender() ! decorateMessage(AccumulatorResultsFound(jobID, accumulatorValues))
case None =>
archive.forward(message)
}
}
ExecuteGraph
获取accumulator的值
/**
* Gets a serialized accumulator map.
* @return The accumulator map with serialized accumulator values.
* @throws IOException
*/
public Map<String, SerializedValue<Object>> getAccumulatorsSerialized() throws IOException { Map<String, Accumulator<?, ?>> accumulatorMap = aggregateUserAccumulators(); Map<String, SerializedValue<Object>> result = new HashMap<String, SerializedValue<Object>>();
for (Map.Entry<String, Accumulator<?, ?>> entry : accumulatorMap.entrySet()) {
result.put(entry.getKey(), new SerializedValue<Object>(entry.getValue().getLocalValue()));
} return result;
}
execution的accumulator聚合,
/**
* Merges all accumulator results from the tasks previously executed in the Executions.
* @return The accumulator map
*/
public Map<String, Accumulator<?,?>> aggregateUserAccumulators() { Map<String, Accumulator<?, ?>> userAccumulators = new HashMap<String, Accumulator<?, ?>>(); for (ExecutionVertex vertex : getAllExecutionVertices()) {
Map<String, Accumulator<?, ?>> next = vertex.getCurrentExecutionAttempt().getUserAccumulators();
if (next != null) {
AccumulatorHelper.mergeInto(userAccumulators, next);
}
} return userAccumulators;
}
具体merge的逻辑,
public static void mergeInto(Map<String, Accumulator<?, ?>> target, Map<String, Accumulator<?, ?>> toMerge) {
for (Map.Entry<String, Accumulator<?, ?>> otherEntry : toMerge.entrySet()) {
Accumulator<?, ?> ownAccumulator = target.get(otherEntry.getKey());
if (ownAccumulator == null) {
// Create initial counter (copy!)
target.put(otherEntry.getKey(), otherEntry.getValue().clone());
}
else {
// Both should have the same type
AccumulatorHelper.compareAccumulatorTypes(otherEntry.getKey(),
ownAccumulator.getClass(), otherEntry.getValue().getClass());
// Merge target counter with other counter
mergeSingle(ownAccumulator, otherEntry.getValue());
}
}
}
更新accumulator
JobManager
收到task发来的heartbeat,其中附带accumulators
case Heartbeat(instanceID, metricsReport, accumulators) =>
updateAccumulators(accumulators)
根据jobid,更新到ExecutionGraph
private def updateAccumulators(accumulators : Seq[AccumulatorSnapshot]) = {
accumulators foreach {
case accumulatorEvent =>
currentJobs.get(accumulatorEvent.getJobID) match {
case Some((jobGraph, jobInfo)) =>
future {
jobGraph.updateAccumulators(accumulatorEvent)
}(context.dispatcher)
case None =>
// ignore accumulator values for old job
}
}
}
根据ExecutionAttemptID, 更新Execution中
/**
* Updates the accumulators during the runtime of a job. Final accumulator results are transferred
* through the UpdateTaskExecutionState message.
* @param accumulatorSnapshot The serialized flink and user-defined accumulators
*/
public void updateAccumulators(AccumulatorSnapshot accumulatorSnapshot) {
Map<AccumulatorRegistry.Metric, Accumulator<?, ?>> flinkAccumulators;
Map<String, Accumulator<?, ?>> userAccumulators;
try {
flinkAccumulators = accumulatorSnapshot.deserializeFlinkAccumulators();
userAccumulators = accumulatorSnapshot.deserializeUserAccumulators(userClassLoader); ExecutionAttemptID execID = accumulatorSnapshot.getExecutionAttemptID();
Execution execution = currentExecutions.get(execID);
if (execution != null) {
execution.setAccumulators(flinkAccumulators, userAccumulators);
}
}
}
对于execution,只要状态不是结束,就直接更新
/**
* Update accumulators (discarded when the Execution has already been terminated).
* @param flinkAccumulators the flink internal accumulators
* @param userAccumulators the user accumulators
*/
public void setAccumulators(Map<AccumulatorRegistry.Metric, Accumulator<?, ?>> flinkAccumulators,
Map<String, Accumulator<?, ?>> userAccumulators) {
synchronized (accumulatorLock) {
if (!state.isTerminal()) {
this.flinkAccumulators = flinkAccumulators;
this.userAccumulators = userAccumulators;
}
}
}
再看TaskManager如何更新accumulator,并发送heartbeat,
/**
* Sends a heartbeat message to the JobManager (if connected) with the current
* metrics report.
*/
protected def sendHeartbeatToJobManager(): Unit = {
try {
val metricsReport: Array[Byte] = metricRegistryMapper.writeValueAsBytes(metricRegistry) val accumulatorEvents =
scala.collection.mutable.Buffer[AccumulatorSnapshot]() runningTasks foreach {
case (execID, task) =>
val registry = task.getAccumulatorRegistry
val accumulators = registry.getSnapshot
accumulatorEvents.append(accumulators)
} currentJobManager foreach {
jm => jm ! decorateMessage(Heartbeat(instanceID, metricsReport, accumulatorEvents))
}
}
}
可以看到会把每个running task的accumulators放到accumulatorEvents,然后通过Heartbeat消息发出
而task的accumlators是通过,task.getAccumulatorRegistry.getSnapshot得到
看看
AccumulatorRegistry
/**
* Main accumulator registry which encapsulates internal and user-defined accumulators.
*/
public class AccumulatorRegistry { protected static final Logger LOG = LoggerFactory.getLogger(AccumulatorRegistry.class); protected final JobID jobID; //accumulators所属的Job
protected final ExecutionAttemptID taskID; //taskID /* Flink's internal Accumulator values stored for the executing task. */
private final Map<Metric, Accumulator<?, ?>> flinkAccumulators = //内部的Accumulators
new HashMap<Metric, Accumulator<?, ?>>(); /* User-defined Accumulator values stored for the executing task. */
private final Map<String, Accumulator<?, ?>> userAccumulators = new HashMap<>(); //用户定义的Accumulators /* The reporter reference that is handed to the reporting tasks. */
private final ReadWriteReporter reporter; /**
* Creates a snapshot of this accumulator registry.
* @return a serialized accumulator map
*/
public AccumulatorSnapshot getSnapshot() {
try {
return new AccumulatorSnapshot(jobID, taskID, flinkAccumulators, userAccumulators);
} catch (IOException e) {
LOG.warn("Failed to serialize accumulators for task.", e);
return null;
}
}
}
snapshot的逻辑也很简单,
public AccumulatorSnapshot(JobID jobID, ExecutionAttemptID executionAttemptID,
Map<AccumulatorRegistry.Metric, Accumulator<?, ?>> flinkAccumulators,
Map<String, Accumulator<?, ?>> userAccumulators) throws IOException {
this.jobID = jobID;
this.executionAttemptID = executionAttemptID;
this.flinkAccumulators = new SerializedValue<Map<AccumulatorRegistry.Metric, Accumulator<?, ?>>>(flinkAccumulators);
this.userAccumulators = new SerializedValue<Map<String, Accumulator<?, ?>>>(userAccumulators);
}
最后,我们如何将统计数据累加到Accumulator上的?
直接看看Flink内部的Accumulator是如何更新的,都是通过这个reporter来更新的
/**
* Accumulator based reporter for keeping track of internal metrics (e.g. bytes and records in/out)
*/
private static class ReadWriteReporter implements Reporter { private LongCounter numRecordsIn = new LongCounter();
private LongCounter numRecordsOut = new LongCounter();
private LongCounter numBytesIn = new LongCounter();
private LongCounter numBytesOut = new LongCounter(); private ReadWriteReporter(Map<Metric, Accumulator<?,?>> accumulatorMap) {
accumulatorMap.put(Metric.NUM_RECORDS_IN, numRecordsIn);
accumulatorMap.put(Metric.NUM_RECORDS_OUT, numRecordsOut);
accumulatorMap.put(Metric.NUM_BYTES_IN, numBytesIn);
accumulatorMap.put(Metric.NUM_BYTES_OUT, numBytesOut);
} @Override
public void reportNumRecordsIn(long value) {
numRecordsIn.add(value);
} @Override
public void reportNumRecordsOut(long value) {
numRecordsOut.add(value);
} @Override
public void reportNumBytesIn(long value) {
numBytesIn.add(value);
} @Override
public void reportNumBytesOut(long value) {
numBytesOut.add(value);
}
}
何处调用到这个report的接口,
对于in, 在反序列化到record的时候会统计Bytesin和Recordsin
AdaptiveSpanningRecordDeserializer
public DeserializationResult getNextRecord(T target) throws IOException {
// check if we can get a full length;
if (nonSpanningRemaining >= 4) {
int len = this.nonSpanningWrapper.readInt();
if (reporter != null) {
reporter.reportNumBytesIn(len);
}
if (len <= nonSpanningRemaining - 4) {
// we can get a full record from here
target.read(this.nonSpanningWrapper);
if (reporter != null) {
reporter.reportNumRecordsIn(1);
}
所以对于out,反之则序列化的时候写入
SpanningRecordSerializer
@Override
public SerializationResult addRecord(T record) throws IOException {
int len = this.serializationBuffer.length();
this.lengthBuffer.putInt(0, len); if (reporter != null) {
reporter.reportNumBytesOut(len);
reporter.reportNumRecordsOut(1);
}
使用accumulator时,需要首先extends RichFunction by callinggetRuntimeContext().addAccumulator
flink - accumulator的更多相关文章
- Flink DataSet API Programming Guide
https://ci.apache.org/projects/flink/flink-docs-release-0.10/apis/programming_guide.html Example ...
- Flink Program Guide (1) -- 基本API概念(Basic API Concepts -- For Java)
false false false false EN-US ZH-CN X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-n ...
- Apache Flink Quickstart
Apache Flink 是新一代的基于 Kappa 架构的流处理框架,近期底层部署结构基于 FLIP-6 做了大规模的调整,我们来看一下在新的版本(1.6-SNAPSHOT)下怎样从源码快速编译执行 ...
- Flink学习(三)状态机制于容错机制,State与CheckPoint
摘自Apache官网 一.State的基本概念 什么叫State?搜了一把叫做状态机制.可以用作以下用途.为了保证 at least once, exactly once,Flink引入了State和 ...
- Flink – WindowedStream
在WindowedStream上可以执行,如reduce,aggregate,min,max等操作 关键是要理解windowOperator对KVState的运用,因为window是用它来存储wind ...
- Flink 中的kafka何时commit?
https://ci.apache.org/projects/flink/flink-docs-release-1.6/internals/stream_checkpointing.html @Ove ...
- Flink 部署文档
Flink 部署文档 1 先决条件 2 下载 Flink 二进制文件 3 配置 Flink 3.1 flink-conf.yaml 3.2 slaves 4 将配置好的 Flink 分发到其他节点 5 ...
- 聊聊flink的Async I/O
// This example implements the asynchronous request and callback with Futures that have the // inter ...
- Flink学习笔记:Flink API 通用基本概念
本文为<Flink大数据项目实战>学习笔记,想通过视频系统学习Flink这个最火爆的大数据计算框架的同学,推荐学习课程: Flink大数据项目实战:http://t.cn/EJtKhaz ...
随机推荐
- PHPCMS V9静态化HTML生成设置及URL规则优化
先讲讲Phpcms V9在后台怎么设置生成静态化HTML,之后再讲解怎么自定义URL规则,进行URL地址优化.在这一篇中,伪静态就不涉及了,大家可以移步到Phpcms V9全站伪静态设置方法. 一.静 ...
- 如何在多模型的情况下进行EF6的结构迁移
所谓多模型就是在一个数据库中包含两个不同模型,或者换句话说就是两个不同DbContext的数据都放到同一个数据库中.这里的多模型不是指多租户的数据库(有谁知道EF很好处理多租户数据库的方案,可以联系我 ...
- Struts2标签实现for循环
感悟:但是不建议使用这种方法,按照MVC框架的思想 ,应该把业务更多放在后台.前台尽量只进行数据展示. 转自:http://blog.csdn.net/guandajian/article/detai ...
- JSP页面中的pageEncoding和contentType两种属性
关于JSP页面中的pageEncoding和contentType两种属性的区别: pageEncoding是jsp文件本身的编码 contentType的charset是指服务器发送给客户端时的内容 ...
- Kinect学习笔记(六)——深度数据测量技术及应用
一.Kinect视角场 1.43°垂直方向和57°水平方向可视范围. 2.视角场常量值定义 属性 描述 Format 获取或设置深度图像格式 MaxDepth 获取最大深度值 MinDepth 获取最 ...
- SQL Server连接数据库失败,可能的问题!
SQL Server Configuration Manager中启动服务 SQL Server外围应用配置器中,打开远程IP连接属性 别的应该没什么问题了!
- helpDB
using System; using System.Collections.Generic; using System.Linq; using System.Web; using System.Da ...
- CodeForces 656B
C - C Time Limit:2000MS Memory Limit:65536KB 64bit IO Format:%I64d & %I64u Submit Status ...
- Mac terminal 解压压缩
tar 解包:tar xvf FileName.tar打包:tar cvf FileName.tar DirName(注:tar是打包,不是压缩!)———————————————.gz解压1:gunz ...
- HTMl5/CSS3/Javascript 学习推荐资源
HTMl5/CSS3/Javascript 学习推荐资源 前端的定义应该是数据内容的展示,在国内大家都觉得前端只是HTML+CSS+Javascript,但是实际上与展示有关的都是前端,所以Ruby/ ...