In case of Sort Merge join in which a shuffle (exchange) will be performed, I have the following questions (Please correct me if my understanding is not correct):
Let's say that relation A is a JSONRelation (640 MB) on the HDFS where the block size is 64MB. This will produce a Scan JSONRelation() of 10 partitions ( 640 / 64 ) where each of these partitions will contain |A| / 10 rows. The second step will be a hashPartitioning(#key, 200) where #key is the equi-join condition and 200 the default number of shuffles (spark.sql.shuffle.partitions). Each partition will be computed in an individual task, in which every row will be hashed on the #key and then will be written in the corresponing chunk (of 200 resulting chunks) directly on disk. Q1: What happens if a resulting hashed row in the executor A must be written in a chunk which is stored in the executor B? Does it use the HashShuffleManager to transfer it over the network? Q2: After the Sort (3rd) step there will be 200, 200 resulting partitions/chunks for relations A and B respectively which will be concatenated into 200 SortMergeJoin tasks where each of them will contain (|A|/200 + |B|/200) rows. For each pair (chunkOfA, chunkOfB) will chunkOfA and chunkOfB contain rows for the same hash key ? Q3: In the SortMergeJoin of Q2, I suppose that each of the 200 SortMergeJoin tasks joins two partitions/chunks with the same hash key. So, if a task corresponds to a hash key X, does it use ShuffleBlockFetchIterator to fetch the two Shuffles/Chunks (of relations A and B) with hash key X? Q4: Which sorting algorithm is being used? -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/Hash-Partitioning-Sort-Merge-Join-tp15275.html Sent from the Apache Spark Developers List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
