Flink的基于两阶段提交协议的事务数据汇实现

背景

在flink中可以通过使用事务性数据汇实现精准一次的保证，本文基于Kakfa的事务处理来看一下在Flink 内部如何实现基于两阶段提交协议的事务性数据汇.

flink kafka事务性数据汇的实现

1。首先在开始进行快照的时候也就是收到checkpoint通知的时候，在snapshot方法中会开启一个新的事务，代码如下：

   public void snapshotState(FunctionSnapshotContext context) throws Exception {// this is like the pre-commit of a 2-phase-commit transaction// we are ready to commit and remember the transactioncheckState(currentTransactionHolder != null,"bug: no transaction object when performing state snapshot");long checkpointId = context.getCheckpointId();LOG.debug("{} - checkpoint {} triggered, flushing transaction '{}'",name(),context.getCheckpointId(),currentTransactionHolder);preCommit(currentTransactionHolder.handle);// 调用kafkaProducer.flush();清理上一个事务的状态(注意不是提交),只是确保前一个事务的所有资源清理完毕pendingCommitTransactions.put(checkpointId, currentTransactionHolder);LOG.debug("{} - stored pending transactions {}", name(), pendingCommitTransactions);
// 调用producer.beginTransaction();方法开启一个新的kafka事务currentTransactionHolder = beginTransactionInternal();LOG.debug("{} - started new transaction '{}'", name(), currentTransactionHolder);state.clear();state.add(new State<>(this.currentTransactionHolder,new ArrayList<>(pendingCommitTransactions.values()),userContext));}

2.其次在JobManager通知检查点完成的通知方法,也就是notifyCheckpointComplete方法中提交事务

Iterator<Map.Entry<Long, TransactionHolder<TXN>>> pendingTransactionIterator =pendingCommitTransactions.entrySet().iterator();Throwable firstError = null;while (pendingTransactionIterator.hasNext()) {Map.Entry<Long, TransactionHolder<TXN>> entry = pendingTransactionIterator.next();Long pendingTransactionCheckpointId = entry.getKey();TransactionHolder<TXN> pendingTransaction = entry.getValue();if (pendingTransactionCheckpointId > checkpointId) {continue;}LOG.info("{} - checkpoint {} complete, committing transaction {} from checkpoint {}",name(),checkpointId,pendingTransaction,pendingTransactionCheckpointId);logWarningIfTimeoutAlmostReached(pendingTransaction);try {//调用producer.commitTransaction()方法提交事务commit(pendingTransaction.handle);} catch (Throwable t) {if (firstError == null) {firstError = t;}}LOG.debug("{} - committed checkpoint transaction {}", name(), pendingTransaction);pendingTransactionIterator.remove();}if (firstError != null) {throw new FlinkRuntimeException("Committing one of transactions failed, logging first encountered failure",firstError);}

至此，一个两阶段提交的flink事务性数据汇完成了，这个事务性数据汇可以构成端到端一致性的一部分