DBMS > Ehcache vs. HEAVY.AI vs. InfinityDB vs. Oracle Rdb

System Properties Comparison Ehcache vs. HEAVY.AI vs. InfinityDB vs. Oracle Rdb

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Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

A Java embedded Key-Value Store which extends the Java Map interface

Primary database model

Secondary database models

Spatial DBMS

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

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Website

www.ehcache.org

github.com/heavyai/heavydb
www.heavy.ai

boilerbay.com

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

Technical documentation

www.ehcache.org/documentation

docs.heavy.ai

boilerbay.com/infinitydb/manual

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

Developer

Terracotta Inc, owned by Software AG

HEAVY.AI, Inc.

Boiler Bay Inc.

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2009

2016

2002

1984

Current release

3.10.0, March 2022

5.10, January 2022

4.0

7.4.1.1, 2021

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache Version 2; enterprise edition available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++ and CUDA

Java

Server operating systems

All OS with a Java VM

Linux

All OS with a Java VM

HP Open VMS

Data scheme

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

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

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

APIs and other access methods

JCache

JDBC
ODBC
Thrift
Vega

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

Supported programming languages

Java

All languages supporting JDBC/ODBC/Thrift
Python

Java

Server-side scripts

Stored procedures

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

ACID

Optimistic locking for transactions; no isolation for bulk loads

yes, on a single node

Concurrency

Support for concurrent manipulation of data

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

fine grained access rights according to SQL-standard

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