DBMS > Apache IoTDB vs. CouchDB vs. Microsoft Azure Cosmos DB vs. TigerGraph

System Properties Comparison Apache IoTDB vs. CouchDB vs. Microsoft Azure Cosmos DB vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Globally distributed, horizontally scalable, multi-model database service

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

Primary database model

Time Series DBMS

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

Spatial DBMS

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

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Score	10.26
Rank	#45	Overall
	#7	Document stores

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

Score	1.83
Rank	#141	Overall
	#13	Graph DBMS

Website

iotdb.apache.org

couchdb.apache.org

azure.microsoft.com/services/cosmos-db

www.tigergraph.com

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.couchdb.org/en/stable

learn.microsoft.com/azure/cosmos-db

docs.tigergraph.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Microsoft

Initial release

2018

2005

2014

2017

Current release

1.1.0, April 2023

3.3.3, December 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache version 2

commercial

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

Java

Erlang

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Android
BSD
Linux
OS X
Solaris
Windows

hosted

Linux

Data scheme

yes

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

via views

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like query language

SQL-like query language (GSQL)

APIs and other access methods

JDBC
Native API

RESTful HTTP/JSON API

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

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

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

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

C++
Java

Server-side scripts

Stored procedures

yes

View functions in JavaScript

JavaScript

yes

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

improved architecture with release 2.0

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session 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 single document possible

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: 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

User concepts

Access control

yes

Access rights for users can be defined per database

Access rights can be defined down to the item level

Role-based access control

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