Pranav Rao created SPARK-23442:
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Summary: Reading from partitioned and bucketed table uses only
bucketSpec.numBuckets partitions in all cases
Key: SPARK-23442
URL: https://issues.apache.org/jira/browse/SPARK-23442
Project: Spark
Issue Type: Bug
Components: Spark Core, SQL
Affects Versions: 2.2.1
Environment: {{{{spark.sql("SET spark.default.parallelism=1000") }}}}
{{spark.sql("set spark.sql.shuffle.partitions=500") }}
{{spark.sql("set spark.sql.files.maxPartitionBytes=134217728")}}
{{-----}}
{{$ hdfs getconf -confKey mapreduce.input.fileinputformat.split.minsize}}
0
$ hdfs getconf -confKey dfs.blocksize
134217728
$ hdfs getconf -confKey mapreduce.job.maps
32
Reporter: Pranav Rao
Through the DataFrameWriter[T] interface I have created a external HIVE table
with 5000 (horizontal) partitions and 50 buckets in each partition. Overall the
dataset is 600GB and the provider is Parquet.
Now this works great when joining with a similarly bucketed dataset - it's able
to avoid a shuffle.
But any action on this Dataframe(from _spark.table("tablename")_), works with
only 50 RDD partitions. This is happening because of
[createBucketedReadRDD|https://github.com/apachttps:/github.com/apache/spark/blob/branch-2.3/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.she/spark/blob/branch-2.3/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.sc].
So the 600GB dataset is only read through 50 tasks, which makes this
partitioning + bucketing scheme not useful at all.
I cannot expose the base directory of the parquet folder for reading the
dataset, because the partition locations don't follow a (basePath + partSpec)
format.
Meanwhile, are there workarounds to use higher parallelism while reading such a
table? Let me know if we
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