Explanation
Reroute a query in case of an executor failure.
Correct. Although this feature exists in Spark, it is not a feature of Adaptive Query Execution. The cluster manager keeps track of executors and will work together with the driver to launch an executor and assign the workload of the failed executor to it (see also link below).
Replace a sort merge join with a broadcast join, where appropriate.
No, this is a feature of Adaptive Query Execution.
Coalesce partitions to accelerate data processing.
Wrong, Adaptive Query Execution does this.
Collect runtime statistics during query execution.
Incorrect, Adaptive Query Execution (AQE) collects these statistics to adjust query plans. This feedback loop is an essential part of accelerating queries via AQE.
Split skewed partitions into smaller partitions to avoid differences in partition processing time.
No, this is indeed a feature of Adaptive Query Execution. Find more information in the Databricks blog post linked below.
More info: Learning Spark, 2nd Edition, Chapter 12, On which way does RDD of spark finish fault-tolerance?
- Stack Overflow, How to Speed up SQL Queries with Adaptive Query Execution