DBMS > Badger vs. Coveo vs. GigaSpaces vs. Sadas Engine

System Properties Comparison Badger vs. Coveo vs. GigaSpaces vs. Sadas Engine

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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

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

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Key-value store

Search engine

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Secondary database models

Graph DBMS
Search engine

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	2.11
Rank	#118	Overall
	#11	Search engines

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

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

github.com/dgraph-io/badger

www.coveo.com

www.gigaspaces.com

www.sadasengine.com

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.coveo.com

docs.gigaspaces.com/latest/landing.html

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

DGraph Labs

Coveo

Gigaspaces Technologies

SADAS s.r.l.

Initial release

2017

2012

2000

2006

Current release

15.5, September 2020

8.0

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2; Commercial licenses available

commercial

free trial version available

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

BSD
Linux
OS X
Solaris
Windows

hosted

Linux
macOS
Solaris
Windows

AIX
Linux
Windows

Data scheme

schema-free

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

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

SQL

Support of SQL

SQL-99 for query and DML statements

yes

APIs and other access methods

RESTful HTTP API

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

JDBC
ODBC
Proprietary protocol

Supported programming languages

C#
Java
JavaScript
Python

.Net
C++
Java
Python
Scala

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

none

yes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

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

none

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

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

managed by 'Learn by Usage'

User concepts

Access control

granular access controls, API key management, content filters

Role-based access control

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

GigaSpaces to hand out almost $14 million in dividends following Cloudify’s acquisition by Dell
19 July 2023, CTech

Data Sciences Corporation partners with GigaSpaces Technologies to usher DIH technology to enterprises in SA
10 October 2023, ITWeb

GigaSpaces Announces Version 16.0 with Breakthrough Data Integration Tools to Ease Enterprises' Digital ...
3 November 2021, PR Newswire

GigaSpaces Spins Off Cloudify, Its Open Source Cloud Orchestration Unit
27 July 2017, Data Center Knowledge

GigaSpaces Orchestrates Cloud Spin-Off
27 July 2017, EnterpriseAI

provided by Google News

Share this page