DBMS > GigaSpaces vs. Google Cloud Datastore vs. MarkLogic vs. Microsoft Azure Cosmos DB

System Properties Comparison GigaSpaces vs. Google Cloud Datastore vs. MarkLogic vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

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

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

Operational and transactional Enterprise NoSQL database

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Document store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Graph DBMS
Search engine

Spatial DBMS

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

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

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

Website

www.gigaspaces.com

cloud.google.com/datastore

www.marklogic.com

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.gigaspaces.com/latest/landing.html

cloud.google.com/datastore/docs

docs.marklogic.com

learn.microsoft.com/azure/cosmos-db

Developer

Gigaspaces Technologies

Google

MarkLogic Corp.

Microsoft

Initial release

2000

2008

2001

2014

Current release

15.5, September 2020

11.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

commercial

restricted free version is 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.

Implementation language

Java, C++, .Net

C++

Server operating systems

Linux
macOS
Solaris
Windows

hosted

Linux
OS X
Windows

hosted

Data scheme

schema-free

Schema can be enforced

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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-99 for query and DML statements

SQL-like query language (GQL)

yes

SQL92

SQL-like query language

APIs and other access methods

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

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

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

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

Supported programming languages

.Net
C++
Java
Python
Scala

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

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

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

Server-side scripts

Stored procedures

yes

using Google App Engine

yes

via XQuery or JavaScript

JavaScript

Triggers

yes, event driven architecture

Callbacks using the Google Apps Engine

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Multi-source replication using Paxos

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

using Google Cloud Dataflow

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

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

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

can act as a resource manager in an XA/JTA transaction

Multi-item ACID transactions with snapshot isolation within a partition

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

yes, with Range Indexes

User concepts

Access control

Role-based access control

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

Role-based access control at the document and subdocument levels

Access rights can be defined down to the item level

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