Supporting Faster File Load Times with Memory Optimizations in Rust | Figma Blog
Figma improved server-side file loading by reducing the memory overhead of its Rust data structures. Replacing per-node `BTreeMap`s with compact sorted vectors made deserialization faster and cut memory usage for large files by nearly 25%, despite worse theoretical operation complexity. The team also explored packing field IDs into unused pointer bits, potentially storing the same information in fewer bytes. ## Smaller, Memory-Efficient Maps - Figma files consist of nodes, each represented by properties such as type, parent, position, and dimensions. - Nodes were stored as `BTreeMap<u16, u64 pointer>` structures because ordered iteration was required for serialization. - Profiling showed these maps consumed more than 60% of a file’s memory, even though they stored metadata rather than large data payloads. - The schema contains fewer than 200 possible fields, and nodes typically contain only a subset of them—about 60 properties on average. - Figma replaced each `BTreeMap` with a sorted flat vector of `(field ID, pointer)` pairs. - Although vectors have theoretically slower insertion, lookup, and editing, their compact linear layout is more cache-friendly and faster during deserialization. - The deployed change reduced memory usage by nearly 25% for large files and improved file-loading performance. ## Saving More Memory with Bit Stuffing - The team also investigated storing the field ID inside the pointer itself. - While pointers are nominally 64 bits, x86 systems currently use only the lower 48 bits for memory addresses, leaving 16 bits available. - Figma’s field IDs require exactly 16 bits, allowing a single `u64` to contain both: - A 16-bit field ID - A 48-bit memory pointer - This representation could eliminate the separate field-ID storage and further reduce memory overhead. - The approach had not yet been productionized because relying on unused pointer bits is architecture-dependent and could change in the future. Figma’s results demonstrate that practical memory layout and CPU cache behavior can outweigh Big O complexity. For compact, bounded data structures, flat vectors—and carefully considered bit packing—can deliver substantial improvements in both memory efficiency and load speed.
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