一:Flink的架构体系
1.基础概念
- JobManager 主节点，老大节点用于管理 Flink 集群，容错机制，状态管理，心跳机制的管理等
- TaskManager 从节点，干活节点，用于具体任务执行
- Slot 任务槽：静态的概念，具备的能力，包含CPU（一个线程）和内存，任务task就是在 Slot 中执行，每个Slot中最多只能执行一个 task。内存分配是静态的，将可执行的内存大小平均的分配给 n 份，slot中内存之间是隔离的，cpu core不隔离的。
- 并行度 parallelism ： 动态的概念，表示当前 task 并行执行的个数。
- 设置并行度的四种方式：
- 在配置文件中设置 flink-conf.yaml ，优先级最低
- 在提交client客户端任务时候，设置 flink run -p 2
- 全局的流执行环境设置并行度 env.setParallelism(2) ，全局并行度设置
- 算子级别的并行度设置，优先级别最高，sink，sink.setParallelism(2)
- Task ： 每个 Flink任务划分出来的多个任务
- subTask ： 将每个 Task 根据并行度划分的子任务，有多少个并行度就有多少个子任务
2.Flink作业执行过程
- JobClient代表分布式系统的面向用户的客户端组件。它用于与JobManager进行通信，因此它负责提交Flink作业，查询已提交作业的状态并接收当前正在运行的作业的状态消息。
- JobManager是中央控制单元，负责执行Flink作业。因此，它控制着资源分配，任务调度和状态报告、容错。
- TaskManager是具体的程序执行
- 总结
- 主从
- Client - > 老大 JobManager -> 小弟 TaskManager
二:部署方式分类
1.Local 本地部署
- 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 flink-conf.yaml , value 前面有个空格
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
2.Standalone
使用Flink自带的资源调度平台进行任务的部署
- 应用场景：开发、测试使用
- 安装部署：
1. 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
2.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
3.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
4.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
5.开启Flink集群
start-cluster.sh
6.查看当前的 Flink集群的状态，webUI
node1:8081
3.Standalone-HA高可用的部署方式
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
1.配置 flink-conf.yaml
- 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 --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/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
# <class-name-of-factory>.
#
# 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
worker 配置文件
node1 node2 node3
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
2.启动集群:
启动步骤： 1.开启hadoop集群 node1 上 start-all.sh 2.开启zookeeper集群 各个窗口上 node1 node2 node3 /export/server/zookeeper/bin/zkServer.sh start 3.开启flink 集群 node1 上 start-cluster.sh
4.Yarn 部署
使用场景：生产环境使用
如何进行部署呢？ Hadoop HDFS、Zookeeper、Yarn
1.修改yarn-site.xml
- 配置<property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>false</value>
</property>
- yarn-site.xml 中修改一下memcheck 置为 false ，不让检查内存是否可用。
2.将 yarn-site.xml 分发到各个节点。
3.重启 Hadoop 集群，启动 yarn
三:任务启动命令区别
standalone ha模式
flink run /export/server/flink/examples/batch/WordCount.jar -p 1 --input hdfs://node1:8020/words.txt
控制台地址:node1:8081
yarn-session模式
# 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
yarn application -kill application_1638083192874_0001
http://node1:8088/cluster
yarn-cluster per-job
flink run \ -m yarn-cluster \ -yjm 1024m \ -ytm 1024m \ /export/server/flink/examples/batch/WordCount.jar \ --input hdfs://node1:8020/words.txt
四:架构区别
1.Standalone
2.Standalone HA
3.on yarn yarn-session
4.on yarn-cluster Per-Job
五:资源使用区别
yarn-session模式
yarn-cluster pre-job模式
application模式
本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
