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DBMS > Apache Impala vs. Kinetica vs. ReductStore

System Properties Comparison Apache Impala vs. Kinetica vs. ReductStore

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonKinetica  Xexclude from comparisonReductStore  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopFully vectorized database across both GPUs and CPUsDesigned to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.
Primary database modelRelational DBMSRelational DBMSTime Series DBMS
Secondary database modelsDocument storeSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score0.00
Rank#383  Overall
#41  Time Series DBMS
Websiteimpala.apache.orgwww.kinetica.comgithub.com/­reductstore
www.reduct.store
Technical documentationimpala.apache.org/­impala-docs.htmldocs.kinetica.comwww.reduct.store/­docs
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaKineticaReductStore LLC
Initial release201320122023
Current release4.1.0, June 20227.1, August 20211.9, March 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoBusiness Source License 1.1
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C, C++C++, Rust
Server operating systemsLinuxLinuxDocker
Linux
macOS
Windows
Data schemeyesyes
Typing infopredefined data types such as float or dateyesyes
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 indexesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
RESTful HTTP API
HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCC++
Java
JavaScript (Node.js)
Python
C++
JavaScript (Node.js)
Python
Rust
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified range
Partitioning methods infoMethods for storing different data on different nodesShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoGPU vRAM or System RAM
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess rights for users and roles on table level

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More resources
Apache ImpalaKineticaReductStore
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