mapper/batcher: add scale benchmarks

Add benchmarks that systematically test node counts from 100 to
50,000 to identify scaling limits and validate performance under
load.
This commit is contained in:
Kristoffer Dalby
2026-03-12 08:42:06 +00:00
parent 3ebe4d99c1
commit 9b24a39943

View File

@@ -0,0 +1,945 @@
package mapper
// Scale benchmarks for the batcher system.
//
// These benchmarks systematically increase node counts to find scaling limits
// and identify bottlenecks. Organized into tiers:
//
// Tier 1 - O(1) operations: should stay flat regardless of node count
// Tier 2 - O(N) lightweight: batch queuing and processing (no MapResponse generation)
// Tier 3 - O(N) heavier: map building, peer diff, peer tracking
// Tier 4 - Concurrent contention: multi-goroutine access under load
//
// Node count progression: 100, 500, 1000, 2000, 5000, 10000, 20000, 50000
import (
"fmt"
"strconv"
"sync"
"testing"
"time"
"github.com/juanfont/headscale/hscontrol/types"
"github.com/juanfont/headscale/hscontrol/types/change"
"github.com/rs/zerolog"
"tailscale.com/tailcfg"
)
// scaleCounts defines the node counts used across all scaling benchmarks.
// Tier 1 (O(1)) tests up to 50k; Tier 2-4 test up to 10k-20k.
var (
scaleCountsO1 = []int{100, 500, 1000, 2000, 5000, 10000, 20000, 50000}
scaleCountsLinear = []int{100, 500, 1000, 2000, 5000, 10000}
scaleCountsHeavy = []int{100, 500, 1000, 2000, 5000, 10000}
scaleCountsConc = []int{100, 500, 1000, 2000, 5000}
)
// ============================================================================
// Tier 1: O(1) Operations — should scale flat
// ============================================================================
// BenchmarkScale_IsConnected tests single-node lookup at increasing map sizes.
func BenchmarkScale_IsConnected(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsO1 {
b.Run(strconv.Itoa(n), func(b *testing.B) {
batcher, _ := benchBatcher(n, 1)
defer func() {
close(batcher.done)
batcher.tick.Stop()
}()
b.ResetTimer()
for i := range b.N {
id := types.NodeID(1 + (i % n)) //nolint:gosec
_ = batcher.IsConnected(id)
}
})
}
}
// BenchmarkScale_AddToBatch_Targeted tests single-node targeted change at
// increasing map sizes. The map size should not affect per-operation cost.
func BenchmarkScale_AddToBatch_Targeted(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsO1 {
b.Run(strconv.Itoa(n), func(b *testing.B) {
batcher, _ := benchBatcher(n, 10)
defer func() {
close(batcher.done)
batcher.tick.Stop()
}()
b.ResetTimer()
for i := range b.N {
targetID := types.NodeID(1 + (i % n)) //nolint:gosec
ch := change.Change{
Reason: "scale-targeted",
TargetNode: targetID,
PeerPatches: []*tailcfg.PeerChange{
{NodeID: tailcfg.NodeID(targetID)}, //nolint:gosec
},
}
batcher.addToBatch(ch)
// Drain every 100 ops to avoid unbounded growth
if i%100 == 99 {
batcher.nodes.Range(func(_ types.NodeID, nc *multiChannelNodeConn) bool {
nc.drainPending()
return true
})
}
}
})
}
}
// BenchmarkScale_ConnectionChurn tests add/remove connection cycle.
// The map size should not affect per-operation cost for a single node.
func BenchmarkScale_ConnectionChurn(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsO1 {
b.Run(strconv.Itoa(n), func(b *testing.B) {
batcher, channels := benchBatcher(n, 10)
defer func() {
close(batcher.done)
batcher.tick.Stop()
}()
b.ResetTimer()
for i := range b.N {
id := types.NodeID(1 + (i % n)) //nolint:gosec
mc, ok := batcher.nodes.Load(id)
if !ok {
continue
}
oldCh := channels[id]
mc.removeConnectionByChannel(oldCh)
newCh := make(chan *tailcfg.MapResponse, 10)
entry := &connectionEntry{
id: fmt.Sprintf("sc-%d", i),
c: newCh,
version: tailcfg.CapabilityVersion(100),
created: time.Now(),
}
entry.lastUsed.Store(time.Now().Unix())
mc.addConnection(entry)
channels[id] = newCh
}
})
}
}
// ============================================================================
// Tier 2: O(N) Lightweight — batch mechanics without MapResponse generation
// ============================================================================
// BenchmarkScale_AddToBatch_Broadcast tests broadcasting a change to ALL nodes.
// Cost should scale linearly with node count.
func BenchmarkScale_AddToBatch_Broadcast(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsLinear {
b.Run(strconv.Itoa(n), func(b *testing.B) {
batcher, _ := benchBatcher(n, 10)
defer func() {
close(batcher.done)
batcher.tick.Stop()
}()
ch := change.DERPMap()
b.ResetTimer()
for range b.N {
batcher.addToBatch(ch)
// Drain to avoid unbounded growth
batcher.nodes.Range(func(_ types.NodeID, nc *multiChannelNodeConn) bool {
nc.drainPending()
return true
})
}
})
}
}
// BenchmarkScale_AddToBatch_FullUpdate tests FullUpdate broadcast cost.
func BenchmarkScale_AddToBatch_FullUpdate(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsLinear {
b.Run(strconv.Itoa(n), func(b *testing.B) {
batcher, _ := benchBatcher(n, 10)
defer func() {
close(batcher.done)
batcher.tick.Stop()
}()
b.ResetTimer()
for range b.N {
batcher.addToBatch(change.FullUpdate())
}
})
}
}
// BenchmarkScale_ProcessBatchedChanges tests draining pending changes into work queue.
func BenchmarkScale_ProcessBatchedChanges(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsLinear {
b.Run(strconv.Itoa(n), func(b *testing.B) {
batcher, _ := benchBatcher(n, 10)
batcher.workCh = make(chan work, n*b.N+1)
defer func() {
close(batcher.done)
batcher.tick.Stop()
}()
b.ResetTimer()
for range b.N {
b.StopTimer()
for i := 1; i <= n; i++ {
if nc, ok := batcher.nodes.Load(types.NodeID(i)); ok { //nolint:gosec
nc.appendPending(change.DERPMap())
}
}
b.StartTimer()
batcher.processBatchedChanges()
}
})
}
}
// BenchmarkScale_BroadcastToN tests end-to-end: addToBatch + processBatchedChanges.
func BenchmarkScale_BroadcastToN(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsLinear {
b.Run(strconv.Itoa(n), func(b *testing.B) {
batcher, _ := benchBatcher(n, 10)
batcher.workCh = make(chan work, n*b.N+1)
defer func() {
close(batcher.done)
batcher.tick.Stop()
}()
ch := change.DERPMap()
b.ResetTimer()
for range b.N {
batcher.addToBatch(ch)
batcher.processBatchedChanges()
}
})
}
}
// BenchmarkScale_SendToAll tests raw channel send cost to N nodes (no batching).
// This isolates the multiChannelNodeConn.send() cost.
// Uses large buffered channels to avoid goroutine drain overhead.
func BenchmarkScale_SendToAll(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsLinear {
b.Run(strconv.Itoa(n), func(b *testing.B) {
// b.N+1 buffer so sends never block
batcher, _ := benchBatcher(n, b.N+1)
defer func() {
close(batcher.done)
batcher.tick.Stop()
}()
data := testMapResponse()
b.ResetTimer()
for range b.N {
batcher.nodes.Range(func(_ types.NodeID, mc *multiChannelNodeConn) bool {
_ = mc.send(data)
return true
})
}
})
}
}
// ============================================================================
// Tier 3: O(N) Heavier — map building, peer diff, peer tracking
// ============================================================================
// BenchmarkScale_ConnectedMap tests building the full connected/disconnected map.
func BenchmarkScale_ConnectedMap(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsHeavy {
b.Run(strconv.Itoa(n), func(b *testing.B) {
batcher, _ := benchBatcher(n, 1)
defer func() {
close(batcher.done)
batcher.tick.Stop()
}()
// 10% disconnected for realism
for i := 1; i <= n; i++ {
if i%10 == 0 {
now := time.Now()
batcher.connected.Store(types.NodeID(i), &now) //nolint:gosec
}
}
b.ResetTimer()
for range b.N {
_ = batcher.ConnectedMap()
}
})
}
}
// BenchmarkScale_ComputePeerDiff tests peer diff computation at scale.
// Each node tracks N-1 peers, with 10% removed.
func BenchmarkScale_ComputePeerDiff(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsHeavy {
b.Run(strconv.Itoa(n), func(b *testing.B) {
mc := newMultiChannelNodeConn(1, nil)
// Track N peers
for i := 1; i <= n; i++ {
mc.lastSentPeers.Store(tailcfg.NodeID(i), struct{}{})
}
// Current: 90% present (every 10th missing)
current := make([]tailcfg.NodeID, 0, n)
for i := 1; i <= n; i++ {
if i%10 != 0 {
current = append(current, tailcfg.NodeID(i))
}
}
b.ResetTimer()
for range b.N {
_ = mc.computePeerDiff(current)
}
})
}
}
// BenchmarkScale_UpdateSentPeers_Full tests full peer list update.
func BenchmarkScale_UpdateSentPeers_Full(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsHeavy {
b.Run(strconv.Itoa(n), func(b *testing.B) {
mc := newMultiChannelNodeConn(1, nil)
peerIDs := make([]tailcfg.NodeID, n)
for i := range peerIDs {
peerIDs[i] = tailcfg.NodeID(i + 1)
}
resp := testMapResponseWithPeers(peerIDs...)
b.ResetTimer()
for range b.N {
mc.updateSentPeers(resp)
}
})
}
}
// BenchmarkScale_UpdateSentPeers_Incremental tests incremental peer updates (10% new).
func BenchmarkScale_UpdateSentPeers_Incremental(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsHeavy {
b.Run(strconv.Itoa(n), func(b *testing.B) {
mc := newMultiChannelNodeConn(1, nil)
// Pre-populate
for i := 1; i <= n; i++ {
mc.lastSentPeers.Store(tailcfg.NodeID(i), struct{}{})
}
addCount := n / 10
if addCount == 0 {
addCount = 1
}
resp := testMapResponse()
resp.PeersChanged = make([]*tailcfg.Node, addCount)
for i := range addCount {
resp.PeersChanged[i] = &tailcfg.Node{ID: tailcfg.NodeID(n + i + 1)}
}
b.ResetTimer()
for range b.N {
mc.updateSentPeers(resp)
}
})
}
}
// BenchmarkScale_MultiChannelBroadcast tests sending to N nodes, each with
// ~1.6 connections on average (every 3rd node has 3 connections).
// Uses large buffered channels to avoid goroutine drain overhead.
func BenchmarkScale_MultiChannelBroadcast(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsHeavy {
b.Run(strconv.Itoa(n), func(b *testing.B) {
// Use b.N+1 buffer so sends never block
batcher, _ := benchBatcher(n, b.N+1)
defer func() {
close(batcher.done)
batcher.tick.Stop()
}()
// Add extra connections to every 3rd node (also buffered)
for i := 1; i <= n; i++ {
if i%3 == 0 {
if mc, ok := batcher.nodes.Load(types.NodeID(i)); ok { //nolint:gosec
for j := range 2 {
ch := make(chan *tailcfg.MapResponse, b.N+1)
entry := &connectionEntry{
id: fmt.Sprintf("extra-%d-%d", i, j),
c: ch,
version: tailcfg.CapabilityVersion(100),
created: time.Now(),
}
entry.lastUsed.Store(time.Now().Unix())
mc.addConnection(entry)
}
}
}
}
data := testMapResponse()
b.ResetTimer()
for range b.N {
batcher.nodes.Range(func(_ types.NodeID, mc *multiChannelNodeConn) bool {
_ = mc.send(data)
return true
})
}
})
}
}
// ============================================================================
// Tier 4: Concurrent Contention — multi-goroutine access
// ============================================================================
// BenchmarkScale_ConcurrentAddToBatch tests parallel addToBatch throughput.
func BenchmarkScale_ConcurrentAddToBatch(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsConc {
b.Run(strconv.Itoa(n), func(b *testing.B) {
batcher, _ := benchBatcher(n, 10)
drainDone := make(chan struct{})
go func() {
defer close(drainDone)
for {
select {
case <-batcher.done:
return
default:
batcher.nodes.Range(func(_ types.NodeID, nc *multiChannelNodeConn) bool {
nc.drainPending()
return true
})
time.Sleep(time.Millisecond) //nolint:forbidigo
}
}
}()
ch := change.DERPMap()
b.ResetTimer()
b.RunParallel(func(pb *testing.PB) {
for pb.Next() {
batcher.addToBatch(ch)
}
})
b.StopTimer()
close(batcher.done)
<-drainDone
batcher.done = make(chan struct{})
batcher.tick.Stop()
})
}
}
// BenchmarkScale_ConcurrentSendAndChurn tests the production hot path:
// sending to all nodes while 10% of connections are churning concurrently.
// Uses large buffered channels to avoid goroutine drain overhead.
func BenchmarkScale_ConcurrentSendAndChurn(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsConc {
b.Run(strconv.Itoa(n), func(b *testing.B) {
batcher, channels := benchBatcher(n, b.N+1)
var mu sync.Mutex
stopChurn := make(chan struct{})
go func() {
i := 0
for {
select {
case <-stopChurn:
return
default:
id := types.NodeID(1 + (i % n)) //nolint:gosec
if i%10 == 0 {
mc, ok := batcher.nodes.Load(id)
if ok {
mu.Lock()
oldCh := channels[id]
mu.Unlock()
mc.removeConnectionByChannel(oldCh)
newCh := make(chan *tailcfg.MapResponse, b.N+1)
entry := &connectionEntry{
id: fmt.Sprintf("sc-churn-%d", i),
c: newCh,
version: tailcfg.CapabilityVersion(100),
created: time.Now(),
}
entry.lastUsed.Store(time.Now().Unix())
mc.addConnection(entry)
mu.Lock()
channels[id] = newCh
mu.Unlock()
}
}
i++
}
}
}()
data := testMapResponse()
b.ResetTimer()
for range b.N {
batcher.nodes.Range(func(_ types.NodeID, mc *multiChannelNodeConn) bool {
_ = mc.send(data)
return true
})
}
b.StopTimer()
close(stopChurn)
close(batcher.done)
batcher.tick.Stop()
})
}
}
// BenchmarkScale_MixedWorkload simulates a realistic production workload:
// - 70% targeted changes (single node updates)
// - 20% DERP map changes (broadcast)
// - 10% full updates (broadcast with full map)
// All while 10% of connections are churning.
func BenchmarkScale_MixedWorkload(b *testing.B) {
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, n := range scaleCountsConc {
b.Run(strconv.Itoa(n), func(b *testing.B) {
batcher, channels := benchBatcher(n, 10)
batcher.workCh = make(chan work, n*100+1)
var mu sync.Mutex
stopChurn := make(chan struct{})
// Background churn on 10% of nodes
go func() {
i := 0
for {
select {
case <-stopChurn:
return
default:
id := types.NodeID(1 + (i % n)) //nolint:gosec
if i%10 == 0 {
mc, ok := batcher.nodes.Load(id)
if ok {
mu.Lock()
oldCh := channels[id]
mu.Unlock()
mc.removeConnectionByChannel(oldCh)
newCh := make(chan *tailcfg.MapResponse, 10)
entry := &connectionEntry{
id: fmt.Sprintf("mix-churn-%d", i),
c: newCh,
version: tailcfg.CapabilityVersion(100),
created: time.Now(),
}
entry.lastUsed.Store(time.Now().Unix())
mc.addConnection(entry)
mu.Lock()
channels[id] = newCh
mu.Unlock()
}
}
i++
}
}
}()
// Background batch processor
stopProc := make(chan struct{})
go func() {
for {
select {
case <-stopProc:
return
default:
batcher.processBatchedChanges()
time.Sleep(time.Millisecond) //nolint:forbidigo
}
}
}()
// Background work channel consumer (simulates workers)
stopWorkers := make(chan struct{})
go func() {
for {
select {
case <-batcher.workCh:
case <-stopWorkers:
return
}
}
}()
b.ResetTimer()
for i := range b.N {
switch {
case i%10 < 7: // 70% targeted
targetID := types.NodeID(1 + (i % n)) //nolint:gosec
batcher.addToBatch(change.Change{
Reason: "mixed-targeted",
TargetNode: targetID,
PeerPatches: []*tailcfg.PeerChange{
{NodeID: tailcfg.NodeID(targetID)}, //nolint:gosec
},
})
case i%10 < 9: // 20% DERP map broadcast
batcher.addToBatch(change.DERPMap())
default: // 10% full update
batcher.addToBatch(change.FullUpdate())
}
}
b.StopTimer()
close(stopChurn)
close(stopProc)
close(stopWorkers)
close(batcher.done)
batcher.tick.Stop()
})
}
}
// ============================================================================
// Tier 5: DB-dependent — AddNode with real MapResponse generation
// ============================================================================
// BenchmarkScale_AddAllNodes measures the cost of connecting ALL N nodes
// to a batcher backed by a real database. Each AddNode generates an initial
// MapResponse containing all peer data, so cost is O(N) per node, O(N²) total.
func BenchmarkScale_AddAllNodes(b *testing.B) {
if testing.Short() {
b.Skip("skipping full pipeline benchmark in short mode")
}
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, nodeCount := range []int{10, 50, 100, 200, 500} {
b.Run(strconv.Itoa(nodeCount), func(b *testing.B) {
testData, cleanup := setupBatcherWithTestData(b, NewBatcherAndMapper, 1, nodeCount, LARGE_BUFFER_SIZE)
defer cleanup()
batcher := testData.Batcher
allNodes := testData.Nodes
for i := range allNodes {
allNodes[i].start()
}
defer func() {
for i := range allNodes {
allNodes[i].cleanup()
}
}()
b.ResetTimer()
for range b.N {
for i := range allNodes {
node := &allNodes[i]
_ = batcher.AddNode(node.n.ID, node.ch, tailcfg.CapabilityVersion(100), nil)
}
b.StopTimer()
for i := range allNodes {
node := &allNodes[i]
batcher.RemoveNode(node.n.ID, node.ch)
}
for i := range allNodes {
for {
select {
case <-allNodes[i].ch:
default:
goto drained
}
}
drained:
}
b.StartTimer()
}
})
}
}
// BenchmarkScale_SingleAddNode measures the cost of adding ONE node to an
// already-populated batcher. This is the real production scenario: a new node
// joins an existing network. The cost should scale with the number of existing
// peers since the initial MapResponse includes all peer data.
func BenchmarkScale_SingleAddNode(b *testing.B) {
if testing.Short() {
b.Skip("skipping full pipeline benchmark in short mode")
}
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, nodeCount := range []int{10, 50, 100, 200, 500, 1000} {
b.Run(strconv.Itoa(nodeCount), func(b *testing.B) {
testData, cleanup := setupBatcherWithTestData(b, NewBatcherAndMapper, 1, nodeCount, LARGE_BUFFER_SIZE)
defer cleanup()
batcher := testData.Batcher
allNodes := testData.Nodes
for i := range allNodes {
allNodes[i].start()
}
defer func() {
for i := range allNodes {
allNodes[i].cleanup()
}
}()
// Connect all nodes except the last one
for i := range len(allNodes) - 1 {
node := &allNodes[i]
err := batcher.AddNode(node.n.ID, node.ch, tailcfg.CapabilityVersion(100), nil)
if err != nil {
b.Fatalf("failed to add node %d: %v", i, err)
}
}
time.Sleep(200 * time.Millisecond) //nolint:forbidigo
// Benchmark: repeatedly add and remove the last node
lastNode := &allNodes[len(allNodes)-1]
b.ResetTimer()
for range b.N {
_ = batcher.AddNode(lastNode.n.ID, lastNode.ch, tailcfg.CapabilityVersion(100), nil)
b.StopTimer()
batcher.RemoveNode(lastNode.n.ID, lastNode.ch)
for {
select {
case <-lastNode.ch:
default:
goto drainDone
}
}
drainDone:
b.StartTimer()
}
})
}
}
// BenchmarkScale_MapResponse_DERPMap measures MapResponse generation for a
// DERPMap change. This is a lightweight change that doesn't touch peers.
func BenchmarkScale_MapResponse_DERPMap(b *testing.B) {
if testing.Short() {
b.Skip("skipping full pipeline benchmark in short mode")
}
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, nodeCount := range []int{10, 50, 100, 200, 500} {
b.Run(strconv.Itoa(nodeCount), func(b *testing.B) {
testData, cleanup := setupBatcherWithTestData(b, NewBatcherAndMapper, 1, nodeCount, LARGE_BUFFER_SIZE)
defer cleanup()
batcher := testData.Batcher
allNodes := testData.Nodes
for i := range allNodes {
allNodes[i].start()
}
defer func() {
for i := range allNodes {
allNodes[i].cleanup()
}
}()
for i := range allNodes {
node := &allNodes[i]
err := batcher.AddNode(node.n.ID, node.ch, tailcfg.CapabilityVersion(100), nil)
if err != nil {
b.Fatalf("failed to add node %d: %v", i, err)
}
}
time.Sleep(200 * time.Millisecond) //nolint:forbidigo
ch := change.DERPMap()
b.ResetTimer()
for i := range b.N {
nodeIdx := i % len(allNodes)
_, _ = batcher.MapResponseFromChange(allNodes[nodeIdx].n.ID, ch)
}
})
}
}
// BenchmarkScale_MapResponse_FullUpdate measures MapResponse generation for a
// FullUpdate change. This forces full peer serialization — the primary bottleneck
// for large networks.
func BenchmarkScale_MapResponse_FullUpdate(b *testing.B) {
if testing.Short() {
b.Skip("skipping full pipeline benchmark in short mode")
}
zerolog.SetGlobalLevel(zerolog.Disabled)
defer zerolog.SetGlobalLevel(zerolog.DebugLevel)
for _, nodeCount := range []int{10, 50, 100, 200, 500} {
b.Run(strconv.Itoa(nodeCount), func(b *testing.B) {
testData, cleanup := setupBatcherWithTestData(b, NewBatcherAndMapper, 1, nodeCount, LARGE_BUFFER_SIZE)
defer cleanup()
batcher := testData.Batcher
allNodes := testData.Nodes
for i := range allNodes {
allNodes[i].start()
}
defer func() {
for i := range allNodes {
allNodes[i].cleanup()
}
}()
for i := range allNodes {
node := &allNodes[i]
err := batcher.AddNode(node.n.ID, node.ch, tailcfg.CapabilityVersion(100), nil)
if err != nil {
b.Fatalf("failed to add node %d: %v", i, err)
}
}
time.Sleep(200 * time.Millisecond) //nolint:forbidigo
ch := change.FullUpdate()
b.ResetTimer()
for i := range b.N {
nodeIdx := i % len(allNodes)
_, _ = batcher.MapResponseFromChange(allNodes[nodeIdx].n.ID, ch)
}
})
}
}