ApacheFlinkJobManagerHA部署-创新互联
1. 下载源代码：
git clone https://github.com/apache/flink.git git branch -a
检出blink分支
git checkout -b blink remotes/origin/blink查看分支
git branch
2. 编译
mvn package -DskipTests
注意：
由于网络问题，编译flink-filesystems/flink-mapr-fs模块时，连接http://repository.mapr.com/maven 仓库速度较慢，修改link-filesystems/flink-mapr-fs/pom.xml文件，切换仓库为aliyun仓库：
aliyun-mapr-releases
https://maven.aliyun.com/repository/mapr-public/
false true
修改nodejs仓库， flink-runtime-web/pom.xml:
npm install
npm
install -g -registry=https://registry.npm.taobao
.org --cache-max=0 --no-save
3. 打包
tar -cjvpf blink-1.5.1.tar.bz2 ./flink-1.5.1/
4. 部署
4.1 前置条件
- 部署有Hadoop集群（HDFS）
- 部署有Zookeeper集群
节点信息：
res-spark-0001 (master)
res-spark-0002 (master)
res-spark-0003 (slave)
res-spark-0004 (slave)
res-spark-0005 (slave)
4.2 解压缩
tar -jxvf blink-1.5.1.tar.bz24.3 配置文件
1).hdfs-site.xml
?xml version="1.0" encoding="UTF-8"?>
dfs.ha.automatic-failover.enabled
true
dfs.nameservices
cluster1
Logical name for this new nameservice
dfs.ha.namenodes.cluster1
nn1,nn2
Unique identifiers for each NameNode in the nameservice
dfs.namenode.rpc-address.cluster1.nn1
res-spark-0001:8020
dfs.namenode.rpc-address.cluster1.nn2
res-spark-0002:8020
dfs.namenode.http-address.cluster1.nn1
res-spark-0001:50070
dfs.namenode.http-address.cluster1.nn2
res-spark-0002:50070
dfs.namenode.shared.edits.dir
qjournal://res-spark-0005:8485;res-spark-0004:8485;res-spark-0002:8485/cluster1
dfs.client.failover.proxy.provider.cluster1
org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider
dfs.ha.fencing.ssh.private-key-file
/root/.ssh/id_rsa
dfs.ha.fencing.methods
sshfence
dfs.ha.fencing.ssh.connect-timeout
30000
dfs.namenode.handler.count
20
dfs.webhdfs.enabled
true
dfs.permissions.enabled
false
dfs.datanode.max.transfer.threads
8192
2). core-site.xml
fs.defaultFS
hdfs://cluster1
true
dfs.journalnode.edits.dir
/data/disk1/hadoop/tmp/journal/node/local/data
hadoop.tmp.dir
/data/disk1/hadoop/tmp/hadoop/hadoop-${user.name}
A bas for other temporary directories
ha.zookeeper.quorum
res-spark-0001:2181,res-spark-0002:2181,res-spark-0003:2181
io.file.buffer.size
131072
fs.file.impl
org.apache.hadoop.fs.LocalFileSystem
The FileSystem for file: uris.
fs.hdfs.impl
org.apache.hadoop.hdfs.DistributedFileSystem
The FileSystem for hdfs: uris.
3). masters
res-spark-0001:8081
res-spark-0002:80814). slaves
res-spark-0003
res-spark-0004
res-spark-00055). 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 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
# The RPC port where the JobManager is reachable.
jobmanager.rpc.port: 6123
# The heap size for the JobManager JVM
jobmanager.heap.size: 1024m
# The heap size for the TaskManager JVM
taskmanager.heap.size: 1024m
# The number of task slots that each TaskManager offers. Each slot runs one parallel pipeline.
taskmanager.numberOfTaskSlots: 6
# 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
#fs.default-scheme: hdfs://cluster1
#==============================================================================
# High Availability
#==============================================================================
# The high-availability mode. Possible options are 'NONE' or 'zookeeper'.
#
# high-availability: 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/
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
high-availability.zookeeper.quorum: res-spark-0001:2181,res-spark-0002:2181,res-spark-0003:2181
high-availability.cluster-id: /cluster_one
# 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
state.checkpoints.dir: hdfs://cluster1/flink-checkpoints
# Default target directory for savepoints, optional.
#
# state.savepoints.dir: hdfs://namenode-host:port/flink-checkpoints
state.savepoints.dir: hdfs://cluster1/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.
#
#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.
#web.submit.enable: false
web.submit.enable: true
#==============================================================================
# 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: 64mb
# taskmanager.network.memory.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/
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/
historyserver.archive.fs.dir: hdfs:///completed-jobs/
# Interval in milliseconds for refreshing the monitored directories.
#historyserver.archive.fs.refresh-interval: 10000 res-spark-0001节点:
jobmanager.rpc.address: res-spark-0001res-spark-0002节点:
jobmanager.rpc.address: res-spark-00026)启动集群
bin/start-cluster.sh
7)启动historyserver
bin/historyserver.sh start另外有需要云服务器可以了解下创新互联scvps.cn，海内外云服务器15元起步，三天无理由+7*72小时售后在线，公司持有idc许可证，提供“云服务器、裸金属服务器、高防服务器、香港服务器、美国服务器、虚拟主机、免备案服务器”等云主机租用服务以及企业上云的综合解决方案，具有“安全稳定、简单易用、服务可用性高、性价比高”等特点与优势，专为企业上云打造定制，能够满足用户丰富、多元化的应用场景需求。
本文名称：ApacheFlinkJobManagerHA部署-创新互联
标题路径：http://scyanting.com/article/cesoog.html
