DBMS > Apache Impala vs. CouchDB vs. Graph Engine vs. Kdb vs. MySQL

System Properties Comparison Apache Impala vs. CouchDB vs. Graph Engine vs. Kdb vs. MySQL

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

High performance Time Series DBMS

Widely used open source RDBMS

Primary database model

Relational DBMS

Document store

Graph DBMS
Key-value store

Time Series DBMS
Vector DBMS

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store

Spatial DBMS

using the Geocouch extension

Relational DBMS

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	0.62
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	7.70
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

docs.couchdb.org/en/stable

www.graphengine.io/docs/manual

code.kx.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

Microsoft

Kx Systems, a division of First Derivatives plc

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2013

2005

2010

2000

kdb was released 2000, kdb+ in 2003

1995

Current release

4.1.0, June 2022

3.3.3, December 2023

3.6, May 2018

8.3.0, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache version 2

Open Source

MIT License

commercial

free 32-bit version

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

.NET and C

C and C++

Server operating systems

Linux

Android
BSD
Linux
OS X
Solaris
Windows

.NET

Linux
OS X
Solaris
Windows

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

via views

yes

table attribute 'grouped'

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (q)

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

RESTful HTTP API

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

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#
C++
F#
Visual Basic

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

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

user defined functions

yes

proprietary syntax

Triggers

yes

with views

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

horizontal partitioning

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

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

similar paradigm used for internal processing

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

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

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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 database

rights management via user accounts

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
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Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
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