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DBMS > Hive vs. IRONdb vs. Microsoft Azure Cosmos DB vs. RDF4J

System Properties Comparison Hive vs. IRONdb vs. Microsoft Azure Cosmos DB vs. RDF4J

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Editorial information provided by DB-Engines
NameHive  Xexclude from comparisonIRONdb  Xexclude from comparisonMicrosoft Azure Cosmos DB infoformer name was Azure DocumentDB  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparison
Descriptiondata warehouse software for querying and managing large distributed datasets, built on HadoopA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicityGlobally distributed, horizontally scalable, multi-model database serviceRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.
Primary database modelRelational DBMSTime Series DBMSDocument store
Graph DBMS
Key-value store
Wide column store
RDF store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score84.22
Rank#14  Overall
#9  Relational DBMS
Score0.05
Rank#328  Overall
#27  Time Series DBMS
Score31.98
Rank#24  Overall
#4  Document stores
#2  Graph DBMS
#3  Key-value stores
#3  Wide column stores
Score0.38
Rank#223  Overall
#10  RDF stores
Websitehive.apache.orgwww.irondb.ioazure.microsoft.com/­services/­cosmos-dbrdf4j.org
Technical documentationcwiki.apache.org/­confluence/­display/­Hive/­Homewww.irondb.io/­docsdocs.microsoft.com/­en-us/­azure/­cosmos-dbdocs.rdf4j.org
DeveloperApache Software Foundation infoinitially developed by FacebookCirconus LLC.MicrosoftSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release2012201720142004
Current release3.1.2, August 2019V0.10.20, January 2018
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialOpen Source infoEclipse Distribution License (EDL), v1.0.
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC and C++Java
Server operating systemsAll OS with a Java VMLinuxhostedLinux
OS X
Unix
Windows
Data schemeyesschema-freeschema-freeyes infoRDF Schemas
Typing infopredefined data types such as float or dateyesyes infotext, numeric, histogramsyes infoJSON typesyes
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
Secondary indexesyesnoyes infoAll properties auto-indexed by defaultyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query language (Circonus Analytics Query Language: CAQL)SQL-like query languageno
APIs and other access methodsJDBC
ODBC
Thrift
HTTP APIDocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Supported programming languagesC++
Java
PHP
Python
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python
Java
PHP
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyes, in LuaJavaScriptyes
TriggersnonoJavaScriptyes
Partitioning methods infoMethods for storing different data on different nodesShardingAutomatic, metric affinity per nodeSharding infoImplicit feature of the cloud servicenone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorconfigurable replication factor, datacenter awareyes infoImplicit feature of the cloud servicenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenowith Hadoop integration infoIntegration with Hadoop/HDInsight on Azure*no
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate consistency per node, eventual consistency across nodesBounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency infoConsistency level configurable on request level
Session Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoMulti-item ACID transactions with snapshot isolation within a partitionACID infoIsolation support depends on the API used
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoin-memory storage is supported as well
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.no
User concepts infoAccess controlAccess rights for users, groups and rolesnoAccess rights can be defined down to the item levelno
More information provided by the system vendor
HiveIRONdbMicrosoft Azure Cosmos DB infoformer name was Azure DocumentDBRDF4J infoformerly known as Sesame
Specific characteristicsIRONdb is a highly available, distributed Time Series Database. It can support dozens...
» more
Competitive advantagesUnmatched Scalability IRONdb is unique among TSDBs in that it does not use a consensus...
» more
Typical application scenariosReal Systems Monitoring Monitor your systems infrastructure in real time across thousands...
» more
Key customersIRONdb serves the needs of the world's largest Time Series Database customers. One...
» more
Market metricsOnly TSDB capable of scaling to billions of metrics. Only TSDB to scale to dozens...
» more
Licensing and pricing modelsIRONdb is licensed under a subscription model based on the number of active metrics...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
HiveIRONdbMicrosoft Azure Cosmos DB infoformer name was Azure DocumentDBRDF4J infoformerly known as Sesame
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