DBMS > Badger vs. MongoDB vs. QuestDB

System Properties Comparison Badger vs. MongoDB vs. QuestDB

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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.

One of the most popular document stores available both as a fully managed cloud service and for deployment on self-managed infrastructure

A high performance open source SQL database for time series data

Primary database model

Key-value store

Document store

Time Series DBMS

Secondary database models

Spatial DBMS
Search engine

integrated Lucene index, currently in MongoDB Atlas only.
Time Series DBMS

Time Series Collections introduced in Release 5.0

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	458.78
Rank	#5	Overall
	#1	Document stores

Score	1.89
Rank	#146	Overall
	#11	Time Series DBMS

Website

github.com/dgraph-io/badger

www.mongodb.com

questdb.io

Technical documentation

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

docs.mongodb.com/manual

questdb.io/docs/introduction

Developer

DGraph Labs

MongoDB, Inc

QuestDB Limited

Initial release

2017

2009

2014

Current release

6.0.1, August 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

MongoDB Inc.'s Server Side Public License v1. Prior versions were published under GNU AGPL v3.0. Commercial licenses are also available.

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

MongoDB available as DBaaS (MongoDB Atlas)

DBaaS offerings (sponsored links)

Database as a Service

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

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Implementation language

C++

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Linux
macOS
Windows

Data scheme

schema-free

Although schema-free, documents of the same collection often follow the same structure. Optionally impose all or part of a schema by defining a JSON schema.

yes

schema-free via InfluxDB Line Protocol

Typing

predefined data types such as float or date

yes

string, integer, double, decimal, boolean, date, object_id, geospatial

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

yes

SQL

Support of SQL

Read-only SQL queries via the MongoDB Connector for BI

SQL-like query language

APIs and other access methods

proprietary protocol using JSON

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC

Supported programming languages

Actionscript

unofficial driver
C
C#
C++
Clojure

unofficial driver
ColdFusion

unofficial driver
D

unofficial driver
Dart

unofficial driver
Delphi

unofficial driver
Erlang
Go
Groovy

unofficial driver
Haskell
Java
JavaScript
Lisp

unofficial driver
Lua

unofficial driver
MatLab

unofficial driver
Perl
PHP
PowerShell

unofficial driver
Prolog

unofficial driver
Python
R

unofficial driver
Ruby
Rust
Scala
Smalltalk

unofficial driver
Swift

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

over HTTP

Server-side scripts

Stored procedures

JavaScript

Triggers

yes

in MongoDB Atlas only

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Partitioned by hashed, ranged, or zoned sharding keys. Live resharding allows users to change their shard keys as an online operation with zero downtime.

horizontal partitioning (by timestamps)

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-Source deployments with MongoDB Atlas Global Clusters
Source-replica replication

Configurable consistency for N replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

Multi-document ACID Transactions with snapshot isolation

ACID for single-table writes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional, enabled by default

yes

In-memory capabilities

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

yes

In-memory storage engine introduced with MongoDB version 3.2

yes

through memory mapped files

User concepts

Access control

Access rights for users and roles

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

MongoDB Time Series Benchmark and Review
20 March 2023

Loading Pandas DataFrames into QuestDB
9 March 2023

How Reliable is Your Distributed Database?
3 March 2023

Running Databases on Kubernetes
2 March 2023

The Tale of Troubleshooting: Unstable Builds and Open Source Infrastructure
1 March 2023

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