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I am trying to precompute partitions for some SparkSql queries. If I compute and persist the the partitions, Spark uses them. If I save the partitioned data to Parquet and reload it later, the partition information is gone and Spark will recompute it.

The actual data is large enough that significant time is spent partitioning. The code below demonstrates the problems sufficiently though. Test2() is currently the only thing that I can get to work, but I would like to jumpstart the actual processing, which is what test3() is attempting to do.

Anyone know what I'm doing wrong? ..or if this is something that Spark can do?

from pyspark import SparkContext from pyspark.sql import SQLContext from pyspark.sql.types import * # NOTE: Need to have python in PATH, SPARK_HOME set to location of spark, HADOOP_HOME set to location of winutils if __name__ == "__main__": sc = SparkContext(appName="PythonPartitionBug") sql_text = "select foo, bar from customer c, orders o where foo < 300 and c.custkey=o.custkey" def setup(): sqlContext = SQLContext(sc) fields1 = [StructField(name, IntegerType()) for name in ['custkey', 'foo']] data1 = [(1, 110), (2, 210), (3, 310), (4, 410), (5, 510)] df1 = sqlContext.createDataFrame(data1, StructType(fields1)) df1.persist() fields2 = [StructField(name, IntegerType()) for name in ['orderkey', 'custkey', 'bar']] data2 = [(1, 1, 10), (2, 1, 20), (3, 2, 30), (4, 3, 40), (5, 4, 50)] df2 = sqlContext.createDataFrame(data2, StructType(fields2)) df2.persist() return sqlContext, df1, df2 def test1(): # Without repartition the final plan includes hashpartitioning # == Physical Plan == # Project [foo#1,bar#14] # +- SortMergeJoin [custkey#0], [custkey#13] # :- Sort [custkey#0 ASC], false, 0 # : +- TungstenExchange hashpartitioning(custkey#0,200), None # : +- Filter (foo#1 < 300) # : +- InMemoryColumnarTableScan [custkey#0,foo#1], [(foo#1 < 300)], InMemoryRelation [custkey#0,foo#1], true, 10000, StorageLevel(false, true, false, false, 1), ConvertToUnsafe, None # +- Sort [custkey#13 ASC], false, 0 # +- TungstenExchange hashpartitioning(custkey#13,200), None # +- InMemoryColumnarTableScan [bar#14,custkey#13], InMemoryRelation [orderkey#12,custkey#13,bar#14], true, 10000, StorageLevel(false, true, false, false, 1), ConvertToUnsafe, None sqlContext, df1, df2 = setup() df1.registerTempTable("customer") df2.registerTempTable("orders") df3 = sqlContext.sql(sql_text) df3.collect() df3.explain(True) def test2(): # With repartition the final plan does not include hashpartitioning # == Physical Plan == # Project [foo#56,bar#69] # +- SortMergeJoin [custkey#55], [custkey#68] # :- Sort [custkey#55 ASC], false, 0 # : +- Filter (foo#56 < 300) # : +- InMemoryColumnarTableScan [custkey#55,foo#56], [(foo#56 < 300)], InMemoryRelation [custkey#55,foo#56], true, 10000, StorageLevel(false, true, false, false, 1), TungstenExchange hashpartitioning(custkey#55,4), None, None # +- Sort [custkey#68 ASC], false, 0 # +- InMemoryColumnarTableScan [bar#69,custkey#68], InMemoryRelation [orderkey#67,custkey#68,bar#69], true, 10000, StorageLevel(false, true, false, false, 1), TungstenExchange hashpartitioning(custkey#68,4), None, None sqlContext, df1, df2 = setup() df1a = df1.repartition(4, 'custkey').persist() df1a.registerTempTable("customer") df2a = df2.repartition(4, 'custkey').persist() df2a.registerTempTable("orders") df3 = sqlContext.sql(sql_text) df3.collect() df3.explain(True) def test3(): # After round tripping the partitioned data, the partitioning is lost and spark repartitions # == Physical Plan == # Project [foo#223,bar#284] # +- SortMergeJoin [custkey#222], [custkey#283] # :- Sort [custkey#222 ASC], false, 0 # : +- TungstenExchange hashpartitioning(custkey#222,200), None # : +- Filter (foo#223 < 300) # : +- InMemoryColumnarTableScan [custkey#222,foo#223], [(foo#223 < 300)], InMemoryRelation [custkey#222,foo#223], true, 10000, StorageLevel(false, true, false, false, 1), Scan ParquetRelation[custkey#222,foo#223] InputPaths: file:/E:/.../df1.parquet, None # +- Sort [custkey#283 ASC], false, 0 # +- TungstenExchange hashpartitioning(custkey#283,200), None # +- InMemoryColumnarTableScan [bar#284,custkey#283], InMemoryRelation [orderkey#282,custkey#283,bar#284], true, 10000, StorageLevel(false, true, false, false, 1), Scan ParquetRelation[orderkey#282,custkey#283,bar#284] InputPaths: file:/E:/.../df2.parquet, None sqlContext, df1, df2 = setup() df1a = df1.repartition(4, 'custkey').persist() df1a.write.parquet("df1.parquet", mode='overwrite') df1a = sqlContext.read.parquet("df1.parquet") df1a.persist() df1a.registerTempTable("customer") df2a = df2.repartition(4, 'custkey').persist() df2a.write.parquet("df2.parquet", mode='overwrite') df2a = sqlContext.read.parquet("df2.parquet") df2a.persist() df2a.registerTempTable("orders") df3 = sqlContext.sql(sql_text) df3.collect() df3.explain(True) test1() test2() test3() sc.stop()
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You're doing nothing wrong - but you can't achieve what you're trying to achieve with Spark: the partitioner used to save a file is necessarily lost when writing to disk. Why? Because Spark doesn't have its own file format, it relies on existing formats (e.g. Parquet, ORC or text files), none of which are even aware of the Partitioner (which is internal to Spark) so they can't persist that information. The data is properly partitioned on disk, but Spark has no way of knowing what partitioner was used when it loads from disk, so it has no choice but to re-partition.

The reason test2() doesn't reveal this is that you reuse the same DataFrame instances, which do store the partitioning information (in memory).

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A better solution would be to use persist(StorageLevel.MEMORY_AND_DISK_ONLY) which will spill the RDD/DF partitions to the Worker's local disk if they're evicted from memory. In this case, rebuilding a partition only requires pulling data from the Worker's local disk which is relatively fast.

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