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287 lines
11 KiB
Go
287 lines
11 KiB
Go
/*
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*
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* Copyright 2022 gRPC authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*
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*/
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// Package roundrobin contains helper functions to check for roundrobin and
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// weighted-roundrobin load balancing of RPCs in tests.
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package roundrobin
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import (
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"context"
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"fmt"
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"math"
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"testing"
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"time"
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"github.com/google/go-cmp/cmp"
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"gonum.org/v1/gonum/stat/distuv"
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"google.golang.org/grpc"
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"google.golang.org/grpc/grpclog"
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"google.golang.org/grpc/peer"
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"google.golang.org/grpc/resolver"
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testgrpc "google.golang.org/grpc/interop/grpc_testing"
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testpb "google.golang.org/grpc/interop/grpc_testing"
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)
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var logger = grpclog.Component("testutils-roundrobin")
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// waitForTrafficToReachBackends repeatedly makes RPCs using the provided
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// TestServiceClient until RPCs reach all backends specified in addrs, or the
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// context expires, in which case a non-nil error is returned.
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func waitForTrafficToReachBackends(ctx context.Context, client testgrpc.TestServiceClient, addrs []resolver.Address) error {
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// Make sure connections to all backends are up. We need to do this two
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// times (to be sure that round_robin has kicked in) because the channel
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// could have been configured with a different LB policy before the switch
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// to round_robin. And the previous LB policy could be sharing backends with
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// round_robin, and therefore in the first iteration of this loop, RPCs
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// could land on backends owned by the previous LB policy.
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for j := 0; j < 2; j++ {
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for i := 0; i < len(addrs); i++ {
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for {
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time.Sleep(time.Millisecond)
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if ctx.Err() != nil {
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return fmt.Errorf("timeout waiting for connection to %q to be up", addrs[i].Addr)
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}
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var peer peer.Peer
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if _, err := client.EmptyCall(ctx, &testpb.Empty{}, grpc.Peer(&peer)); err != nil {
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// Some tests remove backends and check if round robin is
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// happening across the remaining backends. In such cases,
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// RPCs can initially fail on the connection using the
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// removed backend. Just keep retrying and eventually the
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// connection using the removed backend will shutdown and
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// will be removed.
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continue
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}
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if peer.Addr.String() == addrs[i].Addr {
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break
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}
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}
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}
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}
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return nil
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}
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// CheckRoundRobinRPCs verifies that EmptyCall RPCs on the given ClientConn,
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// connected to a server exposing the test.grpc_testing.TestService, are
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// roundrobined across the given backend addresses.
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//
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// Returns a non-nil error if context deadline expires before RPCs start to get
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// roundrobined across the given backends.
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func CheckRoundRobinRPCs(ctx context.Context, client testgrpc.TestServiceClient, addrs []resolver.Address) error {
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if err := waitForTrafficToReachBackends(ctx, client, addrs); err != nil {
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return err
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}
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// At this point, RPCs are getting successfully executed at the backends
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// that we care about. To support duplicate addresses (in addrs) and
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// backends being removed from the list of addresses passed to the
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// roundrobin LB, we do the following:
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// 1. Determine the count of RPCs that we expect each of our backends to
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// receive per iteration.
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// 2. Wait until the same pattern repeats a few times, or the context
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// deadline expires.
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wantAddrCount := make(map[string]int)
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for _, addr := range addrs {
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wantAddrCount[addr.Addr]++
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}
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for ; ctx.Err() == nil; <-time.After(time.Millisecond) {
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// Perform 3 more iterations.
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var iterations [][]string
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for i := 0; i < 3; i++ {
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iteration := make([]string, len(addrs))
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for c := 0; c < len(addrs); c++ {
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var peer peer.Peer
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if _, err := client.EmptyCall(ctx, &testpb.Empty{}, grpc.Peer(&peer)); err != nil {
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return fmt.Errorf("EmptyCall() = %v, want <nil>", err)
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}
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iteration[c] = peer.Addr.String()
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}
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iterations = append(iterations, iteration)
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}
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// Ensure the first iteration contains all addresses in addrs.
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gotAddrCount := make(map[string]int)
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for _, addr := range iterations[0] {
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gotAddrCount[addr]++
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}
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if diff := cmp.Diff(gotAddrCount, wantAddrCount); diff != "" {
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logger.Infof("non-roundrobin, got address count in one iteration: %v, want: %v, Diff: %s", gotAddrCount, wantAddrCount, diff)
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continue
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}
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// Ensure all three iterations contain the same addresses.
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if !cmp.Equal(iterations[0], iterations[1]) || !cmp.Equal(iterations[0], iterations[2]) {
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logger.Infof("non-roundrobin, first iter: %v, second iter: %v, third iter: %v", iterations[0], iterations[1], iterations[2])
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continue
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}
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return nil
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}
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return fmt.Errorf("timeout when waiting for roundrobin distribution of RPCs across addresses: %v", addrs)
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}
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// CheckWeightedRoundRobinRPCs verifies that EmptyCall RPCs on the given
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// ClientConn, connected to a server exposing the test.grpc_testing.TestService,
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// are weighted roundrobined (with randomness) across the given backend
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// addresses.
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//
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// Returns a non-nil error if context deadline expires before RPCs start to get
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// roundrobined across the given backends.
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func CheckWeightedRoundRobinRPCs(ctx context.Context, t *testing.T, client testgrpc.TestServiceClient, addrs []resolver.Address) error {
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if err := waitForTrafficToReachBackends(ctx, client, addrs); err != nil {
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return err
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}
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// At this point, RPCs are getting successfully executed at the backends
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// that we care about. To take the randomness of the WRR into account, we
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// look for approximate distribution instead of exact.
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wantAddrCount := make(map[string]int)
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for _, addr := range addrs {
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wantAddrCount[addr.Addr]++
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}
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attemptCount := attemptCounts(wantAddrCount)
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expectedCount := make(map[string]float64)
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for addr, count := range wantAddrCount {
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expectedCount[addr] = float64(count) / float64(len(addrs)) * float64(attemptCount)
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}
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// There is a small possibility that RPCs are reaching backends that we
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// don't expect them to reach here. The can happen because:
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// - at time T0, the list of backends [A, B, C, D].
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// - at time T1, the test updates the list of backends to [A, B, C], and
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// immediately starts attempting to check the distribution of RPCs to the
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// new backends.
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// - there is no way for the test to wait for a new picker to be pushed on
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// to the channel (which contains the updated list of backends) before
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// starting to attempt the RPC distribution checks.
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// - This is usually a transitory state and will eventually fix itself when
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// the new picker is pushed on the channel, and RPCs will start getting
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// routed to only backends that we care about.
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//
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// We work around this situation by using two loops. The inner loop contains
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// the meat of the calculations, and includes the logic which factors out
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// the randomness in weighted roundrobin. If we ever see an RPCs getting
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// routed to a backend that we don't expect it to get routed to, we break
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// from the inner loop thereby resetting all state and start afresh.
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for {
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observedCount := make(map[string]float64)
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InnerLoop:
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for {
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if ctx.Err() != nil {
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return fmt.Errorf("timeout when waiting for roundrobin distribution of RPCs across addresses: %v", addrs)
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}
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for i := 0; i < attemptCount; i++ {
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var peer peer.Peer
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if _, err := client.EmptyCall(ctx, &testpb.Empty{}, grpc.Peer(&peer)); err != nil {
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return fmt.Errorf("EmptyCall() = %v, want <nil>", err)
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}
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if addr := peer.Addr.String(); wantAddrCount[addr] == 0 {
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break InnerLoop
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}
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observedCount[peer.Addr.String()]++
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}
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return pearsonsChiSquareTest(t, observedCount, expectedCount)
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}
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<-time.After(time.Millisecond)
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}
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}
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// attemptCounts returns the number of attempts needed to verify the expected
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// distribution of RPCs. Having more attempts per category will give the test
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// a greater ability to detect a small but real deviation from the expected
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// distribution.
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func attemptCounts(wantAddrWeights map[string]int) int {
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if len(wantAddrWeights) == 0 {
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return 0
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}
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totalWeight := 0
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minWeight := -1
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for _, weight := range wantAddrWeights {
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totalWeight += weight
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if minWeight == -1 || weight < minWeight {
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minWeight = weight
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}
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}
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minRatio := float64(minWeight) / float64(totalWeight)
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// We want the expected count for the smallest category to be at least 500.
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// ExpectedCount = TotalAttempts * minRatio
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// So, 500 <= TotalAttempts * minRatio
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// which means TotalAttempts >= 500 / minRatio
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const minExpectedPerCategory = 500.0
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requiredAttempts := minExpectedPerCategory / minRatio
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return int(math.Ceil(requiredAttempts))
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}
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// pearsonsChiSquareTest checks if the observed counts match the expected
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// counts.
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// Pearson's Chi-Squared Test Formula:
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//
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// χ² = ∑ (Oᵢ - Eᵢ)² / Eᵢ
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//
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// Where:
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// - χ² is the chi-square statistic
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// - Oᵢ is the observed count for category i
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// - Eᵢ is the expected count for category i
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// - The sum is over all categories (i = 1 to k)
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//
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// This tests how well the observed distribution matches the expected one.
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// Larger χ² values indicate a greater difference between observed and expected
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// counts. The p-value is computed as P(χ² ≥ computed value) under the
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// chi-square distribution with degrees of freedom:
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// df = number of categories - 1
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// See https://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test for more
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// details.
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func pearsonsChiSquareTest(t *testing.T, observedCounts, expectedCounts map[string]float64) error {
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chiSquaredStat := 0.0
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for addr, want := range expectedCounts {
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got := observedCounts[addr]
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chiSquaredStat += (got - want) * (got - want) / want
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}
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degreesOfFreedom := len(expectedCounts) - 1
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const alpha = 1e-6
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chiSquareDist := distuv.ChiSquared{K: float64(degreesOfFreedom)}
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pValue := chiSquareDist.Survival(chiSquaredStat)
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t.Logf("Observed ratio: %v", observedCounts)
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t.Logf("Expected ratio: %v", expectedCounts)
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t.Logf("χ² statistic: %.4f", chiSquaredStat)
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t.Logf("Degrees of freedom: %d\n", degreesOfFreedom)
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t.Logf("p-value: %.10f\n", pValue)
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// Alpha (α) is the threshold we use to decide if the test "fails".
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// It controls how sensitive the chi-square test is.
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//
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// A smaller alpha means we require stronger evidence to say the load
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// balancing is wrong. A larger alpha makes the test more likely to fail,
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// even for small random fluctuations.
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//
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// For CI, we set alpha = 1e-6 to avoid flaky test failures.
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// That means:
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// - There's only a 1-in-a-million chance the test fails due to random
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// variation, assuming the load balancer is working correctly.
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// - If the test *does* fail, it's very likely there's a real bug.
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//
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// TL;DR: smaller alpha = stricter test, fewer false alarms.
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if pValue > alpha {
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return nil
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}
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return fmt.Errorf("observed distribution significantly differs from expectations, observeredCounts: %v, expectedCounts: %v", observedCounts, expectedCounts)
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}
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