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DBMS > Hive vs. Prometheus vs. Spark SQL vs. Tigris

System Properties Comparison Hive vs. Prometheus vs. Spark SQL vs. Tigris

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Editorial information provided by DB-Engines
NameHive  Xexclude from comparisonPrometheus  Xexclude from comparisonSpark SQL  Xexclude from comparisonTigris  Xexclude from comparison
Descriptiondata warehouse software for querying and managing large distributed datasets, built on HadoopOpen-source Time Series DBMS and monitoring systemSpark SQL is a component on top of 'Spark Core' for structured data processingA horizontally scalable, ACID transactional, document database available both as a fully managed cloud service and for deployment on self-managed infrastructure
Primary database modelRelational DBMSTime Series DBMSRelational DBMSDocument store
Key-value store
Search engine
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score59.76
Rank#18  Overall
#12  Relational DBMS
Score7.69
Rank#50  Overall
#3  Time Series DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.09
Rank#363  Overall
#49  Document stores
#54  Key-value stores
#22  Search engines
#38  Time Series DBMS
Websitehive.apache.orgprometheus.iospark.apache.org/­sqlwww.tigrisdata.com
Technical documentationcwiki.apache.org/­confluence/­display/­Hive/­Homeprometheus.io/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.tigrisdata.com/­docs
DeveloperApache Software Foundation infoinitially developed by FacebookApache Software FoundationTigris Data, Inc.
Initial release2012201520142022
Current release3.1.3, April 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache 2.0Open Source infoApache 2.0Open Source infoApache Version 2.0
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 languageJavaGoScala
Server operating systemsAll OS with a Java VMLinux
Windows
Linux
OS X
Windows
Linux
macOS
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesNumeric data onlyyesyes
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 infoImport of XML data possiblenono
Secondary indexesyesnonoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnoSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
ODBC
Thrift
RESTful HTTP/JSON APIJDBC
ODBC
CLI Client
gRPC
RESTful HTTP API
Supported programming languagesC++
Java
PHP
Python
.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby
Java
Python
R
Scala
Go
Java
JavaScript (Node.js)
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenonono
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryes infoby Federationnoneyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencynoneImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes, using FoundationDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlAccess rights for users, groups and rolesnonoAccess rights for users and roles

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HivePrometheusSpark SQLTigris
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