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Bodo 2022.2 Release (Date: 2/28/2022)

This release includes many new features and usability improvements. Overall, 82 code patches were merged since the last release.

New Features and Improvements

  • Reduced the import time of the Bodo package substantially

  • Bodo is now available with pip on x86 Mac

  • Bodo is upgraded to use Numba 0.55.1 (the latest release)

  • Bodo is upgraded to use scikit-learn v1

  • Bodo now supports MPICH version 3.4

  • Connectors:

    • pd.read_sql: Support and getting start documentation for for Oracle DB and PostgreSQL.
    • pd.read_parquet now supports glob patterns
    • Support for escapechar argument in pd.read_csv
    • Decreased compilation time when reading wide schemas with 1000s of columns usings pd.read_parquet.
    • Optimized runtime of pd.read_parquet with head(0) to skip any unnecessary schema collection for each parquet file and just look at the metadata. This optimization is helpful when loading a DataFrame schema.
    • Support using filter pushdown with a single filter consisting of Series.isna, Series.isnull, Series.notna, or Series.notnull.
    • Full filter pushdown support with hdfs and gcs using pd.read_parquet
    • Improved performance and error handling when using DataFrame.to_sql with Snowflake.
    • Bodo now prints a warning if the number of Parquet row groups is too small for effective parallel I/O.
  • Support for using lists and sets as constant global values.

  • Support for distributed global dataframe values

  • Added a compiler optimizations for forcing the columns in a DataFrame to match a DataFrame with an existing schema via DataFrame.dtypes. In particular when Bodo encounters code like:

    def f(df1, df2):
        return df1.astype(df2.dtypes)

    Bodo will automatically use the internal Bodo types for all columns in df2. This enables using astypes for conversions that are typically not possible in Pandas because the column has an object dtype. For example, this can be used to convert a column from datetime64[ns] to with astype.

  • Improved runtime performance when copying a string data from one array to another or when computing an array of string lengths.

  • Pandas:

    • Support for passing multiple columns to values and index with DataFrame.pivot() and DataFrame.pivot_table()
    • Support for using pd.pivot() and pd.pivot_table(). Functionality is equivalent to DataFrame.pivot() and DataFrame.pivot_table()
    • Support for DataFrame.explode()
    • Support for DataFrame.where() and DataFrame.mask()
    • Support for Series.duplicated() and Index.duplicated().
    • Support for Series.rename_axis()
    • Support for using object in DataFrame.astype. Bodo doesn't have a generic "object" type, so the type of the column remains the same.