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DBMS > IRONdb vs. STSdb vs. Vertica

System Properties Comparison IRONdb vs. STSdb vs. Vertica

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Editorial information provided by DB-Engines
NameIRONdb  Xexclude from comparisonSTSdb  Xexclude from comparisonVertica infoVertica Analytics Platform  Xexclude from comparison
DescriptionA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicityKey-Value Store with special method for indexing infooptimized for high performance using a special indexing methodCloud or on-premises 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 modelTime Series DBMSKey-value storeRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.00
Rank#356  Overall
#32  Time Series DBMS
Score0.00
Rank#357  Overall
#56  Key-value stores
Score20.13
Rank#37  Overall
#23  Relational DBMS
Websitewww.circonus.com/­solutions/­time-series-databasegithub.com/­STSSoft/­STSdb4www.vertica.com
Technical documentationdocs.circonus.com/­irondbvertica.com/­documentation
DeveloperCirconus LLC.STS Soft SCMicro Focus infoprior to that Hewlett Packard
Initial release201720112005
Current releaseV0.10.20, January 20184.0.8, September 201511.1, February 2022
License infoCommercial or Open SourcecommercialOpen Source infoGPLv2, commercial license availablecommercial 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.
Vertica Accelerator’s high-performance analytics and machine learning with SQL or Python is available as a managed service.
Implementation languageC and C++C#C++
Server operating systemsLinuxWindowsLinux
Data schemeschema-freeyesyes, but unstructured data can be stored in specific Flex-Tables
Typing infopredefined data types such as float or dateyes infotext, numeric, histogramsyes infoprimitive types and user defined types (classes)yes: BINARY, BOOLEAN, CHAR, VARCHAR, LONG VARCHAR, DATE, TIME, TIMESTAMP, INTERVAL, INTERVAL DAY TO SECOND, INTERVAL YEAR TO MONTH, DOUBLE PRECISION, FLOAT, INTEGER, BIGINT, SMALLINT,NUMERIC, DECIMAL, NUMBER, MONEY,GEOMETRY
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.nono
Secondary indexesnonoNo Indexes Required
SQL infoSupport of SQLSQL-like query language (Circonus Analytics Query Language: CAQL)noFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsHTTP API.NET Client APIADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languages.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
C#
Java
C#
C++
Java
Perl
PHP
Python
R
Server-side scripts infoStored proceduresyes, in Luanoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnonoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesAutomatic, metric affinity per nodenonehorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesconfigurable replication factor, datacenter awarenoneMulti-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 methodsnonono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency per node, eventual consistency across nodesImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
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.nono
User concepts infoAccess controlnonofine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
IRONdbSTSdbVertica infoVertica Analytics Platform
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy on-premises,...
» more
Competitive advantagesFast, scalable and capable of high concurrency. Separation of compute/storage leverages...
» more
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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More resources
IRONdbSTSdbVertica infoVertica Analytics Platform
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