DBMS > Pinecone vs. Riak KV vs. SWC-DB vs. Transbase

System Properties Comparison Pinecone vs. Riak KV vs. SWC-DB vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

A managed, cloud-native vector database

Distributed, fault tolerant key-value store

A high performance, scalable Wide Column DBMS

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Vector DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Wide column store

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	3.16
Rank	#95	Overall
	#2	Vector DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

www.pinecone.io

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

www.transaction.de/en/products/transbase.html

Technical documentation

docs.pinecone.io/docs/overview

www.tiot.jp/riak-docs/riak/kv/latest

www.transaction.de/en/products/transbase/features.html

Developer

Pinecone Systems, Inc

OpenSource, formerly Basho Technologies

Alex Kashirin

Transaction Software GmbH

Initial release

2019

2009

2020

1987

Current release

3.2.0, December 2022

0.5, April 2021

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2, commercial enterprise edition

Open Source

GPL V3

commercial

free development license

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

Erlang

C++

C and C++

Server operating systems

hosted

Linux
OS X

Linux

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

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

restricted

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

RESTful HTTP API

HTTP API
Native Erlang Interface

Proprietary protocol
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C++

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

Erlang

yes

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between data sets can be stored

yes

Transaction concepts

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

yes

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

yes, using Riak Security

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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