前言

SeaTunnel（先前称为WaterDrop）是一个分布式、高性能、易于扩展的数据集成平台，旨在实现海量数据的同步和转换。它支持多种数据处理引擎，包括Apache Spark和Apache Flink，并在某个版本中引入了自主研发的Zeta引擎。SeaTunnel不仅适用于离线数据同步，还能支持CDC（Change Data Capture）实时数据同步，这使得它在处理多样化数据集成场景时表现出色。

本节内容作为官方的一个补充测验，快速开始体验吧。

623f961e2fe94253977c786f4f4cc72c_1d5b84d8e0d5381460c8864ad475bcd0.png

一、Apache Seatunnel是什么？

从官网的介绍看：

Next-generation high-performance, distributed, massive data integration tool.

通过这几个关键词你能看到它的定位：下一代，高性能，分布式，大规模数据集成工具。

那到底好不好用呢？

二、安装

1、下载：https://seatunnel.apache.org/download

推荐：v2.3.5

2、安装

环境：Java8
安装插件：修改 config/plugin_config 以下是一些常用的，不要的暂时不装。
--connectors-v2--
connector-cdc-mysql
connector-clickhouse
connector-file-local
connector-hive
connector-jdbc
connector-kafka
connector-redis
connector-doris
connector-fake
connector-console
connector-elasticsearch
--end--

然后执行：

➜ seatunnel bin/install-plugin.sh 2.3.5

等待执行完毕，就安装完了，很简单。

三、测试

1. 测试 local模式下的用例

修改下模板的测试用例，然后执行如下命令：

bin/seatunnel.sh --config ./config/v2.batch.config -e local
任务的配置很简单：
这里使用了FakeSource来模拟输出两列，通过设置并行度=2 来打印 16 条输出数据。
2024-07-01 21:56:06,617 INFO  [o.a.s.c.s.u.ConfigBuilder     ] [main] - Parsed config file:
{
    "env" : {
        "parallelism" : 2,
        "job.mode" : "BATCH",
        "checkpoint.interval" : 10000
    },
    "source" : [
        {
            "schema" : {
                "fields" : {
                    "name" : "string",
                    "age" : "int"
                }
            },
            "row.num" : 16,
            "parallelism" : 2,
            "result_table_name" : "fake",
            "plugin_name" : "FakeSource"
        }
    ],
    "sink" : [
        {
            "plugin_name" : "Console"
        }
    ]
}

 任务的输出信息，这里的输出组件是 Console所以打印到了控制台
2024-07-01 21:56:07,559 INFO  [o.a.s.c.s.f.s.FakeSourceReader] [BlockingWorker-TaskGroupLocation{jobId=860156818549112833, pipelineId=1, taskGroupId=30000}] - Closed the bounded fake source
2024-07-01 21:56:07,561 INFO  [.a.s.c.s.c.s.ConsoleSinkWriter] [st-multi-table-sink-writer-1] - subtaskIndex=0  rowIndex=1:  SeaTunnelRow#tableId=fake SeaTunnelRow#kind=INSERT : hECbG, 520364021
2024-07-01 21:56:07,561 INFO  [.a.s.c.s.c.s.ConsoleSinkWriter] [st-multi-table-sink-writer-1] - subtaskIndex=1  rowIndex=1:  SeaTunnelRow#tableId=fake SeaTunnelRow#kind=INSERT : LnGDW, 105727523
2024-07-01 21:56:07,561 INFO  [.a.s.c.s.c.s.ConsoleSinkWriter] [st-multi-table-sink-writer-1] - subtaskIndex=0  rowIndex=2:  SeaTunnelRow#tableId=fake SeaTunnelRow#kind=INSERT : UYXBT, 1212484110
2024-07-01 21:56:07,561 INFO  [.a.s.c.s.c.s.ConsoleSinkWriter] [st-multi-table-sink-writer-1] - subtaskIndex=1  rowIndex=2:  SeaTunnelRow#tableId=fake SeaTunnelRow#kind=INSERT : NYiCn, 1208734703
2024-07-01 21:56:07,561 INFO  [.a.s.c.s.c.s.ConsoleSinkWriter] [st-multi-table-sink-writer-1] - subtaskIndex=0  rowIndex=3:  SeaTunnelRow#tableId=fake SeaTunnelRow#kind=INSERT : cSZan, 151817804
任务的统计信息：
***********************************************
           Job Statistic Information
***********************************************
Start Time                : 2024-07-01 21:56:06
End Time                  : 2024-07-01 21:56:08
Total Time(s)             :                   2
Total Read Count          :                  32
Total Write Count         :                  32
Total Failed Count        :                   0
***********************************************

2. 使用 Flink引擎

在上面的测试用例中可以看到如下的日志输出：

 Discovery plugin jar for: PluginIdentifier{engineType='seatunnel', pluginType='source', pluginName='FakeSource'

这表示默认情况下它使用的是 seatunnel engine 执行的，官方称之为 zeta 。这一块内容我们先看下 Flink引擎这边是如何执行的。

1、下载安装 flink1.17

https://nightlies.apache.org/flink/flink-docs-stable/docs/try-flink/local_installation/

启动local cluster 模式

➜  flink bin/start-cluster.sh
Starting cluster.
Starting standalonesession daemon on host MacBook-Pro-2.local.
Starting taskexecutor daemon on host MacBook-Pro-2.local.

2、配置环境变量

➜  config cat seatunnel-env.sh
# Home directory of spark distribution.
SPARK_HOME=${SPARK_HOME:-/Users/mac/apps/spark}
# Home directory of flink distribution.
FLINK_HOME=${FLINK_HOME:-/Users/mac/apps/flink}

3、修改slot插槽数量为大于等于 2

为什么？因为默认的配置中配置了 2 个并行度，而 local启动的默认情况下只有个插槽可供使用，因此任务无法运行。

703ffbb991106ec76b2a13f55f14709a_ec7f3dc38c536844a4775e6cb1b728ab.png

默认启动后资源插槽：

8b5a621ab32a9c3babef6c00f74b6506_bac6d35091b9a9c45ed2687ae685d0e7.png

提交程序运行后，发现一直无法对 sourcez做任务切分：

624434b8c95cde5b9ae40bfb6f0bb48f_31d392ffbfff40a912c9b8ad8ba9679c.png

这是因为 job 的并行度是 2，如下所示：

eaf875a0e1c0947b83ca9492a73f182d_1955133fcbc1cf04a12b7d5ba314ecfe.png

5b22cb455b9881a105eefe8b12fc78d9_a00077bf387f95ec68597dbc43024ec4.png

因此需要修改插槽数量才可以运行，官方这点可没说清楚，需要注意下。

4、运行测试用例

➜  seatunnel bin/start-seatunnel-flink-15-connector-v2.sh --config ./config/v2.streaming.conf.template
Execute SeaTunnel Flink Job: ${FLINK_HOME}/bin/flink run -c org.apache.seatunnel.core.starter.flink.SeaTunnelFlink /Users/mac/server/apache-seatunnel-2.3.5/starter/seatunnel-flink-15-starter.jar --config ./config/v2.streaming.conf.template --name SeaTunnel
Job has been submitted with JobID 9a949409a6f218d50b66ca22cc49b9c4

现在我们修改插槽数量为 2，测试如下：

访问：http://localhost:8081/#/overview

17d4fb65fd88f9ff2cc110642b56d474_b47f610ebab601aa6f800eccd64ce83e.png

TaskManager输出日志如下：

3. 使用 Spark引擎

提交命令

➜  seatunnel bin/start-seatunnel-spark-3-connector-v2.sh \
--master 'local[4]' \
--deploy-mode client \
--config ./config/v2.streaming.conf.template

Execute SeaTunnel Spark Job: ${SPARK_HOME}/bin/spark-submit --class "org.apache.seatunnel.core.starter.spark.SeaTunnelSpark" --name "SeaTunnel" --master "local[4]" --deploy-mode "client" --jars "/Users/mac/server/seatunnel/lib/seatunnel-transforms-v2.jar,/Users/mac/server/seatunnel/lib/seatunnel-hadoop3-3.1.4-uber.jar,/Users/mac/server/seatunnel/connectors/connector-fake-2.3.5.jar,/Users/mac/server/seatunnel/connectors/connector-console-2.3.5.jar" --conf "job.mode=STREAMING" --conf "parallelism=2" --conf "checkpoint.interval=2000" /Users/mac/server/apache-seatunnel-2.3.5/starter/seatunnel-spark-3-starter.jar --config "./config/v2.streaming.conf.template" --master "local[4]" --deploy-mode "client" --name "SeaTunnel"

遇到报错：

2024-07-01 23:25:04,610 INFO v2.V2ScanRelationPushDown:
Pushing operators to SeaTunnelSourceTable
Pushed Filters:
Post-Scan Filters:
Output: name#0, age#1

Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/spark/sql/connector/write/Write

看样子是缺少包导致的导致的，可以参见 issue讨论https://github.com/apache/seatunnel/issues/4879 貌似需要 spark 版本 >=3.2 ,而我的是 3.1.1 因此当前这个问题暂时无解。

Since spark 3.2.0, buildForBatch and buildForStreaming have been deprecated in 
org.apache.spark.sql.connector.write.WriteBuilder. So you should keep spark version >= 3.2.0.

于是，我便下载了 3.2.4(spark -> spark-3.2.4-bin-without-hadoop) 测试后出现了新的问题。


Exception in thread "main" java.lang.NoClassDefFoundError: org/apache/log4j/spi/Filter
	at java.lang.Class.getDeclaredMethods0(Native Method)
	at java.lang.Class.privateGetDeclaredMethods(Class.java:2701)
	at java.lang.Class.privateGetMethodRecursive(Class.java:3048)
	at java.lang.Class.getMethod0(Class.java:3018)
	at java.lang.Class.getMethod(Class.java:1784)
	at sun.launcher.LauncherHelper.validateMainClass(LauncherHelper.java:684)
	at sun.launcher.LauncherHelper.checkAndLoadMain(LauncherHelper.java:666)
Caused by: java.lang.ClassNotFoundException: org.apache.log4j.spi.Filter
	at java.net.URLClassLoader.findClass(URLClassLoader.java:387)
	at java.lang.ClassLoader.loadClass(ClassLoader.java:418)
	at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:359)
	at java.lang.ClassLoader.loadClass(ClassLoader.java:351)
	... 7 more

这说的是 log4j的 jar包似乎不存在，由于我们使用的 spark 版本没有 hadoop的依赖，因此需要在 spark-env.sh里面配置相关的属性，如下：

export JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk-1.8.jdk/Contents/Home
export HADOOP_HOME=/Users/mac/apps/hadoop
export HADOOP_CONF_DIR=/Users/mac/apps/hadoop/etc/hadoop
export SPARK_DIST_CLASSPATH=$(/Users/mac/apps/hadoop/bin/hadoop classpath)
export SPARK_MASTER_HOST=localhost
export SPARK_MASTER_PORT=7077

再次提交测试后，结果如下：

24/07/02 13:40:19 INFO ConfigBuilder: Parsed config file:
{
    "env" : {
        "parallelism" : 2,
        "job.mode" : "STREAMING",
        "checkpoint.interval" : 2000
    },
    "source" : [
        {
            "schema" : {
                "fields" : {
                    "name" : "string",
                    "age" : "int"
                }
            },
            "row.num" : 16,
            "parallelism" : 2,
            "result_table_name" : "fake",
            "plugin_name" : "FakeSource"
        }
    ],
    "sink" : [
        {
            "plugin_name" : "Console"
        }
    ]
}

24/07/02 13:40:19 INFO SparkContext: Running Spark version 3.2.4
24/07/02 13:40:25 INFO FakeSourceReader: wait split!
24/07/02 13:40:25 INFO FakeSourceReader: wait split!
24/07/02 13:40:25 INFO FakeSourceSplitEnumerator: Calculated splits for table fake successfully, the size of splits is 2.
24/07/02 13:40:25 INFO FakeSourceSplitEnumerator: Calculated splits for table fake successfully, the size of splits is 2.
24/07/02 13:40:25 INFO FakeSourceSplitEnumerator: Assigned [FakeSourceSplit(tableId=fake, splitId=1, rowNum=16), FakeSourceSplit(tableId=fake, splitId=0, rowNum=16)] to 2 readers.
24/07/02 13:40:25 INFO FakeSourceSplitEnumerator: Calculated splits successfully, the size of splits is 2.
24/07/02 13:40:25 INFO FakeSourceSplitEnumerator: Assigned [FakeSourceSplit(tableId=fake, splitId=1, rowNum=16), FakeSourceSplit(tableId=fake, splitId=0, rowNum=16)] to 2 readers.
24/07/02 13:40:25 INFO FakeSourceSplitEnumerator: Assigning splits to readers 1 [FakeSourceSplit(tableId=fake, splitId=1, rowNum=16)]
24/07/02 13:40:25 INFO FakeSourceSplitEnumerator: Calculated splits successfully, the size of splits is 2.
24/07/02 13:40:25 INFO FakeSourceSplitEnumerator: Assigning splits to readers 0 [FakeSourceSplit(tableId=fake, splitId=0, rowNum=16)]
24/07/02 13:40:26 INFO FakeSourceReader: 16 rows of data have been generated in split(fake_1) for table fake. Generation time: 1719898826259
24/07/02 13:40:26 INFO FakeSourceReader: 16 rows of data have been generated in split(fake_0) for table fake. Generation time: 1719898826259
24/07/02 13:40:26 INFO ConsoleSinkWriter: subtaskIndex=1  rowIndex=1:  SeaTunnelRow#tableId= SeaTunnelRow#kind=INSERT : eMaly, 2131476727
24/07/02 13:40:26 INFO ConsoleSinkWriter: subtaskIndex=0  rowIndex=1:  SeaTunnelRow#tableId= SeaTunnelRow#kind=INSERT : Osfqi, 257240275
24/07/02 13:40:26 INFO ConsoleSinkWriter: subtaskIndex=1  rowIndex=2:  SeaTunnelRow#tableId= SeaTunnelRow#kind=INSERT : BYVKb, 730735331

看结果符合预期，也就是使用 spark 提交 seatunnl引擎的流任务，通过FakeSource模拟两列输出了 16 条数据。看来的确是需要 spark3.2.x版本的才能成功了。

参考

https://www.modb.pro/db/605827

总结

本节主要总结了单机模式下使用 seatunel完成官方示例程序，初步体会使用，其实使用起来还是很简单的，模式同我之前介绍的 DataX如出一辙，可喜的是它有自己的 web页面可以配置，

因此后面我将分享下如何在页面中进行配置同步任务，最后时间允许的情况下，分析起优秀的源码设计思路，千里之行始于足下，要持续学习，持续成长，然后持续分享，再会～。

原文链接：https://blog.csdn.net/u010834071/article/details/140110947