// Licensed to the LF AI & Data foundation under one // or more contributor license agreements. See the NOTICE file // distributed with this work for additional information // regarding copyright ownership. The ASF licenses this file // to you under the Apache License, Version 2.0 (the // "License"); you may not use this file except in compliance // with the License. You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. package storage import ( "container/heap" "io" "slices" "time" "github.com/apache/arrow/go/v17/arrow" "github.com/apache/arrow/go/v17/arrow/array" "github.com/milvus-io/milvus-proto/go-api/v3/schemapb" "github.com/milvus-io/milvus/pkg/v3/util/merr" ) // SortTimings holds phase-level timing information from the Sort function. type SortTimings struct { ReadCost time.Duration SortCost time.Duration WriteCost time.Duration NumBatches int NumRows int } // Sort materializes the records from rr, stable-selects the rows for which // predicate returns true, sorts them by sortByFieldIDs, and writes them out // through rw in batches of roughly batchSize bytes. // // Performance notes (vs. the naive row-at-a-time approach): // - The row selection is kept in a value slice ([]rowIndex) instead of a // []*rowIndex, avoiding one heap allocation per row. // - Sort keys are extracted into flat per-record slices once. A single int64 // key (the common PK case) is then sorted with an O(N) stable LSD radix // sort; other keys use slices.SortFunc over the flat keys (plain slice // indexing, no Column() map lookup per comparison). // - When writing the output, each source column's array is resolved once per // input record rather than once per row (RecordBuilder.Append would do the // latter); rows are then emitted in order and flushed once the accumulated // batch reaches batchSize bytes. func Sort(batchSize uint64, schema *schemapb.CollectionSchema, rr []RecordReader, rw RecordWriter, predicate func(r Record, ri, i int) bool, sortByFieldIDs []int64, ) (int, *SortTimings, error) { records := make([]Record, 0) indices := make([]rowIndex, 0) // release cgo records defer func() { for _, rec := range records { rec.Release() } }() phaseStart := time.Now() for _, r := range rr { for { rec, err := r.Next() if err == nil { rec.Retain() ri := len(records) records = append(records, rec) for i := 0; i < rec.Len(); i++ { if predicate(rec, ri, i) { indices = append(indices, rowIndex{int32(ri), int32(i)}) } } } else if err == io.EOF { break } else { return 0, nil, err } } } readCost := time.Since(phaseStart) if len(records) == 0 { return 0, &SortTimings{ReadCost: readCost}, nil } phaseStart = time.Now() if len(sortByFieldIDs) > 0 { // Pre-extract the sort key columns into flat per-record slices so the // comparator avoids a Column() map lookup + type assert per comparison. const ( keyInt64 = iota keyString ) kinds := make([]int, len(sortByFieldIDs)) int64Keys := make([][][]int64, len(sortByFieldIDs)) stringKeys := make([][][]string, len(sortByFieldIDs)) for fp, fid := range sortByFieldIDs { switch records[0].Column(fid).(type) { case *array.Int64: kinds[fp] = keyInt64 cols := make([][]int64, len(records)) for ri, rec := range records { cols[ri] = rec.Column(fid).(*array.Int64).Int64Values() } int64Keys[fp] = cols case *array.String: kinds[fp] = keyString cols := make([][]string, len(records)) for ri, rec := range records { a := rec.Column(fid).(*array.String) vals := make([]string, a.Len()) for i := range vals { vals[i] = a.Value(i) } cols[ri] = vals } stringKeys[fp] = cols default: return 0, nil, merr.WrapErrStorageMsg("unsupported type for sorting key") } } // A single int64 sort key (the common PK case) is sorted with a stable // LSD radix sort: O(N) instead of O(N log N) and no comparator calls. // Multi-field or varchar keys fall back to comparison sort. if len(sortByFieldIDs) == 1 && kinds[0] == keyInt64 { radixSortByInt64(indices, int64Keys[0]) } else { slices.SortFunc(indices, func(x, y rowIndex) int { for fp := range sortByFieldIDs { switch kinds[fp] { case keyInt64: xv, yv := int64Keys[fp][x.ri][x.i], int64Keys[fp][y.ri][y.i] if xv != yv { if xv < yv { return -1 } return 1 } case keyString: xv, yv := stringKeys[fp][x.ri][x.i], stringKeys[fp][y.ri][y.i] if xv != yv { if xv < yv { return -1 } return 1 } } } return 0 }) } } sortCost := time.Since(phaseStart) phaseStart = time.Now() rb := NewRecordBuilder(schema) // Resolve each output column's source array once per input record (instead // of once per row, as RecordBuilder.Append would). srcByField := make([][]arrow.Array, len(rb.builders)) defaults := make([]*schemapb.ValueField, len(rb.builders)) for fi := range rb.builders { fid := rb.fields[fi].FieldID cols := make([]arrow.Array, len(records)) for ri := range records { cols[ri] = records[ri].Column(fid) } srcByField[fi] = cols defaults[fi] = rb.fields[fi].GetDefaultValue() } writeRecord := func() error { rec := rb.Build() defer rec.Release() if rec.Len() > 0 { return rw.Write(rec) } return nil } for _, idx := range indices { for fi, builder := range rb.builders { size, err := appendValueAt(builder, srcByField[fi][idx.ri], int(idx.i), defaults[fi]) if err != nil { return 0, nil, merr.Wrapf(err, "failed to append value at row %d for field %s", idx.i, rb.fields[fi].GetName()) } rb.size += size } rb.nRows++ // Flush once the accumulated batch reaches batchSize bytes (exact, like // the original) so a single output record never exceeds the target. if rb.GetSize() >= batchSize { if err := writeRecord(); err != nil { return 0, nil, err } } } // write the last partial batch if err := writeRecord(); err != nil { return 0, nil, err } writeCost := time.Since(phaseStart) timings := &SortTimings{ ReadCost: readCost, SortCost: sortCost, WriteCost: writeCost, NumBatches: len(records), NumRows: len(indices), } return len(indices), timings, nil } // rowIndex addresses a single row as (record index, row-in-record index). It is // stored by value to avoid a per-row heap allocation. type rowIndex struct { ri int32 i int32 } // radixSortByInt64 sorts indices in place so that keys[indices[k].ri][indices[k].i] // is non-decreasing, using a stable LSD radix sort over the 8 bytes of the int64 // key (O(N)). The sign bit is flipped so unsigned byte ordering matches signed // int64 ordering. func radixSortByInt64(indices []rowIndex, keys [][]int64) { n := len(indices) if n < 2 { return } srcKey := make([]uint64, n) for i, idx := range indices { srcKey[i] = uint64(keys[idx.ri][idx.i]) ^ (uint64(1) << 63) } dstKey := make([]uint64, n) srcIdx := indices dstIdx := make([]rowIndex, n) var counts [256]int for shift := uint(0); shift < 64; shift += 8 { counts = [256]int{} for i := 0; i < n; i++ { counts[(srcKey[i]>>shift)&0xff]++ } sum := 0 for b := 0; b < 256; b++ { c := counts[b] counts[b] = sum sum += c } for i := 0; i < n; i++ { b := (srcKey[i] >> shift) & 0xff p := counts[b] counts[b]++ dstIdx[p] = srcIdx[i] dstKey[p] = srcKey[i] } srcIdx, dstIdx = dstIdx, srcIdx srcKey, dstKey = dstKey, srcKey } // 8 passes is even, so the sorted data ends up back in the original `indices` // backing array; copy defensively in case the pass count ever becomes odd. if &srcIdx[0] != &indices[0] { copy(indices, srcIdx) } } // A PriorityQueue implements heap.Interface and holds Items. type PriorityQueue[T any] struct { items []*T less func(x, y *T) bool } var _ heap.Interface = (*PriorityQueue[any])(nil) func (pq PriorityQueue[T]) Len() int { return len(pq.items) } func (pq PriorityQueue[T]) Less(i, j int) bool { return pq.less(pq.items[i], pq.items[j]) } func (pq PriorityQueue[T]) Swap(i, j int) { pq.items[i], pq.items[j] = pq.items[j], pq.items[i] } func (pq *PriorityQueue[T]) Push(x any) { pq.items = append(pq.items, x.(*T)) } func (pq *PriorityQueue[T]) Pop() any { old := pq.items n := len(old) x := old[n-1] old[n-1] = nil pq.items = old[0 : n-1] return x } func (pq *PriorityQueue[T]) Enqueue(x *T) { heap.Push(pq, x) } func (pq *PriorityQueue[T]) Dequeue() *T { return heap.Pop(pq).(*T) } func NewPriorityQueue[T any](less func(x, y *T) bool) *PriorityQueue[T] { pq := PriorityQueue[T]{ items: make([]*T, 0), less: less, } heap.Init(&pq) return &pq } func MergeSort(batchSize uint64, schema *schemapb.CollectionSchema, rr []RecordReader, rw RecordWriter, predicate func(r Record, ri, i int) bool, sortedByFieldIDs []int64, ) (numRows int, err error) { // Fast path: no readers provided if len(rr) == 0 { return 0, nil } type index struct { ri int i int } recs := make([]Record, len(rr)) advanceRecord := func(i int) error { rec, err := rr[i].Next() recs[i] = rec // assign nil if err return err } for i := range rr { err := advanceRecord(i) if err == io.EOF { continue } if err != nil { return 0, err } } comparators := make([]func(x, y *index) int, 0, len(sortedByFieldIDs)) for _, fid := range sortedByFieldIDs { switch recs[0].Column(fid).(type) { case *array.Int64: comparators = append(comparators, func(x, y *index) int { xVal := recs[x.ri].Column(fid).(*array.Int64).Value(x.i) yVal := recs[y.ri].Column(fid).(*array.Int64).Value(y.i) if xVal < yVal { return -1 } if xVal > yVal { return 1 } return 0 }) case *array.String: comparators = append(comparators, func(x, y *index) int { xVal := recs[x.ri].Column(fid).(*array.String).Value(x.i) yVal := recs[y.ri].Column(fid).(*array.String).Value(y.i) if xVal < yVal { return -1 } if xVal > yVal { return 1 } return 0 }) default: return 0, merr.WrapErrStorageMsg("unsupported type for sorting key") } } pq := NewPriorityQueue(func(x, y *index) bool { for _, cmp := range comparators { c := cmp(x, y) if c < 0 { return true } if c > 0 { return false } } if x.ri != y.ri { return x.ri < y.ri } return x.i < y.i }) endPositions := make([]int, len(recs)) var enqueueAll func(ri int) error enqueueAll = func(ri int) error { r := recs[ri] hasValid := false endPosition := 0 for j := 0; j < r.Len(); j++ { if predicate(r, ri, j) { pq.Enqueue(&index{ ri: ri, i: j, }) numRows++ hasValid = true endPosition = j } } if !hasValid { err := advanceRecord(ri) if err == io.EOF { return nil } if err != nil { return err } return enqueueAll(ri) } endPositions[ri] = endPosition return nil } for i, v := range recs { if v != nil { if err := enqueueAll(i); err != nil { return 0, err } } } rb := NewRecordBuilder(schema) writeRecord := func() error { rec := rb.Build() defer rec.Release() if rec.Len() > 0 { return rw.Write(rec) } return nil } for pq.Len() > 0 { idx := pq.Dequeue() if err := rb.Append(recs[idx.ri], idx.i, idx.i+1); err != nil { return 0, err } // Due to current arrow impl (v12), the write performance is largely dependent on the batch size, // small batch size will cause write performance degradation. To work around this issue, we accumulate // records and write them in batches. This requires additional memory copy. if rb.GetSize() >= batchSize { if err := writeRecord(); err != nil { return 0, err } } // If the popped idx reaches the last valid data of the segment, invalidate the cache and advance to the next record if idx.i == endPositions[idx.ri] { err := advanceRecord(idx.ri) if err == io.EOF { continue } if err != nil { return 0, err } if err := enqueueAll(idx.ri); err != nil { return 0, err } } } // write the last batch if rb.GetRowNum() > 0 { if err := writeRecord(); err != nil { return 0, err } } return numRows, nil }