关于Flink:Hudi-on-Flink-快速上手指南

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摘要：本文由阿里巴巴的陈玉兆分享，次要介绍 Flink 集成 Hudi 的最新版本性能以及疾速上手实际指南。内容包含：

  1. 背景
  2. 环境筹备
  3. Batch 模式的读写
  4. Streaming 读
  5. 总结

一、背景

Apache Hudi 是目前最风行的数据湖解决方案之一，Data Lake Analytics[1] 集成了 Hudi 服务高效的数据 MERGE（UPDATE/DELETE）场景；AWS 在 EMR 服务中 预装置 [2] 了 Apache Hudi，为用户提供高效的 record-level updates/deletes 和高效的数据查问治理；Uber [3] 曾经稳固运行 Apache Hudi 服务 4 年多，提供了低提早的数据库同步和高效率的查问[4]。自 2016 年 8 月上线以来，数据湖存储规模曾经超过 100PB[5]。

Apache Flink 作为目前最风行的流计算框架，在流式计算场景有人造的劣势，以后，Flink 社区也在踊跃拥抱 Hudi 社区，施展本身 streaming 写 / 读的劣势，同时也对 batch 的读写做了反对。

Hudi 和 Fink 在 0.8.0 版本做了大量的集成工作[6]。外围的性能包含：

  • 实现了新的 Flink streaming writer
  • 反对 batch 和 streaming 模式 reader
  • 反对 Flink SQL API

Flink streaming writer 通过 state 实现了高效的 index 计划，同时 Hudi 在 UPDATE/DELETE 上的优良设计使得 Flink Hudi 成为以后最有后劲的 CDC 数据入湖计划，因为篇幅关系，将在后续的文章中介绍。

本文用 Flink SQL Client 来简略的演示通过 Flink SQL API 的形式实现 Hudi 表的操作，包含 batch 模式的读写和 streaming 模式的读。

二、环境筹备

本文应用 Flink Sql Client[7] 作为演示工具，SQL CLI 能够比拟不便地执行 SQL 的交互操作。

第一步：下载 Flink jar

Hudi 集成了 Flink 的 1.11 版本。您能够参考这里 [8] 来设置 Flink 环境。hudi-flink-bundle jar 是一个集成了 Flink 相干的 jar 的 uber jar, 目前举荐应用 scala 2.11 来编译。

第二步：设置 Flink 集群

启动一个 standalone 的 Flink 集群。启动之前，倡议将 Flink 的集群配置设置如下：

  • 在 $FLINK_HOME/conf/flink-conf.yaml 中增加配置项 taskmanager.numberOfTaskSlots: 4
  • 在 $FLINK_HOME/conf/workers 中将条目 localhost 设置成 4 行，这里的行数代表了本地启动的 worker 数

启动集群：

# HADOOP_HOME is your hadoop root directory after unpack the binary package.
export HADOOP_CLASSPATH=`$HADOOP_HOME/bin/hadoop classpath`
# Start the flink standalone cluster
./bin/start-cluster.sh

第三步：启动 Flink SQL Client

Hudi 的 bundle jar 应该在 Sql Client 启动的时候加载到 CLASSPATH 中。您能够在门路 hudi-source-dir/packaging/hudi-flink-bundle 下手动编译 jar 包或者从 Apache Official Repository [9]下载。

启动 SQL CLI:

# HADOOP_HOME is your hadoop root directory after unpack the binary package.
export HADOOP_CLASSPATH=`$HADOOP_HOME/bin/hadoop classpath`
./bin/sql-client.sh embedded -j .../hudi-flink-bundle_2.1?-*.*.*.jar shell

备注：

  • 举荐应用 hadoop 2.9.x+ 版本，因为一些对象存储（aliyun-oss）从这个版本开始反对
  • flink-parquet 和 flink-avro 曾经被打进 hudi-flink-bundle jar
  • 您也能够间接将 hudi-flink-bundle jar 拷贝到 $FLINK_HOME/lib 目录下
  • 本文的存储选取了对象存储 aliyun-oss，为了不便，您也能够应用本地门路

演示的工作目录构造如下：

/Users/chenyuzhao/workspace/hudi-demo
  /- flink-1.11.3
  /- hadoop-2.9.2

三、Batch 模式的读写

插入数据

应用如下 DDL 语句创立 Hudi 表：

Flink SQL> create table t2(>   uuid varchar(20),
>   name varchar(10),
>   age int,
>   ts timestamp(3),
>   `partition` varchar(20)
> )
> PARTITIONED BY (`partition`)
> with (
>   'connector' = 'hudi',
>   'path' = 'oss://vvr-daily/hudi/t2'
> );
[INFO] Table has been created.

DDL 里申明了表的 path，record key 为默认值 uuid，pre-combine key 为默认值 ts。

而后通过 VALUES 语句往表中插入数据：

Flink SQL> insert into t2 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');
[INFO] Submitting SQL update statement to the cluster...
[INFO] Table update statement has been successfully submitted to the cluster:
Job ID: 59f2e528d14061f23c552a7ebf9a76bd

这里看到 Flink 的作业曾经胜利提交到集群，能够本地关上 web UI 察看作业的执行状况：

查问数据

作业执行实现后，通过 SELECT 语句查问表后果：

Flink SQL> set execution.result-mode=tableau;
[INFO] Session property has been set.
Flink SQL> select * from t2;
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
| +/- |                 uuid |                 name |         age |                      ts |            partition |
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
|   + |                  id3 |               Julian |          53 |     1970-01-01T00:00:03 |                 par2 |
|   + |                  id4 |               Fabian |          31 |     1970-01-01T00:00:04 |                 par2 |
|   + |                  id7 |                  Bob |          44 |     1970-01-01T00:00:07 |                 par4 |
|   + |                  id8 |                  Han |          56 |     1970-01-01T00:00:08 |                 par4 |
|   + |                  id1 |                Danny |          23 |     1970-01-01T00:00:01 |                 par1 |
|   + |                  id2 |              Stephen |          33 |     1970-01-01T00:00:02 |                 par1 |
|   + |                  id5 |               Sophia |          18 |     1970-01-01T00:00:05 |                 par3 |
|   + |                  id6 |                 Emma |          20 |     1970-01-01T00:00:06 |                 par3 |
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
Received a total of 8 rows

这里执行语句 set execution.result-mode=tableau; 能够让查问后果间接输入到终端。

通过在 WHERE 子句中增加 partition 门路来裁剪 partition：

Flink SQL> select * from t2 where `partition` = 'par1';
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
| +/- |                 uuid |                 name |         age |                      ts |            partition |
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
|   + |                  id1 |                Danny |          23 |     1970-01-01T00:00:01 |                 par1 |
|   + |                  id2 |              Stephen |          33 |     1970-01-01T00:00:02 |                 par1 |
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
Received a total of 2 rows

更新数据

雷同的 record key 的数据会主动笼罩，通过 INSERT 雷同 key 的数据能够实现数据更新：

Flink SQL> insert into t2 values
>   ('id1','Danny',24,TIMESTAMP '1970-01-01 00:00:01','par1'),
>   ('id2','Stephen',34,TIMESTAMP '1970-01-01 00:00:02','par1');
[INFO] Submitting SQL update statement to the cluster...
[INFO] Table update statement has been successfully submitted to the cluster:
Job ID: 944de5a1ecbb7eeb4d1e9e748174fe4c
Flink SQL> select * from t2;
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
| +/- |                 uuid |                 name |         age |                      ts |            partition |
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
|   + |                  id1 |                Danny |          24 |     1970-01-01T00:00:01 |                 par1 |
|   + |                  id2 |              Stephen |          34 |     1970-01-01T00:00:02 |                 par1 |
|   + |                  id3 |               Julian |          53 |     1970-01-01T00:00:03 |                 par2 |
|   + |                  id4 |               Fabian |          31 |     1970-01-01T00:00:04 |                 par2 |
|   + |                  id5 |               Sophia |          18 |     1970-01-01T00:00:05 |                 par3 |
|   + |                  id6 |                 Emma |          20 |     1970-01-01T00:00:06 |                 par3 |
|   + |                  id7 |                  Bob |          44 |     1970-01-01T00:00:07 |                 par4 |
|   + |                  id8 |                  Han |          56 |     1970-01-01T00:00:08 |                 par4 |
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
Received a total of 8 rows

能够看到 uuid 为 id1 和 id2 的数据 age 字段值产生了更新。

再次 insert 新数据察看后果：

Flink SQL> insert into t2 values
>   ('id4','Fabian',32,TIMESTAMP '1970-01-01 00:00:04','par2'),
>   ('id5','Sophia',19,TIMESTAMP '1970-01-01 00:00:05','par3');
[INFO] Submitting SQL update statement to the cluster...
[INFO] Table update statement has been successfully submitted to the cluster:
Job ID: fdeb7fd9f08808e66d77220f43075720
Flink SQL> select * from t2;
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
| +/- |                 uuid |                 name |         age |                      ts |            partition |
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
|   + |                  id5 |               Sophia |          19 |     1970-01-01T00:00:05 |                 par3 |
|   + |                  id6 |                 Emma |          20 |     1970-01-01T00:00:06 |                 par3 |
|   + |                  id3 |               Julian |          53 |     1970-01-01T00:00:03 |                 par2 |
|   + |                  id4 |               Fabian |          32 |     1970-01-01T00:00:04 |                 par2 |
|   + |                  id1 |                Danny |          24 |     1970-01-01T00:00:01 |                 par1 |
|   + |                  id2 |              Stephen |          34 |     1970-01-01T00:00:02 |                 par1 |
|   + |                  id7 |                  Bob |          44 |     1970-01-01T00:00:07 |                 par4 |
|   + |                  id8 |                  Han |          56 |     1970-01-01T00:00:08 |                 par4 |
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
Received a total of 8 rows

四、Streaming 读

通过如下语句创立一张新的表并注入数据：

Flink SQL> create table t1(>   uuid varchar(20),
>   name varchar(10),
>   age int,
>   ts timestamp(3),
>   `partition` varchar(20)
> )
> PARTITIONED BY (`partition`)
> with (
>   'connector' = 'hudi',
>   'path' = 'oss://vvr-daily/hudi/t1',
>   'table.type' = 'MERGE_ON_READ',
>   'read.streaming.enabled' = 'true',
>   'read.streaming.check-interval' = '4'
> );
[INFO] Table has been created.
Flink SQL> 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');
[INFO] Submitting SQL update statement to the cluster...
[INFO] Table update statement has been successfully submitted to the cluster:
Job ID: 9e1dcd37fd0f8ca77534c30c7d87be2c

这里将 table option read.streaming.enabled 设置为 true，表明通过 streaming 的形式读取表数据；opiton read.streaming.check-interval 指定了 source 监控新的 commits 的距离为 4s；option table.type 设置表类型为 MERGE_ON_READ，目前只有 MERGE_ON_READ 表反对 streaming 读。

以上操作产生在一个 terminal 中，咱们称之为 terminal_1。

从新的 terminal（咱们称之为 terminal_2）再次启动 Sql Client，从新创立 t1 表并查问：

Flink SQL> set execution.result-mode=tableau;
[INFO] Session property has been set.
Flink SQL> create table t1(>   uuid varchar(20),
>   name varchar(10),
>   age int,
>   ts timestamp(3),
>   `partition` varchar(20)
> )
> PARTITIONED BY (`partition`)
> with (
>   'connector' = 'hudi',
>   'path' = 'oss://vvr-daily/hudi/t1',
>   'table.type' = 'MERGE_ON_READ',
>   'read.streaming.enabled' = 'true',
>   'read.streaming.check-interval' = '4'
> );
[INFO] Table has been created.
Flink SQL> select * from t1;
2021-03-22 18:36:37,042 INFO  org.apache.hadoop.conf.Configuration.deprecation             [] - mapred.job.map.memory.mb is deprecated. Instead, use mapreduce.map.memory.mb
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
| +/- |                 uuid |                 name |         age |                      ts |            partition |
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
|   + |                  id2 |              Stephen |          33 |     1970-01-01T00:00:02 |                 par1 |
|   + |                  id1 |                Danny |          23 |     1970-01-01T00:00:01 |                 par1 |
|   + |                  id6 |                 Emma |          20 |     1970-01-01T00:00:06 |                 par3 |
|   + |                  id5 |               Sophia |          18 |     1970-01-01T00:00:05 |                 par3 |
|   + |                  id8 |                  Han |          56 |     1970-01-01T00:00:08 |                 par4 |
|   + |                  id7 |                  Bob |          44 |     1970-01-01T00:00:07 |                 par4 |
|   + |                  id4 |               Fabian |          31 |     1970-01-01T00:00:04 |                 par2 |
|   + |                  id3 |               Julian |          53 |     1970-01-01T00:00:03 |                 par2 |

回到 terminal_1，继续执行 batch mode 的 INSERT 操作：

Flink SQL> insert into t1 values
>   ('id1','Danny',27,TIMESTAMP '1970-01-01 00:00:01','par1');
[INFO] Submitting SQL update statement to the cluster...
[INFO] Table update statement has been successfully submitted to the cluster:
Job ID: 2dad24e067b38bc48c3a8f84e793e08b

几秒之后，察看 terminal_2 的输入多了一行：

+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
| +/- |                 uuid |                 name |         age |                      ts |            partition |
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
|   + |                  id2 |              Stephen |          33 |     1970-01-01T00:00:02 |                 par1 |
|   + |                  id1 |                Danny |          23 |     1970-01-01T00:00:01 |                 par1 |
|   + |                  id6 |                 Emma |          20 |     1970-01-01T00:00:06 |                 par3 |
|   + |                  id5 |               Sophia |          18 |     1970-01-01T00:00:05 |                 par3 |
|   + |                  id8 |                  Han |          56 |     1970-01-01T00:00:08 |                 par4 |
|   + |                  id7 |                  Bob |          44 |     1970-01-01T00:00:07 |                 par4 |
|   + |                  id4 |               Fabian |          31 |     1970-01-01T00:00:04 |                 par2 |
|   + |                  id3 |               Julian |          53 |     1970-01-01T00:00:03 |                 par2 |
|   + |                  id1 |                Danny |          27 |     1970-01-01T00:00:01 |                 par1 |

再次在 terminal_1 中执行 INSERT 操作：

Flink SQL> insert into t1 values
>   ('id4','Fabian',32,TIMESTAMP '1970-01-01 00:00:04','par2'),
>   ('id5','Sophia',19,TIMESTAMP '1970-01-01 00:00:05','par3');
[INFO] Submitting SQL update statement to the cluster...
[INFO] Table update statement has been successfully submitted to the cluster:
Job ID: ecafffda3d294a13b0a945feb9acc8a5

察看 terminal_2 的输入变动：

+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
| +/- |                 uuid |                 name |         age |                      ts |            partition |
+-----+----------------------+----------------------+-------------+-------------------------+----------------------+
|   + |                  id2 |              Stephen |          33 |     1970-01-01T00:00:02 |                 par1 |
|   + |                  id1 |                Danny |          23 |     1970-01-01T00:00:01 |                 par1 |
|   + |                  id6 |                 Emma |          20 |     1970-01-01T00:00:06 |                 par3 |
|   + |                  id5 |               Sophia |          18 |     1970-01-01T00:00:05 |                 par3 |
|   + |                  id8 |                  Han |          56 |     1970-01-01T00:00:08 |                 par4 |
|   + |                  id7 |                  Bob |          44 |     1970-01-01T00:00:07 |                 par4 |
|   + |                  id4 |               Fabian |          31 |     1970-01-01T00:00:04 |                 par2 |
|   + |                  id3 |               Julian |          53 |     1970-01-01T00:00:03 |                 par2 |
|   + |                  id1 |                Danny |          27 |     1970-01-01T00:00:01 |                 par1 |
|   + |                  id5 |               Sophia |          19 |     1970-01-01T00:00:05 |                 par3 |
|   + |                  id4 |               Fabian |          32 |     1970-01-01T00:00:04 |                 par2 |

五、总结

通过一些简略的演示，咱们发现 HUDI Flink 的集成曾经绝对欠缺，读写门路均已笼罩，对于具体的配置，能够参考 Flink SQL Config Options[10]。

Hudi 社区正在踊跃的推动和 Flink 的深度集成，包含但不限于：

  • Flink streaming reader 反对 watermark，实现数据湖 / 仓的两头计算层 pipeline
  • Flink 基于 Hudi 的物化视图，实现分钟级的增量视图，服务于线上的近实时查问

正文：
[1] https://www.alibabacloud.com/…
[2]https://aws.amazon.com/cn/emr…
[3]https://www.uber.com/
[4]http://www.slideshare.net/vin…
[5]https://eng.uber.com/uber-big…
[6]https://issues.apache.org/jir…
[7]https://ci.apache.org/project…
[8]https://flink.apache.org/down…
[9]https://repo.maven.apache.org…
[10]https://hudi.apache.org/docs/…

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发表至： flink
2021-03-29
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