DBMS > IBM Cloudant vs. Microsoft Azure Cosmos DB vs. Solr vs. TigerGraph vs. Tkrzw

System Properties Comparison IBM Cloudant vs. Microsoft Azure Cosmos DB vs. Solr vs. TigerGraph vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

Database as a Service offering based on Apache CouchDB

Globally distributed, horizontally scalable, multi-model database service

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

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

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Search engine

Graph DBMS

Key-value store

Secondary database models

Spatial DBMS

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

Score	2.68
Rank	#106	Overall
	#20	Document stores

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

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

www.ibm.com/products/cloudant

azure.microsoft.com/services/cosmos-db

solr.apache.org

www.tigergraph.com

dbmx.net/tkrzw

Technical documentation

cloud.ibm.com/docs/Cloudant

learn.microsoft.com/azure/cosmos-db

solr.apache.org/resources.html

docs.tigergraph.com

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Microsoft

Apache Software Foundation

Mikio Hirabayashi

Initial release

2010

2014

2006

2017

2020

Current release

9.6.0, April 2024

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

Apache 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

Erlang

Java

C++

Server operating systems

hosted

All OS with a Java VM

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

Linux

Linux
macOS

Data scheme

schema-free

yes

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

yes

schema-free

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

SQL

Support of SQL

SQL-like query language

Solr Parallel SQL Interface

SQL-like query language (GSQL)

APIs and other access methods

RESTful HTTP/JSON API

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

Java API
RESTful HTTP/JSON API

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

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

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

C++
Java

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

JavaScript

Java plugins

yes

Triggers

yes

JavaScript

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

Relationships in graphs

Transaction concepts

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

atomic operations within a document possible

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

using specific database classes

User concepts

Access control

Access rights for users can be defined per database

Access rights can be defined down to the item level

yes

Role-based access control

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