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DBMS > Firebird vs. HBase vs. Solr vs. Spark SQL

System Properties Comparison Firebird vs. HBase vs. Solr vs. Spark SQL

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Editorial information provided by DB-Engines
NameFirebird  Xexclude from comparisonHBase  Xexclude from comparisonSolr  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionFirebird is an open source RDBMS forked from Borland's InterBaseWide-column store based on Apache Hadoop and on concepts of BigTableA widely used distributed, scalable search engine based on Apache LuceneSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSWide column storeSearch engineRelational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score20.82
Rank#30  Overall
#18  Relational DBMS
Score30.50
Rank#26  Overall
#2  Wide column stores
Score42.91
Rank#24  Overall
#3  Search engines
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitewww.firebirdsql.orghbase.apache.orgsolr.apache.orgspark.apache.org/­sql
Technical documentationwww.firebirdsql.org/­en/­reference-manualshbase.apache.org/­book.htmlsolr.apache.org/­resources.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperFirebird FoundationApache Software Foundation infoApache top-level project, originally developed by PowersetApache Software FoundationApache Software Foundation
Initial release2000 infoAs fork of Borland's InterBase200820062014
Current release5.0.0, January 20242.3.4, January 20219.6.0, April 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoInitial Developer's Public LicenseOpen Source infoApache version 2Open Source infoApache Version 2Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC and C++JavaJavaScala
Server operating systemsAIX
FreeBSD
HP-UX
Linux
OS X
server-less infoFirebird Embedded Server
Solaris
Unix
Windows
Linux
Unix
Windows infousing Cygwin
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
OS X
Windows
Data schemeyesschema-free, schema definition possibleyes infoDynamic Fields enables on-the-fly addition of new fieldsyes
Typing infopredefined data types such as float or dateyesoptions to bring your own types, AVROyes infosupports customizable data types and automatic typingyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.noyesno
Secondary indexesyesnoyes infoAll search fields are automatically indexedno
SQL infoSupport of SQLyesnoSolr Parallel SQL InterfaceSQL-like DML and DDL statements
APIs and other access methodsADO.NET
C/C++ API
JDBC infoJaybird
ODBC
OLE DB
Java API
RESTful HTTP API
Thrift
Java API
RESTful HTTP/JSON API
JDBC
ODBC
Supported programming languagesC
C#
C++
Delphi
Java
JavaScript infoNode.js
Lua
Perl
PHP
Python
Ruby
C
C#
C++
Groovy
Java
PHP
Python
Scala
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Java
Python
R
Scala
Server-side scripts infoStored proceduresPSQLyes infoCoprocessors in JavaJava pluginsno
Triggersyesyesyes infoUser configurable commands triggered on index changesno
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-source replication
Source-replica replication
yesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual Consistency
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDSingle row ACID (across millions of columns)optimistic lockingno
Concurrency infoSupport for concurrent manipulation of datayes infoFeatures a multi-generational MVCC architecture, readers do not block writersyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
User concepts infoAccess controlUsers with fine-grained authorization conceptAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACyesno

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More resources
FirebirdHBaseSolrSpark SQL
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