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[prototype] storage: Make rebalance decisions at store-level
As outlined in recent comments on cockroachdb#26059, we need to bring back some form of stats-based rebalancing in order to perform well on TPC-C without manual partitioning and replica placement. This commit contains a prototype that demonstrates the effectiveness of changing our approach to making rebalancing decisions from making them in the replicate queue, which operates on arbitrarily ordered replicas of the ranges on a store, to making them at a higher-level. This prototype makes them at a cluster level by running the logic on only one node, but my real proposal is to make them at the store level. This change in abstraction reflects what a human would do if asked to even out the load on a cluster given perfect information about everything happening in the cluster: 1. First, determine which stores have the most load on them (or overfull -- but for the prototype I only considered the one dimension that affects TPC-C the most) 2. Decide whether the most loaded stores are so overloaded that action needs to be taken. 3. Examine the hottest replicas on the store (maybe not the absolute hottest in practice, since moving that one could disrupt user traffic, but in the prototype this seems to work fine) and attempt to move them to under-utilized stores. If this can be done simply by transferring leases to under-utilized stores, then do so. If moving leases isn't enough, then also rebalance replicas from the hottest store to under-utilized stores. 4. Repeat periodically to handle changes in load or cluster membership. In a real versino of this code, the plan is roughly: 1. Each store will independently run their own control loop like this that is only responsible for moving leases/replicas off itself, not off other stores. This avoids needing a centralized coordinator, and will avoid the need to use the raft debug endpoint as long as we start gossiping QPS per store info, since the store already has details about the replicas on itself. 2. The existing replicate queue will stop making decisions motivated by balance. It will switch to only making decisions based on constraints/diversity/lease preferences, which is still needed since the new store-level logic will only check for store-level balance, not that all replicas' constraints are properly met. 3. The new code will have to avoid violating constraints/diversity/lease preferences. 4. The new code should consider range count, disk fullness, and maybe writes per second as well. 5. In order to avoid making decisions based on bad data, I'd like to extend lease transfers to pass along QPS data to the new leaseholder and preemptive snapshots to pass along WPS data to the new replica. This may not be strictly necessary, as shown by the success of this prototype, but should make for more reliable decision making. I tested this out on TPC-C 5k on 15 nodes and am able to consistently get 94% efficiency, which is the max I've seen using a build of the workload generator that erroneously includes the ramp-up period in its final stats. The first run with this code only got 85% because it took a couple minutes to make all the lease transfers it wanted, but then all subsequent runs got the peak efficiency while making negligibly few lease transfers. Note that I didn't even have to implement replica rebalancing to get these results, which oddly contradicts my previous claims. However, I believe that's because I did the initial split/scatter using a binary containing cockroachdb#26438, so the replicas were already better scattered than by default. I ran TPC-C on that build without these changes a couple times, though, and didn't get better than 65% efficiency, so the scatter wasn't the cause of the good results here. Touches cockroachdb#26059, cockroachdb#17979 Release note: None
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// Copyright 2018 The Cockroach Authors. | ||
// | ||
// Licensed under the Apache License, Version 2.0 (the "License"); | ||
// you may not use this file except in compliance with the License. | ||
// You may obtain a copy of the License at | ||
// | ||
// http://www.apache.org/licenses/LICENSE-2.0 | ||
// | ||
// Unless required by applicable law or agreed to in writing, software | ||
// distributed under the License is distributed on an "AS IS" BASIS, | ||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or | ||
// implied. See the License for the specific language governing | ||
// permissions and limitations under the License. | ||
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package server | ||
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import ( | ||
"container/heap" | ||
"context" | ||
"math" | ||
"time" | ||
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"github.com/cockroachdb/cockroach/pkg/roachpb" | ||
"github.com/cockroachdb/cockroach/pkg/server/serverpb" | ||
"github.com/cockroachdb/cockroach/pkg/settings" | ||
"github.com/cockroachdb/cockroach/pkg/util/log" | ||
) | ||
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var testQPSThreshold = settings.RegisterNonNegativeFloatSetting( | ||
"server.test_qps_threshold", | ||
"the maximum fraction a store's qps can differ from the average before store-level rebalancing kicks in", | ||
0.25, | ||
) | ||
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func (s *Server) RunStoreLevelAllocator(ctx context.Context) { | ||
if s.NodeID() != 1 { | ||
return | ||
} | ||
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ticker := time.NewTicker(time.Minute) | ||
defer ticker.Stop() | ||
for { | ||
// Wait out the first tick before doing anything since the store is still | ||
// starting up and we might as well wait for some qps/wps stats to | ||
// accumulate. | ||
select { | ||
case <-s.stopper.ShouldQuiesce(): | ||
return | ||
case <-ticker.C: | ||
} | ||
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/* | ||
stores, _, _ := s.storePool.GetPublicStoreList() | ||
if !needRebalance(stores) { | ||
continue | ||
} | ||
*/ | ||
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log.Infof(ctx, "starting prototype allocator loop") | ||
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resp, err := s.status.RaftDebug(ctx, &serverpb.RaftDebugRequest{}) | ||
if err != nil { | ||
log.Errorf(ctx, "failed to retrieve raft debug info: %s", err) | ||
continue | ||
} | ||
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//sortedStoreQPS, hottestRanges := processResponse(resp) | ||
//if len(sortedStoreQPS) == 0 { | ||
qpsPerStore, hottestRangesByStore := processResponse(resp) | ||
if len(qpsPerStore) == 0 { | ||
log.Infof(ctx, "received no stores to process: %+v", resp) | ||
continue | ||
} | ||
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log.Infof(ctx, "qpsPerStore: %v", qpsPerStore) | ||
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var avgQPS float64 | ||
//for _, store := range sortedStoreQPS { | ||
//avgQPS += store.qps | ||
//} | ||
//avgQPS /= len(sortedStoreQPS) | ||
for _, qps := range qpsPerStore { | ||
avgQPS += qps | ||
} | ||
avgQPS /= float64(len(qpsPerStore)) | ||
upperBound := math.Max(avgQPS*1.15, avgQPS+100) | ||
log.Infof(ctx, "avgQPS: %f, upperBound: %f", avgQPS, upperBound) | ||
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// TODO: Also consider trying to move work to under-utilized stores even | ||
// if there aren't any outliers at the top end. | ||
topLevelLoop: | ||
for { | ||
// Try to lessen the load on the hottest store. | ||
hottestStore, hottestQPS := findHottestStore(qpsPerStore) | ||
log.Infof(ctx, "hottestStore: s%d, hottestQPS: %f", hottestStore, hottestQPS) | ||
if hottestQPS <= upperBound { | ||
break topLevelLoop | ||
} | ||
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hottestRanges := hottestRangesByStore[hottestStore] | ||
var rangeIDs []roachpb.RangeID | ||
for i := range hottestRanges { | ||
rangeIDs = append(rangeIDs, hottestRanges[i].RangeID) | ||
} | ||
log.Infof(ctx, "hottest rangeIDs: %v", rangeIDs) | ||
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// First check if there are any leases we can reasonably move. | ||
for i, r := range hottestRanges { | ||
qps := qps(r) | ||
log.Infof(ctx, "considering r%d, qps=%f", r.RangeID, qps) | ||
for j := range r.Nodes { | ||
storeID := r.Nodes[j].Range.SourceStoreID | ||
// Transfer the lease if we can move it to a store that will still be | ||
// under the average per-store QPS. | ||
if qpsPerStore[storeID]+qps < avgQPS { | ||
// Attempt to transfer the lease, and make sure we don't do | ||
// anything else to the range this go-round. | ||
hottestRangesByStore[hottestStore] = append(hottestRangesByStore[hottestStore][:i], hottestRangesByStore[hottestStore][i+1:]...) | ||
log.Infof(ctx, "transferring lease for r%d (qps=%f) to s%d (qps=%f)", r.RangeID, qps, storeID, qpsPerStore[storeID]) | ||
if err := s.db.AdminTransferLease(ctx, r.Nodes[j].Range.State.ReplicaState.Desc.StartKey, storeID); err != nil { | ||
log.Errorf(ctx, "error transferring lease for r%d to s%d: %s", r.RangeID, storeID, err) | ||
continue topLevelLoop | ||
} | ||
qpsPerStore[storeID] += qps | ||
qpsPerStore[hottestStore] -= qps | ||
continue topLevelLoop | ||
} | ||
} | ||
} | ||
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log.Infof(ctx, "failed to find a store to transfer a lease to") | ||
break topLevelLoop | ||
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/* | ||
// If that didn't work out, then resort to rebalancing replicas. | ||
log.Infof(ctx, "failed to find a store to transfer a lease to; beginning to consider replica rebalances") | ||
hottestRanges := hottestRangesByStore[hottestStore] | ||
var rangeIDs []roachpb.RangeID | ||
for i := range hottestRanges { | ||
rangeIDs = append(rangeIDs, hottestRanges[i].RangeID) | ||
} | ||
log.Infof(ctx, "hottest remaining rangeIDs: %v", rangeIDs) | ||
for i, r := range hottestRanges { | ||
qps := qps(r) | ||
log.Infof(ctx, "considering r%d, qps=%f", r.RangeID, qps) | ||
for j := range r.Nodes { | ||
} | ||
} | ||
// TODO | ||
//storage.TestingRelocateRange(ctx, s.db, rangeDesc, targets) | ||
*/ | ||
} | ||
} | ||
} | ||
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func findHottestStore(qpsPerStore map[roachpb.StoreID]float64) (roachpb.StoreID, float64) { | ||
var storeID roachpb.StoreID | ||
var qps float64 | ||
for s, q := range qpsPerStore { | ||
if q > qps { | ||
storeID = s | ||
qps = q | ||
} | ||
} | ||
return storeID, qps | ||
} | ||
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func findColdestStore(qpsPerStore map[roachpb.StoreID]float64) (roachpb.StoreID, float64) { | ||
var storeID roachpb.StoreID | ||
qps := math.MaxFloat64 | ||
for s, q := range qpsPerStore { | ||
if q < qps { | ||
storeID = s | ||
qps = q | ||
} | ||
} | ||
return storeID, qps | ||
} | ||
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/* | ||
type storeQPS struct { | ||
storeID roachpb.StoreID | ||
qps float64 | ||
} | ||
*/ | ||
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func processResponse( | ||
resp *serverpb.RaftDebugResponse, | ||
) (map[roachpb.StoreID]float64, map[roachpb.StoreID][]*serverpb.RaftRangeStatus) { | ||
qpsPerStore := make(map[roachpb.StoreID]float64) | ||
hottestRangeQueues := make(map[roachpb.StoreID]*PriorityQueue) | ||
for _, r := range resp.Ranges { | ||
r := r | ||
lease, qps := leaseAndQPS(&r) | ||
qpsPerStore[lease] += qps | ||
pq := hottestRangeQueues[lease] | ||
if pq == nil { | ||
pq = &PriorityQueue{} | ||
heap.Init(pq) | ||
hottestRangeQueues[lease] = pq | ||
} | ||
heap.Push(pq, &r) | ||
if pq.Len() > 32 { | ||
heap.Pop(pq) | ||
} | ||
} | ||
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/* | ||
sortedQPS := make([]storeQPS, len(qpsPerStore), 0) | ||
for storeID, qps := range qpsPerStore { | ||
sortedQPS = append(sortedQPS, storeQPS{storeID, qps}) | ||
} | ||
sort.Slice(sortedQPS, func(i, j int) bool { return sortedQPS[i].qps < sortedQPS[j].qps }) | ||
*/ | ||
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hottestRanges := make(map[roachpb.StoreID][]*serverpb.RaftRangeStatus) | ||
for storeID, pq := range hottestRangeQueues { | ||
length := pq.Len() | ||
hottestRanges[storeID] = make([]*serverpb.RaftRangeStatus, length) | ||
rangeQPS := make([]float64, length) | ||
for i := 1; i <= length; i++ { | ||
hottestRanges[storeID][length-i] = heap.Pop(pq).(*serverpb.RaftRangeStatus) | ||
rangeQPS[length-i] = qps(hottestRanges[storeID][length-i]) | ||
} | ||
log.Infof(context.TODO(), "hottest ranges for s%d: %v", storeID, rangeQPS) | ||
} | ||
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return qpsPerStore, hottestRanges | ||
} | ||
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func qps(r *serverpb.RaftRangeStatus) float64 { | ||
_, qps := leaseAndQPS(r) | ||
return qps | ||
} | ||
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func leaseAndQPS(r *serverpb.RaftRangeStatus) (roachpb.StoreID, float64) { | ||
for i := range r.Nodes { | ||
if r.Nodes[i].Range.State.ReplicaState.Lease.Replica.StoreID == r.Nodes[i].Range.SourceStoreID { | ||
return r.Nodes[i].Range.SourceStoreID, r.Nodes[i].Range.Stats.QueriesPerSecond | ||
} | ||
} | ||
return 0, 0 | ||
} | ||
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type PriorityQueue []*serverpb.RaftRangeStatus | ||
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func (pq PriorityQueue) Len() int { return len(pq) } | ||
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func (pq PriorityQueue) Less(i, j int) bool { | ||
return qps(pq[i]) < qps(pq[j]) | ||
} | ||
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func (pq PriorityQueue) Swap(i, j int) { | ||
pq[i], pq[j] = pq[j], pq[i] | ||
} | ||
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func (pq *PriorityQueue) Push(x interface{}) { | ||
item := x.(*serverpb.RaftRangeStatus) | ||
*pq = append(*pq, item) | ||
} | ||
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func (pq *PriorityQueue) Pop() interface{} { | ||
old := *pq | ||
n := len(old) | ||
item := old[n-1] | ||
*pq = old[0 : n-1] | ||
return item | ||
} | ||
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/* | ||
func needRebalance(stores storage.PublicStoreList) bool { | ||
for _, desc := range stores.Stores { | ||
if rangeCountNeedsRebalance(desc.Capacity.RangeCount, stores.avgRangeCount) { | ||
return true | ||
} | ||
if diskUsageNeedsRebalance(desc.Capacity.RangeCount, stores.avgRangeCount) { | ||
return true | ||
} | ||
if wpsNeedsRebalance(desc.Capacity.RangeCount, stores.avgRangeCount) { | ||
return true | ||
} | ||
} | ||
return false | ||
} | ||
func rangeCountNeedsRebalance(rangeCount, avgRangeCount float64) bool { | ||
upperBound := math.Max(avgRangeCount*1.1, avgRangeCount+10) | ||
lowerBound := math.Min(avgRangeCount*0.9, avgRangeCount-10) | ||
return rangeCount > upperBound || rangeCount < lowerBound | ||
} | ||
*/ |
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