DBMS > Badger vs. CouchDB vs. LevelDB vs. TigerGraph

System Properties Comparison Badger vs. CouchDB vs. LevelDB vs. TigerGraph

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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 native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Secondary database models

Spatial DBMS

using the Geocouch extension

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

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

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Website

github.com/dgraph-io/badger

couchdb.apache.org

github.com/google/leveldb

www.tigergraph.com

Technical documentation

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

docs.couchdb.org/en/stable

github.com/google/leveldb/blob/main/doc/index.md

docs.tigergraph.com

Developer

DGraph Labs

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Google

Initial release

2017

2005

2011

2017

Current release

3.3.3, December 2023

1.23, February 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache version 2

Open Source

BSD

commercial

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

Erlang

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Android
BSD
Linux
OS X
Solaris
Windows

Illumos
Linux
OS X
Windows

Linux

Data scheme

schema-free

yes

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

yes

via views

SQL

Support of SQL

SQL-like query language (GSQL)

APIs and other access methods

RESTful HTTP/JSON API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C++
Java

Server-side scripts

Stored procedures

View functions in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

improved architecture with release 2.0

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

none

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

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

atomic operations within a single document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

User concepts

Access control

Access rights for users can be defined per database

Role-based access control

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More resources

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