DBMS > Apache Doris vs. Badger vs. GridGain vs. SWC-DB

System Properties Comparison Apache Doris vs. Badger vs. GridGain vs. SWC-DB

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

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

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

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Key-value store

Key-value store
Relational DBMS

Wide column store

Secondary database models

Time Series DBMS

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

Score	0.57
Rank	#244	Overall
	#113	Relational DBMS

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

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

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

doris.apache.org
github.com/apache/doris

github.com/dgraph-io/badger

www.gridgain.com

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

Technical documentation

github.com/apache/doris/wiki

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

www.gridgain.com/docs/index.html

Developer

Apache Software Foundation, originally contributed from Baidu

DGraph Labs

GridGain Systems, Inc.

Alex Kashirin

Initial release

2017

2007

2020

Current release

1.2.2, February 2023

GridGain 8.5.1

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Java, C++, .Net

C++

Server operating systems

Linux

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-free

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

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

SQL-like query language

APIs and other access methods

JDBC
MySQL client

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

Proprietary protocol
Thrift

Supported programming languages

Java

C#
C++
Java
PHP
Python
Ruby
Scala

C++

Server-side scripts

Stored procedures

user defined functions

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

Triggers

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes (replicated cache)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Security Hooks for custom implementations

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More resources

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