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DBMS > GridDB vs. Hypertable vs. InfinityDB vs. Sphinx

System Properties Comparison GridDB vs. Hypertable vs. InfinityDB vs. Sphinx

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Editorial information provided by DB-Engines
NameGridDB  Xexclude from comparisonHypertable  Xexclude from comparisonInfinityDB  Xexclude from comparisonSphinx  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionScalable in-memory time series database optimized for IoT and Big DataAn open source BigTable implementation based on distributed file systems such as HadoopA Java embedded Key-Value Store which extends the Java Map interfaceOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelTime Series DBMSWide column storeKey-value storeSearch engine
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score0.08
Rank#365  Overall
#55  Key-value stores
Score5.95
Rank#55  Overall
#5  Search engines
Websitegriddb.netboilerbay.comsphinxsearch.com
Technical documentationdocs.griddb.netboilerbay.com/­infinitydb/­manualsphinxsearch.com/­docs
DeveloperToshiba CorporationHypertable Inc.Boiler Bay Inc.Sphinx Technologies Inc.
Initial release2013200920022001
Current release5.1, August 20220.9.8.11, March 20164.03.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoGNU version 3. Commercial license availablecommercialOpen Source infoGPL version 2, commercial licence available
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 languageC++C++JavaC++
Server operating systemsLinuxLinux
OS X
Windows infoan inofficial Windows port is available
All OS with a Java VMFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesschema-freeyes infonested virtual Java Maps, multi-value, logical ‘tuple space’ runtime Schema upgradeyes
Typing infopredefined data types such as float or dateyes infonumerical, string, blob, geometry, boolean, timestampnoyes infoall Java primitives, Date, CLOB, BLOB, huge sparse arraysno
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 indexesyesrestricted infoonly exact value or prefix value scansno infomanual creation possible, using inversions based on multi-value capabilityyes infofull-text index on all search fields
SQL infoSupport of SQLSQL92, SQL-like TQL (Toshiba Query Language)nonoSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
C++ API
Thrift
Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/­docs/­ItemSpaceDataStructures.htm)
Proprietary protocol
Supported programming languagesC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C++
Java
Perl
PHP
Python
Ruby
JavaC++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresnononono
Triggersyesnonono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnoneSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationselectable replication factor on file system levelnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency within container, eventual consistency across containersImmediate ConsistencyImmediate Consistency infoREAD-COMMITTED or SERIALIZED
Foreign keys infoReferential integritynonono infomanual creation possible, using inversions based on multi-value capabilityno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID at container levelnoACID infoOptimistic locking for transactions; no isolation for bulk loadsno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.yesno
User concepts infoAccess controlAccess rights for users can be defined per databasenonono
More information provided by the system vendor
GridDBHypertableInfinityDBSphinx
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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GridDBHypertableInfinityDBSphinx
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