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📝 WalkthroughWalkthroughAdds a CPU-based hot-region dimension: collect store/region CPU stats, propagate them through heartbeats and statistics, extend hot-region config/solver/metrics with CPU support and version gating, update storage/CLI/API outputs, add tests, and bump kvproto dependency versions. Changes
Sequence Diagram(s)sequenceDiagram
participant TiKV as TiKV (store)
participant PD as PD/statistics collector
participant StoreHandler as HeartbeatHandler
participant Scheduler as HotRegionScheduler
TiKV->>PD: send store heartbeat (peers, peer stats, cpu stats)
PD->>PD: aggregate store CPU (unified-read, grpc threads)
PD->>StoreHandler: compute per-region RegionReadCPU (unified + proportional gRPC)
StoreHandler->>Scheduler: publish region loads (bytes, keys, queries, cpu)
Scheduler->>Scheduler: evaluate hot regions (use cpuSupport, thresholds, priorities)
Scheduler->>PD: emit metrics/decisions (including CPU rates)
Estimated code review effort🎯 4 (Complex) | ⏱️ ~60 minutes Possibly related PRs
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✅ Passed checks (4 passed)
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🧪 Generate unit tests (beta)
⚔️ Resolve merge conflicts (beta)
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- Misses 10943 10957 +14
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please link an issue and add some descriptions |
rleungx
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Feb 14, 2026
| storeReadQuery := core.GetReadQueryNum(stats.QueryStats) | ||
| storeWriteQuery := core.GetWriteQueryNum(stats.QueryStats) | ||
| storeTotalQuery := storeReadQuery + storeWriteQuery | ||
| storeGRPCCPU := statistics.StoreGRPCCPUUsage(stats.GetCpuUsages()) |
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Here we intentionally use gRPC CPU only. Unified-read CPU is already in peerStat.CpuStats.UnifiedRead, so using store read CPU here would double count.
rleungx
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pkg/statistics/cpu.go
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| @@ -0,0 +1,74 @@ | |||
| // Copyright 2025 TiKV Project Authors. | |||
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| // Copyright 2025 TiKV Project Authors. | |
| // Copyright 2026 TiKV Project Authors. |
rleungx
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Feb 14, 2026
| return unifiedReadCPU | ||
| } | ||
| grpcCPU := float64(StoreGRPCCPUUsage(cpuUsages)) | ||
| return unifiedReadCPU + grpcCPU*float64(readQuery)/float64(totalQuery) |
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This is an approximation: unified-read CPU is read-only, while grpc-server CPU is shared by read/write requests, so we apportion gRPC CPU by readQuery/totalQuery.
rleungx
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Feb 14, 2026
| rollingWindowsSize = 5 | ||
| // It is used to moving average CPU usage, | ||
| // and the window size is larger than other dimensions to make the CPU usage more stable. | ||
| cpuRollingWindowsSize = 9 |
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A larger window will be more stable for cpu
rleungx
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Feb 14, 2026
| ) | ||
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| // IsHotScheduleWithCPUSupported returns whether TiKV reports CPU info for hot scheduling. | ||
| func IsHotScheduleWithCPUSupported(clusterVersion *semver.Version) bool { |
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What if we wanna cp to release 8.5?
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What problem does this PR solve?
Issue Number: Close #5718
What is changed and how does it work?
Simple description
This pr introduces cpu as a new dimension for hot scheduler, it only serve hot read scheduler
From store heartbeat cpu_usages, sum unified‑read and grpc‑server thread CPU by prefix. Read CPU load is computed as unifiedReadCPU + grpcCPU * readQuery/totalQuery (or just unifiedReadCPU if queries are missing). This feeds the read CPUDim in store loads and hot‑peer stats. CPU uses a longer rolling median window; hotness checks use rolling average for CPU and last‑interval average for other dims. Read priorities become cpu→byte when supported, otherwise fall back to query→byte (or byte→key if query isn’t supported).
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