DBMS > Pinecone vs. RavenDB vs. StarRocks vs. SWC-DB

System Properties Comparison Pinecone vs. RavenDB vs. StarRocks vs. SWC-DB

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Editorial information provided by DB-Engines

Name

Description

A managed, cloud-native vector database

Open Source Operational and Transactional Enterprise NoSQL Document Database

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

Forked from Apache Doris

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

Time Series DBMS

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

Score	3.23
Rank	#92	Overall
	#3	Vector DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	0.93
Rank	#198	Overall
	#93	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

www.pinecone.io

ravendb.net

www.starrocks.io

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.pinecone.io/docs/overview

ravendb.net/docs

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

Developer

Pinecone Systems, Inc

Hibernating Rhinos

The Linux Foundation

since Feb 2023

Alex Kashirin

Initial release

2019

2010

2020

Current release

5.4, July 2022

3.3, June 2024

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

AGPL version 3, commercial license available

Open Source

Apache Version 2.0

Open Source

GPL V3

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.

Implementation language

C++, Java

C++

Server operating systems

hosted

Linux
macOS
Raspberry Pi
Windows

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

SQL-like query language (RQL)

yes

SQL-like query language

APIs and other access methods

RESTful HTTP API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

JDBC
MySQL protocol

Proprietary protocol
Thrift

Supported programming languages

Python

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java

C++

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range and hash)

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID, Cluster-wide transaction available

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

Authorization levels configured per client per database

Role based access control and fine grained access rights

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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