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Cachelot
Lightning-fast LRU cache library and key-value server

What is Cachelot?

Cachelot is a high-performance LRU cache library and distributed caching server designed for speed and efficiency. The library operates within a fixed memory allocation, eliminating garbage collection and minimizing metadata overhead to achieve near-perfect memory utilization with only 5-7% overhead. It ensures smooth responsiveness for both read and write operations, capable of handling up to three million items per second, which translates to approximately 333 nanoseconds per operation. Written in highly optimized C++, Cachelot is suitable for resource-constrained environments like IoT devices and handheld platforms.

As a distributed caching server, cachelotd is Memcached-compatible and focuses on maximizing hardware utilization and performance. It is single-threaded by design, allowing the CPU to operate at 99% efficiency without wasting cycles on locks and RAM access. The system is scalable up to 1024 cores, NUMA-aware, and simple to deploy. Compared to Memcached, Cachelot saves up to 15% RAM by storing more items in the same memory footprint, making it an efficient solution for caching needs across various programming languages through bindings for JS, Python, Go, Java, Ruby, and Erlang.

Features

  • Memory Efficiency: Near-perfect memory utilization with only 5-7% overhead
  • High Performance: Handles up to three million items per second with ~333 nanoseconds per operation
  • Resource Optimization: Suitable for IoT and handheld devices with limited resources
  • Memcached Compatibility: Distributed caching server compatible with Memcached protocols
  • Scalability: Single-threaded design scalable up to 1024 cores and NUMA-aware
  • Language Support: Bindings available for JS, Python, Go, Java, Ruby, and Erlang

Use Cases

  • Accelerating application performance with fast caching
  • Reducing memory usage in distributed systems
  • Optimizing IoT device operations with efficient caching
  • Enhancing server response times in web applications
  • Improving data retrieval speeds in database-driven systems
  • Scaling caching solutions for high-traffic environments

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