Flink Standalone Cluster 集群安装
本文主要介绍如何将Flink以分布式模式运行在集群上（可能是异构的）。
环境准备
Flink 运行在所有类 UNIX 环境上，例如 Linux、Mac OS X 和 Cygwin（对于Windows），而且要求集群由一个master节点和一个或多个worker节点组成。在安装系统之前，确保每台机器上都已经安装了下面的软件：
- Java 1.8.x或更高版本
- ssh（Flink的脚本会用到sshd来管理远程组件）
如果你的集群还没有完全装好这些软件，你需要安装/升级它们。例如，在 Ubuntu Linux 上， 你可以执行下面的命令安装 ssh 和 Java ：
sudo apt-get install ssh
sudo apt-get install openjdk-8-jre
SSH免密码登录
译注：安装过Hadoop、Spark集群的用户应该对这段很熟悉，如果已经了解，可跳过。
为了能够启动/停止远程主机上的进程，master节点需要能免密登录所有worker节点。最方便的方式就是使用ssh的公钥验证了。要安装公钥验证，首先以最终会运行Flink的用户登录master节点。所有的worker节点上也必须要有同样的用户（例如：使用相同用户名的用户）。由于以前安装过ES，用的是es用户，所以本文会以 es用户为例。非常不建议使用 root 账户，这会有很多的安全问题。
当你用需要的用户登录了master节点，你就可以生成一对新的公钥/私钥。下面这段命令会在 ~/.ssh 目录下生成一对新的公钥/私钥。
ssh-keygen -b 2048 -P '' -f ~/.ssh/id_rsa
接下来，将公钥添加到用于认证的authorized_keys文件中：
cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys
最后，将authorized_keys文件分发给集群中所有的worker节点，你可以重复地执行下面这段命令：
scp ~/.ssh/authorized_keys <worker>:~/.ssh/
将上面的<worker>替代成相应worker节点的IP/Hostname。完成了上述拷贝的工作，你应该就可以从master上免密登录其他机器了。
ssh <worker>
配置JAVA_HOME
Flink 需要master和worker节点都配置了JAVA_HOME环境变量。有两种方式可以配置。
一种是，你可以在conf/flink-conf.yaml中设置env.java.home配置项为Java的安装路径。
另一种是，sudo vi /etc/profile，在其中添加JAVA_HOME：
#java
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64
export PATH=$JAVA_HOME/bin:$PATH
export CLASSPATH=.:$JAVA_HOME/lib:$JAVA_HOME/jre/lib:$CLASSPATH
#node
export NODE_HOME=/usr/local/es/node-v9.11.1-linux-x64
export PATH=$NODE_HOME/bin:$PATH
#maven
export MAVEN_HOME=/usr/local/software/maven-3.5.3
export PATH=$MAVEN_HOME/bin:$PATH
#hadoop
export HADOOP_HOME=/usr/local/software/hadoop-2.8.3
export PATH=$PATH:$HADOOP_HOME/bin
export PATH=$PATH:$HADOOP_HOME/sbin
export HADOOP_MAPRED_HOME=$HADOOP_HOME
export HADOOP_COMMON_HOME=$HADOOP_HOME
export HADOOP_HDFS_HOME=$HADOOP_HOME
export YARN_HOME=$HADOOP_HOME
export HADOOP_ROOT_LOGGER=INFO,console
export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native
export HADOOP_OPTS="-Djava.library.path=$HADOOP_HOME/lib"
#hive
export HIVE_HOME=/usr/local/software/hive-2.2.0
export PATH=$HIVE_HOME/bin:$PATH
然后使环境变量生效，并验证 Java 是否安装成功
es@es1:/root$ java -version
openjdk version "1.8.0_162"
OpenJDK Runtime Environment (build 1.8.0_162-8u162-b12-0ubuntu0.16.04.2-b12)
OpenJDK 64-Bit Server VM (build 25.162-b12, mixed mode)
安装Flink
进入下载页面。请选择一个与你的Hadoop版本相匹配的Flink包。如果你不打算使用Hadoop，选择任何版本都可以。
我这里下载的flink是flink-1.5.1，hadoop是hadoop-2.8.3。
在下载了最新的发布包后，拷贝到master节点上，并解压：
tar xzf flink-1.5.1/.tgz
cd flink-1.5.1/
配置Flink
在解压完之后，你需要编辑conf/flink-conf.yaml配置Flink。
设置jobmanager.rpc.address配置项为你的master节点地址。另外为了明确 JVM 在每个节点上所能分配的最大内存，我们需要配置jobmanager.heap.mb和taskmanager.heap.mb，值的单位是 MB。如果对于某些worker节点，你想要分配更多的内存给Flink系统，你可以在相应节点上设置FLINK_TM_HEAP环境变量来覆盖默认的配置。
flink-conf.yaml配置如下：
es@es2:/usr/local/software/flink-1.5.1$ cat conf/flink-conf.yaml
################################################################################
# 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/Mesos
# automatically configure the host name based on the hostname of the node where the
# JobManager runs.
#jobmanager.rpc.address: localhost
jobmanager.rpc.address: es2
# The RPC port where the JobManager is reachable.
jobmanager.rpc.port: 6123
# The heap size for the JobManager JVM
jobmanager.heap.mb: 1024
# The heap size for the TaskManager JVM
taskmanager.heap.mb: 1024
# The number of task slots that each TaskManager offers. Each slot runs one parallel pipeline.
taskmanager.numberOfTaskSlots: 1
# 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-checkpoints
# Flag to enable/disable incremental checkpoints for backends that
# support incremental checkpoints (like the RocksDB state backend).
#
# state.backend.incremental: false
#==============================================================================
# Web Frontend
#==============================================================================
# The address under which the web-based runtime monitor listens.
#
#jobmanager.web.address: 0.0.0.0
# The port under which the web-based runtime monitor listens.
# A value of -1 deactivates the web server.
rest.port: 8081
# Flag to specify whether job submission is enabled from the web-based
# runtime monitor. Uncomment to disable.
#jobmanager.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: /tmp
# Specify whether TaskManager's managed memory should be allocated when starting
# up (true) or when memory is requested.
#
# We recommend to set this value to 'true' only in setups for pure batch
# processing (DataSet API). Streaming setups currently do not use the TaskManager's
# managed memory: The 'rocksdb' state backend uses RocksDB's own memory management,
# while the 'memory' and 'filesystem' backends explicitly keep data as objects
# to save on serialization cost.
#
# taskmanager.memory.preallocate: false
# 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, teh default max is 1GB.
#
# taskmanager.network.memory.fraction: 0.1
# taskmanager.network.memory.min: 67108864
# taskmanager.network.memory.max: 1073741824
#==============================================================================
# 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
最后，你需要提供一个集群中worker节点的列表。因此，就像配置HDFS，编辑conf/slaves文件，然后输入每个worker节点的 IP/Hostname。每一个worker结点之后都会运行一个 TaskManager。
每一条记录占一行，就像下面展示的一样：
es@es2:/usr/local/software/flink-1.5.1$ cat conf/slaves
#localhost
es1
es2
conf:
conf
译注：conf/master文件是用来做JobManager HA的，在这里不需要配置
每一个worker节点上的 Flink 路径必须一致。你可以使用共享的 NSF 目录，或者拷贝整个 Flink 目录到各个worker节点。
cp -r /path/to/flink <worker>:/path/to/
请查阅配置页面了解更多关于Flink的配置。
特别的，这几个
- TaskManager 总共能使用的内存大小（taskmanager.heap.mb）
- 每一台机器上能使用的 CPU 个数（taskmanager.numberOfTaskSlots）
- 集群中的总 CPU 个数（parallelism.default）
- 临时目录（taskmanager.tmp.dirs）
是非常重要的配置项。
启动Flink
下面的脚本会在本地节点启动一个 JobManager，然后通过 SSH 连接所有的worker节点（slaves文件中所列的节点），并在每个节点上运行 TaskManager。现在你的 Flink 系统已经启动并运行了。跑在本地节点上的 JobManager 现在会在配置的 RPC 端口上监听并接收任务。
假定你在master节点上，并在Flink目录中：
bin/start-cluster.sh
master上启动的进程：
slave上启动的进程：
访问8081端口：
可以看到两个taskManager都成功加入进来了。
要停止Flink，也有一个 stop-cluster.sh 脚本。
添加 JobManager/TaskManager 实例到集群中
你可以使用 bin/jobmanager.sh 和 bin/taskmanager 脚本来添加 JobManager 和 TaskManager 实例到你正在运行的集群中。
添加一个 JobManager
bin/jobmanager.sh (start cluster)|stop|stop-all
添加一个 TaskManager
bin/taskmanager.sh start|stop|stop-all
确保你是在需要启动/停止相应实例的节点上运行的这些脚本。
参考：
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