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DBMS > ArcadeDB vs. GridDB vs. HBase vs. Microsoft Azure AI Search

System Properties Comparison ArcadeDB vs. GridDB vs. HBase vs. Microsoft Azure AI Search

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Editorial information provided by DB-Engines
NameArcadeDB  Xexclude from comparisonGridDB  Xexclude from comparisonHBase  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparison
DescriptionFast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewrittenScalable in-memory time series database optimized for IoT and Big DataWide-column store based on Apache Hadoop and on concepts of BigTableSearch-as-a-service for web and mobile app development
Primary database modelDocument store
Graph DBMS
Key-value store
Time Series DBMS infoin next version
Time Series DBMSWide column storeSearch engine
Secondary database modelsKey-value store
Relational DBMS
Vector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.02
Rank#366  Overall
#50  Document stores
#38  Graph DBMS
#53  Key-value stores
#36  Time Series DBMS
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score30.50
Rank#26  Overall
#2  Wide column stores
Score5.59
Rank#63  Overall
#7  Search engines
Websitearcadedb.comgriddb.nethbase.apache.orgazure.microsoft.com/­en-us/­services/­search
Technical documentationdocs.arcadedb.comdocs.griddb.nethbase.apache.org/­book.htmllearn.microsoft.com/­en-us/­azure/­search
DeveloperArcade DataToshiba CorporationApache Software Foundation infoApache top-level project, originally developed by PowersetMicrosoft
Initial release2021201320082015
Current releaseSeptember 20215.1, August 20222.3.4, January 2021V1
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache version 2commercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++Java
Server operating systemsAll OS with a Java VMLinuxLinux
Unix
Windows infousing Cygwin
hosted
Data schemeschema-freeyesschema-free, schema definition possibleyes
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampoptions to bring your own types, AVROyes
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.nononono
Secondary indexesyesyesnoyes
SQL infoSupport of SQLSQL-like query language, no joinsSQL92, SQL-like TQL (Toshiba Query Language)nono
APIs and other access methodsJDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Java API
RESTful HTTP API
Thrift
RESTful HTTP API
Supported programming languagesJavaC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C
C#
C++
Groovy
Java
PHP
Python
Scala
C#
Java
JavaScript
Python
Server-side scripts infoStored proceduresnoyes infoCoprocessors in Javano
Triggersyesyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationSource-replica replicationMulti-source replication
Source-replica replication
yes infoImplicit feature of the cloud service
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency within container, eventual consistency across containersImmediate Consistency or Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes inforelationship in graphsnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID at container levelSingle row ACID (across millions of columns)no
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
User concepts infoAccess controlAccess rights for users can be defined per databaseAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACyes infousing Azure authentication
More information provided by the system vendor
ArcadeDBGridDBHBaseMicrosoft Azure AI Search
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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ArcadeDBGridDBHBaseMicrosoft Azure AI Search
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