Spark Streaming: Data Consistency & Accuracy

In Spark, streaming processing typically utilizes DStreams (Discretized Streams) to represent continuous data streams. To ensure data consistency and accuracy, Spark provides the following mechanisms:

1. Content retention: Spark will cache the received data so that it can be accessed again when needed. This prevents data loss or duplicate processing.
2. Fault tolerance: Spark constructs streaming applications based on Resilient Distributed Datasets (RDD), which have the feature of fault tolerance. In the event of a failure, Spark will automatically recover the data and continue processing.
3. Transactional processing: Spark Streaming supports transactional processing to ensure the integrity and consistency of data. For example, using transactions to guarantee atomicity when writing data to an external storage system.
4. Checkpoint: Spark Streaming supports a checkpoint mechanism that allows the current state to be saved to a reliable storage system. This enables the system to recover the state and continue processing in case of a failure.

In conclusion, the streaming processing in Spark guarantees data consistency and accuracy through its internal mechanisms and features, ensuring that streaming applications can run stably and reliably.