DBMS > Badger vs. QuestDB vs. RocksDB

System Properties Comparison Badger vs. QuestDB vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

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

A high performance open source SQL database for time series data

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

Primary database model

Key-value store

Time Series DBMS

Key-value store

Secondary database models

Relational DBMS

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

Score	0.13
Rank	#371	Overall
	#57	Key-value stores

Score	1.89
Rank	#146	Overall
	#11	Time Series DBMS

Score	4.57
Rank	#87	Overall
	#13	Key-value stores

Website

github.com/dgraph-io/badger

questdb.io

rocksdb.org

Technical documentation

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

questdb.io/docs/introduction

github.com/facebook/rocksdb/wiki

Developer

DGraph Labs

QuestDB Limited

Facebook, Inc.

Initial release

2017

2014

2013

Current release

7.10.2, March 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

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

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
macOS
Windows

Linux

Data scheme

schema-free

yes

schema-free via InfluxDB Line Protocol

schema-free

Typing

predefined data types such as float or date

yes

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

SQL

Support of SQL

SQL-like query language

APIs and other access methods

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC

C++ API
Java API

Supported programming languages

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by timestamps)

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Configurable consistency for N replicas

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID for single-table writes

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

through memory mapped files

yes

User concepts

Access control

More information provided by the system vendor

Specific characteristics

Relational model with native time series support Column based storage and time partitioned...
» more

Competitive advantages

Real-time data ingestion and istant SQL queries for time series High performance...
» more

Typical application scenarios

Application metrics Financial market data and algo trading DevOps monitoring Real-time...
» more

Licensing and pricing models

Apache 2.0.
» more

News

The Inner Workings of Distributed Databases
28 March 2023

Migrating from Relational Databases to Time-series Databases
24 March 2023

MongoDB Time Series Benchmark and Review
20 March 2023

Loading Pandas DataFrames into QuestDB
9 March 2023

Running Databases on Kubernetes
2 March 2023

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.