In the previous article, we have talked about how FireDucks can take care pushdown-projection related optimization for read_parquet(), read_csv() etc. In today’s article, we will focus on the efficient caching mechanism by its JIT compiler. Let’s consider the below sample query for the same data, used in previous article: import pandas as pd df = pd.read_parquet("sample_data.parquet") f_df = df.loc[df["a"] > 3, ["x", "y", "z"]] r1 = f_df.groupby("x")["z"].sum() print(r1) When executing th...| fireducks-dev.github.io
Recently we have updated the result of polars-tpch benchmark on 4th generation Xeon processor. The latest result can be found here, and also below in this artice, explaining how to reproduce the same. For reproducibility, we have used AWS EC2 for this time evaluation. We have used m7i.8xlarge instance type with ubuntu 24.04 image and 128GB EBS SSD. This instance includes: 4th generation Xeon processor: Intel(R) Xeon(R) Platinum 8488C (32cores) 128GB memory Benchmark Result The graph shown bel...| fireducks-dev.github.io