Flink-cdc Schema Evolution 详解

glimpse

源码基于

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~/project/flink_src/flink-cdc master !4 ❯ git remote -v
origin  https://github.com/apache/flink-cdc.git (fetch)
origin  https://github.com/apache/flink-cdc.git (push)
~/project/flink_src/flink-cdc master !4 ❯ git rev-parse HEAD
a5b666a3254b87b44b9a3843a4d001793e86552c

<revision>3.3-SNAPSHOT</revision>

flink-cdc 3.0 重要特性

通过yaml文件定义pipeline
能够感知schema变更

pipeline demo

我们使用一个特殊的sink类型"values"来观察各种事件的产生

values是专门为调试编写的一个sink，会将产生的事件打印在stdout

需要引入包flink-cdc-pipeline-connector-values-3.3-SNAPSHOT.jar，可以从flink-cdc工程中编译flink-cdc-pipeline-connector-values得到

pipeline yaml

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################################################################################
# Description: Sync MySQL all tables to Doris
################################################################################
source:
 type: mysql
 hostname: ${ip}
 port: ${port}
 username: ${username}
 password: ${password}
 tables: ${database}.${table}
 server-id: 5400-5404
 server-time-zone: UTC+8

sink:
  type: values
  name: values Sink

pipeline:
 name: Sync Mysql Database to Values
 parallelism: 2

注意：parallelism > 1 时候一定要在flink中开启checkpoint

这是flink-cdc的已知bug，尚未解决 bug链接
如果想要使用flink-cdc源码调试，需要开启 --use-mini-cluster true
并且修改FlinkPipelineComposer的ofMiniCluster方法，手动设置enableCheckpointing

提交flink-cdc任务

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./bin/flink-cdc.sh mysql-to-values.yaml
Pipeline has been submitted to cluster.
Job ID: a03966de35dc3141c890250daeac9699
Job Description: Sync Mysql Database to Values

在mysql中执行变更操作，观察flink taskmanager日志

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mysql> insert into t1 values(13, 'm');
Query OK, 1 row affected (0.01 sec)

mysql> alter table t1 add column c0 varchar(255);
Query OK, 0 rows affected (0.03 sec)
Records: 0  Duplicates: 0  Warnings: 0

flink日志

日志解析

注意看左侧的 ">"

由于yaml中设置的并发度是2，所有可以看到日志中有两个任务在打印

注意CreateTableEvent和AddColumnEvent这样的关于schema改变的事件会出现在两个并发中，而一个DataChangeEvent事件只会出现在单独一个并发中

flink-cdc 官方文档中描述: schema相关event与DataChangeEvent之间有如下的顺序保证

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a CreateTableEvent must be emitted before any DataChangeEvent if a table is new to the framework, 
and SchemaChangeEvent must be emitted before any DataChangeEvent if the schema of a table is changed. 
This requirement makes sure that the framework has been aware of the schema before processing any data changes.

见understand-flink-cdc-api

schema evolution 实现原理

整体视角

SchemaRegistry运行在JobManager中，继承Coordinator与SchemaOperator交互，负责协调不同流水线中收到schema变更event后的同步

从yaml到pipeline的转化

入口flink-cdc.sh

exec " $JAVA_RUN" -classpath "$ CLASSPATH" " ${LOG_SETTINGS[@]}" org.apache.flink.cdc.cli.CliFrontend "$ @"
入口类 CliFrontend 在 CliFrontend.java

main 调用
createExecutor 调用
new CliExecutor 其中 pipelineDefPath 是yaml文件的路径
CliExecutor.java
1. 通过 YamlPipelineDefinitionParser 将 pipelineDefPath parse为pipelineDef
2. PipelineComposer 通过pipelineDef的定义调用flink的api构建流水线
FlinkPipelineComposer.java

// Build Source Operator
DataSourceTranslator sourceTranslator = new DataSourceTranslator();
DataStream<Event> stream =
sourceTranslator.translate(
pipelineDef.getSource(), env, pipelineDef.getConfig(), parallelism);

...

// Schema operator
SchemaOperatorTranslator schemaOperatorTranslator =
new SchemaOperatorTranslator(
schemaChangeBehavior,
pipelineDef.getConfig().get(PipelineOptions.PIPELINE_SCHEMA_OPERATOR_UID),
pipelineDef
.getConfig()
.get(PipelineOptions.PIPELINE_SCHEMA_OPERATOR_RPC_TIMEOUT));
OperatorIDGenerator schemaOperatorIDGenerator =
new OperatorIDGenerator(schemaOperatorTranslator.getSchemaOperatorUid());

...

// Build DataSink in advance as schema operator requires MetadataApplier
DataSinkTranslator sinkTranslator = new DataSinkTranslator();
DataSink dataSink =
sinkTranslator.createDataSink(pipelineDef.getSink(), pipelineDef.getConfig(), env);
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```
      stream =
              schemaOperatorTranslator.translate(
                      stream,
                      parallelism,
                      dataSink.getMetadataApplier()
                              .setAcceptedSchemaEvolutionTypes(
                                      pipelineDef.getSink().getIncludedSchemaEvolutionTypes()),
                      pipelineDef.getRoute());
```

这里可以看到从yaml的描述到stream的转化

stream 关联-> 当前 env 关联-> FlinkPipelineExecution

最终通过FlinkPipelineExecution.execute()调用用到env.executeAsync()

这里处理用户描述的source和sink节点，flink-cdc还自动插入了一个SchemaOperator节点

schema event的流动

SchemaOperator与sink绑定，这里绑定关系到之后的几个操作

定义一个sink的时候要提供MetadataApplier，运行在JobManager（上方），通过Rpc与SchemaOperator交互

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     schemaOperatorTranslator.translate(
                     ...
                     dataSink.getMetadataApplier()
                             ...);

所有的event都要经过SchemaOperator，SchemaOperator对于SchemaChangeEvent特殊处理 SchemaOperator.java

public void processElement(StreamRecord<Event> streamRecord)
throws InterruptedException, TimeoutException, ExecutionException {
Event event = streamRecord.getValue();
if (event instanceof SchemaChangeEvent) {
processSchemaChangeEvents((SchemaChangeEvent) event);
} else if (event instanceof DataChangeEvent) {
...
}

最终调用到handleSchemaChangeEvent

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private void handleSchemaChangeEvent(TableId tableId, SchemaChangeEvent schemaChangeEvent)
            throws InterruptedException, TimeoutException {
           ...
        // The request will block if another schema change event is being handled
        SchemaChangeResponse response = requestSchemaChange(tableId, schemaChangeEvent);
        if (response.isAccepted()) {
            LOG.info("{}> Sending the FlushEvent for table {}.", subTaskId, tableId);
            output.collect(new StreamRecord<>(new FlushEvent(tableId)));
            ...
            // The request will block until flushing finished in each sink writer
            SchemaChangeResultResponse schemaEvolveResponse = requestSchemaChangeResult();
    }

回想一下刚才在mysql中alter table add column的场景，每一个并发度都有一个AddColumnEvent，都会去调用

requestSchemaChange，向Coordinator发送SchemaChangeRequest

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private SchemaChangeResponse requestSchemaChange(
            TableId tableId, SchemaChangeEvent schemaChangeEvent)
            throws InterruptedException, TimeoutException {
       ...
        while (true) {
            SchemaChangeResponse response =
                    sendRequestToCoordinator(
                            new SchemaChangeRequest(tableId, schemaChangeEvent, subTaskId));
           ...
        }
    }

在SchemaRegistry.java响应请求

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 public CompletableFuture<CoordinationResponse> handleCoordinationRequest(
            CoordinationRequest request) {
        ...
      
                        if (request instanceof SchemaChangeRequest) {
                            SchemaChangeRequest schemaChangeRequest = (SchemaChangeRequest) request;
                            requestHandler.handleSchemaChangeRequest(
                                    schemaChangeRequest, responseFuture);
                        } else if (request instanceof SchemaChangeResultRequest) {
                            requestHandler.getSchemaChangeResult(responseFuture);
                        }
                           ...
    }

这时两个请求只有一个会被处理，另外一个会被认为是duplicate

处理的步骤如下

发起schema变更请求requestSchemaChange

如果被Coordinator Accept，执行output.collect(new StreamRecord<>(new FlushEvent(tableId)));

flushEvent在PrePartitionOperator.java被广播给下游所有的sink

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```
public void processElement(StreamRecord<Event> element) throws Exception {
      ...
      if (event instanceof FlushEvent) {
        // Broadcast FlushEvent
        broadcastEvent(event);
      }
      ...
}
```

flushEvent在sink中会触发当前sink flush所有缓存的事件，之后通知Coordinator完成DataSinkFunctionOperator.java

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```
private void handleFlushEvent(FlushEvent event) throws Exception {
        userFunction.finish();
        schemaEvolutionClient.notifyFlushSuccess(
                getRuntimeContext().getIndexOfThisSubtask(), event.getTableId());
}
```

hang在requestSchemaChangeResult，等待MetadataApplier变更下游数据库schema（比如Doris），天然hang住了上游消息
如果不是第一个requestSchemaChange（相同请求已经在被处理），会hang在requestSchemaChange，也天然hang住上游消息，在Coordinator(SchemaRegistry/MetaAppier)处理好之后会走duplicate分支，只打印日志"{}> Schema change event {} has been handled in another subTask already."
下游sink在处理完flush之后会触发notifyFlushSuccess，SchemaRegistry.java SchemaRegistry会调用handleEventFromOperator响应，最终调用到SchemaRegistryRequestHandler.java中的applySchemaChange, 调用对应sink的metadataApplier
metadataApplier.applySchemaChange(changeEvent);
上面步骤完成之后第一个hang住的requestSchemaChange会返回

MetadataApplier中干了什么

拿Doris举例, 直接去修改后端的列了，这时修改是安全的，因为上游的mysql修改schema之后产生的消息都被hang住，修改schema之前的消息都已经被各个sink flush消费完

DorisMetadataApplier.java

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 public void applySchemaChange(SchemaChangeEvent event) {
        SchemaChangeEventVisitor.<Void, SchemaEvolveException>visit(
                event,
                addColumnEvent -> {
                    applyAddColumnEvent(addColumnEvent);
                    return null;
                },
                alterColumnTypeEvent -> {
                    applyAlterColumnTypeEvent(alterColumnTypeEvent);
                    return null;
                },
                createTableEvent -> {
                    applyCreateTableEvent(createTableEvent);
                    return null;
                },
                dropColumnEvent -> {
                    applyDropColumnEvent(dropColumnEvent);
                    return null;
                },
                ...

glimpse 中没有说清楚的点

schema变更消息会在每个并发度的源头都会产生吗？回答：是的，只有这样SchemaOperator才有机会正确的hang住所有的并发度，并等待SchemaRegistry（MetadataApplier）的响应

总结

flink-cdc 3.0 通过加入了SchemaOperator和MetadataApplier，监控链路上所有消息，当发生schema变更时，同步上下游

hang住上游
flush下游
修改下游schema
恢复运行

这样实现了自动schema变更