DBMS > Microsoft Azure Cosmos DB vs. Qdrant vs. SingleStore vs. StarRocks

System Properties Comparison Microsoft Azure Cosmos DB vs. Qdrant vs. SingleStore vs. StarRocks

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Editorial information provided by DB-Engines

Name

Description

Globally distributed, horizontally scalable, multi-model database service

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

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

Document store
Graph DBMS
Key-value store
Wide column store

Vector DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.28
Rank	#167	Overall
	#6	Vector DBMS

Score	5.38
Rank	#62	Overall
	#35	Relational DBMS

Score	0.93
Rank	#198	Overall
	#93	Relational DBMS

Website

azure.microsoft.com/services/cosmos-db

github.com/qdrant/qdrant
qdrant.tech

www.singlestore.com

www.starrocks.io

Technical documentation

learn.microsoft.com/azure/cosmos-db

qdrant.tech/documentation

docs.singlestore.com

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

Developer

Microsoft

Qdrant

SingleStore Inc.

The Linux Foundation

since Feb 2023

Initial release

2014

2021

2013

2020

Current release

8.5, January 2024

2.5.3, March 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

free developer edition available

Open Source

Apache Version 2.0

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.

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

hosted

Docker
Linux
macOS
Windows

Linux

64 bit version required

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

Numbers, Strings, Geo, Boolean

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 properties auto-indexed by default

yes

Keywords, numberic ranges, geo, full-text

yes

SQL

Support of SQL

SQL-like query language

yes

but no triggers and foreign keys

yes

APIs and other access methods

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

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

JDBC
MySQL protocol

Supported programming languages

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

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

Java

Server-side scripts

Stored procedures

JavaScript

yes

user defined functions

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

hash partitioning

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Collection-level replication

Source-replica replication

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

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency, tunable consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Access rights can be defined down to the item level

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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