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中文版

This is a golang implemented Redis RDB parser for secondary development and memory analysis.

It provides abilities to:

• Generate memory report for rdb file
• Convert RDB files to JSON
• Convert RDB files to Redis Serialization Protocol (or AOF file)
• Find the biggest N keys in RDB files
• Draw FlameGraph to analysis which kind of keys occupied most memory
• Customize data usage
• Generate RDB file

Support RDB version: 1 <= version <= 12(Redis 7.2)

If you read Chinese, you could find a thorough introduction to the RDB file format here: Golang 实现 Redis(11): RDB 文件格式

Thanks sripathikrishnan for his redis-rdb-tools

Install

If you have installed go on your compute, just simply use:

go install github.com/hdt3213/rdb@latest

Package Managers

If you're a Homebrew user, you can install rdb via:

$ brew install rdb

Or, you can download executable binary file from releases and put its path to PATH environment.

use rdb command in terminal, you can see it's manual

This is a tool to parse Redis' RDB files
Options:
  -c command, including: json/memory/aof/bigkey/prefix/flamegraph
  -o output file path, if there is no `-o` option, output to stdout
  -n number of result, using in command: bigkey/prefix
  -port listen port for flame graph web service
  -sep separator for flamegraph, rdb will separate key by it, default value is ":". 
                supporting multi separators: -sep sep1 -sep sep2 
  -regex using regex expression filter keys
  -no-expired reserve expired keys

Examples:
parameters between '[' and ']' is optional
1. convert rdb to json
  rdb -c json -o dump.json dump.rdb
2. generate memory report
  rdb -c memory -o memory.csv dump.rdb
3. convert to aof file
  rdb -c aof -o dump.aof dump.rdb
4. get largest keys
  rdb -c bigkey [-o dump.aof] [-n 10] dump.rdb
5. get number and size by prefix
  rdb -c prefix [-n 10] [-max-depth 3] [-o prefix-report.csv] dump.rdb
6. draw flamegraph
  rdb -c flamegraph [-port 16379] [-sep :] dump.rdb

Convert to Json

Usage:

rdb -c json -o <output_path> <source_path>

example:

rdb -c json -o intset_16.json cases/intset_16.rdb

You can get some rdb examples in cases

The examples for json result:

[
    {"db":0,"key":"hash","size":64,"type":"hash","hash":{"ca32mbn2k3tp41iu":"ca32mbn2k3tp41iu","mddbhxnzsbklyp8c":"mddbhxnzsbklyp8c"}},
    {"db":0,"key":"string","size":10,"type":"string","value":"aaaaaaa"},
    {"db":0,"key":"expiration","expiration":"2022-02-18T06:15:29.18+08:00","size":8,"type":"string","value":"zxcvb"},
    {"db":0,"key":"list","expiration":"2022-02-18T06:15:29.18+08:00","size":66,"type":"list","values":["7fbn7xhcnu","lmproj6c2e","e5lom29act","yy3ux925do"]},
    {"db":0,"key":"zset","expiration":"2022-02-18T06:15:29.18+08:00","size":57,"type":"zset","entries":[{"member":"zn4ejjo4ths63irg","score":1},{"member":"1ik4jifkg6olxf5n","score":2}]},
    {"db":0,"key":"set","expiration":"2022-02-18T06:15:29.18+08:00","size":39,"type":"set","members":["2hzm5rnmkmwb3zqd","tdje6bk22c6ddlrw"]}
]

string

{
    "db": 0,
    "key": "string",
    "size": 10, // estimated memory size
    "type": "string",
	"expiration":"2022-02-18T06:15:29.18+08:00",
    "value": "aaaaaaa"
}

list

{
    "db": 0,
    "key": "list",
    "expiration": "2022-02-18T06:15:29.18+08:00",
    "size": 66,
    "type": "list",
    "values": [
        "7fbn7xhcnu",
        "lmproj6c2e",
        "e5lom29act",
        "yy3ux925do"
    ]
}

set

{
    "db": 0,
    "key": "set",
    "expiration": "2022-02-18T06:15:29.18+08:00",
    "size": 39,
    "type": "set",
    "members": [
        "2hzm5rnmkmwb3zqd",
        "tdje6bk22c6ddlrw"
    ]
}

hash

{
    "db": 0,
    "key": "hash",
    "size": 64,
    "type": "hash",
	"expiration": "2022-02-18T06:15:29.18+08:00",
    "hash": {
        "ca32mbn2k3tp41iu": "ca32mbn2k3tp41iu",
        "mddbhxnzsbklyp8c": "mddbhxnzsbklyp8c"
    }
}

zset

{
    "db": 0,
    "key": "zset",
    "expiration": "2022-02-18T06:15:29.18+08:00",
    "size": 57,
    "type": "zset",
    "entries": [
        {
            "member": "zn4ejjo4ths63irg",
            "score": 1
        },
        {
            "member": "1ik4jifkg6olxf5n",
            "score": 2
        }
    ]
}

stream

{
    "db": 0,
    "key": "mystream",
    "size": 1776,
    "type": "stream",
    "encoding": "",
    "version": 3, // Version 2 means is RDB_TYPE_STREAM_LISTPACKS_2, 3 means is RDB_TYPE_STREAM_LISTPACKS_3
	// StreamEntry is a node in the underlying radix tree of redis stream, of type listpacks, which contains several messages. There is no need to care about which entry the message belongs to when using it.
    "entries": [ 
        {
            "firstMsgId": "1704557973866-0", // ID of the master entry at listpack head 
            "fields": [ // master fields, used for compressing size
                "name",
                "surname"
            ],
            "msgs": [ // messages in entry
                {
                    "id": "1704557973866-0",
                    "fields": {
                        "name": "Sara",
                        "surname": "OConnor"
                    },
                    "deleted": false
                }
            ]
        }
    ],
    "groups": [ // consumer groups
        {
            "name": "consumer-group-name",
            "lastId": "1704557973866-0",
            "pending": [ // pending messages
                {
                    "id": "1704557973866-0",
                    "deliveryTime": 1704557998397,
                    "deliveryCount": 1
                }
            ],
            "consumers": [ // consumers in the group
                {
                    "name": "consumer-name",
                    "seenTime": 1704557998397,
                    "pending": [
                        "1704557973866-0"
                    ],
                    "activeTime": 1704557998397
                }
            ],
            "entriesRead": 1
        }
    ],
    "len": 1, // current number of messages inside this stream
    "lastId": "1704557973866-0",
    "firstId": "1704557973866-0",
    "maxDeletedId": "0-0",
    "addedEntriesCount": 1
}

Generate Memory Report

RDB uses rdb encoded size to estimate redis memory usage.

rdb -c memory -o <output_path> <source_path>

Example:

rdb -c memory -o mem.csv cases/memory.rdb

The examples for csv result:

database,key,type,size,size_readable,element_count
0,hash,hash,64,64B,2
0,s,string,10,10B,0
0,e,string,8,8B,0
0,list,list,66,66B,4
0,zset,zset,57,57B,2
0,large,string,2056,2K,0
0,set,set,39,39B,2

Analyze By Prefix

If you can distinguish modules based on the prefix of the key, for example, the key of user data is User:<uid>, the key of Post is Post:<postid>, the user statistics is Stat:User:???, and the statistics of Post is Stat:Post:???.Then we can get the status of each module through prefix analysis:

database,prefix,size,size_readable,key_count
0,Post:,1170456184,1.1G,701821
0,Stat:,405483812,386.7M,3759832
0,Stat:Post:,291081520,277.6M,2775043
0,User:,241572272,230.4M,265810
0,Topic:,171146778,163.2M,694498
0,Topic:Post:,163635096,156.1M,693758
0,Stat:Post:View,133201208,127M,1387516
0,Stat:User:,114395916,109.1M,984724
0,Stat:Post:Comment:,80178504,76.5M,693758
0,Stat:Post:Like:,77701688,74.1M,693768

Format:

rdb -c prefix [-n <top-n>] [-max-depth <max-depth>] -o <output_path> <source_path>

• The prefix analysis results are arranged in descending order of memory space. The -n option can specify the number of outputs. All are output by default.

• -max-depth can limit the maximum depth of the prefix tree. In the above example, the depth of Stat: is 1, and the depth of Stat:User: and Stat:Post: is 2.

Example:

rdb -c prefix -n 10 -max-depth 2 -o prefix.csv cases/memory.rdb

Flame Graph

In many cases there is not a few very large key but lots of small keys that occupied most memory.

RDB tool could separate keys by the given delimeters, then aggregate keys with same prefix.

Finally RDB tool presents the result as flame graph, with which you could find out which kind of keys consumed most memory.

In this example, the keys of pattern Comment:* use 8.463% memory.

Usage:

rdb -c flamegraph [-port <port>] [-sep <separator1>] [-sep <separator2>] <source_path>

Example:

rdb -c flamegraph -port 16379 -sep : dump.rdb

Find The Biggest Keys

RDB can find biggest N keys in file

rdb -c bigkey -n <result_number> <source_path>

Example:

rdb -c bigkey -n 5 cases/memory.rdb

The examples for csv result:

database,key,type,size,size_readable,element_count
0,large,string,2056,2K,0
0,list,list,66,66B,4
0,hash,hash,64,64B,2
0,zset,zset,57,57B,2
0,set,set,39,39B,2

Convert to AOF

Usage:

rdb -c aof -o <output_path> <source_path>

Example:

rdb -c aof -o mem.aof cases/memory.rdb

The examples for aof result:

*3
$3
SET
$1
s
$7
aaaaaaa

Regex Filter

RDB tool supports using regex expression to filter keys.

Example:

rdb -c json -o regex.json -regex '^l.*' cases/memory.rdb

Customize data usage

package main

import (
	"github.com/hdt3213/rdb/parser"
	"os"
)

func main() {
	rdbFile, err := os.Open("dump.rdb")
	if err != nil {
		panic("open dump.rdb failed")
	}
	defer func() {
		_ = rdbFile.Close()
	}()
	decoder := parser.NewDecoder(rdbFile)
	err = decoder.Parse(func(o parser.RedisObject) bool {
		switch o.GetType() {
		case parser.StringType:
			str := o.(*parser.StringObject)
			println(str.Key, str.Value)
		case parser.ListType:
			list := o.(*parser.ListObject)
			println(list.Key, list.Values)
		case parser.HashType:
			hash := o.(*parser.HashObject)
			println(hash.Key, hash.Hash)
		case parser.ZSetType:
			zset := o.(*parser.ZSetObject)
			println(zset.Key, zset.Entries)
		case parser.StreamType:
			stream := o.(*parser.StreamObject)
			println(stream.Entries, stream.Groups)
		}
		// return true to continue, return false to stop the iteration
		return true
	})
	if err != nil {
		panic(err)
	}
}

Generate RDB file

This library can generate RDB file:

package main

import (
	"github.com/hdt3213/rdb/encoder"
	"github.com/hdt3213/rdb/model"
	"os"
	"time"
)

func main() {
	rdbFile, err := os.Create("dump.rdb")
	if err != nil {
		panic(err)
	}
	defer rdbFile.Close()
	enc := encoder.NewEncoder(rdbFile)
	err = enc.WriteHeader()
	if err != nil {
		panic(err)
	}
	auxMap := map[string]string{
		"redis-ver":    "4.0.6",
		"redis-bits":   "64",
		"aof-preamble": "0",
	}
	for k, v := range auxMap {
		err = enc.WriteAux(k, v)
		if err != nil {
			panic(err)
		}
	}

	err = enc.WriteDBHeader(0, 5, 1)
	if err != nil {
		panic(err)
	}
	expirationMs := uint64(time.Now().Add(time.Hour*8).Unix() * 1000)
	err = enc.WriteStringObject("hello", []byte("world"), encoder.WithTTL(expirationMs))
	if err != nil {
		panic(err)
	}
	err = enc.WriteListObject("list", [][]byte{
		[]byte("123"),
		[]byte("abc"),
		[]byte("la la la"),
	})
	if err != nil {
		panic(err)
	}
	err = enc.WriteSetObject("set", [][]byte{
		[]byte("123"),
		[]byte("abc"),
		[]byte("la la la"),
	})
	if err != nil {
		panic(err)
	}
	err = enc.WriteHashMapObject("list", map[string][]byte{
		"1":  []byte("123"),
		"a":  []byte("abc"),
		"la": []byte("la la la"),
	})
	if err != nil {
		panic(err)
	}
	err = enc.WriteZSetObject("list", []*model.ZSetEntry{
		{
			Score: 1.234,
			Member: "a",
		},
		{
			Score: 2.71828,
			Member: "b",
		},
	})
	if err != nil {
		panic(err)
	}
	err = enc.WriteEnd()
	if err != nil {
		panic(err)
	}
}

Benchmark

Tested on MacBook Pro (16-inch, 2019) 2.6 GHz 6cores Intel Core i7, using a 1.3 GB RDB file encoded with v9 format from Redis 5.0 in production environment.