DBMS > EXASOL vs. Kdb vs. Riak KV vs. Snowflake vs. Trafodion

System Properties Comparison EXASOL vs. Kdb vs. Riak KV vs. Snowflake vs. Trafodion

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

High performance Time Series DBMS

Distributed, fault tolerant key-value store

Cloud-based data warehousing service for structured and semi-structured data

Transactional SQL-on-Hadoop DBMS

Primary database model

Relational DBMS

Time Series DBMS
Vector DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Secondary database models

Relational DBMS

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

Score	2.25
Rank	#120	Overall
	#57	Relational DBMS

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

Score	4.44
Rank	#80	Overall
	#9	Key-value stores

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Website

Technical documentation

www.exasol.com/resources

code.kx.com

www.tiot.jp/riak-docs/riak/kv/latest

docs.snowflake.net/manuals/index.html

trafodion.apache.org/documentation.html

Developer

Exasol

Kx Systems, a division of First Derivatives plc

OpenSource, formerly Basho Technologies

Snowflake Computing Inc.

Apache Software Foundation, originally developed by HP

Initial release

2000

kdb was released 2000, kdb+ in 2003

2009

2014

Current release

3.6, May 2018

3.2.0, December 2022

2.3.0, February 2019

License

Commercial or Open Source

commercial

free 32-bit version

Open Source

Apache version 2, commercial enterprise edition

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Erlang

C++, Java

Server operating systems

Linux
OS X
Solaris
Windows

Linux
OS X

hosted

Linux

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

table attribute 'grouped'

restricted

yes

SQL

Support of SQL

yes

SQL-like query language (q)

yes

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

HTTP API
Native Erlang Interface

CLI Client
JDBC
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

Java
Lua
Python
R

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

JavaScript (Node.js)
Python

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

user defined functions

Erlang

user defined functions

Java Stored Procedures

Triggers

yes

with views

yes

pre-commit hooks and post-commit hooks

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Sharding

no "single point of failure"

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

yes

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

similar paradigm used for internal processing

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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, groups and roles according to SQL-standard

rights management via user accounts

yes, using Riak Security

Users with fine-grained authorization concept, user roles and pluggable authentication

fine grained access rights according to SQL-standard

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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