一、环境准备
1.环境准备：
flink 1.13.1+hudi0.10+hive2.1.1+cdh6.3.0+mysql5.7+flinkcdc2.1+flink web平台
二.编译hudi（这个编译是以前的一个测试版本，编译大同小异）
1.使用git命令下载hudi0.10的代码
steven@wangyuxiangdeMacBook-Pro ~ git clone https://github.com/apache/hudi.git
Cloning into 'hudi'...
remote: Enumerating objects: 122696, done.
remote: Counting objects: 100% (5537/5537), done.
remote: Compressing objects: 100% (674/674), done.
remote: Total 122696 (delta 4071), reused 4988 (delta 3811), pack-reused 117159
Receiving objects: 100% (122696/122696), 75.85 MiB | 5.32 MiB/s, done.
Resolving deltas: 100% (61608/61608), done.
2.使用idea打开hudi更改packging--hudi-flink-bundle下的pom.xml,更改flink-bundel-shade-hive2下的hive-version更改为chd6.3.0的版本。
3.使用命令进行编译
mvn clean install -DskipTests -DskipITs -Dcheckstyle.skip=true -Drat.skip=true -Dhadoop.version=3.0.0 -Pflink-bundle-shade-hive2
1.因为chd6.3.0使用的是hadoop3.0.0,所以要指定hadoop的版本
2.使用的是hive2.1.1的版本，也要指定hive的版本，不然使用sync to hive的时候，会报类的冲突问题。
出现以上的证明编译成功。
4.在packaging下面各个组件中有编译好的jar包。
5.部署同步sync to hive的环境
将hudi-hadoop-mr-bundle-0.10.0-SNAPSHOT.jar包放入到以下路径
路径如下：
[flink@dbos-bigdata-test005 jars]$ pwd
/opt/cloudera/parcels/CDH-6.3.0-1.cdh6.3.0.p0.1279813/jars
进入到hive的lib路径,每一台hive节点都要放
[flink@dbos-bigdata-test005 lib]$ pwd
/opt/cloudera/parcels/CDH-6.3.0-1.cdh6.3.0.p0.1279813/lib/hive/lib
建立软链接
[flink@dbos-bigdata-test005 lib]$ ln -s ../../../jars/hudi-hadoop-mr-bundle-0.10.0-SNAPSHOT.jar hudi-hadoop-mr-bundle-0.10.0-SNAPSHOT.jar
6.进入平台操作安装 YARN MapReduce 框架 JAR
7.hive的辅助jar
因为后面考虑到hudi的数据要存到oss上，所以要放这几个包进来（关于oss的配置详细可参考oss配置文档）
8.重启hive，使配置生效
三、flink环境：
1.配置flink on yarn模式
配置如下：flink-conf.yaml的配置文件如下
################################################################################
# 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.
################################################################################
#==============================================================================
## Execution
##==============================================================================
# execution.target: yarn-per-job
#execution.target: local
execution.checkpointing.externalized-checkpoint-retention: RETAIN_ON_CANCELLATION
#进行checkpointing的间隔时间（单位：毫秒）
execution.checkpointing.interval: 30000
execution.checkpointing.mode: EXACTLY_ONCE
#execution.checkpointing.prefer-checkpoint-for-recovery: true
classloader.check-leaked-classloader: false
#==============================================================================
# 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: dbos-bigdata-test005
# The RPC port where the JobManager is reachable.
jobmanager.rpc.port: 6123
akka.framesize: 10485760b
# 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: 1024m
# 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
taskmanager.heap.size: 1024m
# 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: 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
env.java.home key: /usr/java/jdk1.8.0_181-cloudera
#==============================================================================
# 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
high-availability.zookeeper.quorum: dbos-bigdata-test003:2181,dbos-bigdata-test004:2181,dbos-bigdata-test005:2181
# 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://bigdata/flink-checkpoints
#state.checkpoints.dir: hdfs:///flink/checkpoints
#state.savepoints.dir: hdfs:///flink/savepoints
# 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: 65535-80900
# 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: /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
env.log.dir: /tmp/flink
high-availability.zookeeper.path.root: /flink2.配置flink的环境变量
vim /etc/profile
以下是环境变量，根据自己的版本进行更改
#set default jdk1.8 env
export JAVA_HOME=/usr/java/jdk1.8.0_181-cloudera
export JRE_HOME=/usr/java/jdk1.8.0_181-cloudera/jre
export CLASSPATH=.:${JAVA_HOME}/lib:${JRE_HOME}/lib
export HADOOP_CONF_DIR=/etc/hadoop/conf
export HADOOP_CLASSPATH=`hadoop classpath`
export HBASE_CONF_DIR=/etc/hbase/conf
export FLINK_HOME=/opt/flink
export HIVE_HOME=/opt/cloudera/parcels/CDH-6.3.0-1.cdh6.3.0.p0.1279813/lib/hive
export HIVE_CONF_DIR=/etc/hive/conf
export M2_HOME=/usr/local/maven/apache-maven-3.5.4
export CANAL_ADMIN_HOME=/data/canal/admin
export CANAL_SERVER_HOME=/data/canal/deployer
export PATH=${JAVA_HOME}/bin:${JRE_HOME}/bin:${FLINK_HOME}/bin:${M2_HOME}/bin:${HIVE_HOME}/bin:${CANAL_SERVER_HOME}/bin:${CANAL_ADMIN_HOME}/bin:$PATH3.查看flink是否能正常使用
4.hudi编译好的jar包和flinkcdc的jar包放到flink的lib下
flinkcdc2.1的jar包下载地址
https://github.com/ververica/flink-cdc-connectors/releases
5.以下三个包也要放到flink的lib下，否则同步数据到hive的时候会报错。
6.flink-sql的web的安装与部署
1.github上的下载地址
https://github.com/zhp8341/flink-streaming-platform-web
2.安装地址
https://github.com/zhp8341/flink-streaming-platform-web/blob/master/docs/deploy.md7.编译
mvn clean package -Dmaven.test.skip=true
8.部署
2、flink-streaming-platform-web安装（一定要和flink部署在同一台）
a:下载最新版本 并且解压 https://github.com/zhp8341/flink-streaming-platform-web/releases/
tar -xvf flink-streaming-platform-web.tar.gz
b:执行mysql语句
mysql 版本5.6+以上
创建数据库 数据库名：flink_web
执行表语句
语句地址 https://github.com/zhp8341/flink-streaming-platform-web/blob/master/docs/sql/flink_web.sql
c:修改数据库连接配置
/flink-streaming-platform-web/conf/application.properties
改成上面建好的mysql地址
关于数据库连接配置 需要看清楚你 useSSL=true 你的mysql是否支持 如果不支持可以直接 useSSL=false
d:启动web
cd /XXXX/flink-streaming-platform-web/bin
启动 : sh deploy.sh start
停止 : sh deploy.sh stop
日志目录地址： /XXXX/flink-streaming-platform-web/logs/
一定 一定 一定 要到bin目录下再执行deploy.sh 否则无法启动
e:登录
http://${ip或者hostname}:9084/ 如 : http://hadoop003:9084/admin/index
登录号：admin password: 123456最终的flink-web界面（支持流批一体和jar包）
四.flink cdc到hudi的demo测试
1.mysql的建表语句
CREATE TABLE test_order_v4 (
id int,
username varchar(20),
product varchar(20),
price double,
qty int,
create_time TIMESTAMP,
PRIMARY KEY (id)
);2.插入的测试数据
Insert into test_order_v4 (id,username,product,price,qty,create_time) values(200,'王昱翔','芒果',12,25,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(201,'王昱翔','芒果',12,26,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(202,'王昱翔','芒果',12,27,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(203,'王昱翔','芒果',12,28,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(204,'王昱翔','芒果',12,29,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(205,'王昱翔','芒果',12,30,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(206,'王昱翔','芒果',12,31,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(207,'王昱翔','芒果',12,32,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(208,'王昱翔','芒果',12,33,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(209,'王昱翔','芒果',12,34,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(210,'王昱翔','芒果',12,35,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(211,'王昱翔','芒果',12,36,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(212,'王昱翔','芒果',12,37,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(213,'王昱翔','芒果',12,38,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(214,'王昱翔','芒果',12,39,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(215,'王昱翔','芒果',12,40,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(216,'王昱翔','芒果',12,41,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(217,'王昱翔','芒果',12,42,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(218,'王昱翔','芒果',12,43,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(219,'王昱翔','芒果',12,44,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(220,'王昱翔','芒果',12,45,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(221,'王昱翔','芒果',12,46,current_timestamp());
insert into test_order_v4 (id,username,product,price,qty,create_time) values(222,'王昱翔','芒果',12,47,current_timestamp());3.flink-sql语句
1.创建flink cdc的表
CREATE TABLE test_order_v4 (
id INT,
username STRING,
product STRING,
price DOUBLE,
qty INT,
create_time TIMESTAMP(0),
PRIMARY KEY(id) NOT ENFORCED
) WITH (
'connector' = 'mysql-cdc',
'hostname' = '192.168.100.3',
'port' = '3306',
'username' = 'dmp',
'password' = 'wangyuxiang',
'server-time-zone' = 'Asia/Shanghai',
'debezium.snapshot.mode'='initial',
'database-name' = 'dmp',
'table-name' = 'test_order_v4'
);
2.创建hudi表
CREATE TABLE hudi_test_order_v4(
id INT,
username STRING,
product STRING,
price double,
qty INT,
create_time TIMESTAMP(0)
)
WITH (
'connector' = 'hudi'
, 'path' = 'hdfs://bigdata/hudi/hdm2_v4'
, 'hoodie.datasource.write.recordkey.field' = 'id' -- 主键
, 'write.precombine.field' = 'create_time' -- 相同的键值时，取此字段最大值，默认ts字段
, 'write.tasks' = '1'
, 'compaction.tasks' = '1'
, 'write.rate.limit' = '2000' -- 限制每秒多少条
, 'table.type' = 'MERGE_ON_READ' -- 默认COPY_ON_WRITE
, 'compaction.async.enabled' = 'true' -- 在线压缩
, 'compaction.trigger.strategy' = 'num_commits' -- 按次数压缩
, 'compaction.delta_commits' = '5' -- 默认为5
, 'hive_sync.enable' = 'true' -- 启用hive同步
, 'hive_sync.mode' = 'hms' -- 启用hive hms同步，默认jdbc
, 'hive_sync.metastore.uris' = 'thrift://dbos-bigdata-test002:9083' -- required, metastore的端口
, 'hive_sync.jdbc_url' = 'jdbc:hive2://dbos-bigdata-test002:10000' -- required, hiveServer地址
, 'hive_sync.table' = 'hudi_test_order_v4' -- required, hive 新建的表名
, 'hive_sync.db' = 'hudi2' -- required, hive 新建的数据库名
, 'hive_sync.username' = 'hive' -- required, HMS 用户名
, 'hive_sync.password' = '' -- required, HMS Password
, 'hive_sync.skip_ro_suffix' = 'true' -- 去除ro后缀
);
insert into hudi_test_order_v4 select id,username,product,price,qty,create_time from test_order_v4;4.提交并保存
5.选择开启配置后提交任务
6.任务提交成功
7.yarn上已有这个任务
-
开始insert into插入数据测试
8.flink运行的DAG图上显示已插入条96数据压缩了3次
9.到hdfs上查看生成的文件
10.查看hive上是否生成表（已生成RO和RT表）
11.查询hive表中的数据
select * from hudi_test_order_v4_rt;
select * from hudi_test_order_v4;说明：已自动生产hudi MOR模式的
hudi_test_order_v4(这是一个ro表，因为我在代码中去ro后缀了)
hudi_test_order_v4_rt
以下数据证明mysqlbinlog--hudi--hive的链路是成功的
五：mysql的update操作
1.先查询mysql中ID为200的数据和flink DAG目前的状态
2.mysql做update
更新一条数据
UPDATE test_order_v4 set username = 'Steven' WHERE id = 200;
SELECT * from test_order_v4 WHERE id = 200;
3.更新新一条语句后，提交数从96新增加到97，但是没有进行压缩。
4.查询RT表中有此数据更新的记录
select * from hudi_test_order_v4_rt;
5.查询RO表中数据没有更新。
select * from hudi_test_order_v4;
五：mysql做delete的操作
1.flink DAG的状态提交97次
2.查询一条id为200的数据
3.mysql中删除此数据
DELETE FROM test_order_v4 WHERE id = 200;
4.flink的DAG状态是提交了98次
6.查询hive的rt表
select * from hudi_test_order_v4_rt where id = 200;
hive中id=200的数据已经被删掉
7.查询hive的ro表
select * from hudi_test_order_v4 where id = 200;
数据还是存在的，因为roge表没有达到触发压缩的条件，所以一直没有压缩更新
六：综合模拟频繁的更新、插入、删除测试，达到触发压缩的条件
insert into test_order_v4 (id,username,product,price,qty,create_time) values(224,'王昱翔','芒果',12,47,current_timestamp());
UPDATE test_order_v4 set username = 'Steven' WHERE id = 201;
UPDATE test_order_v4 set username = '王新权' WHERE id = 202;
UPDATE test_order_v4 set username = 'Steven' WHERE id = 203;
DELETE FROM test_order_v4 WHERE id = 210;
DELETE FROM test_order_v4 WHERE id = 211;1.已达到生成parquet
2.查询rt表
select * from hudi_test_order_v4_rt where username = '王昱翔';
3.查询ro表
select * from hudi_test_order_v4 where username = '王昱翔';
4.hudi同步到hive表中的数据做count测试
select count(1) from hudi_test_order_v4 where username = '王昱翔';
rt表比ro表多两条数据
总结：
Hudi 表分为 COW 和 MOR两种类型
COW 表适用于离线批量更新场景，对于更新数据，会先读取旧的 base file，然后合并更新数据，生成新的 base file。
MOR 表适用于实时高频更新场景，更新数据会直接写入 log file 中，读时再进行合并。为了减少读放大的问题，会定期合并 log file 到 base file 中。
ro表和rt表区别：
ro 表全称 read oprimized table，对于 MOR 表同步的 xxx_ro 表，只暴露压缩后的 parquet。其查询方式和COW表类似。设置完 hiveInputFormat 之后 和普通的 Hive 表一样查询即可；
rt表示增量视图，主要针对增量查询的rt表；
ro表只能查parquet文件数据， rt表 parquet文件数据和log文件数据都可查；