# Optimizing High-Performance Bloom Filters for Distributed Systems Leveraging single-hash techniques and atomic operations to reduce latency in backend architectures. - **Source:** [Hacker News: Front Page](https://brifing.si/source/b07aa772-b1a0-4a76-aa18-719c6f9e2dde) - **Published:** 2026-10-19 - **Topic:** [Software & Engineering](https://brifing.si/c/engineering) - **Original article:** - **On Brifing:** https://brifing.si/s/d87d6863-261b-4ba2-bc41-ec1e632583c4 ## Probabilistic Data Structures Basics Bloom filters provide a memory-efficient way to determine if an element exists within a set. By accepting a configurable false positive rate, systems can quickly verify membership without expensive disk I/O. This probabilistic approach is essential for high-performance backends that must handle millions of queries per second efficiently. ## Bluesky Database Scaling Strategy Bluesky utilizes bloom filters to prevent unnecessary database hits for blocks, mutes, and labels. By checking an in-memory filter first, the system achieves a 99% skip rate on database queries. This optimization is critical for maintaining low operational costs and high scalability while managing massive amounts of user data. ## Overcoming Mutex Contention Bottlenecks Initial implementations using the bits-and-blooms library suffered from severe mutex contention during process startup. Profiling with pprof revealed that synchronization overhead caused deployment times to reach 90 minutes. Transitioning to atomic CPU instructions via the atomic-bloom fork eliminated these locks, reducing deployment times to just 15 minutes for the team. ## Transitioning to Single-Hash Filters While double-hashing techniques like Kirsch and Mitzenmacher's approach are standard, further research led to single-hash bloom filter implementations. By using a single 128-bit hash, the team reduced computational overhead. This implementation, known as gloom, has been running in production for nine months, powering both the backend and the jetstream sync service. ## Cache-Aligned Memory Optimization To maximize performance, the implementation splits the filter into 64-byte blocks, matching CPU cache line sizes. Since Go's allocator does not guarantee alignment, the team uses unsafe pointers to ensure memory is cache-aligned. This technique ensures that any bloom filter lookup results in at most one single cache miss. ## Instruction-Level Performance Gains The implementation replaces expensive modulo division instructions with a single MUL instruction to select block indices. This optimization, inspired by Daniel Lemire, significantly reduces the CPU cycles required for each lookup. These micro-optimizations are vital when operating at the scale of millions of requests per second within a distributed system. ## Sharding for Multi-Core Efficiency To prevent multiple CPU cores from competing for the same memory, the team implemented a ShardedAtomicFilter. This approach distributes writes across multiple backing filter shards, effectively minimizing contention on bare metal servers. By spreading the load, the system maintains high throughput even under heavy concurrent access from multiple backend processes. ## Engineering Impact and Takeaways The evolution from mutex-heavy filters to atomic, single-hash, and sharded implementations demonstrates the importance of low-level optimization in backend engineering. By carefully managing memory alignment and CPU instructions, teams can achieve significant performance gains. These techniques remain fundamental for building resilient, high-scale services that handle complex data synchronization tasks. ## Sources covering this story - https://calabro.io/ohbf — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._