Erlang ETS表的实现和性能研究

S. Fritchie
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引用次数: 16

摘要

通过比较基于四种数据结构(AVL平衡二叉树、b -树、可调整大小的线性哈希表和Judy数组)的ETS表的性能,研究了使用一种相对较新的数据结构(称为Judy数组)实现内存数据库Erlang ETS的可行性。基准测试在表填充中使用顺序和随机顺序键的工作负载,从700个键到5400万个键。基准测试结果表明,对于超过CPU数据缓存大小(70,000个键或更多)的表,基于judy的表上的ETS表插入、查找和更新操作明显快于所有其他表类型。当表填充增长到5400万个键,内存使用接近3GB时,基于judi的表的相对速度会提高。基于judi的表的项删除和表遍历操作比基于线性哈希表的类型慢,但是删除操作的额外成本比其他操作节省的总和要小。将哈希表的大小调整为232个桶,由Judy数组管理,可以创建最一致的性能改进,并且只比常规哈希表多使用约6%的内存。其他应用程序可以从Judy数组的应用程序中获益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A study of Erlang ETS table implementations and performance
The viability of implementing an in-memory database, Erlang ETS, using a relatively-new data structure, called a Judy array, was studied by comparing the performance of ETS tables based on four data structures: AVL balanced binary trees, B-trees, resizable linear hash tables, and Judy arrays. The benchmarks used workloads of sequentially- and randomly-ordered keys at table populations from 700 keys to 54 million keys.Benchmark results show that ETS table insertion, lookup, and update operations on Judy-based tables are significantly faster than all other table types for tables that exceed CPU data cache size (70,000 keys or more). The relative speed of Judy-based tables improves as table populations grow to 54 million keys and memory usage approaches 3GB. Term deletion and table traversal operations by Judy-based tables are slower than the linear hash table-based type, but the additional cost of the deletion operation is smaller than the combined savings of the other operations.Resizing a hash table to 232 buckets, managed by a Judy array, creates the most consistent performance improvements and uses only about 6% more memory than a regular hash table. Other applications could benefit substantially by this application of Judy arrays.
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