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DBMS > HugeGraph vs. Microsoft Azure Data Explorer vs. OrigoDB vs. Spark SQL

System Properties Comparison HugeGraph vs. Microsoft Azure Data Explorer vs. OrigoDB vs. Spark SQL

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Editorial information provided by DB-Engines
NameHugeGraph  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonOrigoDB  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionA fast-speed and highly-scalable Graph DBMSFully managed big data interactive analytics platformA fully ACID in-memory object graph databaseSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelGraph DBMSRelational DBMS infocolumn orientedDocument store
Object oriented DBMS
Relational 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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.17
Rank#335  Overall
#31  Graph DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.06
Rank#380  Overall
#50  Document stores
#19  Object oriented DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitegithub.com/­hugegraph
hugegraph.apache.org
azure.microsoft.com/­services/­data-explorerorigodb.comspark.apache.org/­sql
Technical documentationhugegraph.apache.org/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerorigodb.com/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperBaiduMicrosoftRobert Friberg et alApache Software Foundation
Initial release201820192009 infounder the name LiveDB2014
Current release0.9cloud service with continuous releases3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen SourceOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC#Scala
Server operating systemsLinux
macOS
Unix
hostedLinux
Windows
Linux
OS X
Windows
Data schemeyesFixed schema with schema-less datatypes (dynamic)yesyes
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesUser defined using .NET types and collectionsyes
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.noyesno infocan be achieved using .NETno
Secondary indexesyes infoalso supports composite index and range indexall fields are automatically indexedyesno
SQL infoSupport of SQLnoKusto Query Language (KQL), SQL subsetnoSQL-like DML and DDL statements
APIs and other access methodsJava API
RESTful HTTP API
TinkerPop Gremlin
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
.NET Client API
HTTP API
LINQ
JDBC
ODBC
Supported programming languagesGroovy
Java
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.NetJava
Python
R
Scala
Server-side scripts infoStored proceduresasynchronous Gremlin script jobsYes, possible languages: KQL, Python, Ryesno
Triggersnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoDomain Eventsno
Partitioning methods infoMethods for storing different data on different nodesyes infodepending on used storage backend, e.g. Cassandra and HBaseSharding infoImplicit feature of the cloud servicehorizontal partitioning infoclient side managed; servers are not synchronizedyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyes infodepending on used storage backend, e.g. Cassandra and HBaseyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsvia hugegraph-sparkSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyes infoedges in graphnodepending on modelno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoWrite ahead logyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesno
User concepts infoAccess controlUsers, roles and permissionsAzure Active Directory AuthenticationRole based authorizationno

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More resources
HugeGraphMicrosoft Azure Data ExplorerOrigoDBSpark SQL
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