DBMS > Ehcache vs. Heroic vs. LevelDB vs. OrigoDB vs. XTDB

System Properties Comparison Ehcache vs. Heroic vs. LevelDB vs. OrigoDB vs. XTDB

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

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

A fully ACID in-memory object graph database

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Key-value store

Time Series DBMS

Key-value store

Document store
Object oriented DBMS

Document store

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

Score	5.23
Rank	#68	Overall
	#8	Key-value stores

Score	0.57
Rank	#250	Overall
	#21	Time Series DBMS

Score	2.75
Rank	#107	Overall
	#19	Key-value stores

Score	0.03
Rank	#378	Overall
	#51	Document stores
	#18	Object oriented DBMS

Score	0.09
Rank	#351	Overall
	#47	Document stores

Website

www.ehcache.org

github.com/spotify/heroic

github.com/google/leveldb

origodb.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

www.ehcache.org/documentation

spotify.github.io/heroic

github.com/google/leveldb/blob/main/doc/index.md

origodb.com/docs

www.xtdb.com/docs

Developer

Terracotta Inc, owned by Software AG

Spotify

Google

Robert Friberg et al

Juxt Ltd.

Initial release

2009

2014

2011

2009

under the name LiveDB

2019

Current release

3.10.0, March 2022

1.23, February 2021

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

Open Source

BSD

Open Source

MIT License

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++

Clojure

Server operating systems

All OS with a Java VM

Illumos
Linux
OS X
Windows

Linux
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

yes, extensible-data-notation format

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.

can be achieved using .NET

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

limited SQL, making use of Apache Calcite

APIs and other access methods

JCache

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

.NET Client API
HTTP API
LINQ

HTTP REST
JDBC

Supported programming languages

Java

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net

Clojure
Java

Server-side scripts

Stored procedures

yes

Triggers

yes

Cache Event Listeners

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

none

horizontal partitioning

client side managed; servers are not synchronized

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

yes

none

Source-replica replication

yes, each node contains all data

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)

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

with automatic compression on writes

yes

Write ahead log

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

Role based authorization

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.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Jira Data Center user? Here's a critical Ehcache vulnerability to spoil your day
22 July 2021, The Register

Cache replication
19 August 2013, Packt Hub

Atlassian asks customers to patch critical Jira vulnerability
22 July 2021, BleepingComputer

Critical Jira Flaw in Atlassian Could Lead to RCE
22 July 2021, Threatpost

Migration From JBoss 5 to JBoss 7: All It Takes Is 11 Easy Steps
3 June 2021, hackernoon.com

provided by Google News

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

provided by Google News

LevelDB in Ruby — SitePoint
22 October 2014, SitePoint

Microsoft Teams stores auth tokens as cleartext in Windows, Linux, Macs
14 September 2022, BleepingComputer

Pliops unveils XDP-Rocks for RocksDB – Blocks and Files
19 October 2022, Blocks & Files

XanMod, Liquorix Kernels Offer Some Advantages On AMD Ryzen 5 Notebook
26 July 2021, Phoronix

Rust-Based Info Stealers Abuse GitHub Codespaces
19 May 2023, Trend Micro

provided by Google News

Share this page