DBMS > Apache Impala vs. CouchDB vs. Hypertable vs. MarkLogic vs. MySQL

System Properties Comparison Apache Impala vs. CouchDB vs. Hypertable vs. MarkLogic vs. MySQL

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

Analytic DBMS for Hadoop

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

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

Operational and transactional Enterprise NoSQL database

Widely used open source RDBMS

Primary database model

Relational DBMS

Document store

Wide column store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store

Spatial DBMS

using the Geocouch extension

Document store
Spatial DBMS

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

Score	14.03
Rank	#40	Overall
	#24	Relational DBMS

Score	10.26
Rank	#45	Overall
	#7	Document stores

Score	6.50
Rank	#56	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#5	Search engines

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

docs.couchdb.org/en/stable

docs.marklogic.com

dev.mysql.com/doc

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

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

Hypertable Inc.

MarkLogic Corp.

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2013

2005

2009

2001

1995

Current release

4.1.0, June 2022

3.3.3, December 2023

0.9.8.11, March 2016

11.0, December 2022

8.3.0, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache version 2

Open Source

GNU version 3. Commercial license available

commercial

restricted free version is available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Erlang

C++

C and C++

Server operating systems

Linux

Android
BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

an inofficial Windows port is available

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

Schema can be enforced

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.

yes

Secondary indexes

yes

via views

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL92

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

C++ API
Thrift

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

All languages supporting JDBC/ODBC

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

C++
Java
Perl
PHP
Python
Ruby

C
C#
C++
Java
JavaScript (Node.js)
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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

View functions in JavaScript

yes

via XQuery or JavaScript

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

selectable replication factor on file system level

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

atomic operations within a single document possible

ACID

can act as a resource manager in an XA/JTA transaction

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

table locks or row locks depending on storage engine

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, with Range Indexes

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights for users can be defined per database

Role-based access control at the document and subdocument levels

Users with fine-grained authorization concept

no user groups or roles

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