Final answer:
Accumulators in Apache Spark are write-only variables used for aggregating data across nodes in parallel computations such as counting occurrences or summing values.
Step-by-step explanation:
Accumulators are variables that are only "added" to through an associative and commutative operation and are therefore able to be efficiently supported in parallel processing. In Apache Spark, accumulators are used as a tool to aggregate information across the various nodes that are processing data. For example, they can be used to count events that occur during job execution—such as counting the number of errors encountered during data processing.
Accumulators have the following characteristics:
- b. Accumulators are used to store intermediate results in parallel computations. This makes them useful for tasks such as summing up values across partitions or maintaining counters.
- d. While accumulators can be used in Spark Streaming, they are not exclusive to it and can be employed in batch processing as well. Their usability is not limited to Spark Streaming applications only.
It's important to note that accumulators are write-only variables for tasks - they can only be read after the entire computation is finished. Therefore, they are not suitable for tasks that require reading the intermediate value mid-calculation.