1. 环境准备
•Flink 1.12.2_2.11•Hudi 0.9.0-SNAPSHOT(master分支)•Spark 2.4.5、Hadoop 3.1.3、Hive 3.1.2
2. Flink CDC写入Hudi
MySQL建表语句如下
create table users
(
id bigint auto_increment primary key,
name varchar(20) null,
birthday timestamp default CURRENT_TIMESTAMP not null,
ts timestamp default CURRENT_TIMESTAMP not null
);
// 随意插入几条数据
insert into users (name) values ('hello');
insert into users (name) values ('world');
insert into users (name) values ('iceberg');
insert into users (id,name) values (4,'spark');
insert into users (name) values ('hudi');
select * from users;
update users set name = 'hello spark' where id = 5;
delete from users where id = 5;
启动sql-client
$FLINK_HOME/bin/sql-client.sh embedded
//1.创建 mysql-cdc
CREATE TABLE mysql_users (
id BIGINT PRIMARY KEY NOT ENFORCED ,
name STRING,
birthday TIMESTAMP(3),
ts TIMESTAMP(3)
) WITH (
'connector' = 'mysql-cdc',
'hostname' = 'localhost',
'port' = '3306',
'username' = 'root',
'password' = '123456',
'server-time-zone' = 'Asia/Shanghai',
'database-name' = 'mydb',
'table-name' = 'users'
);
// 2.创建hudi表
CREATE TABLE hudi_users2
(
id BIGINT PRIMARY KEY NOT ENFORCED,
name STRING,
birthday TIMESTAMP(3),
ts TIMESTAMP(3),
`partition` VARCHAR(20)
) PARTITIONED BY (`partition`) WITH (
'connector' = 'hudi',
'table.type' = 'MERGE_ON_READ',
'path' = 'hdfs://localhost:9000/hudi/hudi_users2',
'read.streaming.enabled' = 'true',
'read.streaming.check-interval' = '1'
);
//3.mysql-cdc 写入hudi ，会提交有一个flink任务
INSERT INTO hudi_users2 SELECT *, DATE_FORMAT(birthday, 'yyyyMMdd') FROM mysql_users;
Flink任务提交成功后可以查看任务界面
同时可以查看HDFS里的Hudi数据路径，这里需要等Flink 5次checkpoint（默认配置可修改）之后才能查看到这些目录，一开始只有.hoodie一个文件夹
在MySQL执行insert、update、delete等操作，当进行compaction生成parquet文件后就可以用hive/spark-sql/presto(本文只做了hive和spark-sql的测试)进行查询，这里需要注意下：如果没有生成parquet文件，我们建的parquet表是查询不出数据的。
3. Hive查询Hudi表
cd $HIVE_HOMEmkdir auxlib
然后将hudi-hadoop-mr-bundle-0.9.0-SNAPSHOT.jar拷贝过来
使用beeline登录hive
beeline -u jdbc:hive2://localhost:10000 -n hadoop hadoop
创建外部表关联Hudi路径，有两种建表方式
方式一：INPUTFORMAT是org.apache.hudi.hadoop.HoodieParquetInputFormat
这种方式只会查询出来parquet数据文件中的内容，但是刚刚更新或者删除的数据不能查出来
// 创建外部表
CREATE EXTERNAL TABLE `hudi_users_2`(
`_hoodie_commit_time` string,
`_hoodie_commit_seqno` string,
`_hoodie_record_key` string,
`_hoodie_partition_path` string,
`_hoodie_file_name` string,
`id` bigint,
`name` string,
`birthday` bigint,
`ts` bigint)
PARTITIONED BY (
`partition` string)
ROW FORMAT SERDE
'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
STORED AS INPUTFORMAT
'org.apache.hudi.hadoop.HoodieParquetInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
LOCATION
'hdfs://localhost:9000/hudi/hudi_users2';
方式二：INPUTFORMAT是org.apache.hudi.hadoop.realtime.HoodieParquetRealtimeInputFormat
// 这种方式是能够实时读出来写入的数据，也就是Merge On Write，会将基于Parquet的基础列式文件、和基于行的Avro日志文件合并在一起呈现给用户。
CREATE EXTERNAL TABLE `hudi_users_2_mor`(
`_hoodie_commit_time` string,
`_hoodie_commit_seqno` string,
`_hoodie_record_key` string,
`_hoodie_partition_path` string,
`_hoodie_file_name` string,
`id` bigint,
`name` string,
`birthday` bigint,
`ts` bigint)
PARTITIONED BY (
`partition` string)
ROW FORMAT SERDE
'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
STORED AS INPUTFORMAT
'org.apache.hudi.hadoop.realtime.HoodieParquetRealtimeInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
LOCATION
'hdfs://localhost:9000/hudi/hudi_users2';
// 添加分区
alter table hudi_users_2 add if not exists partition(`partition`='20210414') location 'hdfs://localhost:9000/hudi/hudi_users2/20210414';
alter table hudi_users_2_mor add if not exists partition(`partition`='20210414') location 'hdfs://localhost:9000/hudi/hudi_users2/20210414';
// 查询分区的数据
select * from hudi_users_2 where `partition`=20210414;
select * from hudi_users_2_mor where `partition`=20210414;
INPUTFORMAT是org.apache.hudi.hadoop.realtime.HoodieParquetRealtimeInputFormat格式的表在hive3.1.2里面是不能够执行统计操作的
执行select count(1) from hudi_users3_mor where partition='20210414';
查看hive日志 tail -fn 100 hiveserver2.log
需要进行如下设置：set hive.input.format = org.apache.hudi.hadoop.hive.HoodieCombineHiveInputFormat ;具体原因参照这个issue：https://github.com/apache/hudi/issues/2813，或者阿里云技术文档：https://help.aliyun.com/document_detail/193310.html?utm_content=g_1000230851&spm=5176.20966629.toubu.3.f2991ddcpxxvD1#title-ves-82n-odd
再执行一遍依旧报错
但是在本地用hive-2.3.8执行成功了，社群里面的同学测试1.1版本的也报同样的错误，目前猜测是hive版本兼容性有关
4. Spark-SQL查询Hudi表
将hudi-spark-bundle_2.11-0.9.0-SNAPSHOT.jar拷贝到$SPAKR_HOME/jars，每个节点都拷贝一份将hudi-hadoop-mr-bundle-0.9.0-SNAPSHOT.jar拷贝到$HADOOP_HOME/share/hadoop/hdfs下，每个节点都拷贝一份，然后重启hadoop
创建表，同样有两种方式
CREATE EXTERNAL TABLE `hudi_users3_spark`(
`_hoodie_commit_time` string,
`_hoodie_commit_seqno` string,
`_hoodie_record_key` string,
`_hoodie_partition_path` string,
`_hoodie_file_name` string,
`id` bigint,
`name` string,
`birthday` bigint,
`ts` bigint)
PARTITIONED BY (
`partition` string)
ROW FORMAT SERDE
'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
STORED AS INPUTFORMAT
'org.apache.hudi.hadoop.HoodieParquetInputFormat'
OUTPUTFORMAT
'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
LOCATION
'hdfs://localhost:9000/hudi/hudi_users3';
alter table hudi_users3_spark add if not exists partition(`partition`='20210414') location 'hdfs://localhost:9000/hudi/hudi_users3/20210414';
select * from hudi_users3_spark where `partition`='20210414';
// 创建可以实时读表数据的格式
CREATE EXTERNAL TABLE `hudi_users3_spark_mor`(
`_hoodie_commit_time` string,
`_hoodie_commit_seqno` string,
`_hoodie_record_key` string,
`_hoodie_partition_path` string,
`_hoodie_file_name` string,
`id` bigint,
`name` string,
`birthday` bigint,
`ts` bigint)
PARTITIONED BY (
`partition` string)
ROW FORMAT SERDE
'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
STORED AS INPUTFORMAT
'org.apache.hudi.hadoop.HoodieParquetInputFormat'
OUTPUTFORMAT
'org.apache.hudi.hadoop.realtime.HoodieParquetRealtimeInputFormat'
LOCATION
'hdfs://localhost:9000/hudi/hudi_users3';
alter table hudi_users3_spark_mor add if not exists partition(`partition`='20210414') location 'hdfs://localhost:9000/hudi/hudi_users3/20210414';
select * from hudi_users3_spark_mor where `partition`='20210414';
如果Spark-SQL读取实时Hudi数据，必须进行如下设置set spark.sql.hive.convertMetastoreParquet=false;
这里需要注意如果创建表的时候字段类型不对会报错，比如
CREATE EXTERNAL TABLE `hudi_users3_spark_mor`(
`_hoodie_commit_time` string,
`_hoodie_commit_seqno` string,
`_hoodie_record_key` string,
`_hoodie_partition_path` string,
`_hoodie_file_name` string,
`id` string,
`name` string,
`birthday` string,
`ts` string)
PARTITIONED BY (
`partition` string)
ROW FORMAT SERDE
'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
STORED AS INPUTFORMAT
'org.apache.hudi.hadoop.HoodieParquetInputFormat'
OUTPUTFORMAT
'org.apache.hudi.hadoop.realtime.HoodieParquetRealtimeInputFormat'
LOCATION
'hdfs://localhost:9000/hudi/hudi_users3';
id 、ts、birthday都设置为String，会报下面错误。Spark-SQL想读取Hudi数据，字段类型需要严格匹配
5. 后续
目前使用小规模数据测试Flink CDC写入Hudi，后面我们准备用生产数据来走一波，看看Flink-CDC写入Hudi的性能和稳定性。
