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DBMS > HEAVY.AI vs. Kinetica vs. Solr vs. Teradata Aster

System Properties Comparison HEAVY.AI vs. Kinetica vs. Solr vs. Teradata Aster

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Editorial information provided by DB-Engines
NameHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonKinetica  Xexclude from comparisonSolr  Xexclude from comparisonTeradata Aster  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareFully vectorized database across both GPUs and CPUsA widely used distributed, scalable search engine based on Apache LucenePlatform for big data analytics on multistructured data sources and types
Primary database modelRelational DBMSRelational DBMSSearch engineRelational DBMS
Secondary database modelsSpatial DBMSSpatial DBMS
Time Series DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.10
Rank#126  Overall
#61  Relational DBMS
Score0.69
Rank#234  Overall
#107  Relational DBMS
Score44.28
Rank#24  Overall
#3  Search engines
Websitegithub.com/­heavyai/­heavydb
www.heavy.ai
www.kinetica.comsolr.apache.org
Technical documentationdocs.heavy.aidocs.kinetica.comsolr.apache.org/­resources.html
DeveloperHEAVY.AI, Inc.KineticaApache Software FoundationTeradata
Initial release2016201220062005
Current release5.10, January 20227.1, August 20219.5.0, February 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2; enterprise edition availablecommercialOpen Source infoApache Version 2commercial
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 CUDAC, C++Java
Server operating systemsLinuxLinuxAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
Data schemeyesyesyes infoDynamic Fields enables on-the-fly addition of new fieldsFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
Typing infopredefined data types such as float or dateyesyesyes infosupports customizable data types and automatic typingyes
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.nonoyesyes infoin Aster File Store
Secondary indexesnoyesyes infoAll search fields are automatically indexedyes
SQL infoSupport of SQLyesSQL-like DML and DDL statementsSolr Parallel SQL Interfaceyes
APIs and other access methodsJDBC
ODBC
Thrift
Vega
JDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP/JSON API
ADO.NET
JDBC
ODBC
OLE DB
Supported programming languagesAll languages supporting JDBC/ODBC/Thrift
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
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresnouser defined functionsJava pluginsR packages
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangeyes infoUser configurable commands triggered on index changesno
Partitioning methods infoMethods for storing different data on different nodesSharding infoRound robinShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationSource-replica replicationyesyes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonooptimistic lockingACID
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.yesyes infoGPU vRAM or System RAMyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users and roles on table levelyesfine grained access rights according to SQL-standard

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