GraphBit's Memory Efficiency Techniques: Code-Backed Strategies for Optimization

Below is a concise, code-verified summary of GraphBit’s memory efficiency techniques, with short excerpts and file paths.

Memory-optimized executor profile

Purpose: run in constrained environments with reduced footprint by lowering concurrency and disabling extras.

Rust executor profile

Python binding (lower concurrency, disable metrics):

Memory-lean concurrency configuration

Purpose: cap simultaneous work per node type to control peak memory.

Benchmark memory measurement (validation)

Purpose: track actual memory usage with tracemalloc + RSS deltas.

Pre-allocation to avoid reallocs and fragmentation

Purpose: reduce allocations and peak usage in hot structures.

Graph-level caching

Purpose: avoid rebuilding dependency vectors repeatedly.

Provider-level caching

Purpose: avoid redundant model verification and repeated memory churn.

Memory health checks in Node.js bindings

Purpose: surface low-memory conditions before execution.

jemalloc on Unix (allocator choice)

Purpose: improved allocation behavior and memory efficiency on Unix.