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RustInfraJobs High-Yield Rust & AI Systems
#lsm-tree • $180k-$243k USD Base

LSM-Tree in Production Rust: Architecture & Open Roles

Log-Structured Merge-tree storage engines optimized for write throughput & S3

1. Why Teams Deploy LSM-Tree

Log-Structured Merge-trees (LSM-Trees) convert random disk writes into sequential append-only operations, making them the architecture of choice for write-heavy databases, stream processors, and cloud-native storage layers. In the Rust ecosystem, teams build disaggregated LSM engines over S3, such as Neon's Pageserver, RisingWave's Hummock, and SlateDB, or use pure-Rust embedded LSMs like `fjall`.

2. LSM-Tree Kernel & Memory Internals

Incoming writes append to a Write-Ahead Log (WAL) and insert into an in-memory `MemTable` (typically a lock-free concurrent skip list). Once full, the MemTable flushes to immutable Level-0 SSTables (Sorted String Tables) on NVMe or S3. Background compaction threads merge overlapping SSTables across tiers, using Bloom filters and block caches (`foyer`) to bound read amplification.

3. LSM-Tree Interview & Hiring Bar

Storage engine interviews focus heavily on write/read/space amplification trade-offs, Leveled vs. Universal compaction strategies, and tuning compaction I/O throttling to prevent p99 latency spikes.

Open LSM-Tree Engineering Positions (4)