A project to enable GPU-accelerated analytic queries on Cassandra.

See cpp/README.md.

The big picture goal is to allow GPU-accelerated analytic queries on Cassandra. This would enable users to do more analysis using the data and open the path to future developments. This project has taken a few various approaches:

1. Previously, the only "easy" way to do this is to load the database using the Cassandra driver (most likely in Python), and then convert it to a pandas DataFrame for calculations. Then we can convert the pd.DataFrame to a cudf.DataFrame for GPU acceleration. Another solution would be Apache Spark.

2. We marginally optimize the above approach by converting the data from the driver into an Arrow Table instead of a pandas DataFrame. We can then turn the Table into a cudf.DataFrame with minimal overhead.

3. To avoid using the driver and putting load on the Cassandra database, our next approach uses the Cassandra server source code, either OSS or DSE, to read the SSTable files for the respective version. Then, we can use the Apache Arrow Java API to convert the partitions into a series of Arrow RecordBatches (essentially tables). We can then write these batches using the Arrow streaming format to a network socket output stream. On the client side in Python, we can then read the data using pyarrow into a pyarrow.Table, which converts to a cudf.DataFrame as above.

4. Finally, we decided to try and read the SSTable files with our own implementation using C++. To read the complex SSTable binary files, we decided to use the Kaitai Struct library to write declarative format specifications using .ksy (essentially YAML) files. This will greatly decrease the cost of maintaining different database formats in the future and help with migration issues as it is easier to document. It also has the benefit of working across languages, including being able to compile to graphviz for some cool visualizations (see above).

Future:

5. The next potential step would be to introduce GPU parallelization using CUDA while reading the SSTable files, although it's not certain how much of an improvement this would be.

See this blog post for more details.

• cpp/ contains the source code for sstable-to-arrow.
• java/ contains the code for reading SSTables using Cassandra (approach 3 above) and is no longer under development.
• test/ contains CQL queries that generate some useful sample data as well as some helper scripts to profile the different approaches.
• client/ contains sample Python clients to read the tables from the server.
• visualization/ contains modified Kaitai Struct files for visualizing the various data formats via exporting to graphviz. The visualizations are currently quite rudimentary due to limitations of Kaitai exporting to graphviz. It doesn't support Kaitai opaque types (see cpp/README.md for more info) and also doesn't show conditional fields. The current visualization/*_modified.dot files are modified by hand, and the visualization/*.ksy files are not meant to parse actual files but rather just to generate boilerplate graphviz code for the diagrams.