traces/vendor/github.com/twmb/franz-go/pkg/kgo/partitioner.go

package kgo

import (
	"math"
	"math/rand"
	"time"

	"github.com/twmb/franz-go/pkg/kbin"
)

// Partitioner creates topic partitioners to determine which partition messages
// should be sent to.
//
// Note that a record struct is unmodified (minus a potential default topic)
// from producing through partitioning, so you can set fields in the record
// struct before producing to aid in partitioning with a custom partitioner.
type Partitioner interface {
	// forTopic returns a partitioner for an individual topic. It is
	// guaranteed that only one record will use the an individual topic's
	// topicPartitioner at a time, meaning partitioning within a topic does
	// not require locks.
	ForTopic(string) TopicPartitioner
}

// TopicPartitioner partitions records in an individual topic.
type TopicPartitioner interface {
	// RequiresConsistency returns true if a record must hash to the same
	// partition even if a partition is down.
	// If true, a record may hash to a partition that cannot be written to
	// and will error until the partition comes back.
	RequiresConsistency(*Record) bool
	// Partition determines, among a set of n partitions, which index should
	// be chosen to use for the partition for r.
	Partition(r *Record, n int) int
}

// TopicPartitionerOnNewBatch is an optional extension interface to
// TopicPartitioner that calls OnNewBatch before any new batch is created. If
// buffering a record would cause a new batch, OnNewBatch is called.
//
// This interface allows for partitioner implementations that effectively pin
// to a partition until a new batch is created, after which the partitioner can
// choose which next partition to use.
type TopicPartitionerOnNewBatch interface {
	// OnNewBatch is called when producing a record if that record would
	// trigger a new batch on its current partition.
	OnNewBatch()
}

// TopicBackupPartitioner is an optional extension interface to
// TopicPartitioner that can partition by the number of records buffered.
//
// If a partitioner implements this interface, the Partition function will
// never be called.
type TopicBackupPartitioner interface {
	TopicPartitioner

	// PartitionByBackup is similar to Partition, but has an additional
	// backupIter. This iterator will return the number of buffered records
	// per partition index. The iterator's Next function can only be called
	// up to n times, calling it any more will panic.
	PartitionByBackup(r *Record, n int, backupIter TopicBackupIter) int
}

// TopicBackupIter is an iterates through partition indices.
type TopicBackupIter interface {
	// Next returns the next partition index and the total buffered records
	// for the partition. If Rem returns 0, calling this function again
	// will panic.
	Next() (int, int64)
	// Rem returns the number of elements left to iterate through.
	Rem() int
}

////////////
// SIMPLE // - BasicConsistent, Manual, RoundRobin
////////////

// BasicConsistentPartitioner wraps a single function to provide a Partitioner
// and TopicPartitioner (that function is essentially a combination of
// Partitioner.ForTopic and TopicPartitioner.Partition).
//
// As a minimal example, if you do not care about the topic and you set the
// partition before producing:
//
//	kgo.BasicConsistentPartitioner(func(topic) func(*Record, int) int {
//	        return func(r *Record, n int) int {
//	                return int(r.Partition)
//	        }
//	})
func BasicConsistentPartitioner(partition func(string) func(r *Record, n int) int) Partitioner {
	return &basicPartitioner{partition}
}

type (
	basicPartitioner struct {
		fn func(string) func(*Record, int) int
	}

	basicTopicPartitioner struct {
		fn func(*Record, int) int
	}
)

func (b *basicPartitioner) ForTopic(t string) TopicPartitioner {
	return &basicTopicPartitioner{b.fn(t)}
}

func (*basicTopicPartitioner) RequiresConsistency(*Record) bool { return true }
func (b *basicTopicPartitioner) Partition(r *Record, n int) int { return b.fn(r, n) }

// ManualPartitioner is a partitioner that simply returns the Partition field
// that is already set on any record.
//
// Any record with an invalid partition will be immediately failed. This
// partitioner is simply the partitioner that is demonstrated in the
// BasicConsistentPartitioner documentation.
func ManualPartitioner() Partitioner {
	return BasicConsistentPartitioner(func(string) func(*Record, int) int {
		return func(r *Record, _ int) int {
			return int(r.Partition)
		}
	})
}

// RoundRobinPartitioner is a partitioner that round-robin's through all
// available partitions. This algorithm has lower throughput and causes higher
// CPU load on brokers, but can be useful if you want to ensure an even
// distribution of records to partitions.
func RoundRobinPartitioner() Partitioner {
	return new(roundRobinPartitioner)
}

type (
	roundRobinPartitioner struct{}

	roundRobinTopicPartitioner struct {
		on int
	}
)

func (*roundRobinPartitioner) ForTopic(string) TopicPartitioner {
	return new(roundRobinTopicPartitioner)
}

func (*roundRobinTopicPartitioner) RequiresConsistency(*Record) bool { return false }
func (r *roundRobinTopicPartitioner) Partition(_ *Record, n int) int {
	if r.on >= n {
		r.on = 0
	}
	ret := r.on
	r.on++
	return ret
}

//////////////////
// LEAST BACKUP //
//////////////////

// LeastBackupPartitioner prioritizes partitioning by three factors, in order:
//
//  1. pin to the current pick until there is a new batch
//  2. on new batch, choose the least backed up partition (the partition with
//     the fewest amount of buffered records)
//  3. if multiple partitions are equally least-backed-up, choose one at random
//
// This algorithm prioritizes least-backed-up throughput, which may result in
// unequal partitioning. It is likely that this algorithm will talk most to the
// broker that it has the best connection to.
//
// This algorithm is resilient to brokers going down: if a few brokers die, it
// is possible your throughput will be so high that the maximum buffered
// records will be reached in the now-offline partitions before metadata
// responds that the broker is offline. With the standard partitioning
// algorithms, the only recovery is if the partition is remapped or if the
// broker comes back online. With the least backup partitioner, downed
// partitions will see slight backup, but then the other partitions that are
// still accepting writes will get all of the writes and your client will not
// be blocked.
//
// Under ideal scenarios (no broker / connection issues), StickyPartitioner is
// equivalent to LeastBackupPartitioner. This partitioner is only recommended
// if you are a producer consistently dealing with flaky connections or
// problematic brokers and do not mind uneven load on your brokers.
func LeastBackupPartitioner() Partitioner {
	return new(leastBackupPartitioner)
}

type (
	leastBackupInput struct{ mapping []*topicPartition }

	leastBackupPartitioner struct{}

	leastBackupTopicPartitioner struct {
		onPart int
		rng    *rand.Rand
	}
)

func (i *leastBackupInput) Next() (int, int64) {
	last := len(i.mapping) - 1
	buffered := i.mapping[last].records.buffered.Load()
	i.mapping = i.mapping[:last]
	return last, buffered
}

func (i *leastBackupInput) Rem() int {
	return len(i.mapping)
}

func (*leastBackupPartitioner) ForTopic(string) TopicPartitioner {
	return &leastBackupTopicPartitioner{
		onPart: -1,
		rng:    rand.New(rand.NewSource(time.Now().UnixNano())),
	}
}

func (p *leastBackupTopicPartitioner) OnNewBatch()                    { p.onPart = -1 }
func (*leastBackupTopicPartitioner) RequiresConsistency(*Record) bool { return false }
func (*leastBackupTopicPartitioner) Partition(*Record, int) int       { panic("unreachable") }

func (p *leastBackupTopicPartitioner) PartitionByBackup(_ *Record, n int, backup TopicBackupIter) int {
	if p.onPart == -1 || p.onPart >= n {
		leastBackup := int64(math.MaxInt64)
		npicked := 0
		for ; n > 0; n-- {
			pick, backup := backup.Next()
			if backup < leastBackup {
				leastBackup = backup
				p.onPart = pick
				npicked = 1
			} else {
				npicked++ // reservoir sampling with k = 1
				if p.rng.Intn(npicked) == 0 {
					p.onPart = pick
				}
			}
		}
	}
	return p.onPart
}

///////////////////
// UNIFORM BYTES //
///////////////////

// UniformBytesPartitioner is a redux of the StickyPartitioner, proposed in
// KIP-794 and release with the Java client in Kafka 3.3. This partitioner
// returns the same partition until 'bytes' is hit. At that point, a
// re-partitioning happens. If adaptive is false, this chooses a new random
// partition, otherwise this chooses a broker based on the inverse of the
// backlog currently buffered for that broker. If keys is true, this uses
// standard hashing based on record key for records with non-nil keys. hasher
// is optional; if nil, the default hasher murmur2 (Kafka's default).
//
// The point of this hasher is to create larger batches while producing the
// same amount to all partitions over the long run. Adaptive opts in to a
// slight imbalance so that this can produce more to brokers that are less
// loaded.
//
// This implementation differs slightly from Kafka's because this does not
// account for the compressed size of a batch, nor batch overhead. For
// overhead, in practice, the overhead is relatively constant so it would
// affect all batches equally. For compression, this client does not compress
// until after a batch is created and frozen, so it is not possible to track
// compression. This client also uses the number of records for backup
// calculation rather than number of bytes, but the heuristic should be
// similar. Lastly, this client does not have a timeout for partition
// availability. Realistically, these will be the most backed up partitions so
// they should be chosen the least.
//
// NOTE: This implementation may create sub-optimal batches if lingering is
// enabled. This client's default is to disable lingering. The patch used to
// address this in Kafka is KAFKA-14156 (which itself is not perfect in the
// context of disabling lingering). For more details, read KAFKA-14156.
func UniformBytesPartitioner(bytes int, adaptive, keys bool, hasher PartitionerHasher) Partitioner {
	if hasher == nil {
		hasher = KafkaHasher(murmur2)
	}
	return &uniformBytesPartitioner{
		bytes,
		adaptive,
		keys,
		hasher,
	}
}

type (
	uniformBytesPartitioner struct {
		bytes    int
		adaptive bool
		keys     bool
		hasher   PartitionerHasher
	}

	uniformBytesTopicPartitioner struct {
		u      uniformBytesPartitioner
		bytes  int
		onPart int
		rng    *rand.Rand

		calc []struct {
			f float64
			n int
		}
	}
)

func (u *uniformBytesPartitioner) ForTopic(string) TopicPartitioner {
	return &uniformBytesTopicPartitioner{
		u:      *u,
		onPart: -1,
		rng:    rand.New(rand.NewSource(time.Now().UnixNano())),
	}
}

func (p *uniformBytesTopicPartitioner) RequiresConsistency(r *Record) bool {
	return p.u.keys && r.Key != nil
}
func (*uniformBytesTopicPartitioner) Partition(*Record, int) int { panic("unreachable") }

func (p *uniformBytesTopicPartitioner) PartitionByBackup(r *Record, n int, backup TopicBackupIter) int {
	if p.u.keys && r.Key != nil {
		return p.u.hasher(r.Key, n)
	}

	l := 1 + // attributes, int8 unused
		1 + // ts delta, 1 minimum (likely 2 or 3)
		1 + // offset delta, likely 1
		kbin.VarintLen(int32(len(r.Key))) +
		len(r.Key) +
		kbin.VarintLen(int32(len(r.Value))) +
		len(r.Value) +
		kbin.VarintLen(int32(len(r.Headers))) // varint array len headers

	for _, h := range r.Headers {
		l += kbin.VarintLen(int32(len(h.Key))) +
			len(h.Key) +
			kbin.VarintLen(int32(len(h.Value))) +
			len(h.Value)
	}

	p.bytes += l
	if p.bytes >= p.u.bytes {
		p.bytes = l
		p.onPart = -1
	}

	if p.onPart >= 0 && p.onPart < n {
		return p.onPart
	}

	if !p.u.adaptive {
		p.onPart = p.rng.Intn(n)
	} else {
		p.calc = p.calc[:0]

		// For adaptive, the logic is that we pick by broker according
		// to the inverse of the queue size. Presumably this means
		// bytes, but we use records for simplicity.
		//
		// We calculate 1/recs for all brokers and choose the first one
		// in this ordering that takes us negative.
		//
		// e.g., 1/1 + 1/3; pick is 0.2; 0.2*1.3333 = 0.26666; minus 1
		// is negative, meaning our pick is the first. If rng was 0.9,
		// scaled is 1.2, meaning our pick is the second (-1, still
		// positive, second pick takes us negative).
		//
		// To guard floating rounding problems, if we pick nothing,
		// then this means we pick our last.
		var t float64
		for ; n > 0; n-- {
			n, backup := backup.Next()
			backup++ // ensure non-zero
			f := 1 / float64(backup)
			t += f
			p.calc = append(p.calc, struct {
				f float64
				n int
			}{f, n})
		}
		r := p.rng.Float64()
		pick := r * t
		for _, c := range p.calc {
			pick -= c.f
			if pick <= 0 {
				p.onPart = c.n
				break
			}
		}
		if p.onPart == -1 {
			p.onPart = p.calc[len(p.calc)-1].n
		}
	}
	return p.onPart
}

/////////////////////
// STICKY & COMPAT // - Sticky, Kafka (custom hash), Sarama (custom hash)
/////////////////////

// StickyPartitioner is the same as StickyKeyPartitioner, but with no logic to
// consistently hash keys. That is, this only partitions according to the
// sticky partition strategy.
func StickyPartitioner() Partitioner {
	return new(stickyPartitioner)
}

type (
	stickyPartitioner struct{}

	stickyTopicPartitioner struct {
		lastPart int
		onPart   int
		rng      *rand.Rand
	}
)

func (*stickyPartitioner) ForTopic(string) TopicPartitioner {
	p := newStickyTopicPartitioner()
	return &p
}

func newStickyTopicPartitioner() stickyTopicPartitioner {
	return stickyTopicPartitioner{
		lastPart: -1,
		onPart:   -1,
		rng:      rand.New(rand.NewSource(time.Now().UnixNano())),
	}
}

func (p *stickyTopicPartitioner) OnNewBatch()                    { p.lastPart, p.onPart = p.onPart, -1 }
func (*stickyTopicPartitioner) RequiresConsistency(*Record) bool { return false }
func (p *stickyTopicPartitioner) Partition(_ *Record, n int) int {
	if p.onPart == -1 || p.onPart >= n {
		p.onPart = p.rng.Intn(n)
		if p.onPart == p.lastPart {
			p.onPart = (p.onPart + 1) % n
		}
	}
	return p.onPart
}

// StickyKeyPartitioner mirrors the default Java partitioner from Kafka's 2.4
// release (see KIP-480 and KAFKA-8601) until their 3.3 release. This was
// replaced in 3.3 with the uniform sticky partitioner (KIP-794), which is
// reimplemented in this client as the UniformBytesPartitioner.
//
// This is the same "hash the key consistently, if no key, choose random
// partition" strategy that the Java partitioner has always used, but rather
// than always choosing a random partition, the partitioner pins a partition to
// produce to until that partition rolls over to a new batch. Only when rolling
// to new batches does this partitioner switch partitions.
//
// The benefit with this pinning is less CPU utilization on Kafka brokers.
// Over time, the random distribution is the same, but the brokers are handling
// on average larger batches.
//
// hasher is optional; if nil, this will return a partitioner that partitions
// exactly how Kafka does. Specifically, the partitioner will use murmur2 to
// hash keys, will mask out the 32nd bit, and then will mod by the number of
// potential partitions.
func StickyKeyPartitioner(hasher PartitionerHasher) Partitioner {
	if hasher == nil {
		hasher = KafkaHasher(murmur2)
	}
	return &keyPartitioner{hasher}
}

// PartitionerHasher returns a partition to use given the input data and number
// of partitions.
type PartitionerHasher func([]byte, int) int

// KafkaHasher returns a PartitionerHasher using hashFn that mirrors how Kafka
// partitions after hashing data. In Kafka, after hashing into a uint32, the
// hash is converted to an int32 and the high bit is stripped. Kafka by default
// uses murmur2 hashing, and the StickyKeyPartiitoner uses this by default.
// Using this KafkaHasher function is only necessary if you want to change the
// underlying hashing algorithm.
func KafkaHasher(hashFn func([]byte) uint32) PartitionerHasher {
	return func(key []byte, n int) int {
		// https://github.com/apache/kafka/blob/d91a94e/clients/src/main/java/org/apache/kafka/clients/producer/internals/DefaultPartitioner.java#L59
		// https://github.com/apache/kafka/blob/d91a94e/clients/src/main/java/org/apache/kafka/common/utils/Utils.java#L865-L867
		// Masking before or after the int conversion makes no difference.
		return int(hashFn(key)&0x7fffffff) % n
	}
}

// SaramaHasher is a historical misnamed partitioner. This library's original
// implementation of the SaramaHasher was incorrect, if you want an exact
// match for the Sarama partitioner, use the [SaramaCompatHasher].
//
// This partitioner remains because as it turns out, other ecosystems provide
// a similar partitioner and this partitioner is useful for compatibility.
//
// In particular, using this function with a crc32.ChecksumIEEE hasher makes
// this partitioner match librdkafka's consistent partitioner, or the
// zendesk/ruby-kafka partitioner.
func SaramaHasher(hashFn func([]byte) uint32) PartitionerHasher {
	return func(key []byte, n int) int {
		p := int(hashFn(key)) % n
		if p < 0 {
			p = -p
		}
		return p
	}
}

// SaramaCompatHasher returns a PartitionerHasher using hashFn that mirrors how
// Sarama partitions after hashing data.
//
// Sarama has two differences from Kafka when partitioning:
//
// 1) In Kafka, when converting the uint32 hash to an int32, Kafka masks the
// high bit. In Sarama, if the high bit is 1 (i.e., the number as an int32 is
// negative), Sarama negates the number.
//
// 2) Kafka by default uses the murmur2 hashing algorithm. Sarama by default
// uses fnv-1a.
//
// Sarama added a NewReferenceHashPartitioner function that attempted to align
// with Kafka, but the reference partitioner only fixed the first difference,
// not the second. Further customization options were added later that made it
// possible to exactly match Kafka when hashing.
//
// In short, to *exactly* match the Sarama defaults, use the following:
//
//	kgo.StickyKeyPartitioner(kgo.SaramaCompatHasher(fnv32a))
//
// Where fnv32a is a function returning a new 32 bit fnv-1a hasher.
//
//	func fnv32a(b []byte) uint32 {
//		h := fnv.New32a()
//		h.Reset()
//		h.Write(b)
//		return h.Sum32()
//	}
func SaramaCompatHasher(hashFn func([]byte) uint32) PartitionerHasher {
	return func(key []byte, n int) int {
		p := int32(hashFn(key)) % int32(n)
		if p < 0 {
			p = -p
		}
		return int(p)
	}
}

type (
	keyPartitioner struct {
		hasher PartitionerHasher
	}

	stickyKeyTopicPartitioner struct {
		hasher PartitionerHasher
		stickyTopicPartitioner
	}
)

func (k *keyPartitioner) ForTopic(string) TopicPartitioner {
	return &stickyKeyTopicPartitioner{k.hasher, newStickyTopicPartitioner()}
}

func (*stickyKeyTopicPartitioner) RequiresConsistency(r *Record) bool { return r.Key != nil }
func (p *stickyKeyTopicPartitioner) Partition(r *Record, n int) int {
	if r.Key != nil {
		return p.hasher(r.Key, n)
	}
	return p.stickyTopicPartitioner.Partition(r, n)
}

/////////////
// MURMUR2 //
/////////////

// Straight from the C++ code and from the Java code duplicating it.
// https://github.com/apache/kafka/blob/d91a94e/clients/src/main/java/org/apache/kafka/common/utils/Utils.java#L383-L421
// https://github.com/aappleby/smhasher/blob/61a0530f/src/MurmurHash2.cpp#L37-L86
//
// The Java code uses ints but with unsigned shifts; we do not need to.
func murmur2(b []byte) uint32 {
	const (
		seed uint32 = 0x9747b28c
		m    uint32 = 0x5bd1e995
		r           = 24
	)
	h := seed ^ uint32(len(b))
	for len(b) >= 4 {
		k := uint32(b[3])<<24 + uint32(b[2])<<16 + uint32(b[1])<<8 + uint32(b[0])
		b = b[4:]
		k *= m
		k ^= k >> r
		k *= m

		h *= m
		h ^= k
	}
	switch len(b) {
	case 3:
		h ^= uint32(b[2]) << 16
		fallthrough
	case 2:
		h ^= uint32(b[1]) << 8
		fallthrough
	case 1:
		h ^= uint32(b[0])
		h *= m
	}

	h ^= h >> 13
	h *= m
	h ^= h >> 15
	return h
}