DBMS > CouchDB vs. GridGain vs. Microsoft SQL Server vs. Ultipa vs. XTDB

System Properties Comparison CouchDB vs. GridGain vs. Microsoft SQL Server vs. Ultipa vs. XTDB

Editorial information provided by DB-Engines

Name

Description

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

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

Microsofts flagship relational DBMS

High performance Graph DBMS supporting HTAP high availability cluster deployment

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

Primary database model

Document store

Key-value store
Relational DBMS

Relational DBMS

Graph DBMS

Document store

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store
Graph DBMS
Spatial DBMS

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

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.19
Rank	#330	Overall
	#30	Graph DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

couchdb.apache.org

www.gridgain.com

www.microsoft.com/en-us/sql-server

www.ultipa.com

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

Technical documentation

docs.couchdb.org/en/stable

www.gridgain.com/docs/index.html

learn.microsoft.com/en-US/sql/sql-server

www.ultipa.com/document

www.xtdb.com/docs

Developer

Apache Software Foundation

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

GridGain Systems, Inc.

Microsoft

Ultipa

Juxt Ltd.

Initial release

2005

2007

1989

2019

Current release

3.3.3, December 2023

GridGain 8.5.1

SQL Server 2022, November 2022

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache version 2

commercial

restricted free version is available

commercial

Open Source

MIT License

Cloud-based only

Only available as a cloud service

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Implementation language

Erlang

Java, C++, .Net

C++

Clojure

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Linux
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

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

via views

yes

SQL

Support of SQL

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

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP/JSON API

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

RESTful HTTP API

HTTP REST
JDBC

Supported programming languages

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

C#
C++
Java
PHP
Python
Ruby
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C++
Go
Java
JavaScript (Node.js)
Python

Clojure
Java

Server-side scripts

Stored procedures

View functions in JavaScript

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

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes (replicated cache)

yes, but depending on the SQL-Server Edition

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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

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

Access rights for users can be defined per database

Security Hooks for custom implementations

fine grained access rights according to SQL-standard

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