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【运维/安装】Flink + MinIO：实现light-weighting思路下的集群(集群、高可用&&POC、快速搭建)

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导读：本篇文章讲解 【运维/安装】Flink + MinIO：实现light-weighting思路下的集群(集群、高可用&&POC、快速搭建)，希望对大家有帮助，欢迎收藏，转发！站点地址：www.bmabk.com

文章目录

一. 概述

先看下flink 的几种部署模式和适用场景
在这里插入图片描述

本文通过使用flink+MinIO安装实现flink standalone的集群模式，实现“轻量化集群”

  • flink集群本身作为计算资源，去执行flink job
  • Minio 用于存储Flink job产生checkpoint和savepoint、以及存储flink HA的一些信息，也就是作为分布式存储系统。

 
 

二. Flink配置MinIO实现Checkpoint和Savepoint

1. 配置s3文件系统

Apache Flink 1.12 Documentation: Amazon S3
[Apache Flink]: Where is flink-s3-fs-hadoop plugin?

将opt的文件复制到plugins下，如下图
在这里插入图片描述
 

2. 配置checkpoint和savepoint

flink-conf.yaml配置文件修改

fs.allowed-fallback-filesystems: s3
state.backend: filesystem 
state.checkpoints.dir: s3://state/checkpoint
state.savepoints.dir: s3://state/savepoint
s3.endpoint: http://10.0.16.220:9000
s3.path.style.access: true
s3.access-key: minio
s3.secret-key: minio123

配置checkpoint目录时minio上要提前创建好bucket，并且不能使用bucket作为checkpoint根路径。否则不能实例化jobmanager。本文是state。
在这里插入图片描述

 

3. 提交一个flink job到flink集群上

在这里插入图片描述

在这里插入图片描述

 
 

三. minio的安装

1.单节点安装

#新建minio文件夹 
mkdir minio cd minio 
#新建存数据的文件夹 
mkdir data 
#下载minio 如果链接失效访问mino官网获取最新下载地址
wget https://dl.min.io/server/minio/release/linux-amd64/minio 
#加权限 
chmod +x minio 
#自定义账号密码 
export MINIO_ACCESS_KEY=minio 
export MINIO_SECRET_KEY=minio123

后台启动

nohup /data/a 2pp/minio/minio server /data/app/minio/data > /data/log/minio/minio.log 2>&1 &

前端访问

ip:9000

在这里插入图片描述

 

2.多节点安装

多节点minio安装 ing

配置tegine

为了不将所有的读写请求都落到一个节点，造成读写压力，通过利用tengine的反向代理实现负载均衡

#配置minio的upstream主要实现集群的反向代理，和请求转发
cd /data/app/tengine/conf/vhost
vim http_minio.proxy.conf  

#重新启动tegine
bash /data/app/tengine/scripts/tengine restart


# 配置
http_minio.proxy.conf
 
# Minio 主备 配置
upstream minio {
    server 10.0.16.214:18125 weight=1;
    server 10.0.16.231:28125 weight=1;
    server 10.0.16.220:18125 weight=1;
    server 10.0.16.247:18125 weight=1;
    check interval=1000 rise=2 fall=5 timeout=1000 type=http;
    check_http_send "GET /minio/health/live HTTP/1.0\r\n\r\n";
    check_http_expect_alive http_2xx http_3xx;
}
# Minio Server 域 
server {
    # tegine 监听minio请求的端口和tegine的ip
    listen 18095;
    server_name 10.0.16.214;
    
    ignore_invalid_headers off;
    client_max_body_size 0;
    proxy_buffering off;

    location / {
       proxy_set_header X-Real-IP $remote_addr;
       proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
       proxy_set_header X-Forwarded-Proto $scheme;
       proxy_set_header Host $http_host;
       proxy_http_version 1.1;
       
       proxy_set_header Connection "";
       proxy_connect_timeout  300;
       proxy_pass http://minio/;
    }
}

 

三. flink的高可用安装

设置三个节点的flink，实现高可用。集群角色分布如下：

flink-ip1：jobmanager taskmanager
flink-ip2：jobmanager taskmanager
flink-ip3：jobmanager taskmanager

1. 配置flink-conf.yaml

#==============================================================================
# 1. Common 配置
#==============================================================================

# 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.
# 指定所在节点的ip
jobmanager.rpc.address: 10.2.3.155

# The RPC port where the JobManager is reachable.

jobmanager.rpc.port: 18550
jobmanager.rpc.bind-port: 18550


# 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: 8

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

parallelism.default: 1

#==============================================================================
# 2. High Availability
# 以下配置在standalone模式下打开
#==============================================================================
# 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

high-availability: zookeeper
high-availability.storageDir: s3://flink/ha/
high-availability.zookeeper.quorum: 10.2.3.59:127,10.2.3.156:18127,10.2.3.155:18127
high-availability.zookeeper.client.acl: open

### 3. 设置checkpoint 和savepoint
fs.allowed-fallback-filesystems: s3
state.backend: filesystem
state.checkpoints.dir: s3://state/checkpoint
state.savepoints.dir: s3://state/savepoint
s3.endpoint: http://xxx:18095/
s3.path.style.access: true
s3.access-key: admin
s3.secret-key: MINIO@minio_123

#==============================================================================
# 4. Fault tolerance and checkpointing
#==============================================================================

# The backend that will be used to store operator state checkpoints if
# checkpointing is enabled.

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: 18551

# 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: 18551-18590

# 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
#==============================================================================

#flink的ssh参数
env.ssh.opts: -p 36000

##flink日志路径
env.log.dir: /data/logs/flink

## 监控设置
metrics.reporter.promgateway.password: Push$777
metrics.reporter.promgateway.metric.only.report.names: flink_jobmanager_job_lastCheckpointSize,flink_jobmanager_job_lastCheckpointDuration,flink_jobmanager_job_numberOfFailedCheckpoints,flink_jobmanager_Status_JVM_CPU_Load,flink_jobmanager_Status_JVM_Memory_Heap_Used,flink_jobmanager_Status_JVM_GarbageCollector_PS_MarkSweep_Count,flink_jobmanager_Status_JVM_GarbageCollector_MarkSweepCompact_Count,flink_jobmanager_Status_JVM_GarbageCollector_PS_MarkSweep_Time,flink_jobmanager_Status_JVM_GarbageCollector_MarkSweepCompact_Time,flink_taskmanager_Status_JVM_CPU_Load,flink_taskmanager_Status_JVM_Memory_Heap_Used,flink_taskmanager_Status_JVM_GarbageCollector_MarkSweepCompact_Count,flink_taskmanager_Status_JVM_GarbageCollector_G1_Old_Generation_Count,flink_taskmanager_Status_JVM_GarbageCollector_PS_MarkSweep_Count,flink_taskmanager_Status_JVM_GarbageCollector_MarkSweepCompact_Time,flink_taskmanager_Status_JVM_GarbageCollector_G1_Old_Generation_Time,flink_taskmanager_Status_JVM_GarbageCollector_PS_MarkSweep_Time,flink_taskmanager_job_task_Shuffle_Netty_Output_Buffers_outPoolUsage,flink_taskmanager_job_task_Shuffle_Netty_Input_Buffers_inPoolUsage,flink_taskmanager_job_task_Shuffle_Netty_Output_Buffers_outputQueueLength,flink_taskmanager_job_task_Shuffle_Netty_Input_Buffers_inputQueueLength,flink_taskmanager_job_task_currentInputWatermark,flink_jobmanager_job_downtime,flink_taskmanager_job_task_numBytesIn,flink_taskmanager_job_task_numBytesOut,flink_taskmanager_job_task_numRecordsIn,flink_taskmanager_job_task_numRecordsOut
internal.yarn.log-config-file: /data/app/flink/conf/log4j.properties
metrics.reporter.promgateway.host: promgateway-ip
metrics.reporter.promgateway.username: admin
metrics.reporter.promgateway.deleteOnShutdown: false
metrics.reporter.promgateway.class: org.apache.flink.metrics.prometheus.CustomedPrometheusPushGatewayReporter
metrics.reporter.promgateway.groupingKey: mode=2;id=100000153;account_id=110;instance=
metrics.reporter.promgateway.jobName: myJob
metrics.reporter.promgateway.port: 18165
metrics.reporter.promgateway.interval: 15 SECONDS

注意：省略zookeeper的安装

 

2. Masters 和workers设置

masters代表三个节点都有jobmanager角色，对于高可用来说其中两个jobmanager挂掉，flink集群仍然可以运行。

[root@10-101-1-48 conf]# cat masters 
flink-ip1:18551
flink-ip2:18551
flink-ip3:18551
[root@10-101-1-48 conf]# cat workers 
flink-ip1
flink-ip2
flink-ip3

 

3. 文件分发并修改

scp -P 36000 /data/app/flink/conf/masters user@flink-ip:/data/app/flink/conf
scp -P 36000 /data/app/flink/conf/workers user@flink-ip:/data/app/flink/conf
scp -P 36000 /data/app/flink/conf/flink-conf.yaml user@flink-ip:/data/app/flink/conf

修改flink-conf.yaml下的

# 指定所在节点的ip
jobmanager.rpc.address: ip

 

4. 启动

三个节点分别执行

bash /data/app/flink/bin/jobmanager.sh start && bash /data/app/flink/bin/taskmanager.sh start

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