Flink(一)集群配置

三台主机 centos6

已经完成的工作：

• 防火墙已关闭
• 主机名修改完毕，ssh免密登陆配置完成
• jdk已安装
• zookeeper已经部署并运行
• hadoop已经部署并运行

版本：flink-1.8.2-bin-scala_2.11

上传或下载flink，解压缩

[root@node01 software]# tar -zxvf flink-1.8.-bin-scala_2..tgz -C /bigdata/application/

配置环境变量，建立软连接

将官网hadoop的jar包放入lib目录下

编辑flink-conf.yaml

jobmanager.rpc.address：值设置成你master节点的IP地址
taskmanager.heap.mb：每个TaskManager可用的总内存
taskmanager.numberOfTaskSlots：每台机器上可用CPU的总数
parallelism.default：每个Job运行时默认的并行度
taskmanager.tmp.dirs：临时目录
jobmanager.heap.mb：每个节点的JVM能够分配的最大内存
jobmanager.rpc.port: 6123
jobmanager.web.port: 8081

################################################################################
#  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: node03

# The RPC port where the JobManager is reachable.

jobmanager.rpc.port: 

# 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: 

# The parallelism used for programs that did not specify and other parallelism.

parallelism.default: 

# 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: hdfs://ns/

#==============================================================================
# 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://ns/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: )
#
high-availability.zookeeper.quorum: node01:,node02:,node03:
high-availability.zookeeper.path.root: /flink

# 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://ns/flink-checkpoints

# Default target directory for savepoints, optional.
#
state.savepoints.dir: hdfs://ns/flink-checkpoints

# Flag to enable/disable incremental checkpoints for backends that
# support incremental checkpoints (like the RocksDB state backend).
#
# state.backend.incremental: false

#==============================================================================
# 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: 

# 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: -

# 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: 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, the 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:
# . configure the local krb5.conf file
# . provide Kerberos credentials (either a keytab or a ticket cache w/ kinit)
# . make the credentials available to various JAAS login contexts
# . 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: 

# 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: 

yarn.application-attempts: 

编辑master文件

node03:
node01:

编辑slaves文件

node01
node02
node03

编辑zoo.cfg文件

################################################################################
#  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.
################################################################################

# The number of milliseconds of each tick
tickTime=

# The number of ticks that the initial  synchronization phase can take
initLimit=

# The number of ticks that can pass between  sending a request and getting an acknowledgement
syncLimit=

# The directory where the snapshot is stored.
# dataDir=/tmp/zookeeper

# The port at which the clients will connect
clientPort=

# ZooKeeper quorum peers
server.=node01::
server.=node02::
server.=node03::
# server.=host:peer-port:leader-port

复制到各个节点，配置环境变量，软连接

启动

bin下通过start-cluster.sh启动

访问node03:8086

Flink On Yarn模式

flink on yarn

1.第一种方式：yarn-session.sh(开辟资源)+flink run(提交任务)

启动一个一直运行的flink集群

# 下面的命令会申请5个taskmanager，每个2G内存和2个solt，超过集群总资源将会启动失败。
./bin/yarn-session.sh -n  -tm  -s  --nm leo-flink -d

-n ,--container <arg> 分配多少个yarn容器（=taskmanager的数量）

-D <arg> 动态属性

-d, --detached 独立运行

-jm,--jobManagerMemory <arg> JobManager的内存 [in MB]

-nm,--name 在YARN上为一个自定义的应用设置一个名字

-q,--query 显示yarn中可用的资源 (内存, cpu核数)

-qu,--queue <arg> 指定YARN队列.

-s,--slots <arg> 每个TaskManager使用的slots(vcore)数量

-tm,--taskManagerMemory <arg> 每个TaskManager的内存 [in MB]

-z,--zookeeperNamespace <arg> 针对HA模式在zookeeper上创建NameSpace

请注意：

请注意：client必须要设置YARN_CONF_DIR或者HADOOP_CONF_DIR环境变量，通过这个环境变量来读取YARN和HDFS的配置信息，否则启动会失败。
经实验发现，其实如果配置的有HADOOP_HOME环境变量的话也是可以的（只是会出现警告）。HADOOP_HOME ，YARN_CONF_DIR，HADOOP_CONF_DIR 只要配置的有任何一个即可。

运行结果如图：

 

 

yarn-flink

浏览器中访问 http://node4:45559

 

yarn-flink

yarn web-ui中

 

yarn-flink

部署长期运行的flink on yarn实例后，在flink web上看到的TaskManager以及Slots都为0。只有在提交任务的时候，才会依据分配资源给对应的任务执行。</p>

提交Job到长期运行的flink on yarn实例上：

./bin/flink run ./examples/batch/WordCount.jar -input hdfs://leo/test/test.txt -output hdfs://leo/flink-word-count

通过web ui可以看到已经运行完成的任务：

 

task

2.第二种方式：flink run -m yarn-cluster(开辟资源+提交任务)

./bin/flink run -m yarn-cluster -yn  -yjm  -ytm    ./examples/batch/WordCount.jar -input hdfs://leo/test/test.txt -output hdfs://leo/test/flink-word-count2.txt

yarn web ui上查看刚刚提交的任务已经执行成功

 

task

作者：NikolasNull
链接：https://www.jianshu.com/p/4dc0a980e7e9
来源：简书
著作权归作者所有。商业转载请联系作者获得授权，非商业转载请注明出处。

[root@node03 bin]# start-cluster.sh
Starting HA cluster with  masters.
ssh: connect to host node03 port : No buffer space available
Starting standalonesession daemon on host node01.
Starting taskexecutor daemon on host node01.
Starting taskexecutor daemon on host node02.
ssh: connect to host node03 port : No buffer space available
[root@node03 bin]# start-cluster.sh
Starting HA cluster with  masters.
ssh: connect to host node03 port : No buffer space available
[INFO]  instance(s) of standalonesession are already running on node01.
Starting standalonesession daemon on host node01.
[INFO]  instance(s) of taskexecutor are already running on node01.
Starting taskexecutor daemon on host node01.
[INFO]  instance(s) of taskexecutor are already running on node02.
Starting taskexecutor daemon on host node02.
ssh: connect to host node03 port : No buffer space available
[root@node03 bin]# echo  > /proc/sys/net/ipv4/neigh/default/gc_thresh1
[root@node03 bin]# echo  > /proc/sys/net/ipv4/neigh/default/gc_thresh2
[root@node03 bin]# echo  > /proc/sys/net/ipv4/neigh/default/gc_thresh3

ping 或者ssh 发生connect: No buffer space available 错误

 

如果遇到这种情况，一般说明你的本地服务器的arp表缓存太大，而服务器内核设定的回收条数太小，一直被回收造成的。

可以用一下命令扩大arp表可以缓存的记录条数：

echo  > /proc/sys/net/ipv4/neigh/default/gc_thresh1
echo  > /proc/sys/net/ipv4/neigh/default/gc_thresh2
echo  > /proc/sys/net/ipv4/neigh/default/gc_thresh3

这三个值缺省是128，512，1024，我用arp -an |wc -l 看到自己服务器的arp缓存表竟然有300多条记录，修改完成后马上就好了，最后记得把

这三条写入/etc/rc.local 文件中，每次重启都写入下，不然机器重启就又被还原至缺省值了。