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DBMS > Apache Phoenix vs. HugeGraph vs. Machbase Neo vs. Microsoft Azure Data Explorer

System Properties Comparison Apache Phoenix vs. HugeGraph vs. Machbase Neo vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonHugeGraph  Xexclude from comparisonMachbase Neo infoFormer name was Infiniflux  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseA fast-speed and highly-scalable Graph DBMSTimeSeries DBMS for AIoT and BigDataFully managed big data interactive analytics platform
Primary database modelRelational DBMSGraph DBMSTime Series DBMSRelational DBMS infocolumn oriented
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
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score0.13
Rank#336  Overall
#32  Graph DBMS
Score0.12
Rank#339  Overall
#30  Time Series DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websitephoenix.apache.orggithub.com/­hugegraph
hugegraph.apache.org
machbase.comazure.microsoft.com/­services/­data-explorer
Technical documentationphoenix.apache.orghugegraph.apache.org/­docsmachbase.com/­dbmsdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software FoundationBaiduMachbaseMicrosoft
Initial release2014201820132019
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20190.9V8.0, August 2023cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0commercial infofree test version availablecommercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC
Server operating systemsLinux
Unix
Windows
Linux
macOS
Unix
Linux
macOS
Windows
hosted
Data schemeyes infolate-bound, schema-on-read capabilitiesyesyesFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types
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.nononoyes
Secondary indexesyesyes infoalso supports composite index and range indexyesall fields are automatically indexed
SQL infoSupport of SQLyesnoSQL-like query languageKusto Query Language (KQL), SQL subset
APIs and other access methodsJDBCJava API
RESTful HTTP API
TinkerPop Gremlin
gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
Groovy
Java
Python
C
C#
C++
Go
Java
JavaScript
PHP infovia ODBC
Python
R infovia ODBC
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresuser defined functionsasynchronous Gremlin script jobsnoYes, possible languages: KQL, Python, R
Triggersnononoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesShardingyes infodepending on used storage backend, e.g. Cassandra and HBaseShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
yes infodepending on used storage backend, e.g. Cassandra and HBaseselectable replication factoryes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationvia hugegraph-sparknoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyes infoedges in graphnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesnoyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes infovolatile and lookup tableno
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyUsers, roles and permissionssimple password-based access controlAzure Active Directory Authentication

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More resources
Apache PhoenixHugeGraphMachbase Neo infoFormer name was InfinifluxMicrosoft Azure Data Explorer
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