tidb shuffle 源码
tidb shuffle 代码
文件路径：/executor/shuffle.go
// Copyright 2019 PingCAP, Inc.
//
// Licensed 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 executor
import (
"context"
"sync"
"github.com/pingcap/errors"
"github.com/pingcap/failpoint"
"github.com/pingcap/tidb/expression"
"github.com/pingcap/tidb/sessionctx"
"github.com/pingcap/tidb/util/channel"
"github.com/pingcap/tidb/util/chunk"
"github.com/pingcap/tidb/util/execdetails"
"github.com/pingcap/tidb/util/logutil"
"github.com/twmb/murmur3"
"go.uber.org/zap"
)
// ShuffleExec is the executor to run other executors in a parallel manner.
//
// 1. It fetches chunks from M `DataSources` (value of M depends on the actual executor, e.g. M = 1 for WindowExec, M = 2 for MergeJoinExec).
//
// 2. It splits tuples from each `DataSource` into N partitions (Only "split by hash" is implemented so far).
//
// 3. It invokes N workers in parallel, each one has M `receiver` to receive partitions from `DataSources`
//
// 4. It assigns partitions received as input to each worker and executes child executors.
//
// 5. It collects outputs from each worker, then sends outputs to its parent.
//
// +-------------+
// +-------| Main Thread |
// | +------+------+
// | ^
// | |
// | +
// v +++
// outputHolderCh | | outputCh (1 x Concurrency)
// v +++
// | ^
// | |
// | +-------+-------+
// v | |
// +--------------+ +--------------+
// +----- | worker | ....... | worker | worker (N Concurrency): child executor, eg. WindowExec (+SortExec)
// | +------------+-+ +-+------------+
// | ^ ^
// | | |
// | +-+ +-+ ...... +-+
// | | | | | | |
// | ... ... ... inputCh (Concurrency x 1)
// v | | | | | |
// inputHolderCh +++ +++ +++
// v ^ ^ ^
// | | | |
// | +------o----+ |
// | | +-----------------+-----+
// | | |
// | +---+------------+------------+----+-----------+
// | | Partition Splitter |
// | +--------------+-+------------+-+--------------+
// | ^
// | |
// | +---------------v-----------------+
// +----------> | fetch data from DataSource |
// +---------------------------------+
type ShuffleExec struct {
baseExecutor
concurrency int
workers []*shuffleWorker
prepared bool
executed bool
// each dataSource has a corresponding spliter
splitters []partitionSplitter
dataSources []Executor
finishCh chan struct{}
outputCh chan *shuffleOutput
}
type shuffleOutput struct {
chk *chunk.Chunk
err error
giveBackCh chan *chunk.Chunk
}
// Open implements the Executor Open interface.
func (e *ShuffleExec) Open(ctx context.Context) error {
for _, s := range e.dataSources {
if err := s.Open(ctx); err != nil {
return err
}
}
if err := e.baseExecutor.Open(ctx); err != nil {
return err
}
e.prepared = false
e.finishCh = make(chan struct{}, 1)
e.outputCh = make(chan *shuffleOutput, e.concurrency)
for _, w := range e.workers {
w.finishCh = e.finishCh
for _, r := range w.receivers {
r.inputCh = make(chan *chunk.Chunk, 1)
r.inputHolderCh = make(chan *chunk.Chunk, 1)
}
w.outputCh = e.outputCh
w.outputHolderCh = make(chan *chunk.Chunk, 1)
if err := w.childExec.Open(ctx); err != nil {
return err
}
for i, r := range w.receivers {
r.inputHolderCh <- newFirstChunk(e.dataSources[i])
}
w.outputHolderCh <- newFirstChunk(e)
}
return nil
}
// Close implements the Executor Close interface.
func (e *ShuffleExec) Close() error {
var firstErr error
if !e.prepared {
for _, w := range e.workers {
for _, r := range w.receivers {
if r.inputHolderCh != nil {
close(r.inputHolderCh)
}
if r.inputCh != nil {
close(r.inputCh)
}
}
if w.outputHolderCh != nil {
close(w.outputHolderCh)
}
}
if e.outputCh != nil {
close(e.outputCh)
}
}
if e.finishCh != nil {
close(e.finishCh)
}
for _, w := range e.workers {
for _, r := range w.receivers {
if r.inputCh != nil {
channel.Clear(r.inputCh)
}
}
// close child executor of each worker
if err := w.childExec.Close(); err != nil && firstErr == nil {
firstErr = err
}
}
if e.outputCh != nil {
channel.Clear(e.outputCh)
}
e.executed = false
if e.runtimeStats != nil {
runtimeStats := &execdetails.RuntimeStatsWithConcurrencyInfo{}
runtimeStats.SetConcurrencyInfo(execdetails.NewConcurrencyInfo("ShuffleConcurrency", e.concurrency))
e.ctx.GetSessionVars().StmtCtx.RuntimeStatsColl.RegisterStats(e.id, runtimeStats)
}
// close dataSources
for _, dataSource := range e.dataSources {
if err := dataSource.Close(); err != nil && firstErr == nil {
firstErr = err
}
}
// close baseExecutor
if err := e.baseExecutor.Close(); err != nil && firstErr == nil {
firstErr = err
}
return errors.Trace(firstErr)
}
func (e *ShuffleExec) prepare4ParallelExec(ctx context.Context) {
// create a goroutine for each dataSource to fetch and split data
for i := range e.dataSources {
go e.fetchDataAndSplit(ctx, i)
}
waitGroup := &sync.WaitGroup{}
waitGroup.Add(len(e.workers))
for _, w := range e.workers {
go w.run(ctx, waitGroup)
}
go e.waitWorkerAndCloseOutput(waitGroup)
}
func (e *ShuffleExec) waitWorkerAndCloseOutput(waitGroup *sync.WaitGroup) {
waitGroup.Wait()
close(e.outputCh)
}
// Next implements the Executor Next interface.
func (e *ShuffleExec) Next(ctx context.Context, req *chunk.Chunk) error {
req.Reset()
if !e.prepared {
e.prepare4ParallelExec(ctx)
e.prepared = true
}
failpoint.Inject("shuffleError", func(val failpoint.Value) {
if val.(bool) {
failpoint.Return(errors.New("ShuffleExec.Next error"))
}
})
if e.executed {
return nil
}
result, ok := <-e.outputCh
if !ok {
e.executed = true
return nil
}
if result.err != nil {
return result.err
}
req.SwapColumns(result.chk) // `shuffleWorker` will not send an empty `result.chk` to `e.outputCh`.
result.giveBackCh <- result.chk
return nil
}
func recoveryShuffleExec(output chan *shuffleOutput, r interface{}) {
err := errors.Errorf("%v", r)
output <- &shuffleOutput{err: errors.Errorf("%v", r)}
logutil.BgLogger().Error("shuffle panicked", zap.Error(err), zap.Stack("stack"))
}
func (e *ShuffleExec) fetchDataAndSplit(ctx context.Context, dataSourceIndex int) {
var (
err error
workerIndices []int
)
results := make([]*chunk.Chunk, len(e.workers))
chk := newFirstChunk(e.dataSources[dataSourceIndex])
defer func() {
if r := recover(); r != nil {
recoveryShuffleExec(e.outputCh, r)
}
for _, w := range e.workers {
close(w.receivers[dataSourceIndex].inputCh)
}
}()
for {
err = Next(ctx, e.dataSources[dataSourceIndex], chk)
if err != nil {
e.outputCh <- &shuffleOutput{err: err}
return
}
if chk.NumRows() == 0 {
break
}
workerIndices, err = e.splitters[dataSourceIndex].split(e.ctx, chk, workerIndices)
if err != nil {
e.outputCh <- &shuffleOutput{err: err}
return
}
numRows := chk.NumRows()
for i := 0; i < numRows; i++ {
workerIdx := workerIndices[i]
w := e.workers[workerIdx]
if results[workerIdx] == nil {
select {
case <-e.finishCh:
return
case results[workerIdx] = <-w.receivers[dataSourceIndex].inputHolderCh:
break
}
}
results[workerIdx].AppendRow(chk.GetRow(i))
if results[workerIdx].IsFull() {
w.receivers[dataSourceIndex].inputCh <- results[workerIdx]
results[workerIdx] = nil
}
}
}
for i, w := range e.workers {
if results[i] != nil {
w.receivers[dataSourceIndex].inputCh <- results[i]
results[i] = nil
}
}
}
var _ Executor = &shuffleReceiver{}
// shuffleReceiver receives chunk from dataSource through inputCh
type shuffleReceiver struct {
baseExecutor
finishCh <-chan struct{}
executed bool
inputCh chan *chunk.Chunk
inputHolderCh chan *chunk.Chunk
}
// Open implements the Executor Open interface.
func (e *shuffleReceiver) Open(ctx context.Context) error {
if err := e.baseExecutor.Open(ctx); err != nil {
return err
}
e.executed = false
return nil
}
// Close implements the Executor Close interface.
func (e *shuffleReceiver) Close() error {
return errors.Trace(e.baseExecutor.Close())
}
// Next implements the Executor Next interface.
// It is called by `Tail` executor within "shuffle", to fetch data from `DataSource` by `inputCh`.
func (e *shuffleReceiver) Next(ctx context.Context, req *chunk.Chunk) error {
req.Reset()
if e.executed {
return nil
}
select {
case <-e.finishCh:
e.executed = true
return nil
case result, ok := <-e.inputCh:
if !ok || result.NumRows() == 0 {
e.executed = true
return nil
}
req.SwapColumns(result)
e.inputHolderCh <- result
return nil
}
}
// shuffleWorker is the multi-thread worker executing child executors within "partition".
type shuffleWorker struct {
childExec Executor
finishCh <-chan struct{}
// each receiver corresponse to a dataSource
receivers []*shuffleReceiver
outputCh chan *shuffleOutput
outputHolderCh chan *chunk.Chunk
}
func (e *shuffleWorker) run(ctx context.Context, waitGroup *sync.WaitGroup) {
defer func() {
if r := recover(); r != nil {
recoveryShuffleExec(e.outputCh, r)
}
waitGroup.Done()
}()
for {
select {
case <-e.finishCh:
return
case chk := <-e.outputHolderCh:
if err := Next(ctx, e.childExec, chk); err != nil {
e.outputCh <- &shuffleOutput{err: err}
return
}
// Should not send an empty `chk` to `e.outputCh`.
if chk.NumRows() == 0 {
return
}
e.outputCh <- &shuffleOutput{chk: chk, giveBackCh: e.outputHolderCh}
}
}
}
var _ partitionSplitter = &partitionHashSplitter{}
var _ partitionSplitter = &partitionRangeSplitter{}
type partitionSplitter interface {
split(ctx sessionctx.Context, input *chunk.Chunk, workerIndices []int) ([]int, error)
}
type partitionHashSplitter struct {
byItems []expression.Expression
numWorkers int
hashKeys [][]byte
}
func (s *partitionHashSplitter) split(ctx sessionctx.Context, input *chunk.Chunk, workerIndices []int) ([]int, error) {
var err error
s.hashKeys, err = getGroupKey(ctx, input, s.hashKeys, s.byItems)
if err != nil {
return workerIndices, err
}
workerIndices = workerIndices[:0]
numRows := input.NumRows()
for i := 0; i < numRows; i++ {
workerIndices = append(workerIndices, int(murmur3.Sum32(s.hashKeys[i]))%s.numWorkers)
}
return workerIndices, nil
}
func buildPartitionHashSplitter(concurrency int, byItems []expression.Expression) *partitionHashSplitter {
return &partitionHashSplitter{
byItems: byItems,
numWorkers: concurrency,
}
}
type partitionRangeSplitter struct {
byItems []expression.Expression
numWorkers int
groupChecker *vecGroupChecker
idx int
}
func buildPartitionRangeSplitter(ctx sessionctx.Context, concurrency int, byItems []expression.Expression) *partitionRangeSplitter {
return &partitionRangeSplitter{
byItems: byItems,
numWorkers: concurrency,
groupChecker: newVecGroupChecker(ctx, byItems),
idx: 0,
}
}
// This method is supposed to be used for shuffle with sorted `dataSource`
// the caller of this method should guarantee that `input` is grouped,
// which means that rows with the same byItems should be continuous, the order does not matter.
func (s *partitionRangeSplitter) split(ctx sessionctx.Context, input *chunk.Chunk, workerIndices []int) ([]int, error) {
_, err := s.groupChecker.splitIntoGroups(input)
if err != nil {
return workerIndices, err
}
workerIndices = workerIndices[:0]
for !s.groupChecker.isExhausted() {
begin, end := s.groupChecker.getNextGroup()
for i := begin; i < end; i++ {
workerIndices = append(workerIndices, s.idx)
}
s.idx = (s.idx + 1) % s.numWorkers
}
return workerIndices, nil
}
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