How Can I Check if a Flink Job Can Be Restored From a Checkpoint After Restarting It?
What Is Restoration from a Checkpoint?
Flink's checkpointing is a fault tolerance and recovery mechanism. This mechanism ensures that real-time programs can self-recover in case of exceptions or machine issues during runtime.
Principles for Restoration from Checkpoints
- When a job fails to be executed or a resource restarts due to an exception that is not triggered by manual operations, data can be restored from a checkpoint.
- However, if the calculation logic of a job is modified, the job cannot be restored from a checkpoint.
Application Scenarios
Table 1 lists some common scenarios of restoring data from a checkpoint for your reference.
For more scenarios, refer to Principles for Restoration from Checkpoints and assess whether data can be restored from a checkpoint based on the actual situation.
Scenario |
Restoration from a Checkpoint |
Description |
---|---|---|
Adjust or increase the number of concurrent tasks. |
Not supported |
This operation alters the parallelism of the job, thereby changing its execution logic. |
Modify Flink SQL statements and Flink Jar jobs. |
Not supported |
This operation modifies the algorithmic logic of the job with respect to resources. For example, if the original algorithm involves addition and subtraction, but the desired state requires multiplication, division, and modulo operations, it cannot be restored directly from the checkpoint. |
Modify the static stream graph. |
Not supported |
This operation modifies the algorithmic logic of the job with respect to resources. |
Modify the CU(s) per TM parameter. |
Supported |
The modification of compute resources does not affect the operational logic of the job's algorithm or operators. |
A job runs abnormally or there is a physical power outage. |
Supported |
The job parameters and algorithm logic are not modified. |
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