DBMS > Bangdb vs. Kdb vs. SiteWhere vs. Snowflake vs. Trafodion

System Properties Comparison Bangdb vs. Kdb vs. SiteWhere 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

Converged and high performance database for device data, events, time series, document and graph

High performance Time Series DBMS

M2M integration platform for persisting/querying time series data

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Document store
Graph DBMS
Time Series DBMS

Time Series DBMS
Vector DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	7.71
Rank	#49	Overall
	#2	Time Series DBMS
	#1	Vector DBMS

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

bangdb.com

kx.com

github.com/sitewhere/sitewhere

www.snowflake.com

trafodion.apache.org

Technical documentation

docs.bangdb.com

code.kx.com

sitewhere1.sitewhere.io/index.html

docs.snowflake.net/manuals/index.html

trafodion.apache.org/documentation.html

Developer

Sachin Sinha, BangDB

Kx Systems, a division of First Derivatives plc

SiteWhere

Snowflake Computing Inc.

Apache Software Foundation, originally developed by HP

Initial release

2012

2000

kdb was released 2000, kdb+ in 2003

2010

2014

Current release

BangDB 2.0, October 2021

3.6, May 2018

2.3.0, February 2019

License

Commercial or Open Source

Open Source

BSD 3

commercial

free 32-bit version

Open Source

Common Public Attribution License Version 1.0

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

C, C++

Java

C++, Java

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

hosted

Linux

Data scheme

schema-free

yes

predefined scheme

yes

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

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

table attribute 'grouped'

yes

SQL

Support of SQL

SQL like support with command line tool

SQL-like query language (q)

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

HTTP REST

CLI Client
JDBC
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

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

JavaScript (Node.js)
Python

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

user defined functions

Java Stored Procedures

Triggers

yes, Notifications (with Streaming only)

yes

with views

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

horizontal partitioning

Sharding

based on HBase

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Source-replica replication

selectable replication factor

based on HBase

yes

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

rights management via user accounts

Users with fine-grained authorization concept

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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