Spark DataFrame分区器为“无" [英] Spark DataFrame partitioner is None

问题描述

[Spark的新手]创建了DataFrame之后，我尝试根据DataFrame中的列对其进行分区.当我使用 data_frame.rdd.partitioner 检查分区程序时，我得到 None 作为输出.

[New to Spark] After creating a DataFrame I am trying to partition it based on a column in the DataFrame. When I check the partitioner using data_frame.rdd.partitioner I get None as output.

使用->

data_frame.repartition("column_name")

根据Spark文档，默认分区程序是HashPartitioner，如何确认呢?

As per Spark documentation the default partitioner is HashPartitioner, how can I confirm that ?

此外，如何更改分区程序?

Also, how can I change the partitioner ?

推荐答案

这是可以预期的.从 Dataset 转换的 RDD 不保留分区程序，仅数据分发.

That's to be expected. RDD converted from a Dataset doesn't preserve the partitioner, only the data distribution.

如果要检查RDD的分区程序，则应从 queryExecution 检索它:

If you want to inspect partitioner of the RDD you should retrieve it from the queryExecution:

scala> val df = spark.range(100).select($"id" % 3 as "id").repartition(42, $"id")
df: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: bigint]

scala> df.queryExecution.toRdd.partitioner
res1: Option[org.apache.spark.Partitioner] = Some(org.apache.spark.sql.execution.CoalescedPartitioner@4be2340e)

如何更改分区器?

how can I change the partitioner ?

通常您不能.存在 repartitionByRange 方法(请参见链接的线程)，但是否则， Dataset Partitioner 是不可配置的.

In general you cannot. There exist repartitionByRange method (see the linked thread), but otherwise Dataset Partitioner is not configurable.

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