DBMS > Microsoft Azure Cosmos DB vs. Pinecone vs. StarRocks

System Properties Comparison Microsoft Azure Cosmos DB vs. Pinecone vs. StarRocks

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Editorial information provided by DB-Engines

Name

Description

Globally distributed, horizontally scalable, multi-model database service

A managed, cloud-native vector database

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

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

Score	27.12
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	3.13
Rank	#88	Overall
	#3	Vector DBMS

Score	0.75
Rank	#218	Overall
	#101	Relational DBMS

Website

azure.microsoft.com/services/cosmos-db

www.pinecone.io

www.starrocks.io

Technical documentation

learn.microsoft.com/azure/cosmos-db

docs.pinecone.io/docs/overview

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

Developer

Microsoft

Pinecone Systems, Inc

The Linux Foundation

since Feb 2023

Initial release

2014

2019

2020

Current release

3.3, June 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Java

Server operating systems

hosted

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

String, Number, 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

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

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

RESTful HTTP API

JDBC
MySQL protocol

Supported programming languages

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

Python

Java

Server-side scripts

Stored procedures

JavaScript

user defined functions

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

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

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

Access rights can be defined down to the item level

Role based access control and fine grained access rights

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