Tune hlink partition usage and memory options #40
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… range [200..10000] - This prevents very large datasets from requesting ~100000+ partitions
This should allow us to add a --driver_memory argument that lets users adjust the amount of memory required for the Spark driver.
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This PR adjusts the heuristic used to compute the number of partitions to request from Spark and modifies the
hlink.spark.session.SparkConnectionclass to expose thespark.driver.memoryoption through its convenience functions. It also renames a related test file to match the convention we've been using.spark_shuffle_partitions_heuristic()is now capped at 10,000, so the returned value lies in the range [200..10000]. This prevents large datasets from requesting too many partitions. A dataset of size ~2.7 billion would request about 100,000 partitions before this change, but now it requests only 10,000.SparkConnection.connectmethod now has adriver_memoryargument that can be used to adjust the amount of memory required for the Spark driver. Connections made withSparkConnection.localwill now automatically set the driver memory to be the same as the executor memory, since the driver and executor are the same machine in this case. This allows users to tune how much memory they need for their driver instead of always using Spark's default amount.