DBMS > Coveo vs. Drizzle vs. GigaSpaces vs. Google BigQuery vs. PouchDB

System Properties Comparison Coveo vs. Drizzle vs. GigaSpaces vs. Google BigQuery vs. PouchDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Search engine

Relational DBMS

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	2.11
Rank	#118	Overall
	#11	Search engines

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Website

www.coveo.com

www.gigaspaces.com

cloud.google.com/bigquery

pouchdb.com

Technical documentation

docs.coveo.com

docs.gigaspaces.com/latest/landing.html

cloud.google.com/bigquery/docs

pouchdb.com/guides

Developer

Coveo

Drizzle project, originally started by Brian Aker

Gigaspaces Technologies

Google

Apache Software Foundation

Initial release

2012

2008

2000

2010

2012

Current release

7.2.4, September 2012

15.5, September 2020

7.1.1, June 2019

License

Commercial or Open Source

commercial

Open Source

GNU GPL

Open Source

Apache Version 2; Commercial licenses available

commercial

Open Source

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

C++

Java, C++, .Net

JavaScript

Server operating systems

hosted

FreeBSD
Linux
OS X

Linux
macOS
Solaris
Windows

hosted

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

Data scheme

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

yes

schema-free

yes

schema-free

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

SQL

Support of SQL

yes

with proprietary extensions

SQL-99 for query and DML statements

yes

APIs and other access methods

RESTful HTTP API

JDBC

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

RESTful HTTP/JSON API

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

C#
Java
JavaScript
Python

C
C++
Java
PHP

.Net
C++
Java
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

View functions in JavaScript

Triggers

yes

hooks for callbacks inside the server can be used.

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

none

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

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

yes

Transaction concepts

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

yes

ACID

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

In-memory capabilities

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

yes

User concepts

Access control

granular access controls, API key management, content filters

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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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