DBMS > IBM Cloudant vs. Microsoft Azure Cosmos DB vs. TDengine vs. VoltDB

System Properties Comparison IBM Cloudant vs. Microsoft Azure Cosmos DB vs. TDengine vs. VoltDB

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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

Time Series DBMS and big data platform

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

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Relational 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	2.60
Rank	#107	Overall
	#8	Time Series DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

www.ibm.com/products/cloudant

azure.microsoft.com/services/cosmos-db

github.com/taosdata/TDengine
tdengine.com

www.voltdb.com

Technical documentation

cloud.ibm.com/docs/Cloudant

learn.microsoft.com/azure/cosmos-db

docs.tdengine.com

docs.voltdb.com

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Microsoft

TDEngine, previously Taos Data

VoltDB Inc.

Initial release

2010

2014

2019

2010

Current release

3.0, August 2022

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

AGPL V3, also commercial editions available

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Erlang

Java, C++

Server operating systems

hosted

Linux
Windows

Linux
OS X

for development

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

SQL-like query language

Standard SQL with extensions for time-series applications

yes

only a subset of SQL 99

APIs and other access methods

RESTful HTTP/JSON API

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

JDBC
RESTful HTTP API

Java API
JDBC
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

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Rust

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

JavaScript

Java

Triggers

yes

JavaScript

yes, via alarm monitoring

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

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

yes

Multi-source replication
Source-replica replication

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

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

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

User concepts

Access control

Access rights for users can be defined per database

Access rights can be defined down to the item level

yes

Users and roles with access to stored procedures

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Market metrics

TDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
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Licensing and pricing models

TDengine OSS is an open source, cloud native time series database. It includes built-in...
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News

Seamless Data Integration from MQTT and InfluxDB to TDengine
22 May 2024

Solving Long Query Performance Bottlenecks
22 May 2024

What Is Predictive Maintenance?
17 May 2024

Can Typical Time-Series Databases Replace Data Historians?
8 May 2024

TDengine 3.3.0.0 Release Notes
7 May 2024

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