DBMS > Badger vs. Datomic vs. Quasardb vs. RavenDB vs. RocksDB

System Properties Comparison Badger vs. Datomic vs. Quasardb vs. RavenDB vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Distributed, high-performance timeseries database

Open Source Operational and Transactional Enterprise NoSQL Document Database

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

github.com/dgraph-io/badger

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.datomic.com

doc.quasar.ai/master

ravendb.net/docs

github.com/facebook/rocksdb/wiki

Developer

DGraph Labs

Cognitect

quasardb

Hibernating Rhinos

Facebook, Inc.

Initial release

2017

2012

2009

2010

2013

Current release

1.0.7075, December 2023

3.14.1, January 2024

5.4, July 2022

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

limited edition free

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

AGPL version 3, commercial license available

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, Clojure

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

BSD
Linux
OS X
Windows

Linux
macOS
Raspberry Pi
Windows

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (RQL)

APIs and other access methods

RESTful HTTP API

HTTP API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

C++ API
Java API

Supported programming languages

Clojure
Java

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

consistent hashing

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Source-replica replication with selectable replication factor

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

ACID, Cluster-wide transaction available

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

by using LevelDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

Transient mode

yes

User concepts

Access control

Cryptographically strong user authentication and audit trail

Authorization levels configured per client per database

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