DBMS > OpenSearch vs. Sequoiadb vs. StarRocks

System Properties Comparison OpenSearch vs. Sequoiadb vs. StarRocks

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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

NewSQL database with distributed OLTP and SQL

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Search engine
Vector DBMS

Document store
Relational DBMS

Relational DBMS

Secondary database models

Vector DBMS

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

Score	17.19
Rank	#32	Overall
	#4	Search engines
	#2	Vector DBMS

Score	0.33
Rank	#273	Overall
	#43	Document stores
	#126	Relational DBMS

Score	1.08
Rank	#174	Overall
	#78	Relational DBMS

Website

github.com/opensearch-project
opensearch.org

www.sequoiadb.com

www.starrocks.io

Technical documentation

opensearch.org/docs/latest

www.sequoiadb.com/en/index.php?m=Files&a=index

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

The OpenSearch Software Foundation

Sequoiadb Ltd.

The Linux Foundation

since Feb 2023

Initial release

2021

2013

2020

Current release

2.19, February 2025

3.3, June 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Server: AGPL; Client: Apache V2

Open Source

Apache Version 2.0

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.

STACKIT OpenSearch offers OpenSearch in a managed version in enterprise grade, 100% GDPR-compliant, for a wide range of applications like analytics, website search, offering scalability and fast data access.

Implementation language

Java

C++

C++, Java

Server operating systems

All OS with a Java VM

Linux

Data scheme

schema-free

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

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

Java API
RESTful HTTP/JSON API

proprietary protocol using JSON

JDBC
MySQL protocol

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Elasticsearch client

.Net
C++
Java
PHP
Python

Java

Server-side scripts

Stored procedures

yes

JavaScript

user defined functions

Triggers

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

User concepts

Access control

simple password-based access control

Role based access control and fine grained access rights

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More resources

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