DBMS > Pinecone vs. RDFox vs. RocksDB vs. Spark SQL

System Properties Comparison Pinecone vs. RDFox vs. RocksDB vs. Spark SQL

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

A managed, cloud-native vector database

High performance knowledge graph and semantic reasoning engine

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Secondary database models

Relational DBMS

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

Score	3.23
Rank	#92	Overall
	#3	Vector DBMS

Score	0.29
Rank	#300	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.pinecone.io

www.oxfordsemantic.tech

rocksdb.org

spark.apache.org/sql

Technical documentation

docs.pinecone.io/docs/overview

docs.oxfordsemantic.tech

github.com/facebook/rocksdb/wiki

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Pinecone Systems, Inc

Oxford Semantic Technologies

Facebook, Inc.

Apache Software Foundation

Initial release

2019

2017

2013

2014

Current release

6.0, Septermber 2022

9.2.1, May 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Apache 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.

Implementation language

C++

Scala

Server operating systems

hosted

Linux
macOS
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

RDF schemas

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

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

RESTful HTTP API
SPARQL 1.1

C++ API
Java API

JDBC
ODBC

Supported programming languages

Python

C
Java

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

replication via a shared file system

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Foreign keys

Referential integrity

Transaction concepts

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

ACID

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.

yes

User concepts

Access control

Roles, resources, and access types

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.