Bodo is a new just-in-time (JIT) inferential compiler that brings supercomputing-style performance and scalability to native Python analytics code automatically. Bodo has several advantages over other big data analytics systems (which are usually distributed scheduler libraries):

  • Simple programming with native Python APIs like Pandas and Numpy (no “Pandas-like” API layers)

  • Extreme performance and scalability using true parallelism and advanced compiler technology

  • Very high reliability due to binary code generation, which avoids distributed library failures

  • Simple deployment using standard Python workflows

  • Flexible integration with other systems such as cloud storage, data warehouses, and visualization tools

This documentation covers the basics of using Bodo and provides a reference of supported Python features and APIs. In a nutshell, Bodo provides a JIT compilation workflow using the @bodo.jit decorator. It replaces the decorated Python functions with an optimized and parallelized binary version automatically. For example, the program below can perform data transformation on large datasets:

def data_transform(file_name):
    df = pd.read_parquet(file_name)
    df = df[df.C.dt.month == 1]
    df2 = df.groupby("A")["B", "D"].agg(
        lambda S: (S == "ABC").sum()

To run Bodo programs such as this example, programmers can simply use the command line such as mpiexec -n 1024 python (to run on 1024 cores), or use Jupyter Notebook.