DBMS > Hazelcast vs. HyperSQL vs. Lovefield vs. OpenSearch vs. Sphinx

System Properties Comparison Hazelcast vs. HyperSQL vs. Lovefield vs. OpenSearch vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Embeddable relational database for web apps written in pure JavaScript

A distributed, RESTful search and analytics engine forked from Elasticsearch and based on Apache Lucene

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Key-value store

Relational DBMS

Search engine

Secondary database models

Document store

JSON support with IMDG 3.12

Vector DBMS

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

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	16.03
Rank	#37	Overall
	#4	Search engines

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

hazelcast.com

hsqldb.org

google.github.io/lovefield

github.com/opensearch-project
opensearch.org

sphinxsearch.com

Technical documentation

hazelcast.org/imdg/docs

hsqldb.org/web/hsqlDocsFrame.html

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

opensearch.org/docs/latest

sphinxsearch.com/docs

Developer

Hazelcast

Google

Amazon Web Services

Sphinx Technologies Inc.

Initial release

2008

2001

2014

2021

2001

Current release

5.3.6, November 2023

2.7.2, June 2023

2.1.12, February 2017

2.5.0, January 2023

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

based on BSD license

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

Java

JavaScript

Java

C++

Server operating systems

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

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

tested with Chrome, Firefox, IE, Safari

All OS with a Java VM

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

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.

yes

the object must implement a serialization strategy

Secondary indexes

yes

All search fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

via JavaScript builder pattern

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

HTTP API

JDBC via HTTP
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Proprietary protocol

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

All languages supporting JDBC/ODBC
Java

JavaScript

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

Java, SQL

yes

Triggers

yes

Events

yes

Using read-only observers

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Foreign keys

Referential integrity

yes

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

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

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

yes

using MemoryDB

User concepts

Access control

Role-based access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

OpenSearch is a highly scalable and extensible open-source software suite for search,...
» more

Competitive advantages

Distributed as fully open source under Apache License, Version 2.0 (ALv2), OpenSearch...
» more

Typical application scenarios

The OpenSearch platform is used across a broad range of search , analytics, and observability...
» more

Licensing and pricing models

The OpenSearch Project offers open source software licensed under Apache License...
» more

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