Mixing
Spark DataFrame partitioner is None
[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 '18 at 10:43
VijayantVijayant
274
add a comment |
[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 '18 at 10:43
VijayantVijayant
274
add a comment |
[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 '18 at 10:43
VijayantVijayant
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
scala apache-spark
asked Oct 23 '18 at 10:43
VijayantVijayant
274
asked Oct 23 '18 at 10:43
VijayantVijayant
274
asked Oct 23 '18 at 10:43
VijayantVijayant
274
asked Oct 23 '18 at 10:43
VijayantVijayant
274
asked Oct 23 '18 at 10:43
VijayantVijayant
274
274
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
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, $"id")
df: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: bigint]
scala> df.queryExecution.toRdd.partitioner
res1: Option[org.apache.spark.Partitioner] = Some(org.apache.spark.sql.execution.CoalescedPartitioner@4be2340e)
how can I change the partitioner ?
In general you cannot. There exist repartitionByRange method (see the linked thread), but otherwise Dataset Partitioner is not configurable.
answered Oct 23 '18 at 11:03
user10465355user10465355
1,9082416
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
That's to be expected. RDD converted from a Dataset doesn't preserve the partitioner, only the data distribution.
If you want to inspect partitioner of the RDD you should retrieve it from the queryExecution:
scala> val df = spark.range(100).select($"id" % 3 as "id").repartition(42, $"id")
df: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: bigint]
scala> df.queryExecution.toRdd.partitioner
res1: Option[org.apache.spark.Partitioner] = Some(org.apache.spark.sql.execution.CoalescedPartitioner@4be2340e)
how can I change the partitioner ?
In general you cannot. There exist repartitionByRange method (see the linked thread), but otherwise Dataset Partitioner is not configurable.
answered Oct 23 '18 at 11:03
user10465355user10465355
1,9082416
add a comment |
That's to be expected. RDD converted from a Dataset doesn't preserve the partitioner, only the data distribution.
If you want to inspect partitioner of the RDD you should retrieve it from the queryExecution:
scala> val df = spark.range(100).select($"id" % 3 as "id").repartition(42, $"id")
df: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: bigint]
scala> df.queryExecution.toRdd.partitioner
res1: Option[org.apache.spark.Partitioner] = Some(org.apache.spark.sql.execution.CoalescedPartitioner@4be2340e)
how can I change the partitioner ?
In general you cannot. There exist repartitionByRange method (see the linked thread), but otherwise Dataset Partitioner is not configurable.
answered Oct 23 '18 at 11:03
user10465355user10465355
1,9082416
add a comment |
That's to be expected. RDD converted from a Dataset doesn't preserve the partitioner, only the data distribution.
If you want to inspect partitioner of the RDD you should retrieve it from the queryExecution:
scala> val df = spark.range(100).select($"id" % 3 as "id").repartition(42, $"id")
df: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [id: bigint]
scala> df.queryExecution.toRdd.partitioner
res1: Option[org.apache.spark.Partitioner] = Some(org.apache.spark.sql.execution.CoalescedPartitioner@4be2340e)
how can I change the partitioner ?
In general you cannot. There exist repartitionByRange method (see the linked thread), but otherwise Dataset Partitioner is not configurable.
answered Oct 23 '18 at 11:03
user10465355user10465355
1,9082416
That's to be expected. RDD converted from a Dataset doesn't preserve the partitioner, only the data distribution.
If you want to inspect partitioner of the RDD you should retrieve it from the queryExecution:
scala> val df = spark.range(100).select($"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.
answered Oct 23 '18 at 11:03
user10465355user10465355
1,9082416
edited Oct 23 '18 at 12:18
answered Oct 23 '18 at 11:03
user10465355user10465355
1,9082416
answered Oct 23 '18 at 11:03
user10465355user10465355
1,9082416
answered Oct 23 '18 at 11:03
user10465355user10465355
1,9082416
1,9082416
add a comment |
add a comment |
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