Dask for Computing Slots

• Result:

  • Backlog Data Engineering (notion.so)

  • Preliminary results

    • Time it takes in seconds to compute_occupation_snapshots (the inner loop of compute_availability) for all checkouts of January, 2019, against the number of cores (for those implementations that allow multicore computation).

    • 

    • No implementation is covered by unit tests, thus, take these metrics with a grain of salt.

    • Numba has the ability to auto-parallelize code, but it was not used on these tests.

    • In short,

      • Switching to NumPy shortens computation time by a factor of ~4.

      • Parallelizing with Dask, with 16 cores, shortens computation by a factor of ~9 to ~15.

      • The most performant solution (Numba + Dask) shortens computation by a factor of~38.

    • On the most performant solution (Numba + Dask), computing the occupation snapshots for all 2020 checkouts takes:

      • With 10 cores: 4h 55min.

      • With 15 cores: 3h 55min.

  • Lessons Learnt

    • A simple switch to NumPy already offers a dramatic improvement, and changing Pandas code to NumPy was not particularly time consuming.

    • Numba is quite restrictive in the data types that it allows for full optimization (nopythonmode), but such option did not offer much improvement over the NumPy solution in this case.

    • Find the branch with the experiments here.

  • TODO

    • Cleanup code

    • Can I use Numba/Cython to speed up the loop? / Can we get rid of the loop and do it vectorized somehow?

    • Write complete implementation with Numba.

    • Write unit tests.

    • Validate results and intermediate data (Great Expectations, Bulwark´s…).

    • Other ideas:

      • Offloading of certain functions to Big Query (extra steps of ETL? queries on the fly?).

      • Use a columnar data format such as Parquet in the ETLs.