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DBMS > HugeGraph vs. Kinetica vs. ObjectBox vs. Vertica

System Properties Comparison HugeGraph vs. Kinetica vs. ObjectBox vs. Vertica

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Editorial information provided by DB-Engines
NameHugeGraph  Xexclude from comparisonKinetica  Xexclude from comparisonObjectBox  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionA fast-speed and highly-scalable Graph DBMSFully vectorized database across both GPUs and CPUsExtremely fast embedded database for small devices, IoT and MobileCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelGraph DBMSRelational DBMSObject oriented DBMSRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
Time Series DBMSSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.13
Rank#336  Overall
#32  Graph DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score1.20
Rank#170  Overall
#5  Object oriented DBMS
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websitegithub.com/­hugegraph
hugegraph.apache.org
www.kinetica.comobjectbox.iowww.vertica.com
Technical documentationhugegraph.apache.org/­docsdocs.kinetica.comdocs.objectbox.iovertica.com/­documentation
DeveloperBaiduKineticaObjectBox LimitedOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2018201220172005
Current release0.97.1, August 202112.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache License 2.0commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenononono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC, C++C and C++C++
Server operating systemsLinux
macOS
Unix
LinuxAndroid
iOS
Linux
macOS
Windows
Linux
Data schemeyesyesyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateyesyesyesyes
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 indexesyes infoalso supports composite index and range indexyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnoSQL-like DML and DDL statementsnoFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJava API
RESTful HTTP API
TinkerPop Gremlin
JDBC
ODBC
RESTful HTTP API
Proprietary native APIADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesGroovy
Java
Python
C++
Java
JavaScript (Node.js)
Python
C
C++
Dart
Go
Java
JavaScript infoplanned (as of Jan 2019)
Kotlin
Python infoplanned (as of Jan 2019)
Swift
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresasynchronous Gremlin script jobsuser defined functionsnoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangenoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesyes infodepending on used storage backend, e.g. Cassandra and HBaseShardingnonehorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes infodepending on used storage backend, e.g. Cassandra and HBaseSource-replica replicationonline/offline synchronization between client and serverMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsvia hugegraph-sparknonono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infoedges in graphyesyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDACID
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.yesyes infoGPU vRAM or System RAMnono
User concepts infoAccess controlUsers, roles and permissionsAccess rights for users and roles on table levelyesfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
HugeGraphKineticaObjectBoxVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more
Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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HugeGraphKineticaObjectBoxVertica infoOpenText™ Vertica™
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