DBMS > Microsoft Azure Cosmos DB vs. NSDb vs. OrientDB vs. Solr vs. Tigris

System Properties Comparison Microsoft Azure Cosmos DB vs. NSDb vs. OrientDB vs. Solr vs. Tigris

Editorial information provided by DB-Engines

Name

Description

Globally distributed, horizontally scalable, multi-model database service

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

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

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

A horizontally scalable, ACID transactional, document database available both as a fully managed cloud service and for deployment on self-managed infrastructure

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Document store
Graph DBMS
Key-value store

Search engine

Document store
Key-value store
Search engine
Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.02
Rank	#369	Overall
	#51	Document stores
	#55	Key-value stores
	#24	Search engines
	#38	Time Series DBMS

Website

azure.microsoft.com/services/cosmos-db

Technical documentation

learn.microsoft.com/azure/cosmos-db

nsdb.io/Architecture

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

solr.apache.org/resources.html

www.tigrisdata.com/docs

Developer

Microsoft

OrientDB LTD; CallidusCloud; SAP

Apache Software Foundation

Tigris Data, Inc.

Initial release

2014

2017

2010

2006

2022

Current release

3.2.29, March 2024

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache version 2

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, Scala

Java

Server operating systems

hosted

Linux
macOS

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
macOS
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: int, bigint, decimal, string

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

all fields are automatically indexed

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

gRPC
HTTP REST
WebSocket

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

Java API
RESTful HTTP/JSON API

CLI Client
gRPC
RESTful HTTP API

Supported programming languages

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

Java
Scala

.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

Go
Java
JavaScript (Node.js)

Server-side scripts

Stored procedures

JavaScript

Java, Javascript

Java plugins

Triggers

JavaScript

Hooks

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

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

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

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

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

Using Apache Lucene

yes

yes, using FoundationDB

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

Access rights for users and roles

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