使用flink插入数据到hudi数据湖初探

从大数据到人工智能

发布于 2022-01-19 08:14:14

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发布于 2022-01-19 08:14:14

文章被收录于专栏：大数据-BigData

环境：

hadoop 3.2.0
flink 1.11.4-bin-scala_2.11
hudi 0.8.0

本文基于上述组件版本使用flink插入数据到hudi数据湖中。为了确保以下各步骤能够成功完成，请确保hadoop集群正常启动。

确保已经配置环境变量HADOOP_CLASSPATH

对于开源版本hadoop，HADOOP_CLASSPATH配置为：

export HADOOP_CLASSPATH=$HADOOP_CLASSPATH:$HADOOP_HOME/share/hadoop/client/*:$HADOOP_HOME/share/hadoop/common/*:$HADOOP_HOME/share/hadoop/hdfs/*:$HADOOP_HOME/share/hadoop/mapreduce/*:$HADOOP_HOME/share/hadoop/tools/*:$HADOOP_HOME/share/hadoop/yarn/*:$HADOOP_HOME/etc/hadoop/*

本文使用的hdfs为高可用集群，对应hdfs为：hdfs://mycluster

本地安装flink集群

flink下载

wget https://mirrors.tuna.tsinghua.edu.cn/apache/flink/flink-1.11.4/flink-1.11.4-bin-scala_2.11.tgz
tar zxvf flink-1.11.4-bin-scala_2.11.tgz

下载hudi相关jar包，需要下载hudi-flink-bundle_2.11-0.8.0.jar、commons-logging-1.2.jar、htrace-core-3.1.0-incubating.jar以及htrace-core4-4.1.0-incubating.jar这四个jar包到flink的lib目录下，其中

cd flink-1.11.4/lib
wget https://repo.maven.apache.org/maven2/org/apache/hudi/hudi-flink-bundle_2.11/0.8.0/hudi-flink-bundle_2.11-0.8.0.jar
wget https://repo1.maven.org/maven2/commons-logging/commons-logging/1.2/commons-logging-1.2.jar
wget https://repo1.maven.org/maven2/org/apache/htrace/htrace-core/3.1.0-incubating/htrace-core-3.1.0-incubating.jar
wget https://repo1.maven.org/maven2/org/apache/htrace/htrace-core4/4.1.0-incubating/htrace-core4-4.1.0-incubating.jar

修改配置文件

vi conf/workers，写入四个localhost

localhost
localhost
localhost
localhost

vi conf/flink-conf.yaml，修改taskmanager.numberOfTaskSlots的值为4

taskmanager.numberOfTaskSlots: 4

启动flink集群

bin/start-cluster.sh

启动flink-sql client

执行以下命令启动flink sql

./bin/sql-client.sh embedded -j ./lib/hudi-flink-bundle_2.11-0.8.0.jar shell

创建t1表

create table t1(
 uuid VARCHAR(20),
 name VARCHAR(20),
 age INT,
 ts TIMESTAMP(3),
 `partition` VARCHAR(20)
 )
 PARTITIONED BY (`partition`)
 WITH (
   'connector'='hudi',
   'path' = 'hdfs://mycluster/tmp/t1',
   'table.type' = 'MERGE_ON_READ'
 );

插入数据到t1表

 INSERT INTO t1 VALUES
   ('id1','Danny',23,TIMESTAMP '1970-01-01 00:00:01','par1'),
   ('id2','Stephen',33,TIMESTAMP '1970-01-01 00:00:02','par1'),
   ('id3','Julian',53,TIMESTAMP '1970-01-01 00:00:03','par2'),
   ('id4','Fabian',31,TIMESTAMP '1970-01-01 00:00:04','par2'),
   ('id5','Sophia',18,TIMESTAMP '1970-01-01 00:00:05','par3'),
   ('id6','Emma',20,TIMESTAMP '1970-01-01 00:00:06','par3'),
   ('id7','Bob',44,TIMESTAMP '1970-01-01 00:00:07','par4'),
   ('id8','Han',56,TIMESTAMP '1970-01-01 00:00:08','par4');

数据更新

insert into t1 values ('id1','Danny',27,TIMESTAMP '1970-01-01 00:00:01','par1');

数据查询

select * from t1 limit 10;

查询结果：

查看hdfs上对应表的分区

执行命令：

hdfs dfs -ls /tmp/t1

得到

本文为从大数据到人工智能博主「xiaozhch5」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。

原文链接：https://cloud.tencent.com/developer/article/1936504

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原始发表：2021-11-，如有侵权请联系 cloudcommunity@tencent.com 删除

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