Townsourced Tech Blog

Tech musings from a local development agency

BoltDB vs Badger: A Comparison of Go Key-Value databases

When I first started working on BoltHold (a simple querying and index engine that sits on top of BoltDB), Badger didn’t yet exist, and BoltDB was the clear leader of the pack for key-value, pure-go, embeddable databases.

Then Badger was released, and it was shown to be more than just a pure-go version of LSM-tree stores like RocksDB / LevelDB, it actually was faster than RocksDB. Much faster. I knew I wanted to build something with Badger in the future, and when an issue was opened to add Badger support to Bolthold, I jumped on it.

The first thing I had to consider in adding Badger support was whether I wanted to add Badger as another dependency and allow users to switch between the two via a configuration flag, or build an entirely separate library. I ended up following the Go proverb:

A little copying is better than a little dependency

I decided to make BadgerHold a separate library, and now that I have 88% test coverage using the exact same tests as BoltHold, I believe that I made the right choice.

In implementing the exact same library on two different KV stores, I like to think I have gained some perspective on comparing these two databases. Dgraph have already done an excellent comparison of performance between these two libraries. This post instead will focus on comparing criteria other than performance.

Manish Jain from Dgraph has also been kind enough to offer some responses to my limited perspectives in my comparisons below. I’ve included them in-line.

Superficial Differences

When I first approached writing BadgerHold, I was already familiar with BoltDB and had used it on several other projects. Unfortunately, this meant that the onus was on Badger to meet or conflict with my expectations upon first using the library. With that in mind, I found the following differences to be superficial, and not a major impact in my usage of both libraries. That being said, I felt I should list them in case they prove to be more important to you.

File Handling

With BoltDB, you open a database by specifying a file, and passing in some options:

db, err := bolt.Open("my.db", 0600, options)

Badger, on the other hand, requires you to specify a multiple folders as part of an options struct instead of a single file:

opts := badger.DefaultOptions
opts.Dir = "/tmp/badger"
opts.ValueDir = "/tmp/badger"
db, err := badger.Open(opts)

This is due to the nature of LSM-tree databases, where multiple levels are stored across multiple files. I was hard pressed to come up with a legitimate reason why a single file would matter over a directory of files, but Manish explained the reasoning behind the choice.

The benefit of having files be immutable is that they get rsync friendly. That was an explicit requirement for LevelDB at Google.

Manish Jain

Unexpected API Choices

In general I found BoltDB to fall more in line with what I would expect from a Go API compared to Badger. My expectations being defined by those that I am used to seeing in the Go standard library.

For example, a common way to handle options in the Go standard library is to default values if not specified (see http.Server, and sql.Conn).

Bolt’s Open method will accept a nil option and open up the data file with defaults.

db, err := bolt.Open("my.db", 0600, nil)

Badger’s option argument doesn’t accept a pointer, so a struct needs to be passed in. If you want to use the default options, you need to make a copy of an exported struct that contains defaults. Furthermore, you’ll always need to change those defaults because the data directories are stored in the options type.

opts := badger.DefaultOptions
opts.Dir = "/tmp/badger"
opts.ValueDir = "/tmp/badger"
db, err := badger.Open(opts)

That’s intended. It allows flexibility in how you want Badger to behave, and we want users to have a look at the default options and tweak them.

Manish Jain

In the standard Go library, constants and enumerations are usually stored within the same package where they are used, or local enumerations defined from external packages exported enumerations (see gzip and os).

Badger requires you to import the badger/options package to get the enumerations for FileLoadingMode.

Behavior Differences

The following are more significant differences between how BoltDB and Badger behave.


BoltDB has a concept of buckets in a single database. Comparable to tables in a relational database, this allows you to store different types of data in the same database, and not worry about row conflicts.

In BoltHold, I used different buckets to store different types. Since Badger doesn’t have similar functionality, I had to replicate it by prefixing keys with the type being stored, after which I could use Badger’s ValidForPrefix method when iterating to make sure queries only apply to the proper type. This allows the behavior between BoltHold and BadgerHold to be the same when storing different types in the same database. BoltDB most likely does something similar to create it’s bucket functionality, but it’s nice not to have to re-implement it.


BoltDB allows you to create a number of Cursors on any given transaction. Badger, on the other hand, only allows one iterator at a time in Read / Write transactions.

In BoltHold and BadgerHold, you have the ability to write subqueries, which allow you to filter records based on another query from within that same transaction.

bh.Where("Name").MatchFunc(func(ra *bh.RecordAccess) (bool, error) {
	// find where name exists in more than one category
	record, ok := ra.Record().(*ItemTest)
	if !ok {
		return false, fmt.Errorf("Record is not ItemTest, it's a %T", ra.Record())

	var result []ItemTest

	err := ra.SubQuery(&result,
	if err != nil {
		return false, err

	if len(result) > 0 {
		return true, nil

	return false, nil

This type of query is much harder to implement in Badger for Update and Delete queries than in BoltDB due to this single iterator limit from within R/W transactions. I’ve since opened an issue, as this limitation may be fixable.

Memory Usage

The single most surprising difference for me between BoltDB and Badger, was the disparity in memory usage. In my testing I found Badger to use significantly more memory than BoltDB. So much so that I had to rewrite my BadgerHold tests from using a separate database for each test to using a single shared database for all tests. I couldn’t complete the entire suite of tests on my 8GB VM due to out of memory errors. Even after changing all of Badger’s options to their lowest memory settings, I was unable to get the full suite to finish.

To be fair, this is most likely by design, and the source of a lot of the impressive performance differences between BoltDB and Badger. The memory usage was also exasperated by opening multiple databases, which meant there needed to be enough memory for each of those database’s buffer cache. There are many scenarios where this trade off of memory for speed is well worth the cost, however, I was personally surprised by the extent of it, as I hadn’t seen it mentioned in any of the documentation or blog posts.


Criteria BoltDB Badger
File Handling Single File Multiple Directories
API Idiomatic Unexpected
Bucket Support Yes No
Iterators No Restrictions One R/W iterator at a time
Memory Usage Low High

The biggest differences between BoltDB and Badger, stem from their underlying technologies, and not their implementation details. If you are picking one over the other based solely on performance metrics, I would closely consider your usage case instead.

My recommendation would be to use BoltDB for scenarios where you are more memory constrained or where the performance gain isn’t worth the cost in memory usage. Examples of this may be projects on mobile phones, tablets, Raspberry Pi-like devices or command line applications where you need some persistent storage.

For micro-services and projects you’d tend to run on a server where more memory is available, or projects where you expect a lot of writes like for logging, I would recommend Badger.

I would also recommend using one shared Badger database, rather then opening multiple separate databases to limit your memory usage. Using a library like BadgerHold, or using similar key-prefixing methods in your own code, should allow you to manage your data types separately within the same database.

If you choose to use BoltHold, or BadgerHold, I would recommend aliasing the package import as bh and since both library’s APIs are nearly identical, you could easily switch your project from one to another if you find that one library will work better for you.

import (
	bh ""

// this code will run the same on BoltHold and BadgerHold

store.UpdateMatching(&Person{}, bh.Where("Death").Lt(bh.Field("Birth")), func(record interface{}) error {
	update, ok := record.(*Person)
	if !ok {
		return fmt.Errorf("Record isn't the correct type!  Wanted Person, got %T", record)

	update.Birth, update.Death = update.Death, update.Birth

	return nil

comments powered by Disqus