Menu
From my Spark UI. What does it mean by skipped?
284
Skipped stages are cached stages marked in grey, where computation values are stored in memory and not recomputed after accessing HDFS. A glance at the DAG visualization is enough to know if RDD computations are repeatedly performed or cached stages are used.
A stage is a set of independent tasks all computing the same function that need to run as part of a Spark job, where all the tasks have the same shuffle dependencies.
In Apache Spark, a stage is a physical unit of execution. We can say, it is a step in a physical execution plan. It is a set of parallel tasks — one task per partition. In other words, each job gets divided into smaller sets of tasks, is what you call stages.
ResultStage in SparkBy running a function on a spark RDD Stage that executes a Spark action in a user program is a ResultStage. It is considered as a final stage in spark. ResultStage implies as a final stage in a job that applies a function on one or many partitions of the target RDD in Spark.
Typically it means that data has been fetched from cache and there was no need to re-execute given stage. It is consistent with your DAG which shows that the next stage requires shuffling (reduceByKey). Whenever there is shuffling involved Spark automatically caches generated data:
Shuffle also generates a large number of intermediate files on disk. As of Spark 1.3, these files are preserved until the corresponding RDDs are no longer used and are garbage collected. This is done so the shuffle files don’t need to be re-created if the lineage is re-computed.
82
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
