DBMS > OpenSearch vs. Qdrant vs. Sphinx

System Properties Comparison OpenSearch vs. Qdrant vs. Sphinx

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Editorial information provided by DB-Engines

Name

Description

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

A vector similarity Search Engine with neural network or semantic based matching

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

Primary database model

Search engine

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

Score	10.44
Rank	#48	Overall
	#4	Search engines

Score	0.10
Rank	#366	Overall
	#24	Search engines

Score	6.93
Rank	#59	Overall
	#7	Search engines

Website

github.com/opensearch-project
opensearch.org

github.com/qdrant/qdrant
qdrant.tech

sphinxsearch.com

Technical documentation

opensearch.org/docs/latest

qdrant.tech/documentation

sphinxsearch.com/docs

Developer

Amazon Web Services

Qdrant

Sphinx Technologies Inc.

Initial release

2021

2001

Current release

2.2.0, August 2022

3.4.1, July 2021

License

Commercial or Open Source

Open Source

Apache Version 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.

Aiven for OpenSearch: Fully managed open source search and analytics suite including search engine, NoSQL document database, and visualization interface.

Implementation language

Java

Rust

C++

Server operating systems

All OS with a Java VM

Docker

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

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.

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

SQL-like query language (SphinxQL)

APIs and other access methods

Java API
RESTful HTTP/JSON API

RESTful HTTP/JSON API

OpenAPI 3.0

Proprietary protocol

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

Triggers

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

Transaction concepts

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

User concepts

Access control

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