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DBMS > Atos Standard Common Repository vs. Solr vs. Vertica

System Properties Comparison Atos Standard Common Repository vs. Solr vs. Vertica

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Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonSolr  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.
DescriptionHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksA widely used distributed, scalable search engine based on Apache LuceneCloud 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
Key-value store
Search engineRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMSSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score42.91
Rank#24  Overall
#3  Search engines
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositorysolr.apache.orgwww.vertica.com
Technical documentationsolr.apache.org/­resources.htmlvertica.com/­documentation
DeveloperAtos Convergence CreatorsApache Software FoundationOpenText infopreviously Micro Focus and Hewlett Packard
Initial release201620062005
Current release17039.5.0, February 202412.0.3, January 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaC++
Server operating systemsLinuxAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
Data schemeSchema and schema-less with LDAP viewsyes infoDynamic Fields enables on-the-fly addition of new fieldsYes, 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 dateoptionalyes 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.yesyesno
Secondary indexesyesyes infoAll search fields are automatically indexedNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnoSolr Parallel SQL InterfaceFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsLDAPJava API
RESTful HTTP/JSON API
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesAll languages with LDAP bindings.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnoJava pluginsyes, PostgreSQL PL/pgSQL, with minor differences
Triggersyesyes infoUser configurable commands triggered on index changesyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesyesMulti-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 methodsspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceno infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsoptimistic lockingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
User concepts infoAccess controlLDAP bind authenticationyesfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
Atos Standard Common RepositorySolrVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more
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...
» more
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...
» more

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Atos Standard Common RepositorySolrVertica infoOpenText™ Vertica™
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