DBMS > atoti vs. Axibase vs. Dolt vs. Microsoft Azure Cosmos DB vs. RDF4J

System Properties Comparison atoti vs. Axibase vs. Dolt vs. Microsoft Azure Cosmos DB vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

A MySQL compatible DBMS with Git-like versioning of data and schema

Globally distributed, horizontally scalable, multi-model database service

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Object oriented DBMS

Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

RDF store

Secondary database models

Document store

Spatial DBMS

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

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	0.35
Rank	#282	Overall
	#25	Time Series DBMS

Score	1.02
Rank	#191	Overall
	#89	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

atoti.io

axibase.com/docs/atsd/finance

github.com/dolthub/dolt
www.dolthub.com

azure.microsoft.com/services/cosmos-db

rdf4j.org

Technical documentation

docs.atoti.io

docs.dolthub.com

learn.microsoft.com/azure/cosmos-db

rdf4j.org/documentation

Developer

ActiveViam

Axibase Corporation

DoltHub Inc

Microsoft

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2013

2018

2014

2004

Current release

15585

License

Commercial or Open Source

commercial

free versions available

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

Open Source

Apache Version 2.0

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux

Linux
macOS
Windows

hosted

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

SQL-like query language

yes

SQL-like query language

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

CLI Client
HTTP REST

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

Go
Java
PHP
Python
R
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Java
PHP
Python

Server-side scripts

Stored procedures

Python

yes

currently in alpha release

JavaScript

yes

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities

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

yes

User concepts

Access control

Only one user is configurable, and must be specified in the config file at startup

Access rights can be defined down to the item level

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