DBMS > Badger vs. SWC-DB vs. Tigris

System Properties Comparison Badger vs. SWC-DB vs. Tigris

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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, scalable Wide Column DBMS

A horizontally scalable, ACID transactional, document database available both as a fully managed cloud service and for deployment on self-managed infrastructure

Primary database model

Key-value store

Wide column store

Document store
Key-value store
Search engine
Time Series DBMS

Secondary database models

Time Series DBMS

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

Score	0.24
Rank	#300	Overall
	#43	Key-value stores

Score	0.01
Rank	#382	Overall
	#13	Wide column stores

Score	0.05
Rank	#360	Overall
	#50	Document stores
	#53	Key-value stores
	#23	Search engines
	#37	Time Series DBMS

Website

github.com/dgraph-io/badger

github.com/kashirin-alex/swc-db
www.swcdb.org

www.tigrisdata.com

Technical documentation

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

www.tigrisdata.com/docs

Developer

DGraph Labs

Alex Kashirin

Tigris Data, Inc.

Initial release

2017

2020

2022

Current release

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPL V3

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

Linux
macOS
Windows

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

SQL

Support of SQL

SQL-like query language

APIs and other access methods

Proprietary protocol
Thrift

CLI Client
gRPC
RESTful HTTP API

Supported programming languages

C++

Go
Java
JavaScript (Node.js)

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using FoundationDB

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 and roles

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