DBMS > CouchDB vs. GigaSpaces vs. Google BigQuery vs. Sphinx

System Properties Comparison CouchDB vs. GigaSpaces vs. Google BigQuery vs. Sphinx

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Editorial information provided by DB-Engines

Name

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

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

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Document store

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Search engine

Secondary database models

Spatial DBMS

using the Geocouch extension

Graph DBMS
Search engine

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

Score	10.26
Rank	#45	Overall
	#7	Document stores

Score	1.02
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	6.03
Rank	#60	Overall
	#6	Search engines

Website

couchdb.apache.org

www.gigaspaces.com

cloud.google.com/bigquery

sphinxsearch.com

Technical documentation

docs.couchdb.org/en/stable

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

sphinxsearch.com/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Gigaspaces Technologies

Google

Sphinx Technologies Inc.

Initial release

2005

2000

2010

2001

Current release

3.3.3, December 2023

15.5, September 2020

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

Java, C++, .Net

C++

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

Linux
macOS
Solaris
Windows

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

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.

XML can be used for describing objects metadata

Secondary indexes

yes

via views

yes

full-text index on all search fields

SQL

Support of SQL

SQL-99 for query and DML statements

yes

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP/JSON API

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

RESTful HTTP/JSON API

Proprietary protocol

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

View functions in JavaScript

yes

user defined functions

in JavaScript

Triggers

yes

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

atomic operations within a single document possible

ACID

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users can be defined per database

Role-based access control

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

Google Cloud Identity & Access Management (IAM)

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