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DBMS > atoti vs. GridDB vs. HugeGraph vs. Stardog

System Properties Comparison atoti vs. GridDB vs. HugeGraph vs. Stardog

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Editorial information provided by DB-Engines
Nameatoti  Xexclude from comparisonGridDB  Xexclude from comparisonHugeGraph  Xexclude from comparisonStardog  Xexclude from comparison
DescriptionAn in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.Scalable in-memory time series database optimized for IoT and Big DataA fast-speed and highly-scalable Graph DBMSEnterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization
Primary database modelObject oriented DBMSTime Series DBMSGraph DBMSGraph DBMS
RDF store
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.56
Rank#245  Overall
#10  Object oriented DBMS
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score0.13
Rank#336  Overall
#32  Graph DBMS
Score2.02
Rank#123  Overall
#11  Graph DBMS
#6  RDF stores
Websiteatoti.iogriddb.netgithub.com/­hugegraph
hugegraph.apache.org
www.stardog.com
Technical documentationdocs.atoti.iodocs.griddb.nethugegraph.apache.org/­docsdocs.stardog.com
DeveloperActiveViamToshiba CorporationBaiduStardog-Union
Initial release201320182010
Current release5.1, August 20220.97.3.0, May 2020
License infoCommercial or Open Sourcecommercial infofree versions availableOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2.0commercial info60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++JavaJava
Server operating systemsLinuxLinux
macOS
Unix
Linux
macOS
Windows
Data schemeyesyesschema-free and OWL/RDFS-schema support
Typing infopredefined data types such as float or dateyes infonumerical, string, blob, geometry, boolean, timestampyesyes
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.nonono infoImport/export of XML data possible
Secondary indexesyesyes infoalso supports composite index and range indexyes infosupports real-time indexing in full-text and geospatial
SQL infoSupport of SQLMultidimensional Expressions (MDX)SQL92, SQL-like TQL (Toshiba Query Language)noYes, compatible with all major SQL variants through dedicated BI/SQL Server
APIs and other access methodsJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Java API
RESTful HTTP API
TinkerPop Gremlin
GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3
Supported programming languagesC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Groovy
Java
Python
.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby
Server-side scripts infoStored proceduresPythonnoasynchronous Gremlin script jobsuser defined functions and aggregates, HTTP Server extensions in Java
Triggersyesnoyes infovia event handlers
Partitioning methods infoMethods for storing different data on different nodesSharding, horizontal partitioningShardingyes infodepending on used storage backend, e.g. Cassandra and HBasenone
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes infodepending on used storage backend, e.g. Cassandra and HBaseMulti-source replication in HA-Cluster
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsvia hugegraph-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency within container, eventual consistency across containersEventual ConsistencyImmediate Consistency in HA-Cluster
Foreign keys infoReferential integritynoyes infoedges in graphyes inforelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID at container levelACIDACID
Concurrency infoSupport for concurrent manipulation of datayes, multi-version concurrency control (MVCC)yesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyesyes
User concepts infoAccess controlAccess rights for users can be defined per databaseUsers, roles and permissionsAccess rights for users and roles
More information provided by the system vendor
atotiGridDBHugeGraphStardog
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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atotiGridDBHugeGraphStardog
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