Flink on YARN模式下TaskManager的内存分配探究
共 12532字，需浏览 26分钟
·
2021-05-11 00:29
我们使用如下的参数提交了Flink on YARN作业（per-job模式）。
/opt/flink-1.9.0/bin/flink run \
--detached \
--jobmanager yarn-cluster \
--yarnname "x.y.z" \
--yarnjobManagerMemory 2048 \
--yarntaskManagerMemory 4096 \
--yarnslots 2 \
--parallelism 20 \
--class x.y.z \
xyz-1.0.jar
该作业启动了10个TaskManager，并正常运行。来到该任务的Web界面，随便打开一个TaskManager页面，看看它的内存情况。
可见，虽然我们在参数中设置了TaskManager的内存为4GB大，但是上图显示的JVM堆大小只有2.47GB，另外还有一项“Flink Managed Memory”为1.78GB。在用VisualVM监控YarnTaskExecutorRunner时，会发现其JVM内存参数被如下设置：
显然Xmx+MaxDirectMemorySize才是我们在启动参数中设定的TM内存大小（4GB）。那么为什么会这样设置？“Flink Managed Memory”又是什么鬼？下面就来弄懂这些问题。
TaskManager内存布局如下图所示。
为了减少object overhead，Flink主要采用序列化的方式存储各种对象。序列化存储的最小单位叫做MemorySegment，底层为字节数组，大小由taskmanager.memory.segment-size参数指定，默认32KB大。下面分别介绍各块内存：
网络缓存（Network Buffer）：用于网络传输及与网络相关的动作（shuffle、广播等）的内存块，由MemorySegment组成。从Flink 1.5版本之后，网络缓存固定分配在堆外，这样可以充分利用零拷贝等技术。与它相关的三个参数及我们的设定值如下：
# 网络缓存占TM内存的默认比例，默认0.1
taskmanager.network.memory.fraction: 0.15
# 网络缓存的最小值和最大值 ，默认64MB和1GB
taskmanager.network.memory.min: 128mb
taskmanager.network.memory.max: 1gb
托管内存（Flink Managed Memory）：用于所有Flink内部算子逻辑的内存分配，以及中间数据的存储，同样由MemorySegment组成，并通过Flink的MemoryManager组件管理。它默认在堆内分配，如果开启堆外内存分配的开关，也可以在堆内、堆外同时分配。与它相关的两个参数如下：
# 堆内托管内存占TM堆内内存的比例，默认0.7
taskmanager.memory.fraction: 0.7
# 是否允许分配堆外托管内存，默认不允许
taskmanager.memory.off-heap: false
由此也可见，Flink的内存管理不像Spark一样区分Storage和Execution内存，而是直接合二为一，更加灵活。
空闲内存（Free）：虽然名为空闲，但实际上是存储用户代码和数据结构的，固定在堆内，可以理解为堆内内存除去托管内存后剩下的那部分。
如果我们想知道文章开头的问题中各块内存的大小是怎么来的，最好的办法自然是去读源码。下面以Flink 1.9.0源码为例来探索。
TaskManager内存分配逻辑
YARN per-job集群的启动入口位于o.a.f.yarn.YarnClusterDescriptor类中。
public ClusterClient<ApplicationId> deployJobCluster(
ClusterSpecification clusterSpecification,
JobGraph jobGraph,
boolean detached) throws ClusterDeploymentException {
// this is required because the slots are allocated lazily
jobGraph.setAllowQueuedScheduling(true);
try {
return deployInternal(
clusterSpecification,
"Flink per-job cluster",
getYarnJobClusterEntrypoint(),
jobGraph,
detached);
} catch (Exception e) {
throw new ClusterDeploymentException("Could not deploy Yarn job cluster.", e);
}
}
其中，ClusterSpecification对象持有该集群的4个基本参数：JobManager内存大小、TaskManager内存大小、TaskManager数量、每个TaskManager的slot数。而deployInternal()方法在开头调用了o.a.f.yarn.AbstractYarnClusterDescriptor抽象类的validateClusterSpecification()方法，用于校验ClusterSpecification是否合法。
private void validateClusterSpecification(ClusterSpecification clusterSpecification) throws FlinkException {
try {
final long taskManagerMemorySize = clusterSpecification.getTaskManagerMemoryMB();
// We do the validation by calling the calculation methods here
// Internally these methods will check whether the cluster can be started with the provided
// ClusterSpecification and the configured memory requirements
final long cutoff = ContaineredTaskManagerParameters.calculateCutoffMB(flinkConfiguration, taskManagerMemorySize);
TaskManagerServices.calculateHeapSizeMB(taskManagerMemorySize - cutoff, flinkConfiguration);
} catch (IllegalArgumentException iae) {
throw new FlinkException("Cannot fulfill the minimum memory requirements with the provided " +
"cluster specification. Please increase the memory of the cluster.", iae);
}
}
ClusterSpecification.getTaskManagerMemoryMB()方法返回的就是-ytm/--yarntaskManagerMemory参数设定的内存，最终反映在Flink代码中都是taskmanager.heap.size配置项的值。
接下来首先调用ContaineredTaskManagerParameters.calculateCutoffMB()方法，它负责计算一个承载TM的YARN Container需要预留多少内存给TM之外的逻辑来使用。
public static long calculateCutoffMB(Configuration config, long containerMemoryMB) {
Preconditions.checkArgument(containerMemoryMB > 0);
// (1) check cutoff ratio
final float memoryCutoffRatio = config.getFloat(
ResourceManagerOptions.CONTAINERIZED_HEAP_CUTOFF_RATIO);
if (memoryCutoffRatio >= 1 || memoryCutoffRatio <= 0) {
throw new IllegalArgumentException("The configuration value '"
+ ResourceManagerOptions.CONTAINERIZED_HEAP_CUTOFF_RATIO.key() + "' must be between 0 and 1. Value given="
+ memoryCutoffRatio);
}
// (2) check min cutoff value
final int minCutoff = config.getInteger(
ResourceManagerOptions.CONTAINERIZED_HEAP_CUTOFF_MIN);
if (minCutoff >= containerMemoryMB) {
throw new IllegalArgumentException("The configuration value '"
+ ResourceManagerOptions.CONTAINERIZED_HEAP_CUTOFF_MIN.key() + "'='" + minCutoff
+ "' is larger than the total container memory " + containerMemoryMB);
}
// (3) check between heap and off-heap
long cutoff = (long) (containerMemoryMB * memoryCutoffRatio);
if (cutoff < minCutoff) {
cutoff = minCutoff;
}
return cutoff;
}
该方法的执行流程如下：
获取containerized.heap-cutoff-ratio参数，它代表Container预留的非TM内存占设定的TM内存的比例，默认值0.25；
获取containerized.heap-cutoff-min参数，它代表Container预留的非TM内存的最小值，默认值600MB；
按比例计算预留内存，并保证结果不小于最小值。
由此可见，在Flink on YARN时，我们设定的TM内存实际上是Container的内存。也就是说，一个TM能利用的总内存（包含堆内和堆外）是：
tm_total_memory = taskmanager.heap.size - max[containerized.heap-cutoff-min, taskmanager.heap.size * containerized.heap-cutoff-ratio]
用文章开头给的参数实际计算一下：
tm_total_memory = 4096 - max[600, 4096 * 0.25] = 3072
接下来看TaskManagerServices.calculateHeapSizeMB()方法。
public static long calculateHeapSizeMB(long totalJavaMemorySizeMB, Configuration config) {
Preconditions.checkArgument(totalJavaMemorySizeMB > 0);
// all values below here are in bytes
final long totalProcessMemory = megabytesToBytes(totalJavaMemorySizeMB);
final long networkReservedMemory = getReservedNetworkMemory(config, totalProcessMemory);
final long heapAndManagedMemory = totalProcessMemory - networkReservedMemory;
if (config.getBoolean(TaskManagerOptions.MEMORY_OFF_HEAP)) {
final long managedMemorySize = getManagedMemoryFromHeapAndManaged(config, heapAndManagedMemory);
ConfigurationParserUtils.checkConfigParameter(managedMemorySize < heapAndManagedMemory, managedMemorySize,
TaskManagerOptions.MANAGED_MEMORY_SIZE.key(),
"Managed memory size too large for " + (networkReservedMemory >> 20) +
" MB network buffer memory and a total of " + totalJavaMemorySizeMB +
" MB JVM memory");
return bytesToMegabytes(heapAndManagedMemory - managedMemorySize);
}
else {
return bytesToMegabytes(heapAndManagedMemory);
}
}
为了简化问题及符合我们的实际应用，就不考虑开启堆外托管内存的情况了。这里涉及到了计算Network buffer大小的方法```
NettyShuffleEnvironmentConfiguration.calculateNetworkBufferMemory()。
public static long calculateNetworkBufferMemory(long totalJavaMemorySize, Configuration config) {
final int segmentSize = ConfigurationParserUtils.getPageSize(config);
final long networkBufBytes;
if (hasNewNetworkConfig(config)) {
float networkBufFraction = config.getFloat(NettyShuffleEnvironmentOptions.NETWORK_BUFFERS_MEMORY_FRACTION);
long networkBufSize = (long) (totalJavaMemorySize * networkBufFraction);
networkBufBytes = calculateNewNetworkBufferMemory(config, networkBufSize, totalJavaMemorySize);
} else {
// use old (deprecated) network buffers parameter
// 旧版逻辑，不再看了
}
return networkBufBytes;
}
private static long calculateNewNetworkBufferMemory(Configuration config, long networkBufSize, long maxJvmHeapMemory) {
float networkBufFraction = config.getFloat(NettyShuffleEnvironmentOptions.NETWORK_BUFFERS_MEMORY_FRACTION);
long networkBufMin = MemorySize.parse(config.getString(NettyShuffleEnvironmentOptions.NETWORK_BUFFERS_MEMORY_MIN)).getBytes();
long networkBufMax = MemorySize.parse(config.getString(NettyShuffleEnvironmentOptions.NETWORK_BUFFERS_MEMORY_MAX)).getBytes();
int pageSize = ConfigurationParserUtils.getPageSize(config);
checkNewNetworkConfig(pageSize, networkBufFraction, networkBufMin, networkBufMax);
long networkBufBytes = Math.min(networkBufMax, Math.max(networkBufMin, networkBufSize));
ConfigurationParserUtils.checkConfigParameter(/*...*/);
return networkBufBytes;
}
由此可见，网络缓存的大小这样确定：
network_buffer_memory = min[taskmanager.network.memory.max, max(askmanager.network.memory.min, tm_total_memory * taskmanager.network.memory.fraction)]
代入数值：
network_buffer_memory = min[1024, max(128, 3072 * 0.15)] = 460.8
也就是说，TM真正使用的堆内内存为：
tm_heap_memory = tm_total_memory - network_buffer_memory = 3072 - 460.8 ≈ 2611
这完全符合VisualVM截图中的-Xms/-Xmx设定。
同理，可以看一下TaskManager UI中的网络缓存MemorySegment计数。
通过计算得知，网络缓存的实际值与上面算出来的network_buffer_memory值是非常接近的。
那么堆内托管内存的值是怎么计算出来的呢？前面提到了托管内存由MemoryManager管理，来看看TaskManagerServices.createMemoryManager()方法，它用设定好的参数来初始化一个MemoryManager。
private static MemoryManager createMemoryManager(
TaskManagerServicesConfiguration taskManagerServicesConfiguration) throws Exception {
long configuredMemory = taskManagerServicesConfiguration.getConfiguredMemory();
MemoryType memType = taskManagerServicesConfiguration.getMemoryType();
final long memorySize;
boolean preAllocateMemory = taskManagerServicesConfiguration.isPreAllocateMemory();
if (configuredMemory > 0) {
if (preAllocateMemory) {
LOG.info(/*...*/);
} else {
LOG.info(/*...*/);
}
memorySize = configuredMemory << 20; // megabytes to bytes
} else {
// similar to #calculateNetworkBufferMemory(TaskManagerServicesConfiguration tmConfig)
float memoryFraction = taskManagerServicesConfiguration.getMemoryFraction();
if (memType == MemoryType.HEAP) {
long freeHeapMemoryWithDefrag = taskManagerServicesConfiguration.getFreeHeapMemoryWithDefrag();
// network buffers allocated off-heap -> use memoryFraction of the available heap:
long relativeMemSize = (long) (freeHeapMemoryWithDefrag * memoryFraction);
if (preAllocateMemory) {
LOG.info(/*...*/);
} else {
LOG.info(/*...*/);
}
memorySize = relativeMemSize;
} else if (memType == MemoryType.OFF_HEAP) {
long maxJvmHeapMemory = taskManagerServicesConfiguration.getMaxJvmHeapMemory();
// The maximum heap memory has been adjusted according to the fraction (see
// calculateHeapSizeMB(long totalJavaMemorySizeMB, Configuration config)), i.e.
// maxJvmHeap = jvmTotalNoNet - jvmTotalNoNet * memoryFraction = jvmTotalNoNet * (1 - memoryFraction)
// directMemorySize = jvmTotalNoNet * memoryFraction
long directMemorySize = (long) (maxJvmHeapMemory / (1.0 - memoryFraction) * memoryFraction);
if (preAllocateMemory) {
LOG.info(/*...*/);
} else {
LOG.info(/*...*/);
}
memorySize = directMemorySize;
} else {
throw new RuntimeException("No supported memory type detected.");
}
}
// now start the memory manager
final MemoryManager memoryManager;
try {
memoryManager = new MemoryManager(
memorySize,
taskManagerServicesConfiguration.getNumberOfSlots(),
taskManagerServicesConfiguration.getPageSize(),
memType,
preAllocateMemory);
} catch (OutOfMemoryError e) {
// ...
}
return memoryManager;
}
简要叙述一下流程：
获取taskmanager.memory.size参数，用来确定托管内存的绝对大小；
如果taskmanager.memory.size未设置，就继续获取前面提到过的taskmanager.memory.fraction参数；
只考虑堆内内存的情况，调用TaskManagerServicesConfiguration.getFreeHeapMemoryWithDefrag()方法，先主动触发GC，然后获取可用的堆内存量。可见，如果没有意外，程序初始化时该方法返回的值与前文的-Xms/-Xmx应该相同；
计算托管内存大小和其他参数，返回MemoryManager实例。
一般来讲我们都不会简单粗暴地设置taskmanager.memory.size。所以：
flink_managed_memory = tm_heap_memory * taskmanager.memory.fraction = 2611 * 0.7 ≈ 1827
这就是TaskManager UI中显示的托管内存大小了。
