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DBMS > Drizzle vs. Firebird vs. Google Cloud Datastore vs. Solr

System Properties Comparison Drizzle vs. Firebird vs. Google Cloud Datastore vs. Solr

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Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonFirebird  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonSolr  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Firebird is an open source RDBMS forked from Borland's InterBaseAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSRelational DBMSDocument storeSearch engine
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
Score4.47
Rank#76  Overall
#12  Document stores
Score42.91
Rank#24  Overall
#3  Search engines
Websitewww.firebirdsql.orgcloud.google.com/­datastoresolr.apache.org
Technical documentationwww.firebirdsql.org/­en/­reference-manualscloud.google.com/­datastore/­docssolr.apache.org/­resources.html
DeveloperDrizzle project, originally started by Brian AkerFirebird FoundationGoogleApache Software Foundation
Initial release20082000 infoAs fork of Borland's InterBase20082006
Current release7.2.4, September 20125.0.0, January 20249.6.0, April 2024
License infoCommercial or Open SourceOpen Source infoGNU GPLOpen Source infoInitial Developer's Public LicensecommercialOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C and C++Java
Server operating systemsFreeBSD
Linux
OS X
AIX
FreeBSD
HP-UX
Linux
OS X
server-less infoFirebird Embedded Server
Solaris
Unix
Windows
hostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesyesschema-freeyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyes, details hereyes infosupports customizable data types and automatic typing
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.noyes
Secondary indexesyesyesyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLyes infowith proprietary extensionsyesSQL-like query language (GQL)Solr Parallel SQL Interface
APIs and other access methodsJDBCADO.NET
C/C++ API
JDBC infoJaybird
ODBC
OLE DB
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RESTful HTTP/JSON API
Supported programming languagesC
C++
Java
PHP
C
C#
C++
Delphi
Java
JavaScript infoNode.js
Lua
Perl
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresnoPSQLusing Google App EngineJava plugins
Triggersno infohooks for callbacks inside the server can be used.yesCallbacks using the Google Apps Engineyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesShardingnoneShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Source-replica replicationMulti-source replication using Paxosyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infousing Google Cloud Dataflowspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Eventual Consistency
Foreign keys infoReferential integrityyesyesyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyes infoFeatures a multi-generational MVCC architecture, readers do not block writersyesyes
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.noyes
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPUsers with fine-grained authorization conceptAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)yes

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More resources
DrizzleFirebirdGoogle Cloud DatastoreSolr
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