【核心技术】Apache Flink CDC 批流融合技术原理分析
8月份 FlinkCDC 发布2.0.0版本，相较于1.0版本，在全量读取阶段支持分布式读取、支持checkpoint，且在全量 + 增量读取的过程在不锁表的情况下保障数据一致性。 详细介绍参考Flink CDC 2.0 正式发布，详解核心改进。 Flink CDC2.0 数据读取逻辑并不复杂，复杂的是FLIP-27: Refactor Source Interface的设计及对Debezium Api的不了解。本文重点对 Flink CDC 的处理逻辑进行介绍，FLIP-27的设计及 Debezium 的API调用不做过多讲解。 本文先以Flink SQL 案例来介绍Flink CDC2.0的使用，接着介绍CDC中的核心设计包含切片划分、切分读取、增量读取，最后对数据处理过程中涉及flink-mysql-cdc 接口的调用及实现进行代码讲解。 案例 全量读取+增量读取 Mysql表数据，以changelog-json格式写入kafka，观察 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: 增量同步需要开启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");} 全量数据输出： {"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"} ## 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"} 核心设计 切片划分 全量阶段数据读取方式为分布式读取，会先对当前表数据按主键划分成多个Chunk，后续子任务读取Chunk 区间内的数据。根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。 均匀分布 主键列自增且类型为整数类型（int,bigint,decimal）。查询出主键列的最小值，最大值，按 chunkSize 大小将数据均匀划分，因为主键为整数类型，根据当前chunk 起始位置、chunkSize大小，直接计算chunk 的结束位置。 // 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) 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，每次划分需要对未划分的数据按主键进行升序排列，取出前 chunkSize 的最大值为当前 chunk 的结束位置。 // 未拆分的数据排序后，取 chunkSize 条数据取最大值，作为切片的终止位置。 chunkend = SELECT MAX(`order_id`) FROM ( SELECT `order_id` FROM `demo_orders` WHERE `order_id` >= [前一个切片的起始位置] ORDER BY `order_id` ASC LIMIT [chunkSize] ) AS T 全量切片数据读取 Flink 将表数据划分为多个Chunk，子任务在不加锁的情况下，并行读取 Chunk数据。因为全程无锁在数据分片读取过程中，可能有其他事务对切片范围内的数据进行修改，此时无法保证数据一致性。因此，在全量阶段Flink 使用快照记录读取+Binlog数据修正的方式来保证数据的一致性。 快照读取 通过JDBC执行SQL查询切片范围的数据记录。 ## 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] 数据修正 在快照读取操作前、后执行SHOW MASTER STATUS查询binlog文件的当前偏移量，在快照读取完毕后，查询区间内的binlog数据并对读取的快照记录进行修正。 快照读取+Binlog数据读取时的数据组织结构。 BinlogEvents 修正 SnapshotEvents 规则。 未读取到binlog数据，即在执行select阶段没有其他事务进行操作，直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当前切片，下发快照记录。 读取到binlog数据，且数据记录的变更属于当前切片。delete 操作从快照内存中移除该数据，insert 操作向快照内存添加新的数据，update操作向快照内存中添加变更记录，最终会输出更新前后的两条记录到下游。 修正后的数据组织结构： 以读取切片 [1,11）范围的数据为例，描述切片数据的处理过程。c,d,u代表Debezium捕获到的新增、删除、更新操作。 修正前数据及结构： 修正后数据及结构： 单个切片数据处理完毕后会向 SplitEnumerator 发送已完成切片数据的起始位置（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。 增量切片数据读取 全量阶段切片数据读取完成后，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下游可能会有重复数据，偏移量如果设置过大下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完成的全量切片最小的Binlog偏移量，只有满足条件的数据才被下发到下游。数据下发条件： 捕获的Binlog数据的偏移量 > 数据所属分片的Binlog的最大偏移量。 例如，SplitEnumerator 保留的已完成切片信息为。 切片索引 Chunk 数据范围 切片读取的最大Binlog 0 [1,100] 1000 1 [101,200] 800 2 [201,300] 1500 增量读取时，从偏移量 800 开始读取Binlog数据 ，当捕获到数据 <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。 当前偏移量大于快照读的最大偏移量，则下发数据，否则直接丢弃。 代码详解 关于FLIP-27: Refactor Source Interface设计不做详细介绍，本文侧重对 flink-mysql-cdc 接口调用及实现进行讲解。 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 创建 MySqlSourceEnumerator 并调用start方法，做一些初始化工作。 创建 MySqlSourceEnumerator，使用 MySqlHybridSplitAssigner 对全量+增量数据进行切片，使用 MySqlValidator 对 mysql 版本、配置进行校验。 MySqlValidator 校验： mysql版本必须大于等于5.7。 binlog_format 配置必须为 ROW。 binlog_row_image 配置必须为 FULL。 MySqlSplitAssigner 初始化： 创建 ChunkSplitter用来划分切片。 筛选出要读的表名称。 启动周期调度线程，要求 SourceReader 向 SourceEnumerator 发送已完成但未发送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()); } } } MySqlSourceReader 初始化 SourceOperator 集成了SourceReader，通过OperatorEventGateway 和 SourceCoordinator 进行交互。 SourceOperator 在初始化时，通过 MySqlParallelSource 创建 MySqlSourceReader。MySqlSourceReader 通过 SingleThreadFetcherManager 创建Fetcher拉取分片数据，数据以 MySqlRecords 格式写入到 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); } 将创建的 MySqlSourceReader 以事件的形式传递给 SourceCoordinator 进行注册。SourceCoordinator 接收到注册事件后，将reader 地址及索引进行保存。 SourceCoordinator#handleReaderRegistrationEvent // note: SourceCoordinator 处理Reader 注册事件 private void handleReaderRegistrationEvent(ReaderRegistrationEvent event) { context.registerSourceReader(new ReaderInfo(event.subtaskId(), event.location())); enumerator.addReader(event.subtaskId()); } MySqlSourceReader 启动后会向 MySqlSourceEnumerator 发送请求分片事件，从而收集分配的切片数据。 SourceOperator 初始化完毕后，调用 emitNext 由 SourceReaderBase 从 elementsQueue 获取数据集合并下发给 MySqlRecordEmitter。接口调用示意图：! MySqlSourceEnumerator 处理分片请求 MySqlSourceReader 启动时会向 MySqlSourceEnumerator 发送请求 RequestSplitEvent 事件，根据返回的切片范围读取区间数据。MySqlSourceEnumerator 全量读取阶段分片请求处理逻辑，最终返回一个MySqlSnapshotSplit。 处理切片请求事件，为请求的Reader分配切片，通过发送AddSplitEvent时间传递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, 在处理binlog split时优先分配个task-0 readersAwaitingSplit.add(subtaskId); assignSplits(); } // note: 分配切片 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 分配切片 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; } } } MySqlHybridSplitAssigner 处理全量切片、增量切片的逻辑。 任务刚启动时，remainingTables不为空，noMoreSplits返回值为false，创建 SnapshotSplit。 全量阶段分片读取完成后，noMoreSplits返回值为true, 创建 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 切片完成后，创建BinlogSplit。 return Optional.of(createBinlogSplit()); } else { // binlog split is not ready by now return Optional.empty(); } } else { // note: 由MySqlSnapshotSplitAssigner 创建 SnapshotSplit // snapshot assigner still have remaining splits, assign split from it return snapshotSplitAssigner.getNext(); } } MySqlSnapshotSplitAssigner 处理全量切片逻辑，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: 已分配的切片存储到 assignedSplits 集合 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: 初始化阶段创建了 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: 已经完成分片的 Table alreadyProcessedTables.add(nextTable); // note: 递归调用该该方法 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。读取的表必须包含物理主键。 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; } splitTableIntoChunks 根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: 按主键均匀划分 chunks = splitEvenlySizedChunks(min, max); } else { // note: 按主键非均匀划分 // 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; } } /** * 根据拆分列的最小值和最大值将表拆分为大小均匀的块，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; } /** 通过连续计算下一个块最大值，将表拆分为大小不均匀的块。 * 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; } } MySqlSourceReader 处理切片分配请求 MySqlSourceReader接收到切片分配请求后，会为先创建一个 SplitFetcher线程，向 taskQueue 添加、执行AddSplitsTask 任务用来处理添加分片任务，接着执行 FetchTask 使用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。 处理切片分配事件时，创建SplitFetcher向taskQueue添加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); } } // 创建 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); } 执行 SplitFetcher线程，首次执行 AddSplitsTask 线程添加分片，以后执行 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."); } } AddSplitsTask 调用 MySqlSplitReader 的 handleSplitsChanges方法，向切片队列中添加已分配的切片信息。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: 添加切片 到队列。 splits.addAll(splitsChanges.splits()); } MySqlSplitReader 执行fetch()，由DebeziumReader读取数据到事件队列，在对数据修正后以MySqlRecords格式返回。 MySqlSplitReader#fetch @Override public RecordsWithSplitIds<SourceRecord> fetch() throws IOException { // note: 创建Reader 并读取数据 checkSplitOrStartNext(); Iterator<SourceRecord> dataIt = null; try { // note: 对读取的数据进行修正 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: 区分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: 创建SnapshotSplitReader，使用Debezium Api读取分配数据及区间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: 创建BinlogSplitReader，使用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); } } DebeziumReader 数据处理 DebeziumReader 包含全量切片读取、增量切片读取两个阶段，数据读取后存储到 ChangeEventQueue，执行pollSplitRecords 时对数据进行修正。 SnapshotSplitReader 全量切片读取。全量阶段的数据读取通过执行Select语句查询出切片范围内的表数据，在写入队列前后执行 SHOW MASTER STATUS 时，写入当前偏移量。 public void submitSplit(MySqlSplit mySqlSplit) { ...... executor.submit( () -> { try { currentTaskRunning = true; // note: 数据读取，在数据前后插入Binlog当前偏移量 // 1. execute snapshot read task。 final SnapshotSplitChangeEventSourceContextImpl sourceContext = new SnapshotSplitChangeEventSourceContextImpl(); SnapshotResult snapshotResult = splitSnapshotReadTask.execute(sourceContext); // note: 为增量读取做准备，包含了起始偏移量 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; } }); } SnapshotSplitReader 增量切片读取。增量阶段切片读取重点是判断BinlogSplitReadTask什么时候停止，在读取到分片阶段的结束时的偏移量即终止。 MySqlBinlogSplitReadTask#handleEvent protected void handleEvent(Event event) { // note: 事件下发 队列 super.handleEvent(event); // note: 全量读取阶段需要终止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 停止读取 // 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(); } } } SnapshotSplitReader 执行pollSplitRecords 时对队列中的原始数据进行修正。 具体处理逻辑查看 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: 处理队列中写入的 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); // ************ 修正数据 *********** return normalizedSplitRecords(currentSnapshotSplit, sourceRecords, nameAdjuster) .iterator(); } // the data has been polled, no more data reachEnd.compareAndSet(false, true); return null; } 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。 BinlogSplitReader 执行pollSplitRecords 时对队列中的原始数据进行修正，保障数据一致性。 增量阶段的Binlog读取是无界的，数据会全部下发到事件队列，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. 当前 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. * * 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. * * 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. * */ 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 ,直接下发 if (position.isAtOrBefore(maxSplitHighWatermarkMap.get(tableId))) { return true; } Object[] key = getSplitKey( currentBinlogSplit.getSplitKeyType(), sourceRecord, statefulTaskContext.getSchemaNameAdjuster()); for (FinishedSnapshotSplitInfo splitInfo : finishedSplitsInfo.get(tableId)) { /** * note: 当前 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; } MySqlRecordEmitter 数据下发 SourceReaderBase 从队列中获取切片读取的DataChangeEvent数据集合，将数据类型由Debezium的DataChangeEvent 转换为Flink 的RowData类型。 SourceReaderBase 处理切片数据流程。 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; } MySqlRecords 返回单条数据集合。 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(); } } 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: 数据的处理 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); } } MySqlSourceReader 汇报切片读取完成事件 MySqlSourceReader处理完一个全量切片后，会向MySqlSourceEnumerator发送已完成的切片信息，包含切片ID、HighWatermar ，然后继续发送切片请求。 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: 发送切片完成事件 */ reportFinishedSnapshotSplitsIfNeed(); // 上一个spilt处理完成后继续发送切片请求 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); } } MySqlSourceEnumerator 分配增量切片 全量阶段所有分片读取完毕后，MySqlHybridSplitAssigner 会创建BinlogSplit 进行后续增量读取，在创建BinlogSplit 会从全部已完成的全量切片中筛选最小BinlogOffset。注意：2.0.0分支 createBinlogSplit 最小偏移量总是从0开始，最新master分支已经修复这个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); }
