DBMS > BigObject vs. Ehcache vs. Oracle Rdb vs. Pinecone

System Properties Comparison BigObject vs. Ehcache vs. Oracle Rdb vs. Pinecone

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

A widely adopted Java cache with tiered storage options

A managed, cloud-native vector database

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Relational DBMS

Vector DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	3.16
Rank	#95	Overall
	#2	Vector DBMS

Website

bigobject.io

www.ehcache.org

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

www.pinecone.io

Technical documentation

docs.bigobject.io

www.ehcache.org/documentation

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

docs.pinecone.io/docs/overview

Developer

BigObject, Inc.

Terracotta Inc, owned by Software AG

Oracle, originally developed by Digital Equipment Corporation (DEC)

Pinecone Systems, Inc

Initial release

2015

2009

1984

2019

Current release

3.10.0, March 2022

7.4.1.1, 2021

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2; commercial licenses available

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

Java

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

All OS with a Java VM

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

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JCache

RESTful HTTP API

Supported programming languages

Java

Python

Server-side scripts

Stored procedures

Lua

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

by using Terracotta Server

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

by using Terracotta Server

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

yes, on a single node

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

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More resources

DB-Engines blog posts

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2 June 2023, Matthias Gelbmann

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