Luke Francl introduces the new GitHub code search and its capabilities.

• Luke Francl introduces himself and explains his role at GitHub.
• Describes the limitations of the old GitHub code search engine, such as slow performance and limited search capabilities.
• Introduces Blackbird, the new search engine built from the ground up for large-scale source code search, which supports exact substring match, regular expressions, and code navigation.
• Blackbird is fast and can search across billions of files and millions of repositories in a few hundred milliseconds.

Luke details the challenges faced with the old code search system at GitHub.

• Luke worked on the old system, which was slow and limited.
• He was part of three attempts to improve the old code search, all unsuccessful.
• The old system's limitations made it difficult to improve, contributing to the delay in fixing the code search.

Exploring the purpose of code search and technical considerations for indexing.

• Code search aims to help programmers understand code by quickly answering questions, necessitating an index for fast response.
• Indexing is crucial for a search engine, and it involves collecting content and building an inverted index with posting lists.
• Code indexing is unique as repositories change over time, and GitHub's system handles the computational challenges of tracking these changes.

Discussion on the tokenization process in code search and the complexities of indexing forks.

• Tokenization is the process of breaking text into searchable tokens, which is challenging with code due to significant minor differences.
• GitHub's original tokenization process had limitations and couldn't handle certain programming characters, leading to loss of information.
• Forks add another layer of complexity, as they are cheap in git infrastructure but expensive for code search due to the necessity of indexing almost identical content.

Luke outlines the design goals for the new code search and the importance of indexing speed.

• The new GitHub code search had end-user goals such as regular expression search, but the core goal was to make indexing fast, aiming for less than 24 hours to build a new index.
• The team achieved this by rewriting the indexer in Rust, which allowed processing of over 200,000 documents per second.
• Architectural improvements were made, separating the crawl and index processes to optimize performance.

Luke discusses scaling the code search using deduplication and delta compression.

• As the system scaled, the team introduced document deduplication to reduce content indexing.
• Delta compression was used to optimize indexing by reusing previously indexed content across similar repositories.
• A novel data structure, the geometric XOR filter, was invented to accurately estimate the symmetric difference between repository sets, enabling efficient delta compression.

Implementing dynamic shard assignment and a cache server to improve indexing speed.

• The team addressed the challenge of index compaction and resource management by introducing dynamic shard assignment.
• A new cache server was introduced to reuse documents from a previous index, greatly improving indexing speed and efficiency.
• These improvements allowed for a 99.9% cache hit rate for re-indexing and over 50% for incremental indexing.

Luke concludes with lessons learned from the experience of building the new GitHub code search.

• Building software from scratch can be justified if it allows for better use of the data's structure to outperform off-the-shelf tools.
• Software scaling requires continual adaptation and solutions for emerging challenges.
• Efforts to make a system easily changeable are crucial, though not always visible to users, as it enables faster feature delivery.