Spark DataFrame: join stuck on the same stage
I'm working on joining two large datasets with 17M and 2.2M row count.
Partition size of the two datasets are:
mob_join_set:
dth_join_set:
Job is always stuck on the last two tasks of the join:
Have even tried G1GC and spark.sql.shuffle.partitions=500, spark.default.parallelism=500
But no success.
Any help will be greatly appreciated.
apache-spark pyspark apache-spark-sql pyspark-sql
asked Nov 13 '18 at 13:03
Akul NarangAkul Narang
5617
add a comment |
I'm working on joining two large datasets with 17M and 2.2M row count.
Partition size of the two datasets are:
mob_join_set:
dth_join_set:
Job is always stuck on the last two tasks of the join:
Have even tried G1GC and spark.sql.shuffle.partitions=500, spark.default.parallelism=500
But no success.
Any help will be greatly appreciated.
apache-spark pyspark apache-spark-sql pyspark-sql
asked Nov 13 '18 at 13:03
Akul NarangAkul Narang
5617
Are there partitiones with only Null values?
– 5nv
Nov 13 '18 at 13:27
Sounds like you might have a data skew. Check the join keys on both dataframes using groupBy(K1, K2).count().orderby($"count".desc). If there are far too many values of one K1, K2, etc combination, that means they all go to the same partition during join stage.
– alexeipab
Nov 13 '18 at 13:49
add a comment |
I'm working on joining two large datasets with 17M and 2.2M row count.
Partition size of the two datasets are:
mob_join_set:
dth_join_set:
Job is always stuck on the last two tasks of the join:
Have even tried G1GC and spark.sql.shuffle.partitions=500, spark.default.parallelism=500
But no success.
Any help will be greatly appreciated.
apache-spark pyspark apache-spark-sql pyspark-sql
asked Nov 13 '18 at 13:03
Akul NarangAkul Narang
5617
I'm working on joining two large datasets with 17M and 2.2M row count.
Partition size of the two datasets are:
mob_join_set:
dth_join_set:
Job is always stuck on the last two tasks of the join:
Have even tried G1GC and spark.sql.shuffle.partitions=500, spark.default.parallelism=500
But no success.
Any help will be greatly appreciated.
apache-spark pyspark apache-spark-sql pyspark-sql
apache-spark pyspark apache-spark-sql pyspark-sql
asked Nov 13 '18 at 13:03
Akul NarangAkul Narang
5617
asked Nov 13 '18 at 13:03
Akul NarangAkul Narang
5617
asked Nov 13 '18 at 13:03
Akul NarangAkul Narang
5617
asked Nov 13 '18 at 13:03
Akul NarangAkul Narang
5617
asked Nov 13 '18 at 13:03
Akul NarangAkul Narang
5617
5617
Are there partitiones with only Null values?
– 5nv
Nov 13 '18 at 13:27
Sounds like you might have a data skew. Check the join keys on both dataframes using groupBy(K1, K2).count().orderby($"count".desc). If there are far too many values of one K1, K2, etc combination, that means they all go to the same partition during join stage.
– alexeipab
Nov 13 '18 at 13:49
add a comment |
Are there partitiones with only Null values?
– 5nv
Nov 13 '18 at 13:27
Sounds like you might have a data skew. Check the join keys on both dataframes using groupBy(K1, K2).count().orderby($"count".desc). If there are far too many values of one K1, K2, etc combination, that means they all go to the same partition during join stage.
– alexeipab
Nov 13 '18 at 13:49
Are there partitiones with only Null values?
– 5nv
Nov 13 '18 at 13:27
Are there partitiones with only Null values?
– 5nv
Nov 13 '18 at 13:27
Sounds like you might have a data skew. Check the join keys on both dataframes using groupBy(K1, K2).count().orderby($"count".desc). If there are far too many values of one K1, K2, etc combination, that means they all go to the same partition during join stage.
– alexeipab
Nov 13 '18 at 13:49
Sounds like you might have a data skew. Check the join keys on both dataframes using groupBy(K1, K2).count().orderby($"count".desc). If there are far too many values of one K1, K2, etc combination, that means they all go to the same partition during join stage.
– alexeipab
Nov 13 '18 at 13:49
add a comment |
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Are there partitiones with only Null values?
– 5nv
Nov 13 '18 at 13:27
Sounds like you might have a data skew. Check the join keys on both dataframes using groupBy(K1, K2).count().orderby($"count".desc). If there are far too many values of one K1, K2, etc combination, that means they all go to the same partition during join stage.
– alexeipab
Nov 13 '18 at 13:49