otter
A high-performance, in-memory caching library for Go with adaptive eviction policies and low memory overhead.
What is otter?
Otter is a high-performance, in-memory caching library for Go that provides adaptive eviction policies, low memory overhead, and excellent throughput under contention. It solves the problem of efficient data caching in Go applications by implementing advanced algorithms like W-TinyLFU and automatic configuration based on workload patterns.
Go developers building applications that require efficient in-memory caching, such as web servers, APIs, real-time systems, or data-intensive services where performance and low latency are critical.
Developers choose Otter for its combination of high hit rates, excellent throughput, and minimal memory overhead, along with a flexible and configurable API that supports features like automatic loading, asynchronous refresh, and persistence without sacrificing performance.
Overview
A high performance caching library for Go
Use Cases
Best For
- Improving application performance with efficient in-memory caching
- Reducing database load by caching frequently accessed data
- Building high-throughput Go services that require low-latency data access
- Implementing adaptive cache eviction policies for varying workloads
- Minimizing memory overhead in caching layers
- Protecting against cache stampede with deduplicated loader calls
Not Ideal For
- Distributed systems requiring multi-node cache synchronization — Otter is in-memory and single-instance, lacking built-in clustering or distributed coordination.
- Projects stuck on older Go versions (below 1.24) — Otter only supports the two most recent minor Go releases, making it incompatible with legacy or version-locked codebases.
- Ultra-simple caching where a basic sync.Map suffices — Otter's feature set and automatic configuration add complexity unjustified for trivial, low-throughput caching tasks.
- High-availability scenarios needing robust, transactional persistence — Otter's file-based save/load is basic and may not handle failures, large datasets, or real-time replication well.
Pros & Cons
Pros
Implements W-TinyLFU for high hit rates across diverse workloads, as shown in linked benchmark results, ensuring efficient cache admission.
Minimizes lock contention with techniques like BP-Wrapper, delivering excellent throughput under high contention, per the throughput benchmarks.
Uses node code generation to keep memory consumption near-zero for unused features, making it efficient across cache capacities.
Self-tunes data structures based on contention and workload patterns, reducing manual optimization effort for developers.
Cons
Requires Go 1.24+ and only supports the two latest minor versions, which can block adoption in projects with fixed or older Go environments.
Persistence is limited to file-based save/load, lacking integration with databases or distributed storage, making it less suitable for enterprise-scale durability.
While automatic configuration helps, optimizing for edge-case workloads requires understanding advanced algorithms like adaptive W-TinyLFU, which may intimidate some developers.
Frequently Asked Questions
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