DBMS > Google Cloud Datastore vs. Microsoft Access vs. MongoDB vs. openGauss vs. Oracle Coherence

System Properties Comparison Google Cloud Datastore vs. Microsoft Access vs. MongoDB vs. openGauss vs. Oracle Coherence

Editorial information provided by DB-Engines

Name

Description

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

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

One of the most popular document stores available both as a fully managed cloud service and for deployment on self-managed infrastructure

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

Oracles in-memory data grid solution

Primary database model

Secondary database models

Spatial DBMS
Search engine

integrated Lucene index, currently in MongoDB Atlas only.
Time Series DBMS

Time Series Collections introduced in Release 5.0
Vector DBMS

currently available in the MongoDB Atlas cloud service only

Document store
Spatial DBMS

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	421.65
Rank	#5	Overall
	#1	Document stores

Score	1.08
Rank	#183	Overall
	#84	Relational DBMS

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Website

cloud.google.com/datastore

www.microsoft.com/en-us/microsoft-365/access

www.mongodb.com

gitee.com/opengauss
opengauss.org

www.oracle.com/java/coherence

Technical documentation

cloud.google.com/datastore/docs

developer.microsoft.com/en-us/access

www.mongodb.com/docs/manual

docs.opengauss.org/en
gitee.com/opengauss/docs

docs.oracle.com/en/middleware/standalone/coherence

Developer

Google

Microsoft

MongoDB, Inc

Huawei and openGauss community

Oracle

Initial release

2008

1992

2009

2019

2007

Current release

1902 (16.0.11328.20222), March 2019

6.0.7, June 2023

3.0, March 2022

14.1, August 2023

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

Open Source

MongoDB Inc.'s Server Side Public License v1. Prior versions were published under GNU AGPL v3.0. Commercial licenses are also available.

Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

MongoDB available as DBaaS (MongoDB Atlas)

DBaaS offerings (sponsored links)

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Implementation language

C++

C, C++, Java

Java

Server operating systems

hosted

Windows

Not a real database server, but making use of DLLs

Linux
OS X
Solaris
Windows

Linux

All OS with a Java VM

Data scheme

schema-free

yes

schema-free

Although schema-free, documents of the same collection often follow the same structure. Optionally impose all or part of a schema by defining a JSON schema.

yes

schema-free

Typing

predefined data types such as float or date

yes, details here

yes

string, integer, double, decimal, boolean, date, object_id, geospatial

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

SQL

Support of SQL

SQL-like query language (GQL)

yes

but not compliant to any SQL standard

Read-only SQL queries via the MongoDB Atlas SQL Interface

ANSI SQL 2011

APIs and other access methods

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

ADO.NET
DAO
ODBC
OLE DB

GraphQL
HTTP REST
Prisma
proprietary protocol using JSON

JDBC
ODBC

JCache
JPA
RESTful HTTP API

Supported programming languages

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

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

Actionscript

unofficial driver
C
C#
C++
Clojure

unofficial driver
ColdFusion

unofficial driver
D

unofficial driver
Dart

unofficial driver
Delphi

unofficial driver
Erlang
Go
Groovy

unofficial driver
Haskell
Java
JavaScript
Kotlin
Lisp

unofficial driver
Lua

unofficial driver
MatLab

unofficial driver
Perl
PHP
PowerShell

unofficial driver
Prolog

unofficial driver
Python
R

unofficial driver
Ruby
Rust
Scala
Smalltalk

unofficial driver
Swift

C
C++
Java

.Net
C++
Java

Server-side scripts

Stored procedures

using Google App Engine

yes

since Access 2010 using the ACE-engine

JavaScript

yes

Triggers

Callbacks using the Google Apps Engine

yes

since Access 2010 using the ACE-engine

yes

in MongoDB Atlas only

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Partitioned by hashed, ranged, or zoned sharding keys. Live resharding allows users to change their shard keys as an online operation with zero downtime.

horizontal partitioning (by range, list and hash)

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

none

Multi-Source deployments with MongoDB Atlas Global Clusters
Source-replica replication

Source-replica replication

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

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.

Eventual Consistency

can be individually decided for each read operation
Immediate Consistency

default behaviour

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

typically not used, however similar functionality with DBRef possible

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

but no files for transaction logging

Multi-document ACID Transactions with snapshot isolation

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

optional, enabled by default

yes

optionally

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

In-memory storage engine introduced with MongoDB version 3.2

yes

User concepts

Access control

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

a simple user-level security was built in till version Access 2003

Access rights for users and roles

Access rights for users, groups and roles according to SQL-standard

authentification to access the cache via certificates or http basic authentication

More information provided by the system vendor

Specific characteristics

MongoDB provides an integrated suite of cloud database and data services to accelerate...
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Competitive advantages

Built around the flexible document data model and unified API, MongoDB is a developer...
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Typical application scenarios

AI-enriched intelligent apps (Continental, Telefonica, Iron Mountain) Internet of...
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Key customers

ADP, Adobe, Amadeus, AstraZeneca, Auto Trader, Barclays, BBVA, Bosch, Cisco, CERN,...
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Market metrics

Hundreds of millions downloads, over 150,000+ Atlas clusters provisioned every month...
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Licensing and pricing models

MongoDB database server: Server-Side Public License (SSPL) . Commercial licenses...
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