DBMS > AgensGraph vs. Graphite vs. STSdb vs. VoltDB

System Properties Comparison AgensGraph vs. Graphite vs. STSdb vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Graph DBMS
Relational DBMS

Time Series DBMS

Key-value store

Relational DBMS

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

Score	0.16
Rank	#333	Overall
	#30	Graph DBMS
	#146	Relational DBMS

Score	4.75
Rank	#75	Overall
	#5	Time Series DBMS

Score	0.06
Rank	#365	Overall
	#55	Key-value stores

Score	1.46
Rank	#159	Overall
	#74	Relational DBMS

Website

bitnine.net/agensgraph

github.com/graphite-project/graphite-web

github.com/STSSoft/STSdb4

www.voltdb.com

Technical documentation

bitnine.net/documentation

graphite.readthedocs.io

docs.voltdb.com

Developer

Bitnine Global Inc.

Chris Davis

STS Soft SC

VoltDB Inc.

Initial release

2016

2006

2011

2010

Current release

2.1, December 2018

4.0.8, September 2015

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Python

Java, C++

Server operating systems

Linux
OS X
Windows

Linux
Unix

Windows

Linux
OS X

for development

Data scheme

depending on used data model

yes

Typing

predefined data types such as float or date

yes

Numeric data only

yes

primitive types and user defined types (classes)

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.

Secondary indexes

yes

SQL

Support of SQL

yes

only a subset of SQL 99

APIs and other access methods

Cypher Query Language
JDBC

HTTP API
Sockets

.NET Client API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C
Java
JavaScript
Python

JavaScript (Node.js)
Python

C#
Java

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

Java

Triggers

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

locking

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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