tikv-slow-diagnosis

Diagnose TiKV latency regression using a metric-driven workflow that splits TiKV-local latency from upstream transport or TiDB gRPC delays. It identifies the exact slow stage on the read or write path and stops at the earliest proven bottleneck with concrete metric evidence.

Tracing TiKV flow control and snapshot backlog requires Clinic or Grafana metrics, slow-log execution details, or EXPLAIN ANALYZE output. These inputs quantify queue delays and isolate whether slowness originates from flow-control throttling or snapshot backlog accumulation.

Isolate TiKV IO jitter from network issues by applying quantile-escalated metrics to separate disk read/write stalls from network latency. This specialized diagnostic branch pinpointing whether the bottleneck is local storage jitter or upstream transport delay.

Determine TiKV leader drop causes by analyzing Jira incident metrics during rolling restarts to distinguish PD-driven eviction from follower re-election after heartbeat delay. The workflow identifies the earliest abnormal signal triggering the leader movement.

Yes, slow-log execution details and EXPLAIN ANALYZE output are supported inputs to split TiKV read path latency from upstream TiDB gRPC delays. This isolates whether the bottleneck resides in TiKV-local processing or network transport.

Identify the earliest proven bottleneck in TiKV write path slowness by following an evidence-based routing workflow that uses quantile-escalated metrics. This approach stops overclaiming root causes by halting at the first concrete metric evidence of a bottleneck.