// Copyright (c) Tailscale Inc & contributors
// SPDX-License-Identifier: BSD-3-Clause

// Package topk defines a count-min sketch and a cheap probabilistic top-K data
// structure that uses the count-min sketch to track the top K items in
// constant memory and O(log(k)) time.
package topk

import (
	"container/heap"
	"hash/maphash"
	"math"
	"slices"
	"sync"
)

// TopK is a probabilistic counter of the top K items, using a count-min sketch
// to keep track of item counts and a heap to track the top K of them.
type TopK[T any] struct {
	heap minHeap[T]
	k    int
	sf   SerializeFunc[T]
	cms  CountMinSketch
}

// HashFunc is responsible for providing a []byte serialization of a value,
// appended to the provided byte slice. This is used for hashing the value when
// adding to a CountMinSketch.
type SerializeFunc[T any] func([]byte, T) []byte

// New creates a new TopK that stores k values. Parameters for the underlying
// count-min sketch are chosen for a 0.1% error rate and a 0.1% probability of
// error.
func New[T any](k int, sf SerializeFunc[T]) *TopK[T] {
	hashes, buckets := PickParams(0.001, 0.001)
	return NewWithParams(k, sf, hashes, buckets)
}

// NewWithParams creates a new TopK that stores k values, and additionally
// allows customizing the parameters for the underlying count-min sketch.
func NewWithParams[T any](k int, sf SerializeFunc[T], numHashes, numCols int) *TopK[T] {
	ret := &TopK[T]{
		heap: make(minHeap[T], 0, k),
		k:    k,
		sf:   sf,
	}
	ret.cms.init(numHashes, numCols)
	return ret
}

// Add calls AddN(val, 1).
func (tk *TopK[T]) Add(val T) uint64 {
	return tk.AddN(val, 1)
}

var hashPool = &sync.Pool{
	New: func() any {
		buf := make([]byte, 0, 128)
		return &buf
	},
}

// AddN adds the given item to the set with the provided count, returning the
// new estimated count.
func (tk *TopK[T]) AddN(val T, count uint64) uint64 {
	buf := hashPool.Get().(*[]byte)
	defer hashPool.Put(buf)
	ser := tk.sf((*buf)[:0], val)

	vcount := tk.cms.AddN(ser, count)

	// If we don't have a full heap, just push it.
	if len(tk.heap) < tk.k {
		heap.Push(&tk.heap, mhValue[T]{
			count: vcount,
			val:   val,
		})
		return vcount
	}

	// If this item's count surpasses the heap's minimum, update the heap.
	if vcount > tk.heap[0].count {
		tk.heap[0] = mhValue[T]{
			count: vcount,
			val:   val,
		}
		heap.Fix(&tk.heap, 0)
	}
	return vcount
}

// Top returns the estimated top K items as stored by this TopK.
func (tk *TopK[T]) Top() []T {
	ret := make([]T, 0, tk.k)
	for _, item := range tk.heap {
		ret = append(ret, item.val)
	}
	return ret
}

// AppendTop appends the estimated top K items as stored by this TopK to the
// provided slice, allocating only if the slice does not have enough capacity
// to store all items. The provided slice can be nil.
func (tk *TopK[T]) AppendTop(sl []T) []T {
	sl = slices.Grow(sl, tk.k)
	for _, item := range tk.heap {
		sl = append(sl, item.val)
	}
	return sl
}

// CountMinSketch implements a count-min sketch, a probabilistic data structure
// that tracks the frequency of events in a stream of data.
//
// See: https://en.wikipedia.org/wiki/Count%E2%80%93min_sketch
type CountMinSketch struct {
	hashes   []maphash.Seed
	nbuckets int
	matrix   []uint64
}

// NewCountMinSketch creates a new CountMinSketch with the provided number of
// hashes and buckets. Hashes and buckets are often called "depth" and "width",
// or "d" and "w", respectively.
func NewCountMinSketch(hashes, buckets int) *CountMinSketch {
	ret := &CountMinSketch{}
	ret.init(hashes, buckets)
	return ret
}

// PickParams provides good parameters for 'hashes' and 'buckets' when
// constructing a CountMinSketch, given an estimated total number of counts
// (i.e. the sum of all counts ever stored), the error factor ϵ as a float
// (e.g. 1% is 0.001), and the probability factor δ.
//
// Parameters are chosen such that with a probability of 1−δ, the error is at
// most ϵ∗totalCount. Or, in other words: if N is the true count of an event,
// E is the estimate given by a sketch and T the total count of items in the
// sketch, E ≤ N + T*ϵ with probability (1 - δ).
func PickParams(err, probability float64) (hashes, buckets int) {
	d := math.Ceil(math.Log(1 / probability))
	w := math.Ceil(math.E / err)

	return int(d), int(w)
}

func (cms *CountMinSketch) init(hashes, buckets int) {
	for range hashes {
		cms.hashes = append(cms.hashes, maphash.MakeSeed())
	}

	// Need a matrix of hashes * buckets to store counts
	cms.nbuckets = buckets
	cms.matrix = make([]uint64, hashes*buckets)
}

// Add calls AddN(val, 1).
func (cms *CountMinSketch) Add(val []byte) uint64 {
	return cms.AddN(val, 1)
}

// AddN increments the count for the given value by the provided count,
// returning the new count.
func (cms *CountMinSketch) AddN(val []byte, count uint64) uint64 {
	var (
		mh  maphash.Hash
		ret uint64 = math.MaxUint64
	)
	for i, seed := range cms.hashes {
		mh.SetSeed(seed)

		// Generate a hash for this value using Lemire's alternative to modular reduction:
		//    https://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/
		mh.Write(val)
		hash := mh.Sum64()
		hash = multiplyHigh64(hash, uint64(cms.nbuckets))

		// The index in our matrix is (i * buckets) to move "down" i
		// rows in our matrix to the row for this hash, plus 'hash' to
		// move inside this row.
		idx := (i * cms.nbuckets) + int(hash)

		// Add to this row
		cms.matrix[idx] += count
		ret = min(ret, cms.matrix[idx])
	}
	return ret
}

// Get returns the count for the provided value.
func (cms *CountMinSketch) Get(val []byte) uint64 {
	var (
		mh  maphash.Hash
		ret uint64 = math.MaxUint64
	)
	for i, seed := range cms.hashes {
		mh.SetSeed(seed)

		// Generate a hash for this value using Lemire's alternative to modular reduction:
		//    https://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/
		mh.Write(val)
		hash := mh.Sum64()
		hash = multiplyHigh64(hash, uint64(cms.nbuckets))

		// The index in our matrix is (i * buckets) to move "down" i
		// rows in our matrix to the row for this hash, plus 'hash' to
		// move inside this row.
		idx := (i * cms.nbuckets) + int(hash)

		// Select the minimal value among all rows
		ret = min(ret, cms.matrix[idx])
	}
	return ret
}

// multiplyHigh64 implements (x * y) >> 64 "the long way" without access to a
// 128-bit type. This function is adapted from something similar in Tensorflow:
//
//	https://github.com/tensorflow/tensorflow/commit/a47a300185026fe7829990def9113bf3a5109fed
//
// TODO(andrew-d): this could be replaced with a single "MULX" instruction on
// x86_64 platforms, which we can do if this ever turns out to be a performance
// bottleneck.
func multiplyHigh64(x, y uint64) uint64 {
	x_lo := x & 0xffffffff
	x_hi := x >> 32
	buckets_lo := y & 0xffffffff
	buckets_hi := y >> 32
	prod_hi := x_hi * buckets_hi
	prod_lo := x_lo * buckets_lo
	prod_mid1 := x_hi * buckets_lo
	prod_mid2 := x_lo * buckets_hi
	carry := ((prod_mid1 & 0xffffffff) + (prod_mid2 & 0xffffffff) + (prod_lo >> 32)) >> 32
	return prod_hi + (prod_mid1 >> 32) + (prod_mid2 >> 32) + carry
}

type mhValue[T any] struct {
	count uint64
	val   T
}

// An minHeap is a min-heap of ints and associated values.
type minHeap[T any] []mhValue[T]

func (h minHeap[T]) Len() int           { return len(h) }
func (h minHeap[T]) Less(i, j int) bool { return h[i].count < h[j].count }
func (h minHeap[T]) Swap(i, j int)      { h[i], h[j] = h[j], h[i] }

func (h *minHeap[T]) Push(x any) {
	// Push and Pop use pointer receivers because they modify the slice's length,
	// not just its contents.
	*h = append(*h, x.(mhValue[T]))
}

func (h *minHeap[T]) Pop() any {
	old := *h
	n := len(old)
	x := old[n-1]
	*h = old[0 : n-1]
	return x
}
