DBMS > Ehcache vs. RocksDB vs. Tkrzw

System Properties Comparison Ehcache vs. RocksDB vs. Tkrzw

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Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Key-value store

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

Score	6.45
Rank	#64	Overall
	#8	Key-value stores

Score	3.92
Rank	#94	Overall
	#15	Key-value stores

Score	0.00
Rank	#404	Overall
	#67	Key-value stores

Website

www.ehcache.org

rocksdb.org

dbmx.net/tkrzw

Technical documentation

www.ehcache.org/documentation

github.com/facebook/rocksdb/wiki

Developer

Terracotta Inc, owned by Software AG

Facebook, Inc.

Mikio Hirabayashi

Initial release

2009

2013

2020

Current release

3.10.0, March 2022

7.10.2, March 2023

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

BSD

Open Source

Apache Version 2.0

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++

Server operating systems

All OS with a Java VM

Linux

Linux
macOS

Data scheme

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

APIs and other access methods

JCache

C++ API
Java API

Supported programming languages

Java

C
C++
Go
Java
Perl
Python
Ruby

C++
Java
Python
Ruby

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

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

yes

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

Foreign keys

Referential integrity

Transaction concepts

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

yes

supports JTA and can work as an XA resource

yes

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

using specific database classes

User concepts

Access control

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