DBMS > Firebird vs. H2 vs. Spark SQL vs. Yanza

System Properties Comparison Firebird vs. H2 vs. Spark SQL 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

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

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Spark SQL is a component on top of 'Spark Core' for structured data processing

Time Series DBMS for IoT Applications

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	21.06
Rank	#31	Overall
	#19	Relational DBMS

Score	8.22
Rank	#50	Overall
	#32	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

www.firebirdsql.org

www.h2database.com

spark.apache.org/sql

yanza.com

Technical documentation

www.firebirdsql.org/en/reference-manuals

www.h2database.com/html/main.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Firebird Foundation

Thomas Mueller

Apache Software Foundation

Yanza

Initial release

2000

As fork of Borland's InterBase

2005

2014

2015

Current release

5.0.0, January 2024

2.2.220, July 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Initial Developer's Public License

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

Apache 2.0

commercial

free version available

Cloud-based only

Only available as a cloud service

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

C and C++

Java

Scala

Server operating systems

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

All OS with a Java VM

Linux
OS X
Windows

Windows

Data scheme

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

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

JDBC
ODBC

HTTP API

Supported programming languages

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

Java

Java
Python
R
Scala

any language that supports HTTP calls

Server-side scripts

Stored procedures

PSQL

Java Stored Procedures and User-Defined Functions

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

With clustering: 2 database servers on different computers operate on identical copies of a database

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

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

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

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

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

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