DBMS > Google Cloud Datastore vs. InterSystems IRIS vs. Microsoft Azure Cosmos DB vs. TimescaleDB

System Properties Comparison Google Cloud Datastore vs. InterSystems IRIS vs. Microsoft Azure Cosmos DB vs. TimescaleDB

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Editorial information provided by DB-Engines

Name

Description

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

A containerised multi-model DBMS, interoperability and analytics data platform with wide capabilities for vertical and horizontal scalability

Globally distributed, horizontally scalable, multi-model database service

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

Primary database model

Document store

Document store
Key-value store
Object oriented DBMS
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	3.54
Rank	#84	Overall
	#14	Document stores
	#10	Key-value stores
	#1	Object oriented DBMS
	#45	Relational DBMS

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

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

cloud.google.com/datastore

www.intersystems.com/products/intersystems-iris

azure.microsoft.com/services/cosmos-db

www.timescale.com

Technical documentation

cloud.google.com/datastore/docs

docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls

learn.microsoft.com/azure/cosmos-db

docs.timescale.com

Developer

Google

InterSystems

Microsoft

Timescale

Initial release

2008

2018

2014

2017

Current release

2023.3, June 2023

2.15.0, May 2024

License

Commercial or Open Source

commercial

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

Server operating systems

hosted

AIX
Linux
macOS
Ubuntu
Windows

hosted

Linux
OS X
Windows

Data scheme

schema-free

depending on used data model

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

yes

JSON types

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 (GQL)

yes

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

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

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

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C++
Java
JavaScript (Node.js)
Python

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

using Google App Engine

yes

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

Callbacks using the Google Apps Engine

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Source-replica replication

yes

Implicit feature of the cloud service

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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 for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

InterSystems IRIS is a complete cloud-first data platform which includes a multi-model...
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