DBMS > Alibaba Cloud Table Store vs. Ignite vs. Microsoft Azure Cosmos DB vs. TDengine vs. TinkerGraph

System Properties Comparison Alibaba Cloud Table Store vs. Ignite vs. Microsoft Azure Cosmos DB vs. TDengine vs. TinkerGraph

Editorial information provided by DB-Engines

Name

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Globally distributed, horizontally scalable, multi-model database service

Time Series DBMS and big data platform

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Wide column store

Key-value store
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Graph DBMS

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	0.26
Rank	#301	Overall
	#12	Wide column stores

Score	3.16
Rank	#96	Overall
	#15	Key-value stores
	#49	Relational DBMS

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	0.08
Rank	#348	Overall
	#35	Graph DBMS

Website

www.alibabacloud.com/product/table-store

ignite.apache.org

azure.microsoft.com/services/cosmos-db

github.com/taosdata/TDengine
tdengine.com

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

www.alibabacloud.com/help/en/tablestore

apacheignite.readme.io/docs

learn.microsoft.com/azure/cosmos-db

docs.tdengine.com

Developer

Alibaba

Apache Software Foundation

Microsoft

TDEngine, previously Taos Data

Initial release

2016

2015

2014

2019

2009

Current release

Apache Ignite 2.6

3.0, August 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

AGPL V3, also commercial editions available

Open Source

Apache 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

C++, Java, .Net

Java

Server operating systems

hosted

Linux
OS X
Solaris
Windows

hosted

Linux
Windows

Data scheme

schema-free

yes

schema-free

yes

schema-free

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.

yes

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language

Standard SQL with extensions for time-series applications

APIs and other access methods

HTTP API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

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

JDBC
RESTful HTTP API

TinkerPop 3

Supported programming languages

Java
Python

C#
C++
Java
PHP
Python
Ruby
Scala

.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

Groovy
Java

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

JavaScript

Triggers

yes (cache interceptors and events)

JavaScript

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes (replicated cache)

yes

Implicit feature of the cloud service

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

Atomic single-row operations

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional

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 based on subaccounts and tokens

Security Hooks for custom implementations

Access rights can be defined down to the item level

yes

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

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Licensing and pricing models

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