TLeaf

TLeaf is a software framework designed to manage and process tree-structured data. It provides specialized data structures and algorithms for efficiently storing, querying, and updating trees where the primary objects are leaves and internal nodes link to them. The project emphasizes on-disk persistence, concurrency, and scalable queries.

Core concepts include a leaf-first storage model, where leaves carry payloads; internal nodes maintain meta-information such as child pointers, statistics, and locality hints. TLeaf supports multi-level caching, lazy loading of subtrees, and incremental recomputation to limit updates to affected leaves. The API offers typed node representations and a small query language for traversal and aggregation.

Implementations span several languages, with reference implementations in C++ and Rust, plus bindings for Python and JavaScript. It interoperates with common tree formats and can export to JSON, GraphML, or Parquet. The system uses an optimistic concurrency control model to allow parallel updates and provides pluggable storage backends, including in-memory, SSD-backed, and distributed storage.

Typical use cases include large hierarchical datasets, such as source code repositories, organizational charts, phylogenetic trees, and GIS hierarchies, as well as compiler intermediate representations that are tree-centric. It is also used in research on tree-structured data processing and in teaching about tree data structures.

TLeaf emerged in academic circles and small-scale open-source projects in the 2020s. It has seen limited adoption compared to more mature graph and database systems, and opinions vary on its complexity versus benefits. As a niche tool, it is primarily of interest to developers working with large, leaf-heavy tree data.

See also: Tree data structures, Trie, Merkle tree, Graph database, Columnar storage.