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DBMS > Drizzle vs. Google Cloud Datastore vs. IBM Db2 warehouse vs. Spark SQL vs. Sphinx

System Properties Comparison Drizzle vs. Google Cloud Datastore vs. IBM Db2 warehouse vs. Spark SQL vs. Sphinx

Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonIBM Db2 warehouse infoformerly named IBM dashDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonSphinx  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.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformCloud-based data warehousing serviceSpark SQL is a component on top of 'Spark Core' for structured data processingOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelRelational DBMSDocument storeRelational DBMSRelational DBMSSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.36
Rank#72  Overall
#12  Document stores
Score1.37
Rank#160  Overall
#74  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Websitecloud.google.com/­datastorewww.ibm.com/­products/­db2/­warehousespark.apache.org/­sqlsphinxsearch.com
Technical documentationcloud.google.com/­datastore/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsphinxsearch.com/­docs
DeveloperDrizzle project, originally started by Brian AkerGoogleIBMApache Software FoundationSphinx Technologies Inc.
Initial release20082008201420142001
Current release7.2.4, September 20123.5.0 ( 2.13), September 20233.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoGNU GPLcommercialcommercialOpen Source infoApache 2.0Open Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenoyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++ScalaC++
Server operating systemsFreeBSD
Linux
OS X
hostedhostedLinux
OS X
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesyes, details hereyesyesno
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.nono infoImport/export of XML data possibleno
Secondary indexesyesyesyesnoyes infofull-text index on all search fields
SQL infoSupport of SQLyes infowith proprietary extensionsSQL-like query language (GQL)yesSQL-like DML and DDL statementsSQL-like query language (SphinxQL)
APIs and other access methodsJDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
.NET Client API
JDBC
ODBC
OLE DB
JDBC
ODBC
Proprietary protocol
Supported programming languagesC
C++
Java
PHP
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Java
Python
R
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresnousing Google App EnginePL/SQL, SQL PLnono
Triggersno infohooks for callbacks inside the server can be used.Callbacks using the Google Apps Engineyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingyes, utilizing Spark CoreSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replication using Paxosyesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownono
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.Immediate Consistency
Foreign keys infoReferential integrityyesyes infovia ReferenceProperties or Ancestor pathsyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)fine grained access rights according to SQL-standardnono

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More resources
DrizzleGoogle Cloud DatastoreIBM Db2 warehouse infoformerly named IBM dashDBSpark SQLSphinx
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