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DBMS > Apache IoTDB vs. HugeGraph vs. Microsoft Azure AI Search vs. Postgres-XL vs. Sphinx

System Properties Comparison Apache IoTDB vs. HugeGraph vs. Microsoft Azure AI Search vs. Postgres-XL vs. Sphinx

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonHugeGraph  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparisonPostgres-XL  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkA fast-speed and highly-scalable Graph DBMSSearch-as-a-service for web and mobile app developmentBased on PostgreSQL enhanced with MPP and write-scale-out cluster featuresOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelTime Series DBMSGraph DBMSSearch engineRelational DBMSSearch engine
Secondary database modelsVector DBMSDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score0.13
Rank#336  Overall
#32  Graph DBMS
Score5.59
Rank#63  Overall
#7  Search engines
Score0.49
Rank#256  Overall
#117  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Websiteiotdb.apache.orggithub.com/­hugegraph
hugegraph.apache.org
azure.microsoft.com/­en-us/­services/­searchwww.postgres-xl.orgsphinxsearch.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlhugegraph.apache.org/­docslearn.microsoft.com/­en-us/­azure/­searchwww.postgres-xl.org/­documentationsphinxsearch.com/­docs
DeveloperApache Software FoundationBaiduMicrosoftSphinx Technologies Inc.
Initial release2018201820152014 infosince 2012, originally named StormDB2001
Current release1.1.0, April 20230.9V110 R1, October 20183.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0commercialOpen Source infoMozilla public licenseOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaCC++
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
macOS
Unix
hostedLinux
macOS
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyesno
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 infoXML type, but no XML query functionality
Secondary indexesyesyes infoalso supports composite index and range indexyesyesyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query languagenonoyes infodistributed, parallel query executionSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
Native API
Java API
RESTful HTTP API
TinkerPop Gremlin
RESTful HTTP APIADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Proprietary protocol
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Groovy
Java
Python
C#
Java
JavaScript
Python
.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresyesasynchronous Gremlin script jobsnouser defined functionsno
Triggersyesnonoyesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)yes infodepending on used storage backend, e.g. Cassandra and HBaseSharding infoImplicit feature of the cloud servicehorizontal partitioningSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyes infodepending on used storage backend, e.g. Cassandra and HBaseyes infoImplicit feature of the cloud servicenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkvia hugegraph-sparknonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Eventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyes infoedges in graphnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACID infoMVCCno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes 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.yesyesnono
User concepts infoAccess controlyesUsers, roles and permissionsyes infousing Azure authenticationfine grained access rights according to SQL-standardno

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More resources
Apache IoTDBHugeGraphMicrosoft Azure AI SearchPostgres-XLSphinx
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