{"title":"Erlang ETS表的实现和性能研究","authors":"S. Fritchie","doi":"10.1145/940880.940887","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":140676,"journal":{"name":"Erlang Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"A study of Erlang ETS table implementations and performance\",\"authors\":\"S. Fritchie\",\"doi\":\"10.1145/940880.940887\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":140676,\"journal\":{\"name\":\"Erlang Workshop\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Erlang Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/940880.940887\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Erlang Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/940880.940887","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.