DBMS > Coveo vs. HEAVY.AI vs. Lovefield vs. OrigoDB

System Properties Comparison Coveo vs. HEAVY.AI vs. Lovefield vs. OrigoDB

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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

Embeddable relational database for web apps written in pure JavaScript

A fully ACID in-memory object graph database

Primary database model

Search engine

Relational DBMS

Document store
Object oriented DBMS

Secondary database models

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	0.29
Rank	#293	Overall
	#133	Relational DBMS

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

Website

www.coveo.com

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

google.github.io/lovefield

origodb.com

Technical documentation

docs.coveo.com

docs.heavy.ai

github.com/google/lovefield/blob/master/docs/spec_index.md

origodb.com/docs

Developer

Coveo

HEAVY.AI, Inc.

Google

Robert Friberg et al

Initial release

2012

2016

2014

2009

under the name LiveDB

Current release

5.10, January 2022

2.1.12, February 2017

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

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++ and CUDA

JavaScript

Server operating systems

hosted

Linux

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

tested with Chrome, Firefox, IE, Safari

Linux
Windows

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

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
Thrift
Vega

.NET Client API
HTTP API
LINQ

Supported programming languages

C#
Java
JavaScript
Python

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

.Net

Server-side scripts

Stored procedures

yes

Triggers

yes

Using read-only observers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

Round robin

none

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

none

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

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

Write ahead log

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

granular access controls, API key management, content filters

fine grained access rights according to SQL-standard

Role based authorization

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