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DBMS > HEAVY.AI vs. Solr vs. Stardog vs. Yanza

System Properties Comparison HEAVY.AI vs. Solr vs. Stardog vs. Yanza

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Editorial information provided by DB-Engines
NameHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonSolr  Xexclude from comparisonStardog  Xexclude from comparisonYanza  Xexclude from comparison
Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareA widely used distributed, scalable search engine based on Apache LuceneEnterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualizationTime Series DBMS for IoT Applications
Primary database modelRelational DBMSSearch engineGraph DBMS
RDF store
Time Series DBMS
Secondary database modelsSpatial DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Score2.02
Rank#123  Overall
#11  Graph DBMS
#6  RDF stores
Websitegithub.com/­heavyai/­heavydb
www.heavy.ai
solr.apache.orgwww.stardog.comyanza.com
Technical documentationdocs.heavy.aisolr.apache.org/­resources.htmldocs.stardog.com
DeveloperHEAVY.AI, Inc.Apache Software FoundationStardog-UnionYanza
Initial release2016200620102015
Current release5.10, January 20229.5.0, February 20247.3.0, May 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2; enterprise edition availableOpen Source infoApache Version 2commercial info60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/studentscommercial infofree version available
Cloud-based only infoOnly available as a cloud servicenononono infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++ and CUDAJavaJava
Server operating systemsLinuxAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
macOS
Windows
Windows
Data schemeyesyes infoDynamic Fields enables on-the-fly addition of new fieldsschema-free and OWL/RDFS-schema supportschema-free
Typing infopredefined data types such as float or dateyesyes infosupports customizable data types and automatic typingyesno
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.noyesno infoImport/export of XML data possibleno
Secondary indexesnoyes infoAll search fields are automatically indexedyes infosupports real-time indexing in full-text and geospatialno
SQL infoSupport of SQLyesSolr Parallel SQL InterfaceYes, compatible with all major SQL variants through dedicated BI/SQL Serverno
APIs and other access methodsJDBC
ODBC
Thrift
Vega
Java API
RESTful HTTP/JSON API
GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3
HTTP API
Supported programming languagesAll languages supporting JDBC/ODBC/Thrift
Python
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby
any language that supports HTTP calls
Server-side scripts infoStored proceduresnoJava pluginsuser defined functions and aggregates, HTTP Server extensions in Javano
Triggersnoyes infoUser configurable commands triggered on index changesyes infovia event handlersyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodesSharding infoRound robinShardingnonenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationyesMulti-source replication in HA-Clusternone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reducenono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate Consistency in HA-ClusterImmediate Consistency
Foreign keys infoReferential integritynonoyes inforelationships in graphsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanooptimistic lockingACIDno
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.yesyesyes
User concepts infoAccess controlfine grained access rights according to SQL-standardyesAccess rights for users and rolesno

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More resources
HEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022SolrStardogYanza
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