DBMS > Amazon DocumentDB vs. Ehcache vs. Firebird vs. Yanza

System Properties Comparison Amazon DocumentDB vs. Ehcache vs. Firebird vs. Yanza

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Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

A widely adopted Java cache with tiered storage options

Firebird is an open source RDBMS forked from Borland's InterBase

Time Series DBMS for IoT Applications

Primary database model

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

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

Score	20.82
Rank	#30	Overall
	#18	Relational DBMS

Website

aws.amazon.com/documentdb

www.ehcache.org

www.firebirdsql.org

yanza.com

Technical documentation

aws.amazon.com/documentdb/resources

www.ehcache.org/documentation

www.firebirdsql.org/en/reference-manuals

Developer

Terracotta Inc, owned by Software AG

Firebird Foundation

Yanza

Initial release

2019

2009

2000

As fork of Borland's InterBase

2015

Current release

3.10.0, March 2022

5.0.0, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

Initial Developer's Public License

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C and C++

Server operating systems

hosted

All OS with a Java VM

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

Windows

Data scheme

schema-free

yes

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

yes

SQL

Support of SQL

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JCache

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

Java

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

any language that supports HTTP calls

Server-side scripts

Stored procedures

PSQL

Triggers

yes

Cache Event Listeners

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

by using Terracotta Server

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

yes

by using Terracotta Server

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

yes

supports JTA and can work as an XA resource

ACID

Concurrency

Support for concurrent manipulation of data

yes

Features a multi-generational MVCC architecture, readers do not block writers

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

Access rights for users and roles

Users with fine-grained authorization concept

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