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本地单机模式,一般用于测试环境是否搭建成功,很少使用
Flink自带集群,开发测试使用
Flink自带集群,用于开发测试
计算资源统一交给hadoop的yarn进行管理,用于生产环境
点击:
点击下载:
找到安装包,并上传:
上传成功:
tar xzvf flink-1.16.1-bin-scala_2.12.tgz -C /export/servers/
进入 Servers 目录下:
进入 Flink 目录下:
进入 bin 目录下:
需要flink的版本是1.12及以下的版本,在高版本中 scala shell 被舍去了。
上传文件
上传成功:
解压
启动 shell
bin/start-scala-shell.sh local
benv.readTextFile("/root/a.txt").flatMap(_.split(" ")).map((_,1)).groupBy(0).sum(1).print()
输入 :quit 或者 Ctrl + d
启动集群并查看进程
启动失败,需要修改/etc/hosts文件,添加localhost的定义
若直接添加
192.168.92.128 localhost
在启动 Hbase时会出现如下错误
修改完成后,启动成功:
出现错误:org.apache.flink.client.program.ProgramInvocationException: The main method caused an error: java.util.concurrent.ExecutionException: org.apache.flink.runtime.client.JobSubmissionException: Failed to submit JobGraph.
原因:没有启动Flink集群
启动集群:
运行成功:
执行成功后,在/root目录下出现 output 目录
运行结果
点击任务
Flink程序提交任务到 JobClient ,JobClient 提交任务到 JobManager【Master】,JobManager 分发任务给TaskManager,TaskManager执行任务,执行任务后发送状态给 JobManager,JobManager 将结果返回到 JobClient 。
服务器 | JobManager | TaskManager |
---|---|---|
hadoop001 | ✅ | ✅ |
hadoop002 | ❎ | ✅ |
hadoop003 | ❎ | ✅ |
同上
同上
################################################################################ # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ################################################################################ #============================================================================== # Common #============================================================================== # The external address of the host on which the JobManager runs and can be # reached by the TaskManagers and any clients which want to connect. This setting # is only used in Standalone mode and may be overwritten on the JobManager side # by specifying the --host <hostname> parameter of the bin/jobmanager.sh executable. # In high availability mode, if you use the bin/start-cluster.sh script and setup # the conf/masters file, this will be taken care of automatically. Yarn # automatically configure the host name based on the hostname of the node where the # JobManager runs. jobmanager.rpc.address: hadoop001 # The RPC port where the JobManager is reachable. jobmanager.rpc.port: 6123 # The host interface the JobManager will bind to. By default, this is localhost, and will prevent # the JobManager from communicating outside the machine/container it is running on. # On YARN this setting will be ignored if it is set to 'localhost', defaulting to 0.0.0.0. # On Kubernetes this setting will be ignored, defaulting to 0.0.0.0. # # To enable this, set the bind-host address to one that has access to an outside facing network # interface, such as 0.0.0.0. jobmanager.bind-host: 0.0.0.0 # 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: 1600m # The host interface the TaskManager will bind to. By default, this is localhost, and will prevent # the TaskManager from communicating outside the machine/container it is running on. # On YARN this setting will be ignored if it is set to 'localhost', defaulting to 0.0.0.0. # On Kubernetes this setting will be ignored, defaulting to 0.0.0.0. # # To enable this, set the bind-host address to one that has access to an outside facing network # interface, such as 0.0.0.0. taskmanager.bind-host: 0.0.0.0 # The address of the host on which the TaskManager runs and can be reached by the JobManager and # other TaskManagers. If not specified, the TaskManager will try different strategies to identify # the address. # # Note this address needs to be reachable by the JobManager and forward traffic to one of # the interfaces the TaskManager is bound to (see 'taskmanager.bind-host'). # # Note also that unless all TaskManagers are running on the same machine, this address needs to be # configured separately for each TaskManager. taskmanager.host: hadoop001 # 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: 1728m # 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.numberOfTaskSlots: 2 # The parallelism used for programs that did not specify and other parallelism. parallelism.default: 2 # 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: localhost:2181 # ACL options are based on https://zookeeper.apache.org/doc/r3.1.2/zookeeperProgrammers.html#sc_BuiltinACLSchemes # It can be either "creator" (ZOO_CREATE_ALL_ACL) or "open" (ZOO_OPEN_ACL_UNSAFE) # The default value is "open" and it can be changed to "creator" if ZK security is enabled # # high-availability.zookeeper.client.acl: open #============================================================================== # Fault tolerance and checkpointing #============================================================================== # The backend that will be used to store operator state checkpoints if # checkpointing is enabled. Checkpointing is enabled when execution.checkpointing.interval > 0. # # Execution checkpointing related parameters. Please refer to CheckpointConfig and ExecutionCheckpointingOptions for more details. # # execution.checkpointing.interval: 3min # execution.checkpointing.externalized-checkpoint-retention: [DELETE_ON_CANCELLATION, RETAIN_ON_CANCELLATION] # execution.checkpointing.max-concurrent-checkpoints: 1 # execution.checkpointing.min-pause: 0 # execution.checkpointing.mode: [EXACTLY_ONCE, AT_LEAST_ONCE] # execution.checkpointing.timeout: 10min # execution.checkpointing.tolerable-failed-checkpoints: 0 # execution.checkpointing.unaligned: false # # Supported backends are 'hashmap', 'rocksdb', or the # <class-name-of-factory>. # # state.backend: hashmap # Directory for checkpoints filesystem, when using any of the default bundled # state backends. # # state.checkpoints.dir: hdfs://namenode-host:port/flink-checkpoints # Default target directory for savepoints, optional. # # state.savepoints.dir: hdfs://namenode-host:port/flink-savepoints # Flag to enable/disable incremental checkpoints for backends that # support incremental checkpoints (like the RocksDB state backend). # # state.backend.incremental: false # The failover strategy, i.e., how the job computation recovers from task failures. # Only restart tasks that may have been affected by the task failure, which typically includes # downstream tasks and potentially upstream tasks if their produced data is no longer available for consumption. jobmanager.execution.failover-strategy: region #============================================================================== # Rest & web frontend #============================================================================== # The port to which the REST client connects to. If rest.bind-port has # not been specified, then the server will bind to this port as well. # rest.port: 8081 # The address to which the REST client will connect to # rest.address: hadoop001 # Port range for the REST and web server to bind to. # #rest.bind-port: 8080-8090 # The address that the REST & web server binds to # By default, this is localhost, which prevents the REST & web server from # being able to communicate outside of the machine/container it is running on. # # To enable this, set the bind address to one that has access to outside-facing # network interface, such as 0.0.0.0. # rest.bind-address: 0.0.0.0 # Flag to specify whether job submission is enabled from the web-based # runtime monitor. Uncomment to disable. #web.submit.enable: false # Flag to specify whether job cancellation is enabled from the web-based # runtime monitor. Uncomment to disable. #web.cancel.enable: false #============================================================================== # 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, 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 #============================================================================== # Flink Cluster Security Configuration #============================================================================== # Kerberos authentication for various components - Hadoop, ZooKeeper, and connectors - # may be enabled in four steps: # 1. configure the local krb5.conf file # 2. provide Kerberos credentials (either a keytab or a ticket cache w/ kinit) # 3. make the credentials available to various JAAS login contexts # 4. configure the connector to use JAAS/SASL # The below configure how Kerberos credentials are provided. A keytab will be used instead of # a ticket cache if the keytab path and principal are set. # security.kerberos.login.use-ticket-cache: true # security.kerberos.login.keytab: /path/to/kerberos/keytab # security.kerberos.login.principal: flink-user # The configuration below defines which JAAS login contexts # security.kerberos.login.contexts: Client,KafkaClient #============================================================================== # ZK Security Configuration #============================================================================== # Below configurations are applicable if ZK ensemble is configured for security # Override below configuration to provide custom ZK service name if configured # zookeeper.sasl.service-name: zookeeper # The configuration below must match one of the values set in "security.kerberos.login.contexts" # zookeeper.sasl.login-context-name: Client #============================================================================== # HistoryServer #============================================================================== # The HistoryServer is started and stopped via bin/historyserver.sh (start|stop) # Directory to upload completed jobs to. Add this directory to the list of # monitored directories of the HistoryServer as well (see below). #jobmanager.archive.fs.dir: hdfs:///completed-jobs/ # The address under which the web-based HistoryServer listens. #historyserver.web.address: 0.0.0.0 # The port under which the web-based HistoryServer listens. #historyserver.web.port: 8082 # Comma separated list of directories to monitor for completed jobs. #historyserver.archive.fs.dir: hdfs:///completed-jobs/ # Interval in milliseconds for refreshing the monitored directories. #historyserver.archive.fs.refresh-interval: 10000
准备好数据文件
上传hdfs
首先要确保 hdfs 集群已经启动
发现我们以前已经上传过了
提交命令
flink run ./WordCount.jar --input hdfs://hadoop001:9000/input --output hdfs://hadoop001:9000/output
出现错误:
org.apache.flink.core.fs.UnsupportedFileSystemSchemeException: Hadoop is not in the classpath/dependencies.
这个错误需要把flink-1.16.1与hadoop3进行集成。
export HADOOP_CLASSPATH=`hadoop classpath`
去maven中央仓库下载如下jar包并上传到 flink/lib文件夹中
https://mvnrepository.com/artifact/commons-cli/commons-cli/1.5.0
https://mvnrepository.com/artifact/org.apache.flink/flink-shaded-hadoop-3-uber
这是为了集成hadoop,而shaded依赖已经解决了相关的jar包冲突等问题,该jar包属于第三方jar包,官网有链接,但是并没有hadoop 3.X的,这个直接在maven中央仓库搜索倒是可以搜得到。
上传 jar 包到lib目录下
分发 lib 目录到hadoop002和hadoop003
查看 flink web ui
查看 hdfs web UI
点击一个文件查看
服务器 | JobManager | TaskManager |
---|---|---|
hadoop001 | y | y |
hadoop002 | y | y |
hadoop003 | n | y |
分发到Hadoop002:
分发到Hadoop003:
注意:12.7版本下只需要修改一处就可以了,16.1需要修改3处,否则会提交任务失败。
启动ZooKeeper,查看ZooKeeper的状态:
此时查看web ui,hadoop001无法访问,hadoop002还可以继续访问
集群能正常工作,说明高可用在起作用
此时,node2的web ui也无法访问
再次提交任务,出现错误,无法运行任务
重启集群
删除hdfs上以前创建的output文件夹
提交任务,使用之前上传的数据
flink run examples/batch/WordCount.jar --input hdfs://hadoop001:9000/input --output hdfs://hadoop001:9000/output
查看结果
杀掉hadoop001的master进程,并再次提交任务
再次删除hdfs上之前创建的output文件夹
再次提交任务,可以正常运行并查看结果,说明高可用搭建成功
跟standalone保持一致
服务器 | JobManager | TaskManager |
---|---|---|
hadoop001 | y | y |
hadoop002 | y | y |
hadoop003 | n | y |
启动历史服务器
有两种模式
语法:
yarn-session.sh -n 2 -tm 800 -s 1 -d
说明:
启动一个会话
yarn-session.sh -n 2 -tm 800 -s 1 -d
此时的进程
web ui的查看
使用的默认的参数,提交任务
查看yarn的web ui
再次查看yarn的web ui
关闭会话
查看进程
查看yarn的web ui
flink run -m yarn-cluster -yjm 1024 -ytm 1024 examples/batch/WordCount.jar
说明:
执行过程中出现错误
解决错误,可以修改flink的配置
分发配置文件,并重启flink
参考文章:
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