The performance of Ristretto is comparable to Caffeine, which is a high-performance and nearly optimal caching library based on Java 8. Our tests show that Ristretto's cache hit rate is similar to Caffeine's under various workloads. This is an important achievement because achieving similar performance and functionality in Go is a significant challenge.
In summary, Ristretto is a high-performance Go caching library with the following features:
Concurrency: Supports multiple goroutines accessing the cache simultaneously, avoiding contention and blocking.
High cache hit rate: Achieves a high cache hit rate through optimized caching strategies.
Memory-bounded: Can configure the maximum memory usage of the cache to avoid memory leaks.
Scalability: Performs well with increasing core and goroutine counts.
Support for non-uniform key access distributions (such as Zipf distribution).
The design and implementation of Ristretto focus on three main principles: fast access, high concurrency and contention resistance, and memory boundedness. By leveraging the features of the Go language and innovative design, Ristretto successfully achieves these principles and provides a reliable high-performance caching library for the Go community. This article mainly discusses the implementation, optimization, and design philosophy of the Ristretto caching library. The following is a summary of the article:
Ristretto uses a buffer to handle keys, avoiding contention. Background goroutines capture and process Set operations from channels.
Ristretto adopts an eventual consistency model, where Set operations cannot guarantee immediate application to the cache.
Ristretto optimizes for contention, maintaining good performance under high concurrent loads.
Ristretto considers the memory cost of key-value pairs to achieve more realistic cache size limits.
Ristretto uses TinyLFU as an admission policy to estimate the access frequency of keys and decide whether to add new items to the cache.
When the cache reaches capacity, Ristretto uses a sampling LFU strategy to determine which keys to evict.
Ristretto uses a Bloom filter as a guard to prevent contamination of TinyLFU by keys accessed only once.
Ristretto provides rich cache metrics for analysis and optimization of cache behavior. It reduces the overhead of metric collection by reducing false sharing.
Ristretto is a high-performance Go caching library that adapts to various workloads by implementing features such as admission policies and eviction strategies. It also provides detailed cache metrics to help understand cache behavior and further optimize it. In this article, the author introduces the Ristretto caching library and compares it with other popular Go caching libraries in terms of performance. Ristretto uses a sampling LFU (Least Frequently Used) strategy as its eviction policy.
For performance metrics, the author focuses on hit ratios and throughput. Hit ratios are measured using Damian Gryski's cachetest tool and a custom benchmarking suite. These tests cover different types of workloads such as search, database, loop access, and CODASYL database access.
Throughput testing uses the same practical tools as previous blog articles, generating a large number of keys and switching between different goroutines for gets and sets.
In most workloads, Ristretto's sampling LFU strategy performs well. However, in LRU (Least Recently Used) intensive workloads such as the CODASYL benchmark test, Ristretto's performance is affected. The author mentions a paper called "Adaptive Software Cache Management" that explores how to combine LRU and LFU to achieve optimal performance.
The author suggests some future improvements for Ristretto, including placing an LRU "window" before the main cache segment and dynamically adjusting the window size using hill climbing techniques to maximize hit ratios. Caffeine has already achieved good results with this approach, and the author believes Ristretto can also benefit from it in the future.
In conclusion, the author's goal is to create a caching library that competes with Caffeine. While this goal has not been fully achieved, they have successfully created a caching library that performs significantly better than most other caching libraries in the Go domain by using some new techniques. Ristretto will be integrated into Dgraph and Badger in the coming months.