DBMS > Badger vs. GridGain vs. RethinkDB vs. SwayDB vs. XTDB

System Properties Comparison Badger vs. GridGain vs. RethinkDB vs. SwayDB vs. XTDB

Editorial information provided by DB-Engines

Name

Description

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

GridGain is an in-memory computing platform, built on Apache Ignite

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Key-value store

Key-value store
Relational DBMS

Document store

Key-value store

Document store

Secondary database models

Spatial DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

github.com/dgraph-io/badger

www.gridgain.com

rethinkdb.com

swaydb.simer.au

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

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

www.gridgain.com/docs/index.html

rethinkdb.com/docs

www.xtdb.com/docs

Developer

DGraph Labs

GridGain Systems, Inc.

The Linux Foundation

since July 2017

Simer Plaha

Juxt Ltd.

Initial release

2017

2007

2009

2018

2019

Current release

GridGain 8.5.1

2.4.1, August 2020

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2

Open Source

GNU Affero GPL V3.0

Open Source

MIT License

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++, .Net

C++

Scala

Clojure

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

yes, extensible-data-notation format

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.

yes

Secondary indexes

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

limited SQL, making use of Apache Calcite

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

HTTP REST
JDBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Java
Kotlin
Scala

Clojure
Java

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

Triggers

yes (cache interceptors and events)

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

range based

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes (replicated cache)

Source-replica replication

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

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

Atomic single-document operations

Atomic execution of operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

Security Hooks for custom implementations

yes

users and table-level permissions

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More resources

DB-Engines blog posts

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