Importing 4 billion chess games with speed and scale using Elasticsearch and Universal Profiling

Philipp Kahr and Francesco Gualazzi explore the challenges of importing and processing over 4 billion chess games from Lichess, utilizing Elasticsearch and Universal Profiling to address performance issues in their custom Python implementation. The project faced significant hurdles, especially in parsing games using the python-chess library, which was identified as a bottleneck due to its inefficiency in handling large volumes of game data. By employing Elastic APM and Universal Profiling, the authors pinpointed slow areas in their code, leading to the development of a custom parser that significantly improved processing speed. They replaced the python-chess library with a line operator to create game strings and utilized regex for move comparison, resulting in the ability to parse 1.6 million games per minute. This enhancement reduced the time required to process the games dramatically and allowed for advanced data analysis within the Elastic Stack. The post is part of a series that delves into chess game trends and the impact of external factors like YouTube tutorials on chess strategies.