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DBMS > Blueflood vs. HEAVY.AI vs. Microsoft Azure Data Explorer vs. Sphinx

System Properties Comparison Blueflood vs. HEAVY.AI vs. Microsoft Azure Data Explorer vs. Sphinx

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Editorial information provided by DB-Engines
NameBlueflood  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionScalable TimeSeries DBMS based on CassandraA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareFully managed big data interactive analytics platformOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelTime Series DBMSRelational DBMSRelational DBMS infocolumn orientedSearch engine
Secondary database modelsSpatial DBMSDocument 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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.06
Rank#353  Overall
#34  Time Series DBMS
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Websiteblueflood.iogithub.com/­heavyai/­heavydb
www.heavy.ai
azure.microsoft.com/­services/­data-explorersphinxsearch.com
Technical documentationgithub.com/­rax-maas/­blueflood/­wikidocs.heavy.aidocs.microsoft.com/­en-us/­azure/­data-explorersphinxsearch.com/­docs
DeveloperRackspaceHEAVY.AI, Inc.MicrosoftSphinx Technologies Inc.
Initial release2013201620192001
Current release5.10, January 2022cloud service with continuous releases3.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache Version 2; enterprise edition availablecommercialOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++ and CUDAC++
Server operating systemsLinux
OS X
LinuxhostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemepredefined schemeyesFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesno
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.nonoyes
Secondary indexesnonoall fields are automatically indexedyes infofull-text index on all search fields
SQL infoSupport of SQLnoyesKusto Query Language (KQL), SQL subsetSQL-like query language (SphinxQL)
APIs and other access methodsHTTP RESTJDBC
ODBC
Thrift
Vega
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Proprietary protocol
Supported programming languagesAll languages supporting JDBC/ODBC/Thrift
Python
.Net
Go
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 proceduresnonoYes, possible languages: KQL, Python, Rno
Triggersnonoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on CassandraSharding infoRound robinSharding 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 nodesselectable replication factor infobased on CassandraMulti-source replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes 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 controlnofine grained access rights according to SQL-standardAzure Active Directory Authenticationno

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More resources
BluefloodHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Microsoft Azure Data ExplorerSphinx
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