Memory efficient cartesian join in PySpark
pySpark will handle your dataset easily and memory efficient but it will take time to process 10^8 * 10^8 records (this is estimated size of cross join result). See sample code:
from pyspark.sql.types import * df = spark.read.csv('input.csv', header=True, schema=StructType([StructField('id', StringType())])) df.withColumnRenamed('id', 'id1').crossJoin(df.withColumnRenamed('id', 'id2')).show()
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mgoldwasser 4 months
I have a large dataset of string ids, that can fit into memory on a single node in my spark cluster. The issue is that it consumes most of the memory for a single node.
These ids are about 30 characters long. For example:
ids O2LWk4MAbcrOCWo3IVM0GInelSXfcG HbDckDXCye20kwu0gfeGpLGWnJ2yif o43xSMBUJLOKDxkYEQbAEWk4aPQHkm
I am looking to write to file a list of all of the pairs of ids. For example:
id1,id2 O2LWk4MAbcrOCWo3IVM0GInelSXfcG,HbDckDXCye20kwu0gfeGpLGWnJ2yif O2LWk4MAbcrOCWo3IVM0GInelSXfcG,o43xSMBUJLOKDxkYEQbAEWk4aPQHkm HbDckDXCye20kwu0gfeGpLGWnJ2yif,O2LWk4MAbcrOCWo3IVM0GInelSXfcG # etc...
So I need to cross join the dataset on itself. I was hoping to do this on PySpark using a 10 node cluster, but it needs to be memory efficient.