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DBMS > OrigoDB vs. Solr vs. Spark SQL vs. Vertica

System Properties Comparison OrigoDB vs. Solr vs. Spark SQL vs. Vertica

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Editorial information provided by DB-Engines
NameOrigoDB  Xexclude from comparisonSolr  Xexclude from comparisonSpark SQL  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionA fully ACID in-memory object graph databaseA widely used distributed, scalable search engine based on Apache LuceneSpark SQL is a component on top of 'Spark Core' for structured data processingCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelDocument store
Object oriented DBMS
Search engineRelational DBMSRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMSSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.06
Rank#380  Overall
#50  Document stores
#18  Object oriented DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score10.06
Rank#42  Overall
#26  Relational DBMS
Websiteorigodb.comsolr.apache.orgspark.apache.org/­sqlwww.vertica.com
Technical documentationorigodb.com/­docssolr.apache.org/­resources.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.htmlvertica.com/­documentation
DeveloperRobert Friberg et alApache Software FoundationApache Software FoundationOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2009 infounder the name LiveDB200620142005
Current release9.6.1, May 20243.5.0 ( 2.13), September 202312.0.3, January 2023
License infoCommercial or Open SourceOpen SourceOpen Source infoApache Version 2Open Source infoApache 2.0commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenononono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC#JavaScalaC++
Server operating systemsLinux
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
OS X
Windows
Linux
Data schemeyesyes infoDynamic Fields enables on-the-fly addition of new fieldsyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateUser defined using .NET types and collectionsyes infosupports customizable data types and automatic typingyesyes
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.no infocan be achieved using .NETyesnono
Secondary indexesyesyes infoAll search fields are automatically indexednoNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnoSolr Parallel SQL InterfaceSQL-like DML and DDL statementsFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methods.NET Client API
HTTP API
LINQ
Java API
RESTful HTTP/JSON API
JDBC
ODBC
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languages.Net.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Java
Python
R
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresyesJava pluginsnoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersyes infoDomain Eventsyes infoUser configurable commands triggered on index changesnoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning infoclient side managed; servers are not synchronizedShardingyes, utilizing Spark Corehorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyesnoneMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceno infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritydepending on modelnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDoptimistic lockingnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infoWrite ahead logyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnono
User concepts infoAccess controlRole based authorizationyesnofine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
OrigoDBSolrSpark SQLVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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OrigoDBSolrSpark SQLVertica infoOpenText™ Vertica™
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