DBMS > Qdrant vs. SingleStore vs. SQLite vs. StarRocks

System Properties Comparison Qdrant vs. SingleStore vs. SQLite vs. StarRocks

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Editorial information provided by DB-Engines

Name

Description

A high-performance vector database with neural network or semantic-based matching

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Widely used embeddable, in-process RDBMS

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

Vector DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	1.28
Rank	#167	Overall
	#6	Vector DBMS

Score	5.38
Rank	#62	Overall
	#35	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	0.93
Rank	#198	Overall
	#93	Relational DBMS

Website

github.com/qdrant/qdrant
qdrant.tech

www.singlestore.com

www.sqlite.org

www.starrocks.io

Technical documentation

qdrant.tech/documentation

docs.singlestore.com

www.sqlite.org/docs.html

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

Developer

Qdrant

SingleStore Inc.

Dwayne Richard Hipp

The Linux Foundation

since Feb 2023

Initial release

2021

2013

2000

2020

Current release

8.5, January 2024

3.46.0 (23 May 2024), May 2024

2.5.3, March 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free developer edition available

Open Source

Public Domain

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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

Rust

C++, Go

C++, Java

Server operating systems

Docker
Linux
macOS
Windows

Linux

64 bit version required

server-less

Linux

Data scheme

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

Numbers, Strings, Geo, Boolean

yes

not rigid because of 'dynamic typing' concept.

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

Keywords, numberic ranges, geo, full-text

yes

SQL

Support of SQL

yes

but no triggers and foreign keys

yes

SQL-92 is not fully supported

yes

APIs and other access methods

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

JDBC
MySQL protocol

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

Bash
C
C#
Java
JavaScript (Node.js)
Python

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

Java

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

hash partitioning

none

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

Collection-level replication

Source-replica replication

stores two copies of each physical data partition on two separate nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency, tunable consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

yes

In-memory capabilities

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

yes

User concepts

Access control

Key-based authentication

Fine grained access control via users, groups and roles

Role based access control and fine grained access rights

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

Customers in various industries worldwide including US and International Industry...
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Licensing and pricing models

F ree Tier and Enterprise Edition
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