case class OptimizeSkewedJoin(ensureRequirements: EnsureRequirements) extends Rule[SparkPlan] with Product with Serializable
A rule to optimize skewed joins to avoid straggler tasks whose share of data are significantly larger than those of the rest of the tasks.
The general idea is to divide each skew partition into smaller partitions and replicate its matching partition on the other side of the join so that they can run in parallel tasks. Note that when matching partitions from the left side and the right side both have skew, it will become a cartesian product of splits from left and right joining together.
For example, assume the Sort-Merge join has 4 partitions: left: [L1, L2, L3, L4] right: [R1, R2, R3, R4]
Let's say L2, L4 and R3, R4 are skewed, and each of them get split into 2 sub-partitions. This is scheduled to run 4 tasks at the beginning: (L1, R1), (L2, R2), (L3, R3), (L4, R4). This rule expands it to 9 tasks to increase parallelism: (L1, R1), (L2-1, R2), (L2-2, R2), (L3, R3-1), (L3, R3-2), (L4-1, R4-1), (L4-2, R4-1), (L4-1, R4-2), (L4-2, R4-2)
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- new OptimizeSkewedJoin(ensureRequirements: EnsureRequirements)
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A partition is considered as a skewed partition if its size is larger than the median partition size * SKEW_JOIN_SKEWED_PARTITION_FACTOR and also larger than SKEW_JOIN_SKEWED_PARTITION_THRESHOLD.
A partition is considered as a skewed partition if its size is larger than the median partition size * SKEW_JOIN_SKEWED_PARTITION_FACTOR and also larger than SKEW_JOIN_SKEWED_PARTITION_THRESHOLD. Thus we pick the larger one as the skew threshold.
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- object ShuffleStage