记一次flink不做checkpoint的问题

问题现象：Flink UI界面查看checkpoint的metrics发现一直没有做checkpoint，仔细排查发现有部分subtask的状态是finished。
下图是测试环境复现问题

在这里插入图片描述
问题原因：仔细排查代码后发现source是消费kafka的数据，配置的并行度大于kafka的partition数，导致有部分subtask空闲，然后状态变为finished。后来查看了checkpoint过程的源码得以佐证。
在CheckpointCoordinator类的triggerCheckpoint方法中有如下代码段

// check if all tasks that we need to trigger are running.
        // if not, abort the checkpoint
        Execution[] executions = new Execution[tasksToTrigger.length];
        for (int i = 0; i < tasksToTrigger.length; i++) {
            Execution ee = tasksToTrigger[i].getCurrentExecutionAttempt();
            if (ee == null) {
                LOG.info("Checkpoint triggering task {} of job {} is not being executed at the moment. Aborting checkpoint.",
                        tasksToTrigger[i].getTaskNameWithSubtaskIndex(),
                        job);
                throw new CheckpointException(CheckpointFailureReason.NOT_ALL_REQUIRED_TASKS_RUNNING);
            } else if (ee.getState() == ExecutionState.RUNNING) {
                executions[i] = ee;
            } else {
                LOG.info("Checkpoint triggering task {} of job {} is not in state {} but {} instead. Aborting checkpoint.",
                        tasksToTrigger[i].getTaskNameWithSubtaskIndex(),
                        job,
                        ExecutionState.RUNNING,
                        ee.getState());
                throw new CheckpointException(CheckpointFailureReason.NOT_ALL_REQUIRED_TASKS_RUNNING);
            }

ee.getState() == ExecutionState.RUNNING判断execution的状态是否为running，否则不做checkpoint

问题结论：在消费kafka的数据时，source的并发度不能超过kafka的partition数，可以小于partition，但是部分subtask就会消费多个partition的数据，导致吞吐达不到最大，理想状态是source并发度等于partition数。