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DBMS > Fujitsu Enterprise Postgres vs. GridDB vs. HugeGraph vs. Sphinx

System Properties Comparison Fujitsu Enterprise Postgres vs. GridDB vs. HugeGraph vs. Sphinx

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Editorial information provided by DB-Engines
NameFujitsu Enterprise Postgres  Xexclude from comparisonGridDB  Xexclude from comparisonHugeGraph  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionEnterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.Scalable in-memory time series database optimized for IoT and Big DataA fast-speed and highly-scalable Graph DBMSOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelRelational DBMSTime Series DBMSGraph DBMSSearch engine
Secondary database modelsDocument store
Spatial DBMS
Key-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.37
Rank#278  Overall
#128  Relational DBMS
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score0.17
Rank#335  Overall
#31  Graph DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Websitewww.postgresql.fastware.comgriddb.netgithub.com/­hugegraph
hugegraph.apache.org
sphinxsearch.com
Technical documentationwww.postgresql.fastware.com/­product-manualsdocs.griddb.nethugegraph.apache.org/­docssphinxsearch.com/­docs
DeveloperPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyToshiba CorporationBaiduSphinx Technologies Inc.
Initial release201320182001
Current releaseFujitsu Enterprise Postgres 14, January 20225.1, August 20220.93.5.1, February 2023
License infoCommercial or Open SourcecommercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2.0Open Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCC++JavaC++
Server operating systemsLinux
Windows
LinuxLinux
macOS
Unix
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampyesno
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.nono
Secondary indexesyesyesyes infoalso supports composite index and range indexyes infofull-text index on all search fields
SQL infoSupport of SQLyesSQL92, SQL-like TQL (Toshiba Query Language)noSQL-like query language (SphinxQL)
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Java API
RESTful HTTP API
TinkerPop Gremlin
Proprietary protocol
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Groovy
Java
Python
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresuser defined functionsnoasynchronous Gremlin script jobsno
Triggersyesyesnono
Partitioning methods infoMethods for storing different data on different nodespartitioning by range, list and by hashShardingyes infodepending on used storage backend, e.g. Cassandra and HBaseSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationSource-replica replicationyes infodepending on used storage backend, e.g. Cassandra and HBasenone
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 ConsistencyImmediate consistency within container, eventual consistency across containersEventual Consistency
Foreign keys infoReferential integrityyesnoyes infoedges in graphno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID at container levelACIDno
Concurrency infoSupport for concurrent manipulation of datayes, multi-version concurrency control (MVCC)yesyesyes
Durability infoSupport for making data persistentyesyesyesyes 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.yesyes
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users can be defined per databaseUsers, roles and permissionsno
More information provided by the system vendor
Fujitsu Enterprise PostgresGridDBHugeGraphSphinx
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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Fujitsu Enterprise PostgresGridDBHugeGraphSphinx
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