1.Local 本地部署
2. Standalone 使用Flink自带的资源调度平台进行任务的部署
3. Standalone-HA高可用的部署方式
4. Yarn 部署
- 应用场景：开发环境
- 部署步骤：
- 设置 JDK运行环境
- 配置 SSH 免密登录
- 下载并解压缩 Flink-1.13.1 到 /export/server
- 修改配置文件
jobmanager.rpc.address: node1 - 开启flink环境查看web UI监控
开启集群
[root@node1 bin]# start-cluster.sh
#访问监控页面 webUI
http://node1:8081
- 应用场景：开发、测试使用
- 安装部署： flink/conf/flink-conf.yaml 基础配置
# jobManager 的IP地址 jobmanager.rpc.address: node1 # JobManager 的端口号 jobmanager.rpc.port: 6123 # JobManager JVM heap 内存大小 jobmanager.memory.process.size: 1600m # TaskManager JVM heap 内存大小 taskmanager.memory.process.size: 1728m # 每个 TaskManager 提供的任务 slots 数量大小 taskmanager.numberOfTaskSlots: 2 #是否进行预分配内存，默认不进行预分配，这样在我们不使用flink集群时候不会占用集群资源 taskmanager.memory.preallocate: false # 程序默认并行计算的个数 parallelism.default: 1 #JobManager的Web界面的端口（默认：8081） jobmanager.web.port: 8081
- 配置 worker文件
- 将每个从节点 hostname 保存，一行一个
- 将flink的程序及配置拷贝到其他的节点
- scp flink 复制到其他节点
scp /export/server/flink root@node2:/export/server scp /export/server/flink root@node3:/export/server
- 配置环境变量
vim /etc/profile
Flink_HOME=/export/server/flink
PATH=$PATH:$Flink_HOME/bin
# 立即生效
source /etc/profile
-
开启Flink集群
start-cluster.sh -
查看当前的 Flink集群的状态，webUI
node1:8081
-
执行 wordcount 任务执行，run word-count 案例
- 在 hdfs 上上传文件
- flink run 执行这个任务并加载文件
-
执行 wordcount 命令
flink run /export/server/flink/examples/batch/WordCount.jar -p 1 --input hdfs://node1:8020/words.txt```
参数解释
flink run 提交执行任务 类似于 spark-submit
-p 1 并行度设置为1
–input 当前输入的参数
/export/server/flink/examples/batch/WordCount.jar jar包位置
使用场景：开发、测试使用
部署步骤：和 Standalone 部署方式几乎一样，区别：
- 需要将每一台节点的 flink-conf.yaml 中 HA 高可用的zookeeper设置并将zookeeper集群地址设置好
配置 flink-conf.yaml 在 notepad++ 中
具体配置的参数
node1 #============================================================================== # 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 --hostparameter 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/Mesos # automatically configure the host name based on the hostname of the node where the # JobManager runs. jobmanager.rpc.address: node1 # 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: 1600m # 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: 1 # 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. # # Supported backends are 'jobmanager', 'filesystem', 'rocksdb', or the # . # # state.backend: filesystem # 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: 0.0.0.0 # 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 # #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 #============================================================================== # 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: /export/server/flink/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 # jobManager 的IP地址 jobmanager.rpc.address: node1 # JobManager 的端口号 jobmanager.rpc.port: 6123 # JobManager JVM heap 内存大小 jobmanager.memory.process.size: 1600m # TaskManager JVM heap 内存大小 taskmanager.memory.process.size: 1728m # 每个 TaskManager 提供的任务 slots 数量大小 taskmanager.numberOfTaskSlots: 2 #是否进行预分配内存，默认不进行预分配，这样在我们不使用flink集群时候不会占用集群资源 taskmanager.memory.preallocate: false # # 程序默认并行计算的个数 parallelism.default: 1 #JobManager的Web界面的端口（默认：8081） jobmanager.web.port: 8081 #开启HA，使用文件系统作为快照存储 state.backend: filesystem #默认为none，用于指定checkpoint的data files和meta data存储的目录 state.checkpoints.dir: hdfs://node1:8020/flink-checkpoints #默认为none，用于指定savepoints的默认目录 state.savepoints.dir: hdfs://node1:8020/flink-checkpoints #使用zookeeper搭建高可用 high-availability: zookeeper # 存储JobManager的元数据到HDFS,用来恢复JobManager 所需的所有元数据 high-availability.storageDir: hdfs://node1:8020/flink/ha/ high-availability.zookeeper.quorum: node1:2181,node2:2181,node3:2181 #blob存储文件是在群集中分发Flink作业所必需的 blob.storage.directory: /export/server/flink-1.13.1/tmp
- node2 只需要改 node2
- master 配置文件
node1:8081 node2:8081
- HA高可用，设置 HDFS 上的路径用于保存 ha的数据，防止出现当前集群jobmanager挂掉恢复最新状态
- 如何切换 jobmanager 实现HA高可用
关闭node1上的 jobmanager 进程
查看 node2 上 8081 web的log日志，查看是否 granted leadership
jobmanager.sh start 再开启 node1 的jobmanager
使用场景：生产环境使用
如何进行部署呢？ Hadoop HDFS、Zookeeper、Yarn
配置
yarn-site.xml 中修改一下memcheck 置为 false ，不让检查内存是否可用。
yarn.nodemanager.vmem-check-enabled false
- 将 yarn-site.xml 分发到各个节点。
- Flink任务在Yarn上提交方式：
yarn-session + flink run
- 应用场景：大量的小任务，当小任务执行完毕之后并不会关闭session，小任务之间共享session（内存和CPU cores）不隔离资源。
- 如何使用：
# 开启 yarn-session 会话 yarn-session.sh -tm 1024 -s 2 -d # -tm taskmanager 的内存大小 # -s slot 数 # -d daemon 后台执行 flink run -p 2 /export/server/flink/examples/batch/WordCount.jar --input /words.txt
- kill 掉一直运行的 session
yarn application -kill application_1638083192874_0001
每个任务都是直接 flink run 执行 per-job
- 应用场景： 适合于大多数生产环境的，任务的执行，每个任务一个session，程序执行完毕关闭会话。
- 如何使用：
flink run -m yarn-cluster -yjm 1024m -ytm 1024m /export/server/flink/examples/batch/WordCount.jar --input hdfs://node1:8020/words.txt
