DBMS > CouchDB vs. EJDB vs. Hypertable vs. MySQL vs. Teradata

System Properties Comparison CouchDB vs. EJDB vs. Hypertable vs. MySQL vs. Teradata

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

An open source BigTable implementation based on distributed file systems such as Hadoop

Widely used open source RDBMS

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Document store

Wide column store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store
Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

couchdb.apache.org

github.com/Softmotions/ejdb

www.mysql.com

www.teradata.com

Technical documentation

docs.couchdb.org/en/stable

github.com/Softmotions/ejdb/blob/master/README.md

dev.mysql.com/doc

docs.teradata.com

Developer

Apache Software Foundation

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

Softmotions

Hypertable Inc.

Oracle

since 2010, originally MySQL AB, then Sun

Teradata

Initial release

2005

2012

2009

1995

1984

Current release

3.3.3, December 2023

0.9.8.11, March 2016

8.4.0, April 2024

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

GPLv2

Open Source

GNU version 3. Commercial license available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

Erlang

C++

C and C++

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

server-less

Linux
OS X
Windows

an inofficial Windows port is available

FreeBSD
Linux
OS X
Solaris
Windows

hosted
Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

via views

restricted

only exact value or prefix value scans

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

yes

with proprietary extensions

yes

SQL 2016 + extensions

APIs and other access methods

RESTful HTTP/JSON API

in-process shared library

C++ API
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

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

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C++
Java
Perl
PHP
Python
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

View functions in JavaScript

yes

proprietary syntax

yes

UDFs, stored procedures, table functions in parallel

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

selectable replication factor on file system level

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

not for MyISAM storage engine

yes

Transaction concepts

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

atomic operations within a single document possible

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Read/Write Locking

yes

table locks or row locks depending on storage engine

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

Access rights for users can be defined per database

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

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