DBMS > Badger vs. Blueflood vs. Datomic vs. Kdb

System Properties Comparison Badger vs. Blueflood vs. Datomic vs. Kdb

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Scalable TimeSeries DBMS based on Cassandra

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

High performance Time Series DBMS

Primary database model

Key-value store

Time Series DBMS

Relational DBMS

Time Series DBMS
Vector DBMS

Secondary database models

Relational DBMS

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

Score	0.20
Rank	#325	Overall
	#49	Key-value stores

Score	0.09
Rank	#353	Overall
	#33	Time Series DBMS

Score	1.76
Rank	#145	Overall
	#66	Relational DBMS

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

Website

github.com/dgraph-io/badger

blueflood.io

www.datomic.com

kx.com

Technical documentation

godoc.org/github.com/dgraph-io/badger

github.com/rax-maas/blueflood/wiki

docs.datomic.com

code.kx.com

Developer

DGraph Labs

Rackspace

Cognitect

Kx Systems, a division of First Derivatives plc

Initial release

2017

2013

2012

2000

kdb was released 2000, kdb+ in 2003

Current release

1.0.6735, June 2023

3.6, May 2018

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

limited edition free

commercial

free 32-bit version

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Java, Clojure

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X

All OS with a Java VM

Linux
OS X
Solaris
Windows

Data scheme

schema-free

predefined scheme

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'

SQL

Support of SQL

SQL-like query language (q)

APIs and other access methods

HTTP REST

RESTful HTTP API

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

Supported programming languages

Clojure
Java

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

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

Triggers

By using transaction functions

yes

with views

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

none

But extensive use of caching in the application peers

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on Cassandra

none

But extensive use of caching in the application peers

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

rights management via user accounts

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more

Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
» more

Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
» more

Licensing and pricing models

upon request
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Stanchion Turns SQLite Into A Column Store
15 February 2024, iProgrammer

Nubank buys firm behind Clojure programming language
28 July 2020, Finextra

Zoona Case Study
16 December 2017, AWS Blog

Architecting Software for Leverage
13 November 2021, InfoQ.com

TerminusDB Takes on Data Collaboration with a git-Like Approach
1 December 2020, The New Stack

provided by Google News

Turbocharging the Engine: KX Unleashes AI-First Transformation with kdb+
28 February 2024, Business Wire

Introducing Amazon FinSpace with Managed kdb Insights, a fully managed analytics engine, commonly used by capital ...
18 May 2023, AWS Blog

McLaren Applied and KX partner to enhance ATLAS software analytics capabilities
9 August 2023, Professional Motorsport World

KX ANNOUNCES KDB INSIGHTS AS FULLY MANAGED SERVICE ON AMAZON FINSPACE
18 May 2023, Yahoo Finance

KX Brings the Power and Performance of kdb+ to Python Developers with PyKX
7 June 2023, Datanami

provided by Google News

Share this page