DBMS > Graph Engine vs. Heroic vs. Oracle Rdb vs. Pinecone

System Properties Comparison Graph Engine vs. Heroic vs. Oracle Rdb vs. Pinecone

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Editorial information provided by DB-Engines

Name

Description

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

A managed, cloud-native vector database

Primary database model

Graph DBMS
Key-value store

Time Series DBMS

Relational DBMS

Vector DBMS

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

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	3.16
Rank	#95	Overall
	#2	Vector DBMS

Website

www.graphengine.io

github.com/spotify/heroic

www.oracle.com/database/technologies/related/rdb.html

www.pinecone.io

Technical documentation

www.graphengine.io/docs/manual

spotify.github.io/heroic

www.oracle.com/database/technologies/related/rdb-doc.html

docs.pinecone.io/docs/overview

Developer

Microsoft

Spotify

Oracle, originally developed by Digital Equipment Corporation (DEC)

Pinecone Systems, Inc

Initial release

2010

2014

1984

2019

Current release

7.4.1.1, 2021

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache 2.0

commercial

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

.NET and C

Java

Server operating systems

.NET

HP Open VMS

hosted

Data scheme

yes

schema-free

Flexible Schema (defined schema, partial schema, schema free)

Typing

predefined data types such as float or date

yes

String, Number, Boolean

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

via Elasticsearch

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

RESTful HTTP API

Supported programming languages

C#
C++
F#
Visual Basic

Python

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes, on a single node

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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