DBMS > Ehcache vs. Heroic vs. JanusGraph vs. Oracle Berkeley DB vs. OrigoDB

System Properties Comparison Ehcache vs. Heroic vs. JanusGraph vs. Oracle Berkeley DB vs. OrigoDB

Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

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

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Widely used in-process key-value store

A fully ACID in-memory object graph database

Primary database model

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store
Object oriented DBMS

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

Score	4.64
Rank	#68	Overall
	#8	Key-value stores

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Website

www.ehcache.org

github.com/spotify/heroic

janusgraph.org

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

origodb.com

Technical documentation

www.ehcache.org/documentation

spotify.github.io/heroic

docs.janusgraph.org

docs.oracle.com/cd/E17076_05/html/index.html

origodb.com/docs

Developer

Terracotta Inc, owned by Software AG

Spotify

Linux Foundation; originally developed as Titan by Aurelius

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Robert Friberg et al

Initial release

2009

2014

2017

1994

2009

under the name LiveDB

Current release

3.10.0, March 2022

0.6.3, February 2023

18.1.40, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

commercial license available

Open Source

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, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

All OS with a Java VM

Linux
OS X
Unix
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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.

yes

only with the Berkeley DB XML edition

can be achieved using .NET

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JCache

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

.NET Client API
HTTP API
LINQ

Supported programming languages

Java

Clojure
Java
Python

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

.Net

Server-side scripts

Stored procedures

yes

Triggers

yes

Cache Event Listeners

yes

only for the SQL API

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

none

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

depending on model

Transaction concepts

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

yes

supports JTA and can work as an XA resource

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

Write ahead log

In-memory capabilities

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

yes

User concepts

Access control

User authentification and security via Rexster Graph Server

Role based authorization

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