This article examines MySQL’s transition from dual‑master to master‑slave replication, highlights the complexities of position‑based syncing, introduces GTID as a more reliable alternative, and provides practical steps to enable GTID and accurately monitor replication lag using seconds_behind_master, binlog positions, and GTID sets.
The article describes an iOS lag‑monitoring system that detects main‑thread RunLoop blocks, samples stacks every 50 ms into a circular buffer, uses an annealing Fibonacci‑based interval to minimize overhead, visualizes hot paths with flame graphs, and streams data through a Flink‑APM pipeline with only ~2 % CPU and a few megabytes of memory impact.
The article describes an iOS lag‑monitoring system that uses RunLoop state checks to detect main‑thread stalls, captures and caches stack traces, employs a Fibonacci‑based annealing algorithm to limit overhead, symbolises addresses on a server, and visualises results with flame graphs while maintaining low CPU and memory impact.
The article explains the shortcomings of traditional MySQL replication lag measurement, introduces the community heartbeat‑table approach and its limitations, and then details MySQL 8 enhancements (WL#7319, WL#7374) that embed transaction timestamps in binlog events and expose new performance_schema tables, providing precise SQL queries to monitor lag at various stages of the replication pipeline.
