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DBMS > HEAVY.AI vs. JanusGraph vs. Kinetica vs. Solr

System Properties Comparison HEAVY.AI vs. JanusGraph vs. Kinetica vs. Solr

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Editorial information provided by DB-Engines
NameHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonJanusGraph infosuccessor of Titan  Xexclude from comparisonKinetica  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareA Graph DBMS optimized for distributed clusters infoIt was forked from the latest code base of Titan in January 2017Fully vectorized database across both GPUs and CPUsA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSGraph DBMSRelational DBMSSearch engine
Secondary database modelsSpatial DBMSSpatial DBMS
Time Series DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score1.94
Rank#129  Overall
#12  Graph DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Websitegithub.com/­heavyai/­heavydb
www.heavy.ai
janusgraph.orgwww.kinetica.comsolr.apache.org
Technical documentationdocs.heavy.aidocs.janusgraph.orgdocs.kinetica.comsolr.apache.org/­resources.html
DeveloperHEAVY.AI, Inc.Linux Foundation; originally developed as Titan by AureliusKineticaApache Software Foundation
Initial release2016201720122006
Current release5.10, January 20220.6.3, February 20237.1, August 20219.6.0, April 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2; enterprise edition availableOpen Source infoApache 2.0commercialOpen Source infoApache Version 2
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++ and CUDAJavaC, C++Java
Server operating systemsLinuxLinux
OS X
Unix
Windows
LinuxAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesyesyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyesyes infosupports customizable data types and automatic typing
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.nononoyes
Secondary indexesnoyesyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLyesnoSQL-like DML and DDL statementsSolr Parallel SQL Interface
APIs and other access methodsJDBC
ODBC
Thrift
Vega
Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster
JDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP/JSON API
Supported programming languagesAll languages supporting JDBC/ODBC/Thrift
Python
Clojure
Java
Python
C++
Java
JavaScript (Node.js)
Python
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresnoyesuser defined functionsJava plugins
Triggersnoyesyes infotriggers when inserted values for one or more columns fall within a specified rangeyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesSharding infoRound robinyes infodepending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)ShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationyesSource-replica replicationyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infovia Faunus, a graph analytics enginenospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency or Eventual Consistency depending on configurationEventual Consistency
Foreign keys infoReferential integritynoyes infoRelationships in graphsyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infoSupports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcastyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infoGPU vRAM or System RAMyes
User concepts infoAccess controlfine grained access rights according to SQL-standardUser authentification and security via Rexster Graph ServerAccess rights for users and roles on table levelyes

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More resources
HEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022JanusGraph infosuccessor of TitanKineticaSolr
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