DBMS > Citus vs. GigaSpaces vs. Google BigQuery vs. LokiJS vs. Microsoft Access

System Properties Comparison Citus vs. GigaSpaces vs. Google BigQuery vs. LokiJS vs. Microsoft Access

Editorial information provided by DB-Engines

Name

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Large scale data warehouse service with append-only tables

In-memory JavaScript DBMS

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.)

Primary database model

Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Document store

Relational DBMS

Secondary database models

Document store

Graph DBMS
Search engine

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Trend Chart

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.43
Rank	#264	Overall
	#42	Document stores

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Website

www.citusdata.com

www.gigaspaces.com

cloud.google.com/bigquery

github.com/techfort/LokiJS

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

Technical documentation

docs.citusdata.com

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

techfort.github.io/LokiJS

developer.microsoft.com/en-us/access

Developer

Gigaspaces Technologies

Google

Microsoft

Initial release

2010

2000

2010

2014

1992

Current release

8.1, December 2018

15.5, September 2020

1902 (16.0.11328.20222), March 2019

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

commercial

Bundled with Microsoft Office

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

Java, C++, .Net

JavaScript

C++

Server operating systems

Linux

Linux
macOS
Solaris
Windows

hosted

server-less, requires a JavaScript environment (browser, Node.js)

Windows

Not a real database server, but making use of DLLs

Data scheme

yes

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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

specific XML type available, but no XML query functionality

XML can be used for describing objects metadata

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

standard, with numerous extensions

SQL-99 for query and DML statements

yes

but not compliant to any SQL standard

APIs and other access methods

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

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

RESTful HTTP/JSON API

JavaScript API

ADO.NET
DAO
ODBC
OLE DB

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript

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

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

user defined functions

in JavaScript

View functions in JavaScript

yes

since Access 2010 using the ACE-engine

Triggers

yes

yes, event driven architecture

yes

since Access 2010 using the ACE-engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

atomic operations within a single collection possible

ACID

but no files for transaction logging

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

yes

but no files for transaction logging

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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

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