What may happen if checkpoints are not used in Spark Structured Streaming?

When checkpoints are not used in Spark Structured Streaming, the primary risk is that data may be lost during failures. Checkpoints serve as a way to save the state of a streaming application, including information about the progress of processed data. They allow the system to recover from interruptions, such as node failures or application crashes, without losing track of what data has already been consumed.

Without checkpoints, if an error occurs or the system crashes, the application cannot resume from the last known good state. This can lead to scenarios where messages are not processed, resulting in data loss. The system lacks a recovery point, making it difficult to ensure that all incoming data can be reliably processed and acknowledged.

The other options do not accurately capture the implications of missing checkpoints. For instance, applications might not necessarily run faster without checkpoints; in fact, they could become slower or less efficient due to the need to reprocess data from the beginning in case of a failure. Data would not be unprocessed simply due to the absence of checkpoints — it would more likely be a matter of whether the application can recover after a failure. Finally, the absence of checkpoints would likely complicate workload management rather than making it easier, as the risk of losing data would increase, leading to more difficult