DBMS > Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. ObjectBox vs. OpenMLDB vs. TimescaleDB

System Properties Comparison Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. ObjectBox vs. OpenMLDB vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

Globally distributed, horizontally scalable, multi-model database service

A Wide Column Store for rapid development using massive semi-structured datasets

Extremely fast embedded database for small devices, IoT and Mobile

An open-source machine learning database that provides a feature platform for training and inference

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Object oriented DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

Time Series DBMS

Relational DBMS

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

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/services/storage/tables

objectbox.io

openmldb.ai

www.timescale.com

Technical documentation

learn.microsoft.com/azure/cosmos-db

docs.objectbox.io

openmldb.ai/docs/zh/main

docs.timescale.com

Developer

Microsoft

ObjectBox Limited

4 Paradigm Inc.

Timescale

Initial release

2014

2012

2017

2020

2017

Current release

2024-2 February 2024

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

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 and C++

C++, Java, Scala

Server operating systems

hosted

Android
iOS
Linux
macOS
Windows

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

Fixed schema

yes

Typing

predefined data types such as float or date

yes

JSON types

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

SQL

Support of SQL

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

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

RESTful HTTP API

Proprietary native API

JDBC
SQLAlchemy

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

C++
Go
Java
Python
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

JavaScript

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

none

horizontal partitioning

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

online/offline synchronization between client and server

Source-replica replication

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

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

Access rights can be defined down to the item level

Access rights based on private key authentication or shared access signatures

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

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