DBMS > HEAVY.AI vs. Lovefield vs. Microsoft Azure AI Search vs. Solr vs. SQLite

System Properties Comparison HEAVY.AI vs. Lovefield vs. Microsoft Azure AI Search vs. Solr vs. SQLite

Editorial information provided by DB-Engines

Name

Description

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

Search-as-a-service for web and mobile app development

A widely used distributed, scalable search engine based on Apache Lucene

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Search engine

Relational DBMS

Secondary database models

Spatial DBMS

Vector DBMS

Spatial DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

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

google.github.io/lovefield

azure.microsoft.com/en-us/services/search

solr.apache.org

www.sqlite.org

Technical documentation

docs.heavy.ai

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

learn.microsoft.com/en-us/azure/search

solr.apache.org/resources.html

www.sqlite.org/docs.html

Developer

HEAVY.AI, Inc.

Google

Microsoft

Apache Software Foundation

Dwayne Richard Hipp

Initial release

2016

2014

2015

2006

2000

Current release

5.10, January 2022

2.1.12, February 2017

9.6.0, April 2024

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2

Open Source

Public Domain

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

Java

Server operating systems

Linux

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

tested with Chrome, Firefox, IE, Safari

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

server-less

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

dynamic column types

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

yes

not rigid because of 'dynamic typing' concept.

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

Solr Parallel SQL Interface

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC
Thrift
Vega

RESTful HTTP API

Java API
RESTful HTTP/JSON API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

JavaScript

C#
Java
JavaScript
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

Java plugins

Triggers

Using read-only observers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Sharding

Implicit feature of the cloud service

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

yes

Implicit feature of the cloud service

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

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

yes

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

fine grained access rights according to SQL-standard

yes

using Azure authentication

yes

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