DBMS > Blueflood vs. Brytlyt vs. EJDB vs. Kinetica

System Properties Comparison Blueflood vs. Brytlyt vs. EJDB vs. Kinetica

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Fully vectorized database across both GPUs and CPUs

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.06
Rank	#352	Overall
	#36	Time Series DBMS

Score	0.27
Rank	#292	Overall
	#132	Relational DBMS

Score	0.13
Rank	#331	Overall
	#46	Document stores

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Website

blueflood.io

brytlyt.io

github.com/Softmotions/ejdb

www.kinetica.com

Technical documentation

github.com/rax-maas/blueflood/wiki

docs.brytlyt.io

github.com/Softmotions/ejdb/blob/master/README.md

docs.kinetica.com

Developer

Rackspace

Brytlyt

Softmotions

Kinetica

Initial release

2013

2016

2012

Current release

5.0, August 2023

7.1, August 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

GPLv2

commercial

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

C, C++ and CUDA

C, C++

Server operating systems

Linux
OS X

Linux
OS X
Windows

server-less

Linux

Data scheme

predefined scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

HTTP REST

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

in-process shared library

JDBC
ODBC
RESTful HTTP API

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Source-replica replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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

GPU vRAM or System RAM

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

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