Hello,
I have partitioned parquet files based on "event_hour" column.
After reading parquet files to spark:
spark.read.format("parquet").load("...")
Files from the same parquet partition are scattered in many spark
partitions.
Example of mapping spark partition -> parquet partition:
Spark partition 1 -> 2019050101, 2019050102, 2019050103
Spark partition 2 -> 2019050101, 2019050103, 2019050104
...
Spark partition 20 -> 2019050101, ...
Spark partition 21 -> 2019050101, ...
As you can see parquet partition 2019050101 is present in Spark partition
1, 2, 20, 21.
As a result when I write out the dataFrame:
df.write.partitionBy("event_hour").format("parquet").save("...")
There are many files created in one parquet partition (In case of our
example its 4 files, but in reality its much more)
To speed up queries, my goal is to write 1 file per parquet partition (1
file per hour).
So far my only solution is to use repartition:
df.repartition(col("event_hour"))
But there is a lot of overhead with unnecessary shuffle. I'd like to force
spark to "pickup" the parquet partitioning.
In my investigation I've found
org.apache.spark.sql.execution.FileSourceScanExec#createNonBucketedReadRDD
<https://github.com/apache/spark/blob/a44880ba74caab7a987128cb09c4bee41617770a/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala#L452>
where the initial partitioning is happening based on file sizes. There is
an explicit ordering which causes parquet partition shuffle.
thank you for your help,
Tomas
