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DBMS > Apache Impala vs. Apache Phoenix vs. GridDB vs. Hive

System Properties Comparison Apache Impala vs. Apache Phoenix vs. GridDB vs. Hive

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonApache Phoenix  Xexclude from comparisonGridDB  Xexclude from comparisonHive  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopA scale-out RDBMS with evolutionary schema built on Apache HBaseScalable in-memory time series database optimized for IoT and Big Datadata warehouse software for querying and managing large distributed datasets, built on Hadoop
Primary database modelRelational DBMSRelational DBMSTime Series DBMSRelational DBMS
Secondary database modelsDocument storeKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score61.17
Rank#18  Overall
#12  Relational DBMS
Websiteimpala.apache.orgphoenix.apache.orggriddb.nethive.apache.org
Technical documentationimpala.apache.org/­impala-docs.htmlphoenix.apache.orgdocs.griddb.netcwiki.apache.org/­confluence/­display/­Hive/­Home
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software FoundationToshiba CorporationApache Software Foundation infoinitially developed by Facebook
Initial release2013201420132012
Current release4.1.0, June 20225.0-HBase2, July 2018 and 4.15-HBase1, December 20195.1, August 20223.1.3, April 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2.0Open Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaC++Java
Server operating systemsLinuxLinux
Unix
Windows
LinuxAll OS with a Java VM
Data schemeyesyes infolate-bound, schema-on-read capabilitiesyesyes
Typing infopredefined data types such as float or dateyesyesyes infonumerical, string, blob, geometry, boolean, timestampyes
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.nonono
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyesSQL92, SQL-like TQL (Toshiba Query Language)SQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
JDBCJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JDBC
ODBC
Thrift
Supported programming languagesAll languages supporting JDBC/ODBCC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C++
Java
PHP
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functionsnoyes infouser defined functions and integration of map-reduce
Triggersnonoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-source replication
Source-replica replication
Source-replica replicationselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceHadoop integrationConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyes infoquery execution via MapReduce
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual ConsistencyImmediate consistency within container, eventual consistency across containersEventual Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID at container levelno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users can be defined per databaseAccess rights for users, groups and roles
More information provided by the system vendor
Apache ImpalaApache PhoenixGridDBHive
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» 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
Apache ImpalaApache PhoenixGridDBHive
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