Checkpointing
A streaming application, as given in examples earlier such as fraud detection and next-best offer, typically operate 24/7 and hence it is of the utmost importance that the framework is resilient enough to recover from failures (which will happen). Spark Streaming provides the option to checkpoint information to a fault-tolerant storage system so that it can recover from failures. Checkpointing consist of two major types:
Metadata checkpointing: Metadata checkpoint is essential if you would like to recover from driver program failures. As an application architect/engineer you would want to save the metadata about your job, that is, information defining the streaming computation to be performed to a fault-tolerant system such as HDFS or S3. If the node running the driver program fails, you will have to fall back on this checkpoint to get to the latest state of your application. A typical application metadata will include the following:
Configuration: The initial configuration used...
