Hi Dear All, 请教各位一个问题，下面是我的集群配置： 1、我现在使用的是flink1.12版本； 2、基于CDH6.3.2搭建的hadoop三个节点的集群，使用CDH自带的yarn集群； 3、flink运行模式：Per-Job Cluster on yarn（三个节点，没每个节点48核64G内存）; 4、以下是我三个节点的 flink-conf.yaml 的配置，三个flink节点除了jobmanager.rpc.address不同外其它配置都一样：
#============================================================================== # Common 通用设置选项 #============================================================================== jobmanager.rpc.address: cdh1 # The RPC port where the JobManager is reachable. jobmanager.rpc.port: 6123 # The total process memory size for the JobManager. # # Note this accounts for all memory usage within the JobManager process, including JVM metaspace and other overhead. jobmanager.memory.process.size: 2048m # The total process memory size for the TaskManager. # Note this accounts for all memory usage within the TaskManager process, including JVM metaspace and other overhead. taskmanager.memory.process.size: 6144m # To exclude JVM metaspace and overhead, please, use total Flink memory size instead of 'taskmanager.memory.process.size'. # It is not recommended to set both 'taskmanager.memory.process.size' and Flink memory. # taskmanager.memory.flink.size: 1280m # The number of task slots that each TaskManager offers. Each slot runs one parallel pipeline. #TaskManager提供的插槽数（默认值：1）。每个插槽可以执行一项任务或管道。TaskManager中具有多个插槽可以帮助 #分摊跨并行任务或管道的某些恒定开销（JVM，应用程序库或网络连接的开销） taskmanager.numberOfTaskSlots: 1 # The parallelism used for programs that did not specify and other parallelism. #当未在任何地方指定并行度时使用的默认并行性（默认值：1） parallelism.default: 1 #添加如下配置，指定taskmananger的地址，如果是单机部署，指定localhost #taskmanager.host: 0.0.0.0 # The default file system scheme and authority. # By default file paths without scheme are interpreted relative to the local # root file system 'file:///'. Use this to override the default and interpret # relative paths relative to a different file system, # for example 'hdfs://mynamenode:12345' # # fs.default-scheme #============================================================================== # High Availability #============================================================================== # The high-availability mode. Possible options are 'NONE' or 'zookeeper'. high-availability: zookeeper # The path where metadata for master recovery is persisted. While ZooKeeper stores # the small ground truth for checkpoint and leader election, this location stores # the larger objects, like persisted dataflow graphs. # Must be a durable file system that is accessible from all nodes # (like HDFS, S3, Ceph, nfs, ...) high-availability.storageDir: hdfs:///flink/ha/ # The list of ZooKeeper quorum peers that coordinate the high-availability # setup. This must be a list of the form: # "host1:clientPort,host2:clientPort,..." (default clientPort: 2181) high-availability.zookeeper.quorum: cdh1:2181,cdh2:2181,cdh3:2181 high-availability.zookeeper.client.acl: open high-availability.zookeeper.path.root: /flink #============================================================================== # Fault tolerance、checkpointing and state backends 容错能力、检查点和状态后端 #============================================================================== state.backend: rocksdb #选择状态后端是否应创建增量检查点默认false，如果可能对于增量检查点，仅存储与前一个检查点的差异， #而不存储完整的检查点状态。启用后，显示在Web UI中或从rest API获取的状态大小仅代表增量检查点大小， #而不是完整的检查点大小。某些状态后端可能不支持增量检查点，因此会忽略此选项 state.backend.incremental: true #是否为状态后端配置本地恢复。默认情况下，本地恢复处于禁用状态。本地恢复当前仅涵盖键控状态后端。当前，MemoryStateBackend不支持本地恢复 state.backend.local-recovery: true #RocksDB中数据块的缓存数量，单位比特。RocksDB的默认块缓存大小为“ 8MB” state.backend.rocksdb.block.cache-size: 268435456 #这确定了计时器服务状态实现的工厂。对于基于RocksDB的实现，选项可以是HEAP（基于堆）或ROCKSDB state.backend.rocksdb.timer-service.factory: HEAP # Directory for checkpoints filesystem, when using any of the default bundled # state backends. 用于在Flink支持的文件系统中存储检查点的数据文件和元数据的默认目录 state.checkpoints.dir: hdfs:///flink/flink-checkpoints # Default target directory for savepoints, optional. #保存点的默认目录。由状态后端用于将保存点写入文件系统 state.savepoints.dir: hdfs:///flink/flink-savepoints # 要保留的最大已完成检查点数 state.checkpoints.num-retained: 3 #此选项指定作业计算如何从任务失败中恢复。可接受的值为： #'full'：重新启动所有任务以恢复作业。 #“region”：重新启动可能受任务故障影响的所有任务。可以在此处找到更多详细信息。 jobmanager.execution.failover-strategy: region #============================================================================== # Advanced #============================================================================== # Override the directories for temporary files. If not specified, the # system-specific Java temporary directory (java.io.tmpdir property) is taken. # # For framework setups on Yarn or Mesos, Flink will automatically pick up the # containers' temp directories without any need for configuration. # # Add a delimited list for multiple directories, using the system directory # delimiter (colon ':' on unix) or a comma, e.g.: # /data1/tmp:/data2/tmp:/data3/tmp # # Note: Each directory entry is read from and written to by a different I/O # thread. You can include the same directory multiple times in order to create # multiple I/O threads against that directory. This is for example relevant for # high-throughput RAIDs. # # io.tmp.dirs: /tmp # The classloading resolve order. Possible values are 'child-first' (Flink's default) # and 'parent-first' (Java's default). # # Child first classloading allows users to use different dependency/library # versions in their application than those in the classpath. Switching back # to 'parent-first' may help with debugging dependency issues. # # classloader.resolve-order: child-first # The amount of memory going to the network stack. These numbers usually need # no tuning. Adjusting them may be necessary in case of an "Insufficient number # of network buffers" error. The default min is 64MB, the default max is 1GB. # # taskmanager.memory.network.fraction: 0.1 # taskmanager.memory.network.min: 64mb # taskmanager.memory.network.max: 1gb #============================================================================== # YARN Configuration #============================================================================== #ApplicationMaster重新启动的次数。默认情况下，该值将设置为1。如果启用了高可用性，则默认值将为2。 #重新启动次数也受YARN限制（通过yarn.resourcemanager.am.max-attempts配置）。请注意，整个Flink群集将重新启动，并且YARN Client将失去连接 yarn.application-attempts: 10 #yarn.container-start-command-template: %java% %jvmmem% %jvmopts% -DyarnContainerId=$CONTAINER_ID %logging% %class% %args% %redirects% #yarn.maximum-failed-containers: 100 #yarn.tags: flink #============================================================================== # HistoryServer #============================================================================== heartbeat.timeout: 1800000 请教的问题： 通过 ./bin/flink run \ -d -t yarn-per-job \ -yjm 1536 \ -ytm 3072 \ -yD jobmanager.memory.process.size=1.5GB \ -yD taskmanager.memory.process.size=3GB \ -yD heartbeat.timeout=1800000 \ /opt/flink-1.12.0/examples/myProject/bi-wxfx-fqd-1.0.0.jar 这个命令提交运行flink job之后 命令中指定的内存参数没有被使用，在flink webUI里面观察到的使用内存是 flink-conf.yaml 里面配置的大小，cli命令指定的并未起作用，是我使用的不正确吗？ 祝好！ 刘海 | | 刘海 | | liuha...@163.com | 签名由网易邮箱大师定制
