DBMS > Apache Impala vs. Cassandra vs. CouchDB vs. HyperSQL vs. MySQL

System Properties Comparison Apache Impala vs. Cassandra vs. CouchDB vs. HyperSQL vs. MySQL

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

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

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Widely used open source RDBMS

Primary database model

Key/Value like access via memcached API

Secondary database models

Document store

Vector DBMS

starting with release V5

Spatial DBMS

using the Geocouch extension

Document store
Spatial DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

cassandra.apache.org/doc/latest

docs.couchdb.org/en/stable

hsqldb.org/web/hsqlDocsFrame.html

dev.mysql.com/doc

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

Apache top level project, originally developped by Facebook

Apache Software Foundation

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

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2013

2008

2005

2001

1995

Current release

4.1.0, June 2022

4.1.3, July 2023

3.3.3, December 2023

2.7.2, June 2023

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache version 2

Open Source

Apache version 2

Open Source

based on BSD license

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Erlang

Java

C and C++

Server operating systems

Linux

BSD
Linux
OS X
Windows

Android
BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

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

restricted

only equality queries, not always the best performing solution

yes

via views

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like SELECT, DML and DDL statements (CQL)

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

RESTful HTTP/JSON API

HTTP API

JDBC via HTTP
JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

All languages supporting JDBC/ODBC

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

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

All languages supporting JDBC/ODBC
Java

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

Java, SQL

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

improved architecture with release 2.0

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

Multi-source replication
Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

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

Atomicity and isolation are supported for single operations

atomic operations within a single document possible

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

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 object

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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