DBMS > Graph Engine vs. NSDb vs. RavenDB vs. Valentina Server vs. VoltDB

System Properties Comparison Graph Engine vs. NSDb vs. RavenDB vs. Valentina Server vs. VoltDB

Editorial information provided by DB-Engines

Name

Description

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Open Source Operational and Transactional Enterprise NoSQL Document Database

Object-relational database and reports server

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
Key-value store

Time Series DBMS

Document store

Relational DBMS

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

Technical documentation

www.graphengine.io/docs/manual

nsdb.io/Architecture

ravendb.net/docs

valentina-db.com/docs/dokuwiki/v5/doku.php

docs.voltdb.com

Developer

Microsoft

Hibernating Rhinos

Paradigma Software

VoltDB Inc.

Initial release

2010

2017

2010

1999

2010

Current release

5.4, July 2022

5.7.5

11.3, April 2022

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache Version 2.0

Open Source

AGPL version 3, commercial license available

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

.NET and C

Java, Scala

Java, C++

Server operating systems

.NET

Linux
macOS

Linux
macOS
Raspberry Pi
Windows

Linux
OS X
Windows

Linux
OS X

for development

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (RQL)

yes

only a subset of SQL 99

APIs and other access methods

RESTful HTTP API

gRPC
HTTP REST
WebSocket

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C#
C++
F#
Visual Basic

Java
Scala

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

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

Eventual Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

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, Cluster-wide transaction available

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

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

Using Apache Lucene

yes

Snapshots and command logging

In-memory capabilities

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

yes

User concepts

Access control

Authorization levels configured per client per database

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

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More resources

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