Support CPUNearestNeighbor for benchmarking exact nearest neighbors. #655
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eordentlich
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May 22, 2024
| from pyspark.sql.functions import udf | ||
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| spark_func = udf(py_func, "array<float>") | ||
| df = spark.range(len(X)).select("id", spark_func("id").alias("features")) |
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Any advantage to doing this way vs createDataFrame from pandas df?
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It seems does not throw out a "task size larger than 1000k" warning on large dataset, but it looks no different on small dataset.
| def cache_df(dfA: DataFrame, dfB: DataFrame) -> Tuple[DataFrame, DataFrame]: | ||
| dfA = dfA.cache() | ||
| dfB = dfB.cache() | ||
| dfA.count() |
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Did you verify that count actually caches the dataframe? I think sometimes it can be short-circuited via metadata (e.g. parquet files).
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May 25, 2024
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| yield pd.DataFrame({"dummy": [1]}) | ||
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| dfA.mapInPandas(func_dummy, schema="dummy int").count() |
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Better to avoid python udfs for this kind of thing but probably ok.
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cpu LSH will be moved to bench_approx_nearest_neighbors.py for benchmarking with GPU IVFFlat.