Comments (2)
@stinodego any clue?
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These are default values that will let you run scale factor 1 locally without any problems. If we use PySpark defaults, certain queries fail due to memory issues.
The benchmark blog has the actual values used during the benchmark:
For PySpark, driver memory and executor memory were set to 20g and 10g respectively.
I have tried a few different settings, and these seemed to work best for scale factor 10.
If these benchmarks are being run on a single node, we should probably set the shuffle partitions to be like 1-4 instead of 200 (which is the default).
Could be - I am by no means a PySpark optimization expert. Perhaps they should implement better/dynamic defaults.
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Related Issues (20)
- use `ends_with` instead of `contains`
- Query #2 Inaccurate Output HOT 2
- Explain tpch in readme HOT 2
- The rest of the queries? HOT 5
- Get the results without having to run it HOT 1
- Unexpected arguments HOT 1
- Non-idiomatic usage in q7 HOT 1
- Add potential quick fix for macOS compilation error in README HOT 2
- Queries should be expressed in SQL as well to test polars SQL coverage of TPCH HOT 4
- Plot results failed - arr was deprecated HOT 4
- Missing parquet files HOT 1
- Usage of Python subprocess.run HOT 1
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