关于Flink:Apache-Flink-CDC-批流融合技术原理分析

本文转载自「好将来技术」公众号，以 Flink SQL 案例来介绍 Flink CDC 2.0 的应用，并解读 CDC 中的外围设计。次要内容为：

1. 案例
2. 外围设计
3. 代码详解

8 月份 Flink CDC 公布 2.0.0 版本，相较于 1.0 版本，在全量读取阶段反对分布式读取、反对 checkpoint，且在全量 + 增量读取的过程在不锁表的状况下保障数据一致性。具体介绍参考 Flink CDC 2.0 正式公布，详解外围改良。

Flink CDC 2.0 数据读取逻辑并不简单，简单的是 FLIP-27: Refactor Source Interface 的设计及对 Debezium Api 的不理解。本文重点对 Flink CDC 的解决逻辑进行介绍，FLIP-27 的设计及 Debezium 的 API 调用不做过多解说。

本文应用 CDC 2.0.0 版本，先以 Flink SQL 案例来介绍 Flink CDC 2.0 的应用，接着介绍 CDC 中的外围设计蕴含切片划分、切分读取、增量读取，最初对数据处理过程中波及 flink-mysql-cdc 接口的调用及实现进行代码解说。

一、案例

全量读取 + 增量读取 Mysql 表数据，以changelog-json 格局写入 kafka，察看 RowKind 类型及影响的数据条数。

public static void main(String[] args) {StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        EnvironmentSettings envSettings = EnvironmentSettings.newInstance()
                .useBlinkPlanner()
                .inStreamingMode()
                .build();
        env.setParallelism(3);
        // note: 增量同步须要开启 CK
        env.enableCheckpointing(10000);
        StreamTableEnvironment tableEnvironment = StreamTableEnvironment.create(env, envSettings);
            
        tableEnvironment.executeSql("CREATE TABLE demoOrders (\n" +
                "`order_id` INTEGER ,\n" +
                "`order_date` DATE ,\n" +
                "`order_time` TIMESTAMP(3),\n" +
                "`quantity` INT ,\n" +
                "`product_id` INT ,\n" +
                "`purchaser` STRING,\n" +
                "primary key(order_id)  NOT ENFORCED" +
                ") WITH (\n" +
                "'connector' = 'mysql-cdc',\n"+"          'hostname' = 'localhost',\n"+"          'port' = '3306',\n"+"          'username' = 'cdc',\n"+"          'password' = '123456',\n"+"          'database-name' = 'test',\n"+"          'table-name' = 'demo_orders'," +
                            //  全量 + 增量同步   
                "'scan.startup.mode' = 'initial'      "+" )");

              tableEnvironment.executeSql("CREATE TABLE sink (\n" +
                "`order_id` INTEGER ,\n" +
                "`order_date` DATE ,\n" +
                "`order_time` TIMESTAMP(3),\n" +
                "`quantity` INT ,\n" +
                "`product_id` INT ,\n" +
                "`purchaser` STRING,\n" +
                "primary key (order_id)  NOT ENFORCED" +
                ") WITH (\n" +
                "'connector' = 'kafka',\n"+"    'properties.bootstrap.servers' = 'localhost:9092',\n"+"    'topic' = 'mqTest02',\n"+"    'format' = 'changelog-json' "+")");

             tableEnvironment.executeSql("insert into sink select * from demoOrders");}

全量数据输入：

{"data":{"order_id":1010,"order_date":"2021-09-17","order_time":"2021-09-22 10:52:12.189","quantity":53,"product_id":502,"purchaser":"flink"},"op":"+I"}
{"data":{"order_id":1009,"order_date":"2021-09-17","order_time":"2021-09-22 10:52:09.709","quantity":31,"product_id":500,"purchaser":"flink"},"op":"+I"}
{"data":{"order_id":1008,"order_date":"2021-09-17","order_time":"2021-09-22 10:52:06.637","quantity":69,"product_id":503,"purchaser":"flink"},"op":"+I"}
{"data":{"order_id":1007,"order_date":"2021-09-17","order_time":"2021-09-22 10:52:03.535","quantity":52,"product_id":502,"purchaser":"flink"},"op":"+I"}
{"data":{"order_id":1002,"order_date":"2021-09-17","order_time":"2021-09-22 10:51:51.347","quantity":69,"product_id":503,"purchaser":"flink"},"op":"+I"}
{"data":{"order_id":1001,"order_date":"2021-09-17","order_time":"2021-09-22 10:51:48.783","quantity":50,"product_id":502,"purchaser":"flink"},"op":"+I"}
{"data":{"order_id":1000,"order_date":"2021-09-17","order_time":"2021-09-17 17:40:32.354","quantity":30,"product_id":500,"purchaser":"flink"},"op":"+I"}
{"data":{"order_id":1006,"order_date":"2021-09-17","order_time":"2021-09-22 10:52:01.249","quantity":31,"product_id":500,"purchaser":"flink"},"op":"+I"}
{"data":{"order_id":1005,"order_date":"2021-09-17","order_time":"2021-09-22 10:51:58.813","quantity":69,"product_id":503,"purchaser":"flink"},"op":"+I"}
{"data":{"order_id":1004,"order_date":"2021-09-17","order_time":"2021-09-22 10:51:56.153","quantity":50,"product_id":502,"purchaser":"flink"},"op":"+I"}
{"data":{"order_id":1003,"order_date":"2021-09-17","order_time":"2021-09-22 10:51:53.727","quantity":30,"product_id":500,"purchaser":"flink"},"op":"+I"}

批改表数据，增量捕捉：

## 更新 1005 的值 
{"data":{"order_id":1005,"order_date":"2021-09-17","order_time":"2021-09-22 02:51:58.813","quantity":69,"product_id":503,"purchaser":"flink"},"op":"-U"}
{"data":{"order_id":1005,"order_date":"2021-09-17","order_time":"2021-09-22 02:55:43.627","quantity":80,"product_id":503,"purchaser":"flink"},"op":"+U"}

## 删除 1000 
{"data":{"order_id":1000,"order_date":"2021-09-17","order_time":"2021-09-17 09:40:32.354","quantity":30,"product_id":500,"purchaser":"flink"},"op":"-D"}

二、外围设计

1. 切片划分

全量阶段数据读取形式为分布式读取，会先对以后表数据按主键划分成多个 Chunk，后续子工作读取 Chunk 区间内的数据。依据主键列是否为自增整数类型，对表数据划分为均匀分布的 Chunk 及非均匀分布的 Chunk。

1.1 均匀分布

主键列自增且类型为整数类型（int,bigint,decimal）。查问出主键列的最小值，最大值，按 chunkSize 大小将数据平均划分，因为主键为整数类型，依据以后 chunk 起始地位、chunkSize 大小，间接计算 chunk 的完结地位。

留神：最新版本均匀分布的触发条件不再依赖主键列是否自增，要求主键列卫整数类型且依据 max(id) – min(id)/rowcount 计算出数据分布系数，只有散布系数 <= 配置的散布系数 (evenly-distribution.factor 默认为 1000.0d) 才会进行数据平均划分。

//  计算主键列数据区间
select min(`order_id`), max(`order_id`) from demo_orders;

//  将数据划分为 chunkSize 大小的切片
chunk-0：[min，start + chunkSize)
chunk-1：[start + chunkSize, start + 2chunkSize)
.......
chunk-last：[max，null)

1.2 非均匀分布

主键列非自增或者类型为非整数类型。主键为非数值类型，每次划分须要对未划分的数据按主键进行升序排列，取出前 chunkSize 的最大值为以后 chunk 的完结地位。

留神：最新版本非均匀分布触发条件为主键列为非整数类型，或者计算出的散布系数 (distributionFactor) > 配置的散布系数 (evenly-distribution.factor)。

// 未拆分的数据排序后，取 chunkSize 条数据取最大值，作为切片的终止地位。chunkend = SELECT MAX(`order_id`) FROM (
        SELECT `order_id`  FROM `demo_orders` 
        WHERE `order_id` >= [前一个切片的起始地位] 
        ORDER BY `order_id` ASC 
        LIMIT   [chunkSize]  
    ) AS T

2. 全量切片数据读取

Flink 将表数据划分为多个 Chunk，子工作在不加锁的状况下，并行读取 Chunk 数据。因为全程无锁在数据分片读取过程中，可能有其余事务对切片范畴内的数据进行批改，此时无奈保证数据一致性。因而，在全量阶段 Flink 应用快照记录读取 + Binlog 数据修改的形式来保证数据的一致性。

2.1 快照读取

通过 JDBC 执行 SQL 查问切片范畴的数据记录。

## 快照记录数据读取 SQL 
SELECT * FROM `test`.`demo_orders` 
WHERE order_id >= [chunkStart] 
AND NOT (order_id = [chunkEnd]) 
AND order_id <= [chunkEnd]

2.2 数据修改

在快照读取操作前、后执行 SHOW MASTER STATUS 查问 binlog 文件的以后偏移量，在快照读取结束后，查问区间内的 binlog 数据并对读取的快照记录进行修改。

快照读取 + Binlog 数据读取时的数据组织构造：

BinlogEvents 修改 SnapshotEvents 规定。

• 未读取到 binlog 数据，即在执行 select 阶段没有其余事务进行操作，间接下发所有快照记录。
• 读取到 binlog 数据，且变更的数据记录不属于以后切片，下发快照记录。
• 读取到 binlog 数据，且数据记录的变更属于以后切片。delete 操作从快照内存中移除该数据，insert 操作向快照内存增加新的数据，update 操作向快照内存中增加变更记录，最终会输入更新前后的两条记录到上游。

修改后的数据组织构造：

![image.png](https://img.alicdn.com/imgextra/i1/O1CN01POsozI1WMXPFo2J0W_!!6000000002774-2-tps-1080-93.png)

以读取切片 [1,11] 范畴的数据为例，形容切片数据的处理过程。c、d、u 代表 Debezium 捕捉到的新增、删除、更新操作。

修改前数据及构造：

修改后数据及构造：

单个切片数据处理结束后会向 SplitEnumerator 发送已实现切片数据的起始地位（ChunkStart, ChunkStartEnd）、Binlog 的最大偏移量（High watermark），用来为增量读取指定起始偏移量。

3. 增量切片数据读取

全量阶段切片数据读取实现后，SplitEnumerator 会下发一个 BinlogSplit 进行增量数据读取。BinlogSplit 读取最重要的属性就是起始偏移量，偏移量如果设置过小上游可能会有反复数据，偏移量如果设置过大上游可能是已超期的脏数据。而 Flink CDC 增量读取的起始偏移量为 所有已实现的全量切片最小的 Binlog 偏移量，只有满足条件的数据才被下发到上游。数据下发条件：

• 捕捉的 Binlog 数据的偏移量 > 数据所属分片的 Binlog 的最大偏移量。

例如，SplitEnumerator 保留的已实现切片信息为：

​ 增量读取时，从偏移量 800 开始读取 Binlog 数据，当捕捉到数据 <data:123, offset:1500> 时，先找到 123 所属快照分片，并找到对应的最大 Binlog 偏移量 800。以后偏移量大于快照读的最大偏移量，则下发数据，否则间接抛弃。

三、代码详解

对于 FLIP-27: Refactor Source Interface 设计不做具体介绍，本文偏重对 flink-mysql-cdc 接口调用及实现进行解说。

1. MySqlSourceEnumerator 初始化

SourceCoordinator 作为 OperatorCoordinator 对 Source 的实现，运行在 Master 节点，在启动时通过调用 MySqlParallelSource#createEnumerator 创立 MySqlSourceEnumerator 并调用 start 办法，做一些初始化工作。

1. 创立 MySqlSourceEnumerator，应用 MySqlHybridSplitAssigner 对全量 + 增量数据进行切片，应用 MySqlValidator 对 mysql 版本、配置进行校验。

2. MySqlValidator 校验：

   1. mysql 版本必须大于等于 5.7。
   2. binlog_format 配置必须为 ROW。
   3. binlog_row_image 配置必须为 FULL。

3. MySqlSplitAssigner 初始化：

   1. 创立 ChunkSplitter 用来划分切片。
   2. 筛选出要读的表名称。
4. 启动周期调度线程，要求 SourceReader 向 SourceEnumerator 发送已实现但未发送 ACK 事件的切片信息。

private void syncWithReaders(int[] subtaskIds, Throwable t) {if (t != null) {throw new FlinkRuntimeException("Failed to list obtain registered readers due to:", t);
    }
    // when the SourceEnumerator restores or the communication failed between
    // SourceEnumerator and SourceReader, it may missed some notification event.
    // tell all SourceReader(s) to report there finished but unacked splits.
    if (splitAssigner.waitingForFinishedSplits()) {for (int subtaskId : subtaskIds) {
            // note: 发送 FinishedSnapshotSplitsRequestEvent 
            context.sendEventToSourceReader(subtaskId, new FinishedSnapshotSplitsRequestEvent());
        }
    }
}

2. MySqlSourceReader 初始化

SourceOperator 集成了 SourceReader，通过 OperatorEventGateway 和 SourceCoordinator 进行交互。

1. SourceOperator 在初始化时，通过 MySqlParallelSource 创立 MySqlSourceReader。MySqlSourceReader 通过 SingleThreadFetcherManager 创立 Fetcher 拉取分片数据，数据以 MySqlRecords 格局写入到 elementsQueue。

MySqlParallelSource#createReader

public SourceReader<T, MySqlSplit> createReader(SourceReaderContext readerContext) throws Exception {
    // note:  数据存储队列
FutureCompletingBlockingQueue<RecordsWithSplitIds<SourceRecord>> elementsQueue =
        new FutureCompletingBlockingQueue<>();
final Configuration readerConfiguration = getReaderConfig(readerContext);

    // note: Split Reader 工厂类
Supplier<MySqlSplitReader> splitReaderSupplier =
        () -> new MySqlSplitReader(readerConfiguration, readerContext.getIndexOfSubtask());

return new MySqlSourceReader<>(
        elementsQueue,
        splitReaderSupplier,
        new MySqlRecordEmitter<>(deserializationSchema),
        readerConfiguration,
        readerContext);
}

2. 将创立的 MySqlSourceReader 以事件的模式传递给 SourceCoordinator 进行注册。SourceCoordinator 接管到注册事件后，将 reader 地址及索引进行保留。

SourceCoordinator#handleReaderRegistrationEvent
// note: SourceCoordinator 解决 Reader 注册事件
private void handleReaderRegistrationEvent(ReaderRegistrationEvent event) {context.registerSourceReader(new ReaderInfo(event.subtaskId(), event.location()));
    enumerator.addReader(event.subtaskId());
}

3. MySqlSourceReader 启动后会向 MySqlSourceEnumerator 发送申请分片事件，从而收集调配的切片数据。

4. SourceOperator 初始化结束后，调用 emitNext 由 SourceReaderBase 从 elementsQueue 获取数据汇合并下发给 MySqlRecordEmitter。接口调用示意图：

3. MySqlSourceEnumerator 解决分片申请

MySqlSourceReader 启动时会向 MySqlSourceEnumerator 发送申请 RequestSplitEvent 事件，依据返回的切片范畴读取区间数据。MySqlSourceEnumerator 全量读取阶段分片申请解决逻辑，最终返回一个 MySqlSnapshotSplit。

1. 解决切片申请事件，为申请的 Reader 调配切片，通过发送 AddSplitEvent 工夫传递 MySqlSplit (全量阶段 MySqlSnapshotSplit、增量阶段 MySqlBinlogSplit)。

MySqlSourceEnumerator#handleSplitRequest
public void handleSplitRequest(int subtaskId, @Nullable String requesterHostname) {if (!context.registeredReaders().containsKey(subtaskId)) {
        // reader failed between sending the request and now. skip this request.
        return;
    }
    // note:  将 reader 所属的 subtaskId 存储到 TreeSet, 在解决 binlog split 时优先调配个 task-0
    readersAwaitingSplit.add(subtaskId);

    assignSplits();}

// note: 调配切片
private void assignSplits() {final Iterator<Integer> awaitingReader = readersAwaitingSplit.iterator();
    while (awaitingReader.hasNext()) {int nextAwaiting = awaitingReader.next();
        // if the reader that requested another split has failed in the meantime, remove
        // it from the list of waiting readers
        if (!context.registeredReaders().containsKey(nextAwaiting)) {awaitingReader.remove();
            continue;
        }

        //note: 由 MySqlSplitAssigner 调配切片
        Optional<MySqlSplit> split = splitAssigner.getNext();
        if (split.isPresent()) {final MySqlSplit mySqlSplit = split.get();
            //  note: 发送 AddSplitEvent, 为 Reader 返回切片信息
            context.assignSplit(mySqlSplit, nextAwaiting);
            awaitingReader.remove();

            LOG.info("Assign split {} to subtask {}", mySqlSplit, nextAwaiting);
        } else {
            // there is no available splits by now, skip assigning
            break;
        }
    }
}

2. MySqlHybridSplitAssigner 解决全量切片、增量切片的逻辑。

   1. 工作刚启动时，remainingTables 不为空，noMoreSplits 返回值为 false，创立 SnapshotSplit。
   2. 全量阶段分片读取实现后，noMoreSplits 返回值为 true, 创立 BinlogSplit。

MySqlHybridSplitAssigner#getNext
@Override
public Optional<MySqlSplit> getNext() {if (snapshotSplitAssigner.noMoreSplits()) {
        // binlog split assigning
        if (isBinlogSplitAssigned) {
            // no more splits for the assigner
            return Optional.empty();} else if (snapshotSplitAssigner.isFinished()) {
            // we need to wait snapshot-assigner to be finished before
            // assigning the binlog split. Otherwise, records emitted from binlog split
            // might be out-of-order in terms of same primary key with snapshot splits.
            isBinlogSplitAssigned = true;

            //note: snapshot split 切片实现后，创立 BinlogSplit。return Optional.of(createBinlogSplit());
        } else {
            // binlog split is not ready by now
            return Optional.empty();}
    } else {
        // note: 由 MySqlSnapshotSplitAssigner 创立 SnapshotSplit
        // snapshot assigner still have remaining splits, assign split from it
        return snapshotSplitAssigner.getNext();}
}

3. MySqlSnapshotSplitAssigner 解决全量切片逻辑，通过 ChunkSplitter 生成切片，并存储到 Iterator 中。

@Override
public Optional<MySqlSplit> getNext() {if (!remainingSplits.isEmpty()) {
        // return remaining splits firstly
        Iterator<MySqlSnapshotSplit> iterator = remainingSplits.iterator();
        MySqlSnapshotSplit split = iterator.next();
        iterator.remove();
        
        //note: 已调配的切片存储到 assignedSplits 汇合
        assignedSplits.put(split.splitId(), split);

        return Optional.of(split);
    } else {
        // note: 初始化阶段 remainingTables 存储了要读取的表名
        TableId nextTable = remainingTables.pollFirst();
        if (nextTable != null) {// split the given table into chunks (snapshot splits)
            //  note: 初始化阶段创立了 ChunkSplitter，调用 generateSplits 进行切片划分
            Collection<MySqlSnapshotSplit> splits = chunkSplitter.generateSplits(nextTable);
            //  note: 保留所有切片信息
            remainingSplits.addAll(splits);
            //  note: 曾经实现分片的 Table
            alreadyProcessedTables.add(nextTable);
            //  note: 递归调用该该办法
            return getNext();} else {return Optional.empty();
        }
    }
}

4. ChunkSplitter 将表划分为均匀分布 or 不均匀分布切片的逻辑。读取的表必须蕴含物理主键。

public Collection<MySqlSnapshotSplit> generateSplits(TableId tableId) {Table schema = mySqlSchema.getTableSchema(tableId).getTable();
    List<Column> primaryKeys = schema.primaryKeyColumns();
    // note: 必须有主键
    if (primaryKeys.isEmpty()) {
        throw new ValidationException(
                String.format(
                        "Incremental snapshot for tables requires primary key,"
                                + "but table %s doesn't have primary key.",
                        tableId));
    }
    // use first field in primary key as the split key
    Column splitColumn = primaryKeys.get(0);

    final List<ChunkRange> chunks;
    try {
         // note: 按主键列将数据划分成多个切片
        chunks = splitTableIntoChunks(tableId, splitColumn);
    } catch (SQLException e) {throw new FlinkRuntimeException("Failed to split chunks for table" + tableId, e);
    }
    //note: 主键数据类型转换、ChunkRange 包装成 MySqlSnapshotSplit。// convert chunks into splits
    List<MySqlSnapshotSplit> splits = new ArrayList<>();
    RowType splitType = splitType(splitColumn);
 
    for (int i = 0; i < chunks.size(); i++) {ChunkRange chunk = chunks.get(i);
        MySqlSnapshotSplit split =
                createSnapshotSplit(tableId, i, splitType, chunk.getChunkStart(), chunk.getChunkEnd());
        splits.add(split);
    }
    return splits;
}

5. splitTableIntoChunks 依据物理主键划分切片。

private List<ChunkRange> splitTableIntoChunks(TableId tableId, Column splitColumn)
        throws SQLException {final String splitColumnName = splitColumn.name();
    //  select min, max
    final Object[] minMaxOfSplitColumn = queryMinMax(jdbc, tableId, splitColumnName);
    final Object min = minMaxOfSplitColumn[0];
    final Object max = minMaxOfSplitColumn[1];
    if (min == null || max == null || min.equals(max)) {
        // empty table, or only one row, return full table scan as a chunk
        return Collections.singletonList(ChunkRange.all());
    }

    final List<ChunkRange> chunks;
    if (splitColumnEvenlyDistributed(splitColumn)) {
        // use evenly-sized chunks which is much efficient
        // note: 按主键平均划分
        chunks = splitEvenlySizedChunks(min, max);
    } else {
        // note: 按主键非平均划分
        // use unevenly-sized chunks which will request many queries and is not efficient.
        chunks = splitUnevenlySizedChunks(tableId, splitColumnName, min, max);
    }

    return chunks;
}

/** Checks whether split column is evenly distributed across its range. */
private static boolean splitColumnEvenlyDistributed(Column splitColumn) {
    // only column is auto-incremental are recognized as evenly distributed.
    // TODO: we may use MAX,MIN,COUNT to calculate the distribution in the future.
    if (splitColumn.isAutoIncremented()) {DataType flinkType = MySqlTypeUtils.fromDbzColumn(splitColumn);
        LogicalTypeRoot typeRoot = flinkType.getLogicalType().getTypeRoot();
        // currently, we only support split column with type BIGINT, INT, DECIMAL
        return typeRoot == LogicalTypeRoot.BIGINT
                || typeRoot == LogicalTypeRoot.INTEGER
                || typeRoot == LogicalTypeRoot.DECIMAL;
    } else {return false;}
}


/**
 *  依据拆分列的最小值和最大值将表拆分为大小平均的块，并以 {@link #chunkSize} 步长滚动块。* Split table into evenly sized chunks based on the numeric min and max value of split column,
 * and tumble chunks in {@link #chunkSize} step size.
 */
private List<ChunkRange> splitEvenlySizedChunks(Object min, Object max) {if (ObjectUtils.compare(ObjectUtils.plus(min, chunkSize), max) > 0) {
        // there is no more than one chunk, return full table as a chunk
        return Collections.singletonList(ChunkRange.all());
    }

    final List<ChunkRange> splits = new ArrayList<>();
    Object chunkStart = null;
    Object chunkEnd = ObjectUtils.plus(min, chunkSize);
    //  chunkEnd <= max
    while (ObjectUtils.compare(chunkEnd, max) <= 0) {splits.add(ChunkRange.of(chunkStart, chunkEnd));
        chunkStart = chunkEnd;
        chunkEnd = ObjectUtils.plus(chunkEnd, chunkSize);
    }
    // add the ending split
    splits.add(ChunkRange.of(chunkStart, null));
    return splits;
}

/**   通过间断计算下一个块最大值，将表拆分为大小不平均的块。* Split table into unevenly sized chunks by continuously calculating next chunk max value. */
private List<ChunkRange> splitUnevenlySizedChunks(TableId tableId, String splitColumnName, Object min, Object max) throws SQLException {final List<ChunkRange> splits = new ArrayList<>();
    Object chunkStart = null;

    Object chunkEnd = nextChunkEnd(min, tableId, splitColumnName, max);
    int count = 0;
    while (chunkEnd != null && ObjectUtils.compare(chunkEnd, max) <= 0) {// we start from [null, min + chunk_size) and avoid [null, min)
        splits.add(ChunkRange.of(chunkStart, chunkEnd));
        // may sleep a while to avoid DDOS on MySQL server
        maySleep(count++);
        chunkStart = chunkEnd;
        chunkEnd = nextChunkEnd(chunkEnd, tableId, splitColumnName, max);
    }
    // add the ending split
    splits.add(ChunkRange.of(chunkStart, null));
    return splits;
}

private Object nextChunkEnd(Object previousChunkEnd, TableId tableId, String splitColumnName, Object max)
        throws SQLException {
    // chunk end might be null when max values are removed
    Object chunkEnd =
            queryNextChunkMax(jdbc, tableId, splitColumnName, chunkSize, previousChunkEnd);
    if (Objects.equals(previousChunkEnd, chunkEnd)) {
        // we don't allow equal chunk start and end,
        // should query the next one larger than chunkEnd
        chunkEnd = queryMin(jdbc, tableId, splitColumnName, chunkEnd);
    }
    if (ObjectUtils.compare(chunkEnd, max) >= 0) {return null;} else {return chunkEnd;}
}

4. MySqlSourceReader 解决切片调配申请

MySqlSourceReader 接管到切片调配申请后，会为先创立一个 SplitFetcher 线程，向 taskQueue 增加、执行 AddSplitsTask 工作用来解决增加分片工作，接着执行 FetchTask 应用 Debezium API 进行读取数据，读取的数据存储到 elementsQueue 中，SourceReaderBase 会从该队列中获取数据，并下发给 MySqlRecordEmitter。

1. 解决切片调配事件时，创立 SplitFetcher 向 taskQueue 增加 AddSplitsTask。

SingleThreadFetcherManager#addSplits
public void addSplits(List<SplitT> splitsToAdd) {SplitFetcher<E, SplitT> fetcher = getRunningFetcher();
    if (fetcher == null) {fetcher = createSplitFetcher();
        // Add the splits to the fetchers.
        fetcher.addSplits(splitsToAdd);
        startFetcher(fetcher);
    } else {fetcher.addSplits(splitsToAdd);
    }
}

// 创立 SplitFetcher
protected synchronized SplitFetcher<E, SplitT> createSplitFetcher() {if (closed) {throw new IllegalStateException("The split fetcher manager has closed.");
    }
    // Create SplitReader.
    SplitReader<E, SplitT> splitReader = splitReaderFactory.get();

    int fetcherId = fetcherIdGenerator.getAndIncrement();
    SplitFetcher<E, SplitT> splitFetcher =
            new SplitFetcher<>(
                    fetcherId,
                    elementsQueue,
                    splitReader,
                    errorHandler,
                    () -> {fetchers.remove(fetcherId);
                        elementsQueue.notifyAvailable();});
    fetchers.put(fetcherId, splitFetcher);
    return splitFetcher;
}

public void addSplits(List<SplitT> splitsToAdd) {enqueueTask(new AddSplitsTask<>(splitReader, splitsToAdd, assignedSplits));
    wakeUp(true);
}

2. 执行 SplitFetcher 线程，首次执行 AddSplitsTask 线程增加分片，当前执行 FetchTask 线程拉取数据。

SplitFetcher#runOnce
void runOnce() {
    try {if (shouldRunFetchTask()) {runningTask = fetchTask;} else {runningTask = taskQueue.take();
        }
        
        if (!wakeUp.get() && runningTask.run()) {LOG.debug("Finished running task {}", runningTask);
            runningTask = null;
            checkAndSetIdle();}
    } catch (Exception e) {
        throw new RuntimeException(
                String.format(
                        "SplitFetcher thread %d received unexpected exception while polling the records",
                        id),
                e);
    }

    maybeEnqueueTask(runningTask);
    synchronized (wakeUp) {
        // Set the running task to null. It is necessary for the shutdown method to avoid
        // unnecessarily interrupt the running task.
        runningTask = null;
        // Set the wakeUp flag to false.
        wakeUp.set(false);
        LOG.debug("Cleaned wakeup flag.");
    }
}

3. AddSplitsTask 调用 MySqlSplitReader 的 handleSplitsChanges 办法，向切片队列中增加已调配的切片信息。在下一次 fetch() 调用时，从队列中获取切片并读取切片数据。

AddSplitsTask#run
public boolean run() {for (SplitT s : splitsToAdd) {assignedSplits.put(s.splitId(), s);
    }
    splitReader.handleSplitsChanges(new SplitsAddition<>(splitsToAdd));
    return true;
}
MySqlSplitReader#handleSplitsChanges
public void handleSplitsChanges(SplitsChange<MySqlSplit> splitsChanges) {if (!(splitsChanges instanceof SplitsAddition)) {
        throw new UnsupportedOperationException(
                String.format(
                        "The SplitChange type of %s is not supported.",
                        splitsChanges.getClass()));
    }

    //note: 增加切片 到队列。splits.addAll(splitsChanges.splits());
}

4. MySqlSplitReader 执行 fetch()，由 DebeziumReader 读取数据到事件队列，在对数据修改后以 MySqlRecords 格局返回。

MySqlSplitReader#fetch
@Override
public RecordsWithSplitIds<SourceRecord> fetch() throws IOException {
    // note: 创立 Reader 并读取数据
    checkSplitOrStartNext();

    Iterator<SourceRecord> dataIt = null;
    try {
        // note:  对读取的数据进行修改
        dataIt = currentReader.pollSplitRecords();} catch (InterruptedException e) {LOG.warn("fetch data failed.", e);
        throw new IOException(e);
    }

    //  note: 返回的数据被封装为 MySqlRecords 进行传输
    return dataIt == null
            ? finishedSnapshotSplit()   
            : MySqlRecords.forRecords(currentSplitId, dataIt);
}

private void checkSplitOrStartNext() throws IOException {
    // the binlog reader should keep alive
    if (currentReader instanceof BinlogSplitReader) {return;}

    if (canAssignNextSplit()) {
        // note:  从切片队列读取 MySqlSplit
        final MySqlSplit nextSplit = splits.poll();
        if (nextSplit == null) {throw new IOException("Cannot fetch from another split - no split remaining");
        }

        currentSplitId = nextSplit.splitId();
        // note:  辨别全量切片读取还是增量切片读取
        if (nextSplit.isSnapshotSplit()) {if (currentReader == null) {final MySqlConnection jdbcConnection = getConnection(config);
                final BinaryLogClient binaryLogClient = getBinaryClient(config);

                final StatefulTaskContext statefulTaskContext =
                        new StatefulTaskContext(config, binaryLogClient, jdbcConnection);
                // note: 创立 SnapshotSplitReader，应用 Debezium Api 读取调配数据及区间 Binlog 值
                currentReader = new SnapshotSplitReader(statefulTaskContext, subtaskId);
            }

        } else {
            // point from snapshot split to binlog split
            if (currentReader != null) {LOG.info("It's turn to read binlog split, close current snapshot reader");
                currentReader.close();}

            final MySqlConnection jdbcConnection = getConnection(config);
            final BinaryLogClient binaryLogClient = getBinaryClient(config);
            final StatefulTaskContext statefulTaskContext =
                    new StatefulTaskContext(config, binaryLogClient, jdbcConnection);
            LOG.info("Create binlog reader");
            // note: 创立 BinlogSplitReader，应用 Debezium API 进行增量读取
            currentReader = new BinlogSplitReader(statefulTaskContext, subtaskId);
        }
        // note: 执行 Reader 进行数据读取
        currentReader.submitSplit(nextSplit);
    }
}

5. DebeziumReader 数据处理

DebeziumReader 蕴含全量切片读取、增量切片读取两个阶段，数据读取后存储到 ChangeEventQueue，执行 pollSplitRecords 时对数据进行修改。

1. SnapshotSplitReader 全量切片读取。全量阶段的数据读取通过执行 Select 语句查问出切片范畴内的表数据，在写入队列前后执行 SHOW MASTER STATUS 时，写入以后偏移量。

public void submitSplit(MySqlSplit mySqlSplit) {
    ......
    executor.submit(() -> {
                try {
                    currentTaskRunning = true;
                    // note: 数据读取，在数据前后插入 Binlog 以后偏移量
                    // 1. execute snapshot read task。final SnapshotSplitChangeEventSourceContextImpl sourceContext =
                            new SnapshotSplitChangeEventSourceContextImpl();
                    SnapshotResult snapshotResult =
                            splitSnapshotReadTask.execute(sourceContext);

                    //  note: 为增量读取做筹备，蕴含了起始偏移量
                    final MySqlBinlogSplit appendBinlogSplit = createBinlogSplit(sourceContext);
                    final MySqlOffsetContext mySqlOffsetContext =
                            statefulTaskContext.getOffsetContext();
                    mySqlOffsetContext.setBinlogStartPoint(appendBinlogSplit.getStartingOffset().getFilename(),
                            appendBinlogSplit.getStartingOffset().getPosition());

                    //  note: 从起始偏移量开始读取           
                    // 2. execute binlog read task
                    if (snapshotResult.isCompletedOrSkipped()) {
                        // we should only capture events for the current table,
                        Configuration dezConf =
                                statefulTaskContext
                                        .getDezConf()
                                        .edit()
                                        .with(
                                                "table.whitelist",
                                                currentSnapshotSplit.getTableId())
                                        .build();

                        // task to read binlog for current split
                        MySqlBinlogSplitReadTask splitBinlogReadTask =
                                new MySqlBinlogSplitReadTask(new MySqlConnectorConfig(dezConf),
                                        mySqlOffsetContext,
                                        statefulTaskContext.getConnection(),
                                        statefulTaskContext.getDispatcher(),
                                        statefulTaskContext.getErrorHandler(),
                                        StatefulTaskContext.getClock(),
                                        statefulTaskContext.getTaskContext(),
                                        (MySqlStreamingChangeEventSourceMetrics)
                                                statefulTaskContext
                                                        .getStreamingChangeEventSourceMetrics(),
                                        statefulTaskContext
                                                .getTopicSelector()
                                                .getPrimaryTopic(),
                                        appendBinlogSplit);

                        splitBinlogReadTask.execute(new SnapshotBinlogSplitChangeEventSourceContextImpl());
                    } else {
                        readException =
                                new IllegalStateException(
                                        String.format(
                                                "Read snapshot for mysql split %s fail",
                                                currentSnapshotSplit));
                    }
                } catch (Exception e) {
                    currentTaskRunning = false;
                    LOG.error(
                            String.format(
                                    "Execute snapshot read task for mysql split %s fail",
                                    currentSnapshotSplit),
                            e);
                    readException = e;
                }
            });
}

2. SnapshotSplitReader 增量切片读取。增量阶段切片读取重点是判断 BinlogSplitReadTask 什么时候进行，在读取到分片阶段的完结时的偏移量即终止。

MySqlBinlogSplitReadTask#handleEvent
protected void handleEvent(Event event) {
    // note: 事件下发 队列
    super.handleEvent(event);
    // note: 全量读取阶段须要终止 Binlog 读取
    // check do we need to stop for read binlog for snapshot split.
    if (isBoundedRead()) {
        final BinlogOffset currentBinlogOffset =
                new BinlogOffset(offsetContext.getOffset().get(BINLOG_FILENAME_OFFSET_KEY).toString(),
                        Long.parseLong(
                                offsetContext
                                        .getOffset()
                                        .get(BINLOG_POSITION_OFFSET_KEY)
                                        .toString()));
        // note: currentBinlogOffset > HW 进行读取
        // reach the high watermark, the binlog reader should finished
        if (currentBinlogOffset.isAtOrBefore(binlogSplit.getEndingOffset())) {
            // send binlog end event
            try {
                signalEventDispatcher.dispatchWatermarkEvent(
                        binlogSplit,
                        currentBinlogOffset,
                        SignalEventDispatcher.WatermarkKind.BINLOG_END);
            } catch (InterruptedException e) {logger.error("Send signal event error.", e);
                errorHandler.setProducerThrowable(new DebeziumException("Error processing binlog signal event", e));
            }
            //  终止 binlog 读取
            // tell reader the binlog task finished
            ((SnapshotBinlogSplitChangeEventSourceContextImpl) context).finished();}
    }
}

3. SnapshotSplitReader 执行 pollSplitRecords 时对队列中的原始数据进行修改。具体解决逻辑查看 RecordUtils#normalizedSplitRecords。

public Iterator<SourceRecord> pollSplitRecords() throws InterruptedException {if (hasNextElement.get()) {// data input: [low watermark event][snapshot events][high watermark event][binlogevents][binlog-end event]
        // data output: [low watermark event][normalized events][high watermark event]
        boolean reachBinlogEnd = false;
        final List<SourceRecord> sourceRecords = new ArrayList<>();
        while (!reachBinlogEnd) {
            // note: 解决队列中写入的 DataChangeEvent 事件
            List<DataChangeEvent> batch = queue.poll();
            for (DataChangeEvent event : batch) {sourceRecords.add(event.getRecord());
                if (RecordUtils.isEndWatermarkEvent(event.getRecord())) {
                    reachBinlogEnd = true;
                    break;
                }
            }
        }
        // snapshot split return its data once
        hasNextElement.set(false);
        //  ************   修改数据  ***********
        return normalizedSplitRecords(currentSnapshotSplit, sourceRecords, nameAdjuster)
                .iterator();}
    // the data has been polled, no more data
    reachEnd.compareAndSet(false, true);
    return null;
}

4. BinlogSplitReader 数据读取。读取逻辑比较简单，重点是起始偏移量的设置，起始偏移量为所有切片的 HW。
5. BinlogSplitReader 执行 pollSplitRecords 时对队列中的原始数据进行修改，保障数据一致性。增量阶段的 Binlog 读取是无界的，数据会全副下发到事件队列，BinlogSplitReader 通过 shouldEmit() 判断数据是否下发。

BinlogSplitReader#pollSplitRecords
public Iterator<SourceRecord> pollSplitRecords() throws InterruptedException {checkReadException();
    final List<SourceRecord> sourceRecords = new ArrayList<>();
    if (currentTaskRunning) {List<DataChangeEvent> batch = queue.poll();
        for (DataChangeEvent event : batch) {if (shouldEmit(event.getRecord())) {sourceRecords.add(event.getRecord());
            }
        }
    }
    return sourceRecords.iterator();}

事件下发条件：

1. 新收到的 event post 大于 maxwm；
2. 以后 data 值所属某个 snapshot spilt & 偏移量大于 HWM, 下发数据。

/**
 *
 * Returns the record should emit or not.
 *
 * <p>The watermark signal algorithm is the binlog split reader only sends the binlog event that
 * belongs to its finished snapshot splits. For each snapshot split, the binlog event is valid
 * since the offset is after its high watermark.
 *
 * <pre> E.g: the data input is :
 *    snapshot-split-0 info : [0,    1024) highWatermark0
 *    snapshot-split-1 info : [1024, 2048) highWatermark1
 *  the data output is:
 *  only the binlog event belong to [0,    1024) and offset is after highWatermark0 should send,
 *  only the binlog event belong to [1024, 2048) and offset is after highWatermark1 should send.
 * </pre>
 */
private boolean shouldEmit(SourceRecord sourceRecord) {if (isDataChangeRecord(sourceRecord)) {TableId tableId = getTableId(sourceRecord);
        BinlogOffset position = getBinlogPosition(sourceRecord);
        // aligned, all snapshot splits of the table has reached max highWatermark
       
        // note:  新收到的 event post 大于 maxwm , 间接下发
        if (position.isAtOrBefore(maxSplitHighWatermarkMap.get(tableId))) {return true;}
        Object[] key =
                getSplitKey(currentBinlogSplit.getSplitKeyType(),
                        sourceRecord,
                        statefulTaskContext.getSchemaNameAdjuster());

        for (FinishedSnapshotSplitInfo splitInfo : finishedSplitsInfo.get(tableId)) {
            /**
             *  note: 以后 data 值所属某个 snapshot spilt &  偏移量大于 HWM, 下发数据
             */
            if (RecordUtils.splitKeyRangeContains(key, splitInfo.getSplitStart(), splitInfo.getSplitEnd())
                    && position.isAtOrBefore(splitInfo.getHighWatermark())) {return true;}
        }
        // not in the monitored splits scope, do not emit
        return false;
    }

    // always send the schema change event and signal event
    // we need record them to state of Flink
    return true;
}

6. MySqlRecordEmitter 数据下发

SourceReaderBase 从队列中获取切片读取的 DataChangeEvent 数据汇合，将数据类型由 Debezium 的 DataChangeEvent 转换为 Flink 的 RowData 类型。

1. SourceReaderBase 解决切片数据流程。

org.apache.flink.connector.base.source.reader.SourceReaderBase#pollNext
public InputStatus pollNext(ReaderOutput<T> output) throws Exception {
    // make sure we have a fetch we are working on, or move to the next
    RecordsWithSplitIds<E> recordsWithSplitId = this.currentFetch;
    if (recordsWithSplitId == null) {recordsWithSplitId = getNextFetch(output);
        if (recordsWithSplitId == null) {return trace(finishedOrAvailableLater());
        }
    }

    // we need to loop here, because we may have to go across splits
    while (true) {
        // Process one record.
        // note:  通过 MySqlRecords 从迭代器中读取单条数据
        final E record = recordsWithSplitId.nextRecordFromSplit();
        if (record != null) {
            // emit the record.
            recordEmitter.emitRecord(record, currentSplitOutput, currentSplitContext.state);
            LOG.trace("Emitted record: {}", record);

            // We always emit MORE_AVAILABLE here, even though we do not strictly know whether
            // more is available. If nothing more is available, the next invocation will find
            // this out and return the correct status.
            // That means we emit the occasional 'false positive' for availability, but this
            // saves us doing checks for every record. Ultimately, this is cheaper.
            return trace(InputStatus.MORE_AVAILABLE);
        } else if (!moveToNextSplit(recordsWithSplitId, output)) {
            // The fetch is done and we just discovered that and have not emitted anything, yet.
            // We need to move to the next fetch. As a shortcut, we call pollNext() here again,
            // rather than emitting nothing and waiting for the caller to call us again.
            return pollNext(output);
        }
        // else fall through the loop
    }
}

private RecordsWithSplitIds<E> getNextFetch(final ReaderOutput<T> output) {splitFetcherManager.checkErrors();

    LOG.trace("Getting next source data batch from queue");
    // note: 从 elementsQueue 获取数据
    final RecordsWithSplitIds<E> recordsWithSplitId = elementsQueue.poll();
    if (recordsWithSplitId == null || !moveToNextSplit(recordsWithSplitId, output)) {return null;}

    currentFetch = recordsWithSplitId;
    return recordsWithSplitId;
}

2. MySqlRecords 返回单条数据汇合。

com.ververica.cdc.connectors.mysql.source.split.MySqlRecords#nextRecordFromSplit

public SourceRecord nextRecordFromSplit() {
    final Iterator<SourceRecord> recordsForSplit = this.recordsForCurrentSplit;
    if (recordsForSplit != null) {if (recordsForSplit.hasNext()) {return recordsForSplit.next();
        } else {return null;}
    } else {throw new IllegalStateException();
    }
}

3. MySqlRecordEmitter 通过 RowDataDebeziumDeserializeSchema 将数据转换为 Rowdata。

com.ververica.cdc.connectors.mysql.source.reader.MySqlRecordEmitter#emitRecord
public void emitRecord(SourceRecord element, SourceOutput<T> output, MySqlSplitState splitState)
    throws Exception {if (isWatermarkEvent(element)) {BinlogOffset watermark = getWatermark(element);
    if (isHighWatermarkEvent(element) && splitState.isSnapshotSplitState()) {splitState.asSnapshotSplitState().setHighWatermark(watermark);
    }
} else if (isSchemaChangeEvent(element) && splitState.isBinlogSplitState()) {HistoryRecord historyRecord = getHistoryRecord(element);
    Array tableChanges =
            historyRecord.document().getArray(HistoryRecord.Fields.TABLE_CHANGES);
    TableChanges changes = TABLE_CHANGE_SERIALIZER.deserialize(tableChanges, true);
    for (TableChanges.TableChange tableChange : changes) {splitState.asBinlogSplitState().recordSchema(tableChange.getId(), tableChange);
    }
} else if (isDataChangeRecord(element)) {
    //  note: 数据的解决
    if (splitState.isBinlogSplitState()) {BinlogOffset position = getBinlogPosition(element);
        splitState.asBinlogSplitState().setStartingOffset(position);
    }
    debeziumDeserializationSchema.deserialize(
            element,
            new Collector<T>() {
                @Override
                public void collect(final T t) {output.collect(t);
                }

                @Override
                public void close() {// do nothing}
            });
} else {
    // unknown element
    LOG.info("Meet unknown element {}, just skip.", element);
}
}

RowDataDebeziumDeserializeSchema 序列化过程。

com.ververica.cdc.debezium.table.RowDataDebeziumDeserializeSchema#deserialize
public void deserialize(SourceRecord record, Collector<RowData> out) throws Exception {Envelope.Operation op = Envelope.operationFor(record);
    Struct value = (Struct) record.value();
    Schema valueSchema = record.valueSchema();
    if (op == Envelope.Operation.CREATE || op == Envelope.Operation.READ) {GenericRowData insert = extractAfterRow(value, valueSchema);
        validator.validate(insert, RowKind.INSERT);
        insert.setRowKind(RowKind.INSERT);
        out.collect(insert);
    } else if (op == Envelope.Operation.DELETE) {GenericRowData delete = extractBeforeRow(value, valueSchema);
        validator.validate(delete, RowKind.DELETE);
        delete.setRowKind(RowKind.DELETE);
        out.collect(delete);
    } else {GenericRowData before = extractBeforeRow(value, valueSchema);
        validator.validate(before, RowKind.UPDATE_BEFORE);
        before.setRowKind(RowKind.UPDATE_BEFORE);
        out.collect(before);

        GenericRowData after = extractAfterRow(value, valueSchema);
        validator.validate(after, RowKind.UPDATE_AFTER);
        after.setRowKind(RowKind.UPDATE_AFTER);
        out.collect(after);
    }
}

7. MySqlSourceReader 汇报切片读取实现事件

MySqlSourceReader 解决完一个全量切片后，会向 MySqlSourceEnumerator 发送已实现的切片信息，蕴含切片 ID、HighWatermar，而后持续发送切片申请。

com.ververica.cdc.connectors.mysql.source.reader.MySqlSourceReader#onSplitFinished
protected void onSplitFinished(Map<String, MySqlSplitState> finishedSplitIds) {for (MySqlSplitState mySqlSplitState : finishedSplitIds.values()) {MySqlSplit mySqlSplit = mySqlSplitState.toMySqlSplit();

    finishedUnackedSplits.put(mySqlSplit.splitId(), mySqlSplit.asSnapshotSplit());
}
/**
 *   note: 发送切片实现事件
 */
reportFinishedSnapshotSplitsIfNeed();

//  上一个 spilt 解决实现后持续发送切片申请
context.sendSplitRequest();}

private void reportFinishedSnapshotSplitsIfNeed() {if (!finishedUnackedSplits.isEmpty()) {final Map<String, BinlogOffset> finishedOffsets = new HashMap<>();
        for (MySqlSnapshotSplit split : finishedUnackedSplits.values()) {
            // note: 发送切片 ID，及最大偏移量
            finishedOffsets.put(split.splitId(), split.getHighWatermark());
        }
        FinishedSnapshotSplitsReportEvent reportEvent =
                new FinishedSnapshotSplitsReportEvent(finishedOffsets);

        context.sendSourceEventToCoordinator(reportEvent);
        LOG.debug("The subtask {} reports offsets of finished snapshot splits {}.",
                subtaskId,
                finishedOffsets);
    }
}

8. MySqlSourceEnumerator 调配增量切片

全量阶段所有分片读取结束后，MySqlHybridSplitAssigner 会创立 BinlogSplit 进行后续增量读取，在创立 BinlogSplit 会从全副已实现的全量切片中筛选最小 BinlogOffset。留神：2.0.0 分支 createBinlogSplit 最小偏移量总是从 0 开始，最新 master 分支曾经修复这个 BUG。

private MySqlBinlogSplit createBinlogSplit() {
    final List<MySqlSnapshotSplit> assignedSnapshotSplit =
            snapshotSplitAssigner.getAssignedSplits().values().stream()
                    .sorted(Comparator.comparing(MySqlSplit::splitId))
                    .collect(Collectors.toList());

    Map<String, BinlogOffset> splitFinishedOffsets =
            snapshotSplitAssigner.getSplitFinishedOffsets();
    final List<FinishedSnapshotSplitInfo> finishedSnapshotSplitInfos = new ArrayList<>();
    final Map<TableId, TableChanges.TableChange> tableSchemas = new HashMap<>();

    BinlogOffset minBinlogOffset = null;
    // note: 从所有 assignedSnapshotSplit 中筛选最小偏移量
    for (MySqlSnapshotSplit split : assignedSnapshotSplit) {
        // find the min binlog offset
        BinlogOffset binlogOffset = splitFinishedOffsets.get(split.splitId());
        if (minBinlogOffset == null || binlogOffset.compareTo(minBinlogOffset) < 0) {minBinlogOffset = binlogOffset;}
        finishedSnapshotSplitInfos.add(
                new FinishedSnapshotSplitInfo(split.getTableId(),
                        split.splitId(),
                        split.getSplitStart(),
                        split.getSplitEnd(),
                        binlogOffset));
        tableSchemas.putAll(split.getTableSchemas());
    }

    final MySqlSnapshotSplit lastSnapshotSplit =
            assignedSnapshotSplit.get(assignedSnapshotSplit.size() - 1).asSnapshotSplit();
       
    return new MySqlBinlogSplit(
            BINLOG_SPLIT_ID,
            lastSnapshotSplit.getSplitKeyType(),
            minBinlogOffset == null ? BinlogOffset.INITIAL_OFFSET : minBinlogOffset,
            BinlogOffset.NO_STOPPING_OFFSET,
            finishedSnapshotSplitInfos,
            tableSchemas);
}

更多 Flink 相干技术问题，可扫码退出社区钉钉交换群；

第一工夫获取最新技术文章和社区动静，请关注公众号～