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DBMS > HEAVY.AI vs. Heroic vs. Oracle Berkeley DB vs. SpatiaLite vs. Sphinx

System Properties Comparison HEAVY.AI vs. Heroic vs. Oracle Berkeley DB vs. SpatiaLite vs. Sphinx

Editorial information provided by DB-Engines
NameHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonHeroic  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonSpatiaLite  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchWidely used in-process key-value storeSpatial extension of SQLiteOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelRelational DBMSTime Series DBMSKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Spatial DBMSSearch engine
Secondary database modelsSpatial DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score0.51
Rank#255  Overall
#21  Time Series DBMS
Score2.21
Rank#117  Overall
#20  Key-value stores
#3  Native XML DBMS
Score1.60
Rank#149  Overall
#3  Spatial DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Websitegithub.com/­heavyai/­heavydb
www.heavy.ai
github.com/­spotify/­heroicwww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlwww.gaia-gis.it/­fossil/­libspatialite/­indexsphinxsearch.com
Technical documentationdocs.heavy.aispotify.github.io/­heroicdocs.oracle.com/­cd/­E17076_05/­html/­index.htmlwww.gaia-gis.it/­gaia-sins/­spatialite_topics.htmlsphinxsearch.com/­docs
DeveloperHEAVY.AI, Inc.SpotifyOracle infooriginally developed by Sleepycat, which was acquired by OracleAlessandro FurieriSphinx Technologies Inc.
Initial release20162014199420082001
Current release5.10, January 202218.1.40, May 20205.0.0, August 20203.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2; enterprise edition availableOpen Source infoApache 2.0Open Source infocommercial license availableOpen Source infoMPL 1.1, GPL v2.0 or LGPL v2.1Open Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++ and CUDAJavaC, Java, C++ (depending on the Berkeley DB edition)C++C++
Server operating systemsLinuxAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
server-lessFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesschema-freeschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesnoyesno
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.nonoyes infoonly with the Berkeley DB XML editionno
Secondary indexesnoyes infovia Elasticsearchyesyesyes infofull-text index on all search fields
SQL infoSupport of SQLyesnoyes infoSQL interfaced based on SQLite is availableyesSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
ODBC
Thrift
Vega
HQL (Heroic Query Language, a JSON-based language)
HTTP API
Proprietary protocol
Supported programming languagesAll languages supporting JDBC/ODBC/Thrift
Python
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresnonononono
Triggersnonoyes infoonly for the SQL APIyesno
Partitioning methods infoMethods for storing different data on different nodesSharding infoRound robinShardingnonenoneSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationyesSource-replica replicationnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes 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.yesnoyesyes
User concepts infoAccess controlfine grained access rights according to SQL-standardnonono

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More resources
HEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022HeroicOracle Berkeley DBSpatiaLiteSphinx
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