mapreduce

mrkit-go

mrkit-go is an easy-to-use MapReduce toolkit in Go. It is based on an existing Go MapReduce implementation and repackaged as a more plug-and-play library/runner for local use.

Quickstart (Run in 10 Minutes)

This section is for first-time users who just want to run an end-to-end MySQL batch job:

MySQL source table -> MapReduce -> MySQL sink table

0) Prerequisites

• Go 1.21+ installed
• MySQL running and reachable (example below uses localhost:3306)
• MySQL account with read/write permission on source/sink databases

1) Prepare demo source data

cd /path/to/mrkit-go
MYSQL_HOST=localhost MYSQL_PORT=3306 MYSQL_USER=root MYSQL_PASSWORD=123456 \
MYSQL_DB=mysql SOURCE_TABLE=source_events TARGET_TABLE=agg_results \
ROWS=5000 KEY_MOD=100 \
go run ./cmd/batch -mode prepare

2) Validate config schema (v1)

go run ./cmd/batch -check -config example/batch-minimal/mysqlbatch/flow.mysql.count.json

Expected output:

config check pass

3) Run config-driven flow

Run one of these:

go run ./cmd/batch -config example/batch-minimal/mysqlbatch/flow.mysql.count.json
go run ./cmd/batch -config example/batch-minimal/mysqlbatch/flow.mysql.minmax.json
go run ./cmd/batch -config example/batch-minimal/mysqlbatch/flow.mysql.topn.json

Cross-DB examples:

# mysql -> redis
go run ./cmd/batch -config example/batch-minimal/mysqlbatch/flow.mysql_to_redis.count.json

# redis -> mysql
go run ./cmd/batch -config example/batch-minimal/redisbatch/flow.redis_to_mysql.count.json

# redis -> redis
go run ./cmd/batch -config example/batch-minimal/redisbatch/flow.redis_to_redis.count.json

Flow benchmark by config (works for mysql/redis source/sink):

go run ./cmd/batch -mode benchmark -config example/batch-minimal/mysqlbatch/flow.mysql_to_redis.count.json
go run ./cmd/batch -mode benchmark -config example/batch-minimal/redisbatch/flow.redis_to_redis.count.json

4) Verify success

Expected log includes:

flow done

Result tables (default sink DB: mr_target):

• agg_count_results
• agg_minmax_results
• agg_topn_results

Notes

• count is additive and can run with multiple reducers.
• minmax and topN are non-additive. Use reducers=1 in config to avoid cross-reducer merge distortion.
• For built-in transforms (count/minmax/topN), no prebuilt .so is required.

Features

• Multiple worker goroutines in one process on a single machine.
• Multiple worker processes on a single machine.
• Fault tolerance.
• Easy to parallel your code with Map and Reduce functions.

Library Usage - Your own map and reduce function

Here's a simply example for word count program. wc.go

package main
import (
	"strconv"
	"strings"
	"unicode"

	"github.com/emptyOVO/mrkit-go/worker"
)
func Map(filename string, contents string, ctx worker.MrContext) {
	// function to detect word separators.
	ff := func(r rune) bool { return !unicode.IsLetter(r) }

	// split contents into an array of words.
	words := strings.FieldsFunc(contents, ff)

	for _, w := range words {
		ctx.EmitIntermediate(w, "1")
	}
}
func Reduce(key string, values []string, ctx worker.MrContext) {
	// return the number of occurrences of this word.
	ctx.Emit(key, strconv.Itoa(len(values)))
}

Usage - 1 program with master, worker goroutine

main.go

package main

import (
	mp "github.com/emptyOVO/mrkit-go"
)

func main() {
	mp.StartSingleMachineJob(mp.ParseArg())
}

Run with :

# Compile plugin
go build -race -buildmode=plugin -o wc.so wc.go

# Word count
go run -race main.go -i 'input/files' -p 'wc.so' -r 1 -w 8

Output file name is mr-out-0.txt

More example can be found in the mrapps/ folder, and we will add more example in the future.

Usage - Master program, and worker program (Isolate master and workers)

master.go

package main

import (
	mp "github.com/emptyOVO/mrkit-go"
)

func main() {
	mp.StartMaster(mp.ParseArg())
}

worker.go

package main

import (
	mp "github.com/emptyOVO/mrkit-go"
)

func main() {
	mp.StartWorker(mp.ParseArg())
}

Run with :

# Compile plugin
go build -race -buildmode=plugin -o wc.so wc.go

# Word count
go run -race cmd/master.go -i 'txt/*' -p 'cmd/wc.so' -r 1 -w 8 &
sleep 1
go run -race cmd/worker.go -i 'txt/*' -p 'cmd/wc.so' -r 1 -w 1 &
go run -race cmd/worker.go -i 'txt/*' -p 'cmd/wc.so' -r 1 -w 2 &
go run -race cmd/worker.go -i 'txt/*' -p 'cmd/wc.so' -r 1 -w 3 &
go run -race cmd/worker.go -i 'txt/*' -p 'cmd/wc.so' -r 1 -w 4 &
go run -race cmd/worker.go -i 'txt/*' -p 'cmd/wc.so' -r 1 -w 5 &
go run -race cmd/worker.go -i 'txt/*' -p 'cmd/wc.so' -r 1 -w 6 &
go run -race cmd/worker.go -i 'txt/*' -p 'cmd/wc.so' -r 1 -w 7 &
go run -race cmd/worker.go -i 'txt/*' -p 'cmd/wc.so' -r 1 -w 8 

Help

MapReduce is an easy-to-use MapReduce Go parallel-computing toolkit inspired by 2021 6.824 lab1.
It supports multiple workers threads on a single machine and multiple processes on a single machine right now.

Usage:
  mapreduce [flags]

Flags:
  -h, --help            help for mapreduce
  -m, --inRAM           Whether write the intermediate file in RAM (default true)
  -i, --input strings   Input files
  -p, --plugin string   Plugin .so file
      --port int        Port number (default 10000)
  -r, --reduce int      Number of Reducers (default 1)
  -w, --worker int      Number of Workers(for master node)
                        ID of worker(for worker node) (default 4)

MySQL Batch Processing (Go Library)

The framework now provides a Go library for MySQL source/sink and benchmark workflows:

• package: batch
• source read: primary-key range sharding (id range split)
• sink write: batch stage insert + grouped upsert
• end-to-end: MySQL -> MapReduce -> MySQL in one Go call
• supports cross-database pipeline: source DB and target DB can be configured separately

Reducer partitioning is fixed to hash(key) % nReduce.

Library usage

package main

import (
	"context"

	"github.com/emptyOVO/mrkit-go/batch"
)

func main() {
	_ = batch.RunPipeline(context.Background(), batch.PipelineConfig{
		DB: batch.DBConfig{
			Host:     "127.0.0.1",
			Port:     3306,
			User:     "root",
			Password: "123456",
			Database: "mysql",
		},
		SourceDB: batch.DBConfig{
			Host:     "127.0.0.1",
			Port:     3306,
			User:     "root",
			Password: "123456",
			Database: "mysql",
		},
		SinkDB: batch.DBConfig{
			Host:     "127.0.0.1",
			Port:     3306,
			User:     "root",
			Password: "123456",
			Database: "mr_target",
		},
		Source: batch.SourceConfig{
			Table: "source_events",
		},
		Sink: batch.SinkConfig{
			TargetTable: "agg_results",
			Replace:     true,
		},
		PluginPath: "cmd/agg.so",
		Reducers:   8,
		Workers:    16,
		InRAM:      false,
		Port:       10000,
	})
}

Built-in Go runner

This repo also includes a runner command based on the same library:

# 1) prepare synthetic source table (default 10m rows)
MYSQL_HOST=127.0.0.1 MYSQL_PORT=3306 MYSQL_USER=root MYSQL_PASSWORD=123456 MYSQL_DB=mysql \
SOURCE_TABLE=source_events TARGET_TABLE=agg_results ROWS=10000000 \
go run ./cmd/batch -mode prepare

# 2) run end-to-end pipeline (source mysql -> target mr_target)
MYSQL_HOST=localhost MYSQL_PORT=3306 MYSQL_USER=root MYSQL_PASSWORD=123456 MYSQL_DB=mysql \
MYSQL_SOURCE_DB=mysql MYSQL_TARGET_DB=mr_target \
SOURCE_TABLE=source_events TARGET_TABLE=agg_results \
go run ./cmd/batch -mode pipeline -plugin cmd/agg.so

# 3) validate correctness
MYSQL_HOST=127.0.0.1 MYSQL_PORT=3306 MYSQL_USER=root MYSQL_PASSWORD=123456 MYSQL_DB=mysql \
SOURCE_TABLE=source_events TARGET_TABLE=agg_results \
go run ./cmd/batch -mode validate

Config-driven flow (source/transform/sink)

For a plug-and-play experience (SeaTunnel-like), you can run by a single JSON config:

go run ./cmd/batch -check -config example/batch-minimal/mysqlbatch/flow.mysql.json
go run ./cmd/batch -config example/batch-minimal/mysqlbatch/flow.mysql.json

Config sections:

• source: MySQL source connection + extract config
• transform: built-in transform (count / minmax / topN) or plugin mode
• sink: MySQL or Redis sink config

Production template (source/sink split + concurrency):

{
  "version": "v1",
  "source": {
    "type": "mysql",
    "db": {
      "host": "10.20.1.11",
      "port": 3306,
      "user": "etl_reader",
      "password": "REPLACE_ME",
      "database": "biz_source",
      "params": {
        "readTimeout": "60s",
        "writeTimeout": "60s",
        "timeout": "10s",
        "loc": "Local",
        "multiStatements": "false"
      }
    },
    "config": {
      "table": "order_events",
      "pkcolumn": "id",
      "keycolumn": "biz_key",
      "valcolumn": "metric",
      "where": "event_time >= '2026-02-01 00:00:00' AND event_time < '2026-02-02 00:00:00'",
      "shards": 64,
      "parallel": 16,
      "outputdir": "txt/mysql_source",
      "fileprefix": "chunk"
    }
  },
  "transform": {
    "type": "builtin",
    "builtin": "count",
    "reducers": 16,
    "workers": 32,
    "in_ram": false,
    "port": 18000
  },
  "sink": {
    "type": "mysql",
    "db": {
      "host": "10.20.2.15",
      "port": 3306,
      "user": "etl_writer",
      "password": "REPLACE_ME",
      "database": "biz_dw",
      "params": {
        "readTimeout": "60s",
        "writeTimeout": "60s",
        "timeout": "10s",
        "loc": "Local",
        "multiStatements": "false"
      }
    },
    "config": {
      "targettable": "order_metric_daily",
      "keycolumn": "biz_key",
      "valcolumn": "metric_sum",
      "inputglob": "mr-out-*.txt",
      "replace": true,
      "batchsize": 5000
    }
  }
}

Run:

go run ./cmd/batch -config /absolute/path/flow.prod.json

Plugin mode is still available (advanced use case):

{
  "version": "v1",
  "transform": {
    "type": "mapreduce",
    "plugin_path": "cmd/agg.so",
    "reducers": 8,
    "workers": 16,
    "in_ram": false,
    "port": 10000
  }
}

Failure rerun suggestions:

• Keep where windowed (time range or batch id) so each run has deterministic input.
• If sink.config.replace=true, rerunning the same batch is idempotent (overwrite behavior).
• If you need history accumulation, use replace=false and ensure batch filters avoid duplicates.
• Before rerun, clean local artifacts: mr-out-*.txt, txt/mysql_source/chunk-*.txt.
• Start with moderate parallelism: shards=4x~8x reducers, workers=2x reducers, then tune by DB load.

Minimal external integration example:

• example/batch-minimal/go.mod
• example/batch-minimal/main.go

Benchmark

MYSQL_HOST=127.0.0.1 MYSQL_PORT=3306 MYSQL_USER=root MYSQL_PASSWORD=123456 MYSQL_DB=mysql \
SOURCE_TABLE=source_events TARGET_TABLE=agg_results PREPARE_DATA=true ROWS=10000000 \
go run ./cmd/batch -mode benchmark -plugin cmd/agg.so

Contributions

Pull requests are always welcome!