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DBMS > Microsoft Azure Data Explorer vs. Postgres-XL vs. PostgreSQL vs. Sphinx vs. VelocityDB

System Properties Comparison Microsoft Azure Data Explorer vs. Postgres-XL vs. PostgreSQL vs. Sphinx vs. VelocityDB

Editorial information provided by DB-Engines
NameMicrosoft Azure Data Explorer  Xexclude from comparisonPostgres-XL  Xexclude from comparisonPostgreSQL  Xexclude from comparisonSphinx  Xexclude from comparisonVelocityDB  Xexclude from comparison
DescriptionFully managed big data interactive analytics platformBased on PostgreSQL enhanced with MPP and write-scale-out cluster featuresWidely used open source RDBMS infoDeveloped as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQLOpen source search engine for searching in data from different sources, e.g. relational databasesA .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)
Primary database modelRelational DBMS infocolumn orientedRelational DBMSRelational DBMS infowith object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.Search engineGraph DBMS
Object oriented DBMS
Secondary database modelsDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
Document store
Spatial DBMS
Document store
Graph DBMS infowith Apache Age
Spatial DBMS
Vector DBMS infowith pgvector extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.53
Rank#254  Overall
#117  Relational DBMS
Score636.25
Rank#4  Overall
#4  Relational DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Score0.11
Rank#354  Overall
#37  Graph DBMS
#15  Object oriented DBMS
Websiteazure.microsoft.com/­services/­data-explorerwww.postgres-xl.orgwww.postgresql.orgsphinxsearch.comvelocitydb.com
Technical documentationdocs.microsoft.com/­en-us/­azure/­data-explorerwww.postgres-xl.org/­documentationwww.postgresql.org/­docssphinxsearch.com/­docsvelocitydb.com/­UserGuide
DeveloperMicrosoftPostgreSQL Global Development Group infowww.postgresql.org/­developerSphinx Technologies Inc.VelocityDB Inc
Initial release20192014 infosince 2012, originally named StormDB1989 info1989: Postgres, 1996: PostgreSQL20012011
Current releasecloud service with continuous releases10 R1, October 201816.3, May 20243.5.1, February 20237.x
License infoCommercial or Open SourcecommercialOpen Source infoMozilla public licenseOpen Source infoBSDOpen Source infoGPL version 2, commercial licence availablecommercial
Cloud-based only infoOnly available as a cloud serviceyesnononono
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Implementation languageCCC++C#
Server operating systemshostedLinux
macOS
FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Any that supports .NET
Data schemeFixed schema with schema-less datatypes (dynamic)yesyesyesyes
Typing infopredefined data types such as float or dateyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesyesnoyes
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.yesyes infoXML type, but no XML query functionalityyes infospecific XML-type available, but no XML query functionality.no
Secondary indexesall fields are automatically indexedyesyesyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLKusto Query Language (KQL), SQL subsetyes infodistributed, parallel query executionyes infostandard with numerous extensionsSQL-like query language (SphinxQL)no
APIs and other access methodsMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Proprietary protocol.Net
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
.Net
C
C++
Delphi
Java infoJDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
.Net
Server-side scripts infoStored proceduresYes, possible languages: KQL, Python, Ruser defined functionsuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.nono
Triggersyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesyesnoCallbacks are triggered when data changes
Partitioning methods infoMethods for storing different data on different nodesSharding infoImplicit feature of the cloud servicehorizontal partitioningpartitioning by range, list and (since PostgreSQL 11) by hashSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replication infoother methods possible by using 3rd party extensionsnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsSpark connector (open source): github.com/­Azure/­azure-kusto-sparknononono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoMVCCACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nononoyes
User concepts infoAccess controlAzure Active Directory Authenticationfine grained access rights according to SQL-standardfine grained access rights according to SQL-standardnoBased on Windows Authentication

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More resources
Microsoft Azure Data ExplorerPostgres-XLPostgreSQLSphinxVelocityDB
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