https://www.chenshaowen.com/en/tags/performance/Recent content in Performance on Shaowen Chen's WebsiteHugo -- gohugo.ioen©2016 - {year}, All Rights Reserved.Sun, 08 Feb 2026 00:00:00 +0000weeklyhttps://www.chenshaowen.com/en/blog/multi-node-multi-disk-minio-cluster-performance-test-in-different-erasure-code-configurations.htmlSun, 08 Feb 2026 00:00:00 +0000Sun, 08 Feb 2026 00:00:00 +0000https://www.chenshaowen.com/en/blog/multi-node-multi-disk-minio-cluster-performance-test-in-different-erasure-code-configurations.html1. Erasure Code Configuration The default erasure parity configuration is as follows: Erasure Set Size Default Parity (EC:M) 1 EC:0 2-3 EC:1 4-5 EC:2 6 - 7 EC:3 8 - 16 EC:4 The maximum parity is ERASURE_SET_SIZE/2, meaning the number of parity blocks cannot exceed the number of data blocks.微信公众号MinIOStorageOperationsPerformanceTestingBenchmarkingPerformance Testinghttps://www.chenshaowen.com/en/blog/database-query-optimization-of-django-performance.htmlWed, 20 Mar 2019 00:00:00 +0000Wed, 20 Mar 2019 00:00:00 +0000https://www.chenshaowen.com/en/blog/database-query-optimization-of-django-performance.htmlThis article mainly offers optimization advice on Django fields and queries, and also introduces a performance analysis tool called Django-silk. I hope it helps you develop high-performance Django projects. 1. DBA’s Advice 1.1 Table Field Design Avoid null values; null values are hard to optimize queries around and take up extra index space Prefer INT over BIGINT, and describe fields as accurately as possible Use enums or integers instead of string types Use TIMESTAMP instead of DATETIME Do not put more than 20 fields in a single table Store IPs as integers 1.微信公众号DatabaseOptimizationPerformanceDjangoPythonR&Dhttps://www.chenshaowen.com/en/blog/sub-db-and-table-of-django-performance.htmlTue, 19 Mar 2019 00:00:00 +0000Tue, 19 Mar 2019 00:00:00 +0000https://www.chenshaowen.com/en/blog/sub-db-and-table-of-django-performance.html1. The Problem We Hit The frontend requests are heavy, concurrency is high, and access is slow. The bottlenecks show up mainly as: Large single tables Large single databases Slow network IO Slow disk IO Optimizing network and disk IO mainly relies on hardware upgrades. In theory, a database imposes no limit on the size of a single database or a single table, but an oversized single database or table means more requests land on a single machine, putting pressure on IO.微信公众号DjangoDatabaseShardingPerformanceDatabase ShardingTable Sharding