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1. Failed, if the job does not enable HA.
2. Restart, if the job enable HA. If it’s a streaming job, the job will be resumed from the last successful checkpoint. If it’s a batch job, it have has to run from beginning, all previous progress will be lost.

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1. ExecutionJobVertexInitializedEvent: This event is responsible for recording the initialization information of ExecutionJobVertex, its content contains the decided parallelism of this job vertex, and its input information. This event will be triggered and written out when an execution job vertex ExecutionJobVertex is initialized.

2. ExecutionVertexFinishedEvent: This event is responsible for recording the information of finished task. Our goal is that all finished tasks don’t need to re-run, so the simple idea is to trigger an event when a task is finished.  The content of this event contains:

   1. The state of the finished task/ExecutionVertex, including IO metrics, accumulators, etc. These contents can be easily obtained from ExecutionGraph.

   2. If the job vertex which this task belongs to has operator coordinators, the states of the operator coordinators also need to be recorded.

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Consider that the operator coordinators may have large state, we believe it may cause large overhead if we snapshot the operator coordinator at each execution vertex finished. To solve this problem, we will add a new configuration option "execution.batch.job-recovery.operator-coordinator-snapshot.min-pause" to control the minimum interval pause between snapshots. When restoring, we will also reconcile the execution job vertex state with the operator coordinator state to be consistent. In other words, we will adjust the execution job vertex to the state at the time of the lastest snapshot operator coordinators.

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