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DBMS > AgensGraph vs. Firebase Realtime Database vs. GridDB vs. HugeGraph

System Properties Comparison AgensGraph vs. Firebase Realtime Database vs. GridDB vs. HugeGraph

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Editorial information provided by DB-Engines
NameAgensGraph  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonGridDB  Xexclude from comparisonHugeGraph  Xexclude from comparison
DescriptionMulti-model database supporting relational and graph data models and built upon PostgreSQLCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.Scalable in-memory time series database optimized for IoT and Big DataA fast-speed and highly-scalable Graph DBMS
Primary database modelGraph DBMS
Relational DBMS
Document storeTime Series DBMSGraph DBMS
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.23
Rank#315  Overall
#26  Graph DBMS
#140  Relational DBMS
Score13.64
Rank#39  Overall
#6  Document stores
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score0.17
Rank#335  Overall
#31  Graph DBMS
Websitebitnine.net/­agensgraphfirebase.google.com/­products/­realtime-databasegriddb.netgithub.com/­hugegraph
hugegraph.apache.org
Technical documentationbitnine.net/­documentationfirebase.google.com/­docs/­databasedocs.griddb.nethugegraph.apache.org/­docs
DeveloperBitnine Global Inc.Google infoacquired by Google 2014Toshiba CorporationBaidu
Initial release2016201220132018
Current release2.1, December 20185.1, August 20220.9
License infoCommercial or Open SourceOpen Source infoApache License 2.0commercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCC++Java
Server operating systemsLinux
OS X
Windows
hostedLinuxLinux
macOS
Unix
Data schemedepending on used data modelschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyes infonumerical, string, blob, geometry, boolean, timestampyes
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 indexesyesyesyesyes infoalso supports composite index and range index
SQL infoSupport of SQLyesnoSQL92, SQL-like TQL (Toshiba Query Language)no
APIs and other access methodsCypher Query Language
JDBC
Android
iOS
JavaScript API
RESTful HTTP API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Java API
RESTful HTTP API
TinkerPop Gremlin
Supported programming languagesC
Java
JavaScript
Python
Java
JavaScript
Objective-C
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Groovy
Java
Python
Server-side scripts infoStored proceduresyeslimited functionality with using 'rules'noasynchronous Gremlin script jobs
TriggersnoCallbacks are triggered when data changesyesno
Partitioning methods infoMethods for storing different data on different nodesno, but can be realized using table inheritanceShardingyes infodepending on used storage backend, e.g. Cassandra and HBase
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationSource-replica replicationyes infodepending on used storage backend, e.g. Cassandra and HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsvia hugegraph-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Immediate consistency within container, eventual consistency across containersEventual Consistency
Foreign keys infoReferential integrityyesnonoyes infoedges in graph
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyesACID at container levelACID
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.noyesyes
User concepts infoAccess controlfine grained access rights according to SQL-standardyes, based on authentication and database rulesAccess rights for users can be defined per databaseUsers, roles and permissions
More information provided by the system vendor
AgensGraphFirebase Realtime DatabaseGridDBHugeGraph
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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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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AgensGraphFirebase Realtime DatabaseGridDBHugeGraph
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