This article explains how to handle MySQL disk‑space alerts by using the pt‑online‑schema‑change (pt‑osc) tool to selectively retain needed rows, migrate data to a temporary table, and drop the original table, thereby achieving fast, low‑impact data cleanup.
To efficiently purge old user‑operation logs while retaining the most recent three months and preserving type‑c entries, the article recommends abandoning offset pagination and MyBatis PageHelper, using primary‑key‑based pagination with a tracked startId, in‑memory filtering, intelligent startId recalculation, and optional sleep intervals to avoid database overload.
A Java developer recounts a production MySQL outage caused by a faulty batch insert, walks through failed delete and lock‑free attempts, then details a series of table‑recreation, truncate, and index‑rebuilding steps that finally restored data integrity and performance.
When a user reported errors, I discovered a MySQL table flooded with millions of bad rows from a new group, causing slow queries and crashes; I detail the step‑by‑step investigation, failed attempts with DELETE and lock‑free changes, and the final successful rebuild using temporary tables, DROP, TRUNCATE, and index reconstruction.
This article explains how to use Redis's SCAN command to iterate over dictionary data, clean up stale keys, understand the command's underlying mechanics during dictionary resizing, and avoid common pitfalls through practical code examples and performance analysis.
The article explains why massive MySQL tables need periodic cleanup, compares risky drop/truncate approaches, and provides a step‑by‑step guide to safely delete historical data in batches using pt‑archiver with proper parameters, session handling, and post‑deletion maintenance.
This article explains why dropping large amounts of data in a TiDB cluster can trigger compaction flow‑control, leading to write stalls and QPS jitter, and provides step‑by‑step troubleshooting, configuration tweaks, and best‑practice recommendations to resolve the issue.
A large‑scale delete operation in a MySQL PXC cluster caused transaction rollback, table locks, and flow control, leading to service slowdown, and was resolved by redesigning the cleanup mechanism with partitioned tables, stored procedures, and scheduled events.
This article walks through the problem of duplicate rows in a MySQL table, shows an initial complex SQL attempt, compares a slow PHP‑based cleanup, and finally presents a concise, three‑step DELETE statement that eliminates duplicates in under a second.
This article explains how to handle a 200‑million‑row MySQL 5.6 table by adding indexes, cleaning 99% of old data, converting it to a partitioned table, and using online DDL and auxiliary tables to maintain continuous business operations.
