DBMS > EJDB vs. HEAVY.AI vs. Newts vs. STSdb vs. SWC-DB

System Properties Comparison EJDB vs. HEAVY.AI vs. Newts vs. STSdb vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Time Series DBMS based on Cassandra

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Spatial DBMS

Time Series DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

github.com/Softmotions/ejdb

github.com/heavyai/heavydb
www.heavy.ai

opennms.github.io/newts

github.com/STSSoft/STSdb4

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

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

docs.heavy.ai

github.com/OpenNMS/newts/wiki

Developer

Softmotions

HEAVY.AI, Inc.

OpenNMS Group

STS Soft SC

Alex Kashirin

Initial release

2012

2016

2014

2011

2020

Current release

5.10, January 2022

4.0.8, September 2015

0.5, April 2021

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

Open Source

GPL V3

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

C++ and CUDA

Java

C++

Server operating systems

server-less

Linux

Linux
OS X
Windows

Windows

Linux

Data scheme

schema-free

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

primitive types and user defined types (classes)

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.

Secondary indexes

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

in-process shared library

JDBC
ODBC
Thrift
Vega

HTTP REST
Java API

.NET Client API

Proprietary protocol
Thrift

Supported programming languages

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

All languages supporting JDBC/ODBC/Thrift
Python

Java

C#
Java

C++

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Sharding

based on Cassandra

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

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

User concepts

Access control

fine grained access rights according to SQL-standard

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