Apache Flink CDC 批流融合技术原理分析
问题导读：
1、怎样使用Flink CDC实现全量读取 + 增量读取 Mysql 表数据？
2、怎样使用Flink CDC实现切片划分、切分读取？
3、怎样调用flink-mysql-cdc 接口？
8 月份 Flink CDC 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 https://flink-learning.org.cn/ar ... 1c4d5eeb75a141d9e1e。
Flink CDC 2.0 数据读取逻辑并不复杂，复杂的是 https://cwiki.apache.org/conflue ... or+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， https://cwiki.apache.org/conflue ... or+Source+Interface 的设计及 Debezium 的 API 调用不做过多讲解。
本文使用 CDC 2.0.0 版本，先以 Flink SQL 案例来介绍 Flink CDC 2.0 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。
一、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"}
修改表数据，增量捕获：
## 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、核心设计
1. 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。
1.1 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。
注意：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根据 max(id) - min(id)/rowcount 计算出数据分布系数，只有分布系数 <= 配置的分布系数 (evenly-distribution.factor 默认为 1000.0d) 才会进行数据均匀划分。
//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：
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。
注意：最新版本非均匀分布触发条件为主键列为非整数类型，或者计算出的分布系数 (distributionFactor) > 配置的分布系数 (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
) AS T
2. 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。
2.1 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 >=
AND NOT (order_id = )
AND order_id <=
2.2 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 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。
3. 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 1000
1 800
2 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。
三、代码详解
关于 https://cwiki.apache.org/conflue ... or+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。
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 创建 MySqlSourceEnumerator 并调用 start 方法，做一些初始化工作。
（1）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。
（2）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。
（3）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());
}
}
}
2. 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。
```sql MySqlParallelSource#createReader
public SourceReader<T, MySqlSplit> createReader(SourceReaderContext readerContext) throws Exception { // note: 数据存储队列 FutureCompletingBlockingQueue<RecordsWithSplitIds> elementsQueue = new FutureCompletingBlockingQueue<>(); final Configuration readerConfiguration = getReaderConfig(readerContext);
// note: Split Reader 工厂类
Supplier 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。
```sql
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：
3. 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)。
```sql 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 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。
```sql
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();
}
}
}
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。
```sql private List 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; final Object max = minMaxOfSplitColumn; 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 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 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 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 splitUnevenlySizedChunks(
TableId tableId, String splitColumnName, Object min, Object max) throws SQLException {
final List 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
https://img.alicdn.com/imgextra/i3/O1CN01aSKwvE28TMhykoPfg_!!6000000007933-2-tps-1080-396.png
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。
```sql
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);
}
}
5. 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:
// data output:
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：
[*]新收到的 event post 大于 maxwm；
[*]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.
*
* <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 ,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;
}
6. 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。
```java org.apache.flink.connector.base.source.reader.SourceReaderBase#pollNext public InputStatus pollNext(ReaderOutput output) throws Exception { // make sure we have a fetch we are working on, or move to the next RecordsWithSplitIds 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 getNextFetch(final ReaderOutput 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。
```java
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);
}
}
7. 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);
}
}
8. 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);
}
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---------------------
作者：任建旭
来源：flink-learning
原文：Apache Flink CDC 批流融合技术原理分析
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