DBMS > 4D vs. BigObject vs. Google Cloud Datastore vs. InterSystems Caché vs. Microsoft Azure Cosmos DB

System Properties Comparison 4D vs. BigObject vs. Google Cloud Datastore vs. InterSystems Caché vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

Application development environment with integrated database management system

Analytic DBMS for real-time computations and queries

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

A multi-model DBMS and application server

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Document store

Key-value store
Object oriented DBMS
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store

Spatial DBMS

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

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

Website

www.4d.com

bigobject.io

cloud.google.com/datastore

www.intersystems.com/products/cache

azure.microsoft.com/services/cosmos-db

Technical documentation

developer.4d.com

docs.bigobject.io

cloud.google.com/datastore/docs

docs.intersystems.com

learn.microsoft.com/azure/cosmos-db

Developer

4D, Inc

BigObject, Inc.

Google

InterSystems

Microsoft

Initial release

1984

2015

2008

1997

2014

Current release

v20, April 2023

2018.1.4, May 2020

License

Commercial or Open Source

commercial

free community edition available

commercial

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.

Server operating systems

OS X
Windows

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

hosted

Data scheme

yes

schema-free

depending on used data model

schema-free

Typing

predefined data types such as float or date

yes

yes, details here

yes

JSON 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

SQL

Support of SQL

yes

close to SQL 92

SQL-like DML and DDL statements

SQL-like query language (GQL)

yes

SQL-like query language

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

fluentd
ODBC
RESTful HTTP API

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

.NET Client API
JDBC
ODBC
RESTful HTTP API

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

Supported programming languages

4D proprietary IDE
PHP

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

C#
C++
Java

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

Server-side scripts

Stored procedures

yes

Lua

using Google App Engine

yes

JavaScript

Triggers

yes

Callbacks using the Google Apps Engine

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

Multi-source replication using Paxos

Source-replica replication

yes

Implicit feature of the cloud service

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

none

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

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

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

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

Users and groups

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

Access rights for users, groups and roles

Access rights can be defined down to the item level

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