asim: add randomized range generation

This patch enables random range configuration to be generated.

TestRandomized can now take another setting parameter rangeGen (default: uniform
rangeGenType, uniform keySpaceGenType, empty weightedRand).

These generators are part of the framework fields which persist across
iterations. The numbers produced by the generator shape the distribution across
iterations.

- rangeKeyGenType: determines range generator type across iterations (default:
uniformGenerator, min = 1, max = 1000)

- keySpaceGenType: determines key space generator type across iterations
(default: uniformGenerator, min = 1000, max = 200000)

- weightedRand: if non-empty, enables weighted randomization for range
distribution

This provides three modes for range generation:
1. Default: currently set to uniform distribution
2. Random: randomly generates range distribution across stores
3. Weighted Randomization: enables weighted randomization for range distribution
if and only if given weightedRand is non-empty

Part of: #106311

Release note: None

pkg/kv/kvserver/asim/gen/generator.go

@@ -192,34 +192,43 @@ type PlacementType int
 const (
 	Uniform PlacementType = iota
 	Skewed
+	Random
+	WeightedRandom
 )
 
 // BaseRanges provide fundamental range functionality and are embedded in
 // specialized range structs. These structs implement the RangeGen interface
 // which is then utilized to generate allocator simulation. Key structs that
-// embed BaseRanges are: BasicRanges.
+// embed BaseRanges are: BasicRanges, RandomizedBasicRanges, and
+// WeightedRandomizedBasicRanges.
 type BaseRanges struct {
 	Ranges            int
 	KeySpace          int
 	ReplicationFactor int
 	Bytes             int64
 }
 
-// getRangesInfo generates and distributes ranges across stores based on
+// GetRangesInfo generates and distributes ranges across stores based on
 // PlacementType while using other BaseRanges fields for range configuration.
-func (b BaseRanges) getRangesInfo(pType PlacementType, numOfStores int) state.RangesInfo {
+func (b BaseRanges) GetRangesInfo(
+	pType PlacementType, numOfStores int, randSource *rand.Rand, weightedRandom []float64,
+) state.RangesInfo {
 	switch pType {
 	case Uniform:
 		return state.RangesInfoEvenDistribution(numOfStores, b.Ranges, b.KeySpace, b.ReplicationFactor, b.Bytes)
 	case Skewed:
 		return state.RangesInfoSkewedDistribution(numOfStores, b.Ranges, b.KeySpace, b.ReplicationFactor, b.Bytes)
+	case Random:
+		return state.RangesInfoRandDistribution(randSource, numOfStores, b.Ranges, b.KeySpace, b.ReplicationFactor, b.Bytes)
+	case WeightedRandom:
+		return state.RangesInfoWeightedRandDistribution(randSource, weightedRandom, b.Ranges, b.KeySpace, b.ReplicationFactor, b.Bytes)
 	default:
 		panic(fmt.Sprintf("unexpected range placement type %v", pType))
 	}
 }
 
-// loadRangeInfo loads the given state with the specified rangesInfo.
-func (b BaseRanges) loadRangeInfo(s state.State, rangesInfo state.RangesInfo) {
+// LoadRangeInfo loads the given state with the specified rangesInfo.
+func (b BaseRanges) LoadRangeInfo(s state.State, rangesInfo state.RangesInfo) {
 	for _, rangeInfo := range rangesInfo {
 		rangeInfo.Size = b.Bytes
 	}
@@ -239,8 +248,11 @@ type BasicRanges struct {
 func (br BasicRanges) Generate(
 	seed int64, settings *config.SimulationSettings, s state.State,
 ) state.State {
-	rangesInfo := br.getRangesInfo(br.PlacementType, len(s.Stores()))
-	br.loadRangeInfo(s, rangesInfo)
+	if br.PlacementType == Random || br.PlacementType == WeightedRandom {
+		panic("BasicRanges generate only uniform or skewed distributions")
+	}
+	rangesInfo := br.GetRangesInfo(br.PlacementType, len(s.Stores()), nil, []float64{})
+	br.LoadRangeInfo(s, rangesInfo)
 	return s
 }
 

pkg/kv/kvserver/asim/state/new_state.go

@@ -12,6 +12,7 @@ package state
 
 import (
 	"fmt"
+	"math/rand"
 	"sort"
 
 	"github.com/cockroachdb/cockroach/pkg/kv/kvserver/asim/config"
@@ -65,6 +66,79 @@ func exactDistribution(counts []int) []float64 {
 	return distribution
 }
 
+// weighted struct handles weighted random index selection from an input array,
+// weightedStores.
+//
+// For example, consider input weightedStores = [0.1, 0.2, 0.7].
+// - newWeighted constructs cumulative weighs, creating cumulativeWeighted [0.1,
+// 0.3, 1.0].
+// - rand function then randomly selects a number n within the range of [0.0,
+// 1.0) and finds which bucket ([0.0, 0.1], (0.1, 0.3], (0.3, 1.0]) n falls
+// under. It finds the smallest index within cumulativeWeights that >= n. Thus,
+// indices with greater weights have a higher probability of being selected as
+// they cover larger cumulative weights range. For instance, if it selects 0.5,
+// Rand would return index 2 since 0.7 is the smallest index that is >= 0.5.
+type weighted struct {
+	cumulativeWeights []float64
+}
+
+// newWeighted constructs cumulative weights that are used later to select a
+// single random index from weightedStores based on the associated weights.
+func newWeighted(weightedStores []float64) weighted {
+	cumulativeWeights := make([]float64, len(weightedStores))
+	prefixSumWeight := float64(0)
+	for i, item := range weightedStores {
+		prefixSumWeight += item
+		cumulativeWeights[i] = prefixSumWeight
+	}
+	if cumulativeWeights[len(weightedStores)-1] != float64(1) {
+		panic(fmt.Sprintf("total cumulative weights for all stores should sum up to one but got %.2f\n",
+			cumulativeWeights[len(weightedStores)-1]))
+	}
+	return weighted{cumulativeWeights: cumulativeWeights}
+}
+
+// rand randomly picks an index from weightedStores based on the associated
+// weights.
+func (w weighted) rand(randSource *rand.Rand) int {
+	r := randSource.Float64()
+	index := sort.Search(len(w.cumulativeWeights), func(i int) bool { return w.cumulativeWeights[i] >= r })
+	return index
+}
+
+// weightedRandDistribution generates a weighted random distribution across
+// stores. It achieves this by randomly selecting an index from weightedStores
+// 10 times while considering the weights, and repeating this process ten times.
+// The output is a weighted random distribution reflecting the selections made.
+func weightedRandDistribution(randSource *rand.Rand, weightedStores []float64) []float64 {
+	w := newWeighted(weightedStores)
+	numSamples := 10
+	votes := make([]int, len(weightedStores))
+	for i := 0; i < numSamples; i++ {
+		index := w.rand(randSource)
+		votes[index] += 1
+	}
+	return exactDistribution(votes)
+}
+
+// randDistribution generates a random distribution across stores. It achieves
+// this by creating an array of size n, selecting random numbers from [0, 10)
+// for each index, and returning the exact distribution of this result.
+func randDistribution(randSource *rand.Rand, n int) []float64 {
+	total := float64(0)
+	distribution := make([]float64, n)
+	for i := 0; i < n; i++ {
+		num := float64(randSource.Intn(10))
+		distribution[i] = num
+		total += num
+	}
+
+	for i := 0; i < n; i++ {
+		distribution[i] = distribution[i] / total
+	}
+	return distribution
+}
+
 // RangesInfoWithDistribution returns a RangesInfo, where the stores given are
 // initialized with the specified % of the replicas. This is done on a best
 // effort basis, given the replication factor. It may be impossible to satisfy
@@ -250,6 +324,61 @@ func RangesInfoEvenDistribution(
 		int64(MinKey), int64(keyspace), rangeSize)
 }
 
+// RangesInfoWeightedRandDistribution returns a RangesInfo, where ranges are
+// generated with a weighted random distribution across stores.
+func RangesInfoWeightedRandDistribution(
+	randSource *rand.Rand,
+	weightedStores []float64,
+	ranges int,
+	keyspace int,
+	replicationFactor int,
+	rangeSize int64,
+) RangesInfo {
+	if randSource == nil || len(weightedStores) == 0 {
+		panic("randSource cannot be nil and weightedStores must be non-empty in order to generate weighted random range info")
+	}
+	distribution := weightedRandDistribution(randSource, weightedStores)
+	storeList := makeStoreList(len(weightedStores))
+	spanConfig := defaultSpanConfig
+	spanConfig.NumReplicas = int32(replicationFactor)
+	spanConfig.NumVoters = int32(replicationFactor)
+	return RangesInfoWithDistribution(
+		storeList,
+		distribution,
+		distribution,
+		ranges,
+		spanConfig,
+		int64(MinKey),
+		int64(keyspace),
+		rangeSize, /* rangeSize */
+	)
+}
+
+// RangesInfoRandDistribution returns a RangesInfo, where ranges are generated
+// with a random distribution across stores.
+func RangesInfoRandDistribution(
+	randSource *rand.Rand,
+	stores int,
+	ranges int,
+	keyspace int,
+	replicationFactor int,
+	rangeSize int64,
+) RangesInfo {
+	if randSource == nil {
+		panic("randSource cannot be nil in order to generate random range info")
+	}
+	distribution := randDistribution(randSource, stores)
+	storeList := makeStoreList(stores)
+
+	spanConfig := defaultSpanConfig
+	spanConfig.NumReplicas = int32(replicationFactor)
+	spanConfig.NumVoters = int32(replicationFactor)
+
+	return RangesInfoWithDistribution(
+		storeList, distribution, distribution, ranges, spanConfig,
+		int64(MinKey), int64(keyspace), rangeSize)
+}
+
 // NewStateWithDistribution returns a State where the stores given are
 // initialized with the specified % of the replicas. This is done on a best
 // effort basis, given the replication factor. It may be impossible to satisfy
@@ -320,3 +449,35 @@ func NewStateSkewedDistribution(
 	rangesInfo := RangesInfoSkewedDistribution(stores, ranges, keyspace, replicationFactor, 0 /* rangeSize */)
 	return LoadConfig(clusterInfo, rangesInfo, settings)
 }
+
+// NewStateRandDistribution returns a new State where the replica count per
+// store is randomized.
+func NewStateRandDistribution(
+	seed int64,
+	stores int,
+	ranges int,
+	keyspace int,
+	replicationFactor int,
+	settings *config.SimulationSettings,
+) State {
+	randSource := rand.New(rand.NewSource(seed))
+	clusterInfo := ClusterInfoWithStoreCount(stores, 1 /* storesPerNode */)
+	rangesInfo := RangesInfoRandDistribution(randSource, stores, ranges, keyspace, replicationFactor, 0 /* rangeSize */)
+	return LoadConfig(clusterInfo, rangesInfo, settings)
+}
+
+// NewStateWeightedRandDistribution returns a new State where the replica count
+// per store is weighted randomized based on weightedStores.
+func NewStateWeightedRandDistribution(
+	seed int64,
+	weightedStores []float64,
+	ranges int,
+	keyspace int,
+	replicationFactor int,
+	settings *config.SimulationSettings,
+) State {
+	randSource := rand.New(rand.NewSource(seed))
+	clusterInfo := ClusterInfoWithStoreCount(len(weightedStores), 1 /* storesPerNode */)
+	rangesInfo := RangesInfoWeightedRandDistribution(randSource, weightedStores, ranges, keyspace, replicationFactor, 0 /* rangeSize */)
+	return LoadConfig(clusterInfo, rangesInfo, settings)
+}

pkg/kv/kvserver/asim/state/state_test.go

@@ -384,13 +384,20 @@ func TestOrderedStateLists(t *testing.T) {
 	// Test a skewed distribution with 100 stores, 10k ranges and 1m keyspace.
 	s = NewStateSkewedDistribution(100, 10000, 3, 1000000, settings)
 	assertListsOrdered(s)
+
+	const defaultSeed = 42
+	s = NewStateRandDistribution(defaultSeed, 7, 1400, 10000, 3, settings)
+	assertListsOrdered(s)
+
+	s = NewStateWeightedRandDistribution(defaultSeed, []float64{0.0, 0.1, 0.3, 0.6}, 1400, 10000, 3, settings)
+	assertListsOrdered(s)
 }
 
 // TestNewStateDeterministic asserts that the state returned from the new state
 // utility functions is deterministic.
 func TestNewStateDeterministic(t *testing.T) {
 	settings := config.DefaultSimulationSettings()
-
+	const defaultSeed = 42
 	testCases := []struct {
 		desc       string
 		newStateFn func() State
@@ -409,6 +416,18 @@ func TestNewStateDeterministic(t *testing.T) {
 				return NewStateWithDistribution([]float64{0.2, 0.2, 0.2, 0.2, 0.2}, 5, 3, 10000, settings)
 			},
 		},
+		{
+			desc: "rand distribution ",
+			newStateFn: func() State {
+				return NewStateRandDistribution(defaultSeed, 7, 1400, 10000, 3, settings)
+			},
+		},
+		{
+			desc: "weighted rand distribution ",
+			newStateFn: func() State {
+				return NewStateWeightedRandDistribution(defaultSeed, []float64{0.0, 0.1, 0.3, 0.6}, 1400, 10000, 3, settings)
+			},
+		},
 	}
 
 	for _, tc := range testCases {
@@ -421,6 +440,35 @@ func TestNewStateDeterministic(t *testing.T) {
 	}
 }
 
+// TestRandDistribution asserts that the distribution returned from
+// randDistribution and weightedRandDistribution sum up to 1.
+func TestRandDistribution(t *testing.T) {
+	const defaultSeed = 42
+	randSource := rand.New(rand.NewSource(defaultSeed))
+	testCases := []struct {
+		desc         string
+		distribution []float64
+	}{
+		{
+			desc:         "random distribution",
+			distribution: randDistribution(randSource, 7),
+		},
+		{
+			desc:         "weighted random distribution",
+			distribution: weightedRandDistribution(randSource, []float64{0.0, 0.1, 0.3, 0.6}),
+		},
+	}
+	for _, tc := range testCases {
+		t.Run(tc.desc, func(t *testing.T) {
+			total := float64(0)
+			for i := 0; i < len(tc.distribution); i++ {
+				total += tc.distribution[i]
+			}
+			require.Equal(t, float64(1), total)
+		})
+	}
+}
+
 // TestSplitRangeDeterministic asserts that range splits are deterministic.
 func TestSplitRangeDeterministic(t *testing.T) {
 	settings := config.DefaultSimulationSettings()

pkg/kv/kvserver/asim/tests/default_settings.go

@@ -108,3 +108,24 @@ func defaultPlotSettings() plotSettings {
 		width:  defaultWidth,
 	}
 }
+
+type rangeGenSettings struct {
+	rangeKeyGenType generatorType
+	keySpaceGenType generatorType
+	weightedRand    []float64
+}
+
+const (
+	defaultRangeKeyGenType = uniformGenerator
+	defaultKeySpaceGenType = uniformGenerator
+)
+
+var defaultWeightedRand []float64
+
+func defaultRangeGenSettings() rangeGenSettings {
+	return rangeGenSettings{
+		rangeKeyGenType: defaultRangeKeyGenType,
+		keySpaceGenType: defaultKeySpaceGenType,
+		weightedRand:    defaultWeightedRand,
+	}
+}