SPARK-2203: PySpark defaults to use same num reduce partitions as map side #1138
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… partitions For shuffle-based operators, such as rdd.groupBy() or rdd.sortByKey(), PySpark will always assume that the default parallelism to use for the reduce side is ctx.defaultParallelism, which is a constant typically determined by the number of cores in cluster. In contrast, Spark's Partitioner#defaultPartitioner will use the same number of reduce partitions as map partitions unless the defaultParallelism config is explicitly set. This tends to be a better default in order to avoid OOMs, and should also be the behavior of PySpark. JIRA: https://issues.apache.org/jira/browse/SPARK-2203
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Good catch. Change looks good to me. |
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Merging this in master |
pdeyhim
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… side For shuffle-based operators, such as rdd.groupBy() or rdd.sortByKey(), PySpark will always assume that the default parallelism to use for the reduce side is ctx.defaultParallelism, which is a constant typically determined by the number of cores in cluster. In contrast, Spark's Partitioner#defaultPartitioner will use the same number of reduce partitions as map partitions unless the defaultParallelism config is explicitly set. This tends to be a better default in order to avoid OOMs, and should also be the behavior of PySpark. JIRA: https://issues.apache.org/jira/browse/SPARK-2203 Author: Aaron Davidson <aaron@databricks.com> Closes apache#1138 from aarondav/pyfix and squashes the following commits: 1bd5751 [Aaron Davidson] SPARK-2203: PySpark defaults to use same num reduce partitions as map partitions
xiliu82
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… side For shuffle-based operators, such as rdd.groupBy() or rdd.sortByKey(), PySpark will always assume that the default parallelism to use for the reduce side is ctx.defaultParallelism, which is a constant typically determined by the number of cores in cluster. In contrast, Spark's Partitioner#defaultPartitioner will use the same number of reduce partitions as map partitions unless the defaultParallelism config is explicitly set. This tends to be a better default in order to avoid OOMs, and should also be the behavior of PySpark. JIRA: https://issues.apache.org/jira/browse/SPARK-2203 Author: Aaron Davidson <aaron@databricks.com> Closes apache#1138 from aarondav/pyfix and squashes the following commits: 1bd5751 [Aaron Davidson] SPARK-2203: PySpark defaults to use same num reduce partitions as map partitions
mapr-devops
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Co-authored-by: Egor Krivokon <>
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For shuffle-based operators, such as rdd.groupBy() or rdd.sortByKey(), PySpark will always assume that the default parallelism to use for the reduce side is ctx.defaultParallelism, which is a constant typically determined by the number of cores in cluster.
In contrast, Spark's Partitioner#defaultPartitioner will use the same number of reduce partitions as map partitions unless the defaultParallelism config is explicitly set. This tends to be a better default in order to avoid OOMs, and should also be the behavior of PySpark.
JIRA: https://issues.apache.org/jira/browse/SPARK-2203