Spark DataFrame partitioner is None

up vote
2
down vote

favorite

[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.

Partitioning using ->

data_frame.repartition("column_name")

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

Also, how can I change the partitioner ?

asked Oct 23 at 10:43

Vijayant

274

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up vote
2
down vote

favorite

[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.

Partitioning using ->

data_frame.repartition("column_name")

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

Also, how can I change the partitioner ?

asked Oct 23 at 10:43

Vijayant

274

add a comment |

up vote
2
down vote

favorite

[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.

Partitioning using ->

data_frame.repartition("column_name")

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

Also, how can I change the partitioner ?

asked Oct 23 at 10:43

Vijayant

274

[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.

Partitioning using ->

data_frame.repartition("column_name")

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

Also, how can I change the partitioner ?

scala apache-spark

asked Oct 23 at 10:43

Vijayant

274

asked Oct 23 at 10:43

Vijayant

274

asked Oct 23 at 10:43

Vijayant

274

asked Oct 23 at 10:43

Vijayant

274

asked Oct 23 at 10:43

Vijayant

274

add a comment |

1 Answer
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up vote
1
down vote

accepted

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

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)

how can I change the partitioner ?

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

edited Oct 23 at 12:18

answered Oct 23 at 11:03

user10465355

1,169310

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1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

up vote
1
down vote

accepted

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

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)

how can I change the partitioner ?

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

edited Oct 23 at 12:18

answered Oct 23 at 11:03

user10465355

1,169310

add a comment |

up vote
1
down vote

accepted

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

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)

how can I change the partitioner ?

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

edited Oct 23 at 12:18

answered Oct 23 at 11:03

user10465355

1,169310

add a comment |

up vote
1
down vote

accepted

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

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)

how can I change the partitioner ?

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

edited Oct 23 at 12:18

answered Oct 23 at 11:03

user10465355

1,169310

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

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)

how can I change the partitioner ?

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

edited Oct 23 at 12:18

answered Oct 23 at 11:03

user10465355

1,169310

edited Oct 23 at 12:18

answered Oct 23 at 11:03

user10465355

1,169310

answered Oct 23 at 11:03

user10465355

1,169310

answered Oct 23 at 11:03

user10465355

1,169310

add a comment |

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