spark使用多个日志文件来确保容错性。与查询相关的是偏移日志和提交日志。从streamexecution类文档：

/**
   * A write-ahead-log that records the offsets that are present in each batch. In order to ensure
   * that a given batch will always consist of the same data, we write to this log *before* any
   * processing is done.  Thus, the Nth record in this log indicated data that is currently being
   * processed and the N-1th entry indicates which offsets have been durably committed to the sink.
   */
  val offsetLog = new OffsetSeqLog(sparkSession, checkpointFile("offsets"))

  /**
   * A log that records the batch ids that have completed. This is used to check if a batch was
   * fully processed, and its output was committed to the sink, hence no need to process it again.
   * This is used (for instance) during restart, to help identify which batch to run next.
   */
  val commitLog = new CommitLog(sparkSession, checkpointFile("commits"))

因此，当它从Kafka读取时，它会将偏移量写入 offsetLog 只有在处理数据并将其写入接收器（在您的cassandra中）之后，它才会将偏移量写入 commitLog .

spark结构化流媒体并不像一个“正常”的Kafka消费者那样向Kafka提供补偿。spark使用检查点机制在内部管理偏移量。
请看下面这个问题的第一个回答，它很好地解释了如何使用检查点和commitslog管理状态：如何为结构化查询获取kafka偏移量，以便进行手动可靠的偏移量管理？