DBMS > Coveo vs. HEAVY.AI vs. OrigoDB vs. Postgres-XL

System Properties Comparison Coveo vs. HEAVY.AI vs. OrigoDB vs. Postgres-XL

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

A fully ACID in-memory object graph database

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Search engine

Relational DBMS

Document store
Object oriented DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	2.28
Rank	#114	Overall
	#11	Search engines

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

www.coveo.com

github.com/heavyai/heavydb
www.heavy.ai

origodb.com

www.postgres-xl.org

Technical documentation

docs.coveo.com

docs.heavy.ai

origodb.com/docs

www.postgres-xl.org/documentation

Developer

Coveo

HEAVY.AI, Inc.

Robert Friberg et al

Initial release

2012

2016

2009

under the name LiveDB

2014

since 2012, originally named StormDB

Current release

5.10, January 2022

10 R1, October 2018

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Mozilla public license

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++ and CUDA

Server operating systems

hosted

Linux

Linux
Windows

Linux
macOS

Data scheme

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

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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.

can be achieved using .NET

yes

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

distributed, parallel query execution

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
Thrift
Vega

.NET Client API
HTTP API
LINQ

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

C#
Java
JavaScript
Python

All languages supporting JDBC/ODBC/Thrift
Python

.Net

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

Round robin

horizontal partitioning

client side managed; servers are not synchronized

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

depending on model

yes

Transaction concepts

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

yes

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

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

granular access controls, API key management, content filters

fine grained access rights according to SQL-standard

Role based authorization

fine grained access rights 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

Coveo Board Member Steps Down, Search for Successor On - TipRanks.com
28 May 2024, TipRanks

Coveo Solutions (CVO) Set to Announce Earnings on Monday
27 May 2024, Defense World

Coveo Debuts GenAI Tools on Genesys Cloud and AppFoundry
17 May 2024, CX Today

Coveo Announces Resignation of Board Member – Company Announcement
28 May 2024, Financial Times

Coveo Announces Resignation Of Board Member
28 May 2024, Barchart

provided by Google News

Big Data Analytics: A Game Changer for Infrastructure
13 July 2023, Spiceworks News and Insights

HEAVY.AI Launches HEAVY 7.0, Introducing Real-Time Machine Learning Capabilities
19 April 2023, Business Wire

OmniSci Gets HEAVY New Name and New CEO
1 March 2022, Datanami

Making the most of geospatial intelligence
14 April 2023, InfoWorld

The insideBIGDATA IMPACT 50 List for Q4 2023
11 October 2023, insideBIGDATA

provided by Google News

Share this page