merge multiple small files in to few larger files in Spark

问题:

I using hive through Spark. I have a Insert into partitioned table query in my spark code. The input data is in 200+gb. When Spark is writing to a partitioned table, it is spitting very small files(files in kb's). so now the output partitioned table folder have 5000+ small kb files. I want to merge these in to few large MB files, may be about few 200mb files. I tired using hive merge settings, but they don't seem to work.

'val result7A = hiveContext.sql("set hive.exec.dynamic.partition=true")   val result7B = hiveContext.sql("set hive.exec.dynamic.partition.mode=nonstrict")  val result7C = hiveContext.sql("SET hive.merge.size.per.task=256000000")  val result7D = hiveContext.sql("SET hive.merge.mapfiles=true")  val result7E = hiveContext.sql("SET hive.merge.mapredfiles=true")  val result7F = hiveContext.sql("SET hive.merge.sparkfiles = true")  val result7G = hiveContext.sql("set hive.aux.jars.path=c:\\Applications\\json-serde-1.1.9.3-SNAPSHOT-jar-with-dependencies.jar")  val result8 = hiveContext.sql("INSERT INTO TABLE partition_table PARTITION (date) select a,b,c from partition_json_table")' 

The above hive settings work in a mapreduce hive execution and spits out files of specified size. Is there any option to do this Spark or Scala?

回答1:

You may want to try using the DataFrame.coalesce method; it returns a DataFrame with the specified number of partitions (each of which becomes a file on insertion). So using the number of records you are inserting and the typical size of each record, you can estimate how many partitions to coalesce to if you want files of ~200MB.

回答2:

I had the same issue. Solution was to add DISTRIBUTE BY clause with the partition columns. This ensures that data for one partition goes to single reducer. Example in your case:

INSERT INTO TABLE partition_table PARTITION (date) select a,b,c from partition_json_table DISTRIBUTE BY date 

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