DBMS > GigaSpaces vs. Google BigQuery vs. HarperDB vs. IBM Cloudant

System Properties Comparison GigaSpaces vs. Google BigQuery vs. HarperDB vs. IBM Cloudant

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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

Large scale data warehouse service with append-only tables

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Database as a Service offering based on Apache CouchDB

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Document store

Secondary database models

Graph DBMS
Search engine

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	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	2.75
Rank	#104	Overall
	#19	Document stores

Website

www.gigaspaces.com

cloud.google.com/bigquery

www.harperdb.io

www.ibm.com/products/cloudant

Technical documentation

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

docs.harperdb.io/docs

cloud.ibm.com/docs/Cloudant

Developer

Gigaspaces Technologies

Google

HarperDB

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Initial release

2000

2010

2017

2010

Current release

15.5, September 2020

3.1, August 2021

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial licenses available

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.

Implementation language

Java, C++, .Net

Node.js

Erlang

Server operating systems

Linux
macOS
Solaris
Windows

hosted

Linux
OS X

hosted

Data scheme

schema-free

yes

dynamic schema

schema-free

Typing

predefined data types such as float or date

yes

JSON 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.

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

SQL-like data manipulation statements

APIs and other access methods

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

RESTful HTTP/JSON API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

RESTful HTTP/JSON API

Supported programming languages

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C#
Java
JavaScript
Objective-C
PHP
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Custom Functions

since release 3.1

View functions (Map-Reduce) in JavaScript

Triggers

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

A table resides as a whole on one (or more) nodes in a cluster

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

the nodes on which a table resides can be defined

Multi-source replication
Source-replica replication

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

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Atomic execution of specific operations

atomic operations within a document possible

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

Durability

Support for making data persistent

yes

yes, using LMDB

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

Role-based access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles

Access rights for users can be defined per database

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