DBMS > AgensGraph vs. LevelDB vs. MonetDB vs. TigerGraph vs. VoltDB

System Properties Comparison AgensGraph vs. LevelDB vs. MonetDB vs. TigerGraph vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

A relational database management system that stores data in columns

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

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

Secondary database models

Document store
Spatial DBMS

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

bitnine.net/agensgraph

github.com/google/leveldb

www.monetdb.org

www.tigergraph.com

www.voltdb.com

Technical documentation

bitnine.net/documentation

github.com/google/leveldb/blob/main/doc/index.md

www.monetdb.org/Documentation

docs.tigergraph.com

docs.voltdb.com

Developer

Bitnine Global Inc.

Google

MonetDB BV

VoltDB Inc.

Initial release

2016

2011

2004

2017

2010

Current release

2.1, December 2018

1.23, February 2021

Dec2023 (11.49), December 2023

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

BSD

Open Source

Mozilla Public License 2.0

commercial

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

C++

Java, C++

Server operating systems

Linux
OS X
Windows

Illumos
Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Linux
OS X

for development

Data scheme

depending on used data model

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL 2003 with some extensions

SQL-like query language (GSQL)

yes

only a subset of SQL 99

APIs and other access methods

Cypher Query Language
JDBC

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C
Java
JavaScript
Python

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C++
Java

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

yes, in SQL, C, R

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

none

Sharding via remote tables

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Source-replica replication available in experimental status

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

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

Data access is serialized by the server

Durability

Support for making data persistent

yes

with automatic compression on writes

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

Role-based access control

Users and roles with access to stored procedures

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