mr

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mapreduce

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Why MapReduce is needed

In practical business scenarios we often need to get the corresponding properties from different rpc services to assemble complex objects.

For example, to query product details.

1. product service - query product attributes
2. inventory service - query inventory properties
3. price service - query price attributes
4. marketing service - query marketing properties

If it is a serial call, the response time will increase linearly with the number of rpc calls, so we will generally change serial to parallel to optimize response time.

Simple scenarios using WaitGroup can also meet the needs, but what if we need to check the data returned by the rpc call, data processing, data aggregation? The official go library does not have such a tool (CompleteFuture is provided in java), so we implemented an in-process data batching MapReduce concurrent tool based on the MapReduce architecture.

Design ideas

Let's try to put ourselves in the author's shoes and sort out the possible business scenarios for the concurrency tool:

1. querying product details: supporting concurrent calls to multiple services to combine product attributes, and supporting call errors that can be ended immediately.
2. automatic recommendation of user card coupons on product details page: support concurrently verifying card coupons, automatically rejecting them if they fail, and returning all of them.

The above is actually processing the input data and finally outputting the cleaned data. There is a very classic asynchronous pattern for data processing: the producer-consumer pattern. So we can abstract the life cycle of data batch processing, which can be roughly divided into three phases.

1. data production generate
2. data processing mapper
3. data aggregation reducer

Data producing is an indispensable stage, data processing and data aggregation are optional stages, data producing and processing support concurrent calls, data aggregation is basically a pure memory operation, so a single concurrent process can do it.

Since different stages of data processing are performed by different goroutines, it is natural to consider the use of channel to achieve communication between goroutines.

How can I terminate the process at any time?

It's simple, just receive from a channel or the given context in the goroutine.

A simple example

Calculate the sum of squares, simulating the concurrency.

package main

import (
    "fmt"
    "log"

    "github.com/sanhuanshisanshao/go-zero/core/mr"
)

func main() {
    val, err := mr.MapReduce(func(source chan<- interface{}) {
        // generator
        for i := 0; i < 10; i++ {
            source <- i
        }
    }, func(item interface{}, writer mr.Writer, cancel func(error)) {
        // mapper
        i := item.(int)
        writer.Write(i * i)
    }, func(pipe <-chan interface{}, writer mr.Writer, cancel func(error)) {
        // reducer
        var sum int
        for i := range pipe {
            sum += i.(int)
        }
        writer.Write(sum)
    })
    if err != nil {
        log.Fatal(err)
    }
    fmt.Println("result:", val)
}

More examples: https://github.com/zeromicro/zero-examples/tree/main/mapreduce

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Documentation

Index

• Variables
• func Finish(fns ...func() error) error
• func FinishVoid(fns ...func())
• func ForEach(generate GenerateFunc, mapper ForEachFunc, opts ...Option)
• func MapReduce(generate GenerateFunc, mapper MapperFunc, reducer ReducerFunc, opts ...Option) (interface{}, error)
• func MapReduceChan(source <-chan interface{}, mapper MapperFunc, reducer ReducerFunc, ...) (interface{}, error)
• func MapReduceVoid(generate GenerateFunc, mapper MapperFunc, reducer VoidReducerFunc, ...) error
• type ForEachFunc
• type GenerateFunc
• type MapFunc
• type MapperFunc
• type Option
  • func WithContext(ctx context.Context) Option
  • func WithWorkers(workers int) Option
• type ReducerFunc
• type VoidReducerFunc
• type Writer

Variables

var (
	// ErrCancelWithNil is an error that mapreduce was cancelled with nil.
	ErrCancelWithNil = errors.New("mapreduce cancelled with nil")
	// ErrReduceNoOutput is an error that reduce did not output a value.
	ErrReduceNoOutput = errors.New("reduce not writing value")
)

Functions

func Finish

func Finish(fns ...func() error) error

Finish runs fns parallelly, cancelled on any error.

func FinishVoid

func FinishVoid(fns ...func())

FinishVoid runs fns parallelly.

func ForEach

func ForEach(generate GenerateFunc, mapper ForEachFunc, opts ...Option)

ForEach maps all elements from given generate but no output.

func MapReduce

func MapReduce(generate GenerateFunc, mapper MapperFunc, reducer ReducerFunc,
	opts ...Option) (interface{}, error)

MapReduce maps all elements generated from given generate func, and reduces the output elements with given reducer.

func MapReduceChan

func MapReduceChan(source <-chan interface{}, mapper MapperFunc, reducer ReducerFunc,
	opts ...Option) (interface{}, error)

MapReduceChan maps all elements from source, and reduce the output elements with given reducer.

func MapReduceVoid

func MapReduceVoid(generate GenerateFunc, mapper MapperFunc, reducer VoidReducerFunc, opts ...Option) error

MapReduceVoid maps all elements generated from given generate, and reduce the output elements with given reducer.

Types

type ForEachFunc

type ForEachFunc func(item interface{})

ForEachFunc is used to do element processing, but no output.

type GenerateFunc

type GenerateFunc func(source chan<- interface{})

GenerateFunc is used to let callers send elements into source.

type MapFunc

type MapFunc func(item interface{}, writer Writer)

MapFunc is used to do element processing and write the output to writer.

type MapperFunc

type MapperFunc func(item interface{}, writer Writer, cancel func(error))

MapperFunc is used to do element processing and write the output to writer, use cancel func to cancel the processing.

type Option

type Option func(opts *mapReduceOptions)

Option defines the method to customize the mapreduce.

func WithContext

func WithContext(ctx context.Context) Option

WithContext customizes a mapreduce processing accepts a given ctx.

func WithWorkers

func WithWorkers(workers int) Option

WithWorkers customizes a mapreduce processing with given workers.

type ReducerFunc

type ReducerFunc func(pipe <-chan interface{}, writer Writer, cancel func(error))

ReducerFunc is used to reduce all the mapping output and write to writer, use cancel func to cancel the processing.

type VoidReducerFunc

type VoidReducerFunc func(pipe <-chan interface{}, cancel func(error))

VoidReducerFunc is used to reduce all the mapping output, but no output. Use cancel func to cancel the processing.

type Writer

type Writer interface {
	Write(v interface{})
}

Writer interface wraps Write method.