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DBMS > Microsoft Azure Data Explorer vs. Sphinx

System Properties Comparison Microsoft Azure Data Explorer vs. Sphinx

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Our visitors often compare Microsoft Azure Data Explorer and Sphinx with Elasticsearch, Google BigQuery and MongoDB.

Editorial information provided by DB-Engines
NameMicrosoft Azure Data Explorer  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionFully managed big data interactive analytics platformOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelRelational DBMSSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.51
Rank#120  Overall
#62  Relational DBMS
Score7.23
Rank#46  Overall
#5  Search engines
Websiteazure.microsoft.com/­services/­data-explorersphinxsearch.com
Technical documentationdocs.microsoft.com/­en-us/­azure/­data-explorersphinxsearch.com/­docs
DeveloperMicrosoftSphinx Technologies Inc.
Initial release20192001
Current release3.1.1, October 2018
License infoCommercial or Open SourcecommercialOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud serviceyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++
Server operating systemshostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesyes
Typing infopredefined data types such as float or dateyesno
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.yes
Secondary indexesyes infofull-text index on all search fields
SQL infoSupport of SQLKusto Query Language (KQL), SQL subsetSQL-like query language (SphinxQL)
APIs and other access methodsMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Proprietary protocol
Supported programming languages.Net
Java
JavaScript (Node.js)
PowerShell
Python
R
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresYes. Languages: KQL, Python, Rno
TriggersTable-level update policy rules can be used to automatically run a query and ingest its results when data is ingested into another table.no
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud serviceSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes 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.no
User concepts infoAccess controlAzure Active Directory Authenticationno

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More resources
Microsoft Azure Data ExplorerSphinx
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Recent citations in the news

Azure Data Explorer for IoT Data Analytics | Internet of Things Show - Microsoft
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