DBMS > Microsoft Azure Cosmos DB vs. OrientDB vs. Solr vs. Titan

System Properties Comparison Microsoft Azure Cosmos DB vs. OrientDB vs. Solr vs. Titan

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Editorial information provided by DB-Engines

Name

Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.

Description

Globally distributed, horizontally scalable, multi-model database service

Multi-model DBMS (Document, Graph, Key/Value)

A widely used distributed, scalable search engine based on Apache Lucene

Titan is a Graph DBMS optimized for distributed clusters.

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Document store
Graph DBMS
Key-value store

Search engine

Graph DBMS

Secondary database models

Spatial DBMS

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

Score	29.85
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	3.27
Rank	#97	Overall
	#16	Document stores
	#6	Graph DBMS
	#15	Key-value stores

Score	44.28
Rank	#24	Overall
	#3	Search engines

Website

azure.microsoft.com/services/cosmos-db

orientdb.org

solr.apache.org

github.com/thinkaurelius/titan

Technical documentation

learn.microsoft.com/azure/cosmos-db

www.orientdb.com/docs/last/index.html

solr.apache.org/resources.html

github.com/thinkaurelius/titan/wiki

Developer

Microsoft

OrientDB LTD; CallidusCloud; SAP

Apache Software Foundation

Aurelius, owned by DataStax

Initial release

2014

2010

2006

2012

Current release

3.2.29, March 2024

9.5.0, February 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Apache Version 2

Open Source

Apache license, version 2.0

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

hosted

All OS with a Java JDK (>= JDK 6)

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
OS X
Unix
Windows

Data scheme

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

JSON types

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

All properties auto-indexed by default

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language, no joins

Solr Parallel SQL Interface

APIs and other access methods

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

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Java API
RESTful HTTP/JSON API

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Supported programming languages

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

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Clojure
Java
Python

Server-side scripts

Stored procedures

JavaScript

Java, Javascript

Java plugins

yes

Triggers

JavaScript

Hooks

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

yes

via pluggable storage backends

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

could be achieved with distributed queries

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

yes

via Faunus, a graph analytics engine

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

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

yes

Relationships in graph

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

In-memory capabilities

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

yes

User concepts

Access control

Access rights can be defined down to the item level

Access rights for users and roles; record level security configurable

yes

User authentification and security via Rexster Graph Server

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