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DBMS > Apache Impala vs. chDB vs. Hive vs. Spark SQL

System Properties Comparison Apache Impala vs. chDB vs. Hive vs. Spark SQL

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonchDB  Xexclude from comparisonHive  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopAn embedded SQL OLAP Engine powered by ClickHousedata warehouse software for querying and managing large distributed datasets, built on HadoopSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument storeTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score0.00
Rank#383  Overall
#158  Relational DBMS
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websiteimpala.apache.orggithub.com/­chdb-io/­chdbhive.apache.orgspark.apache.org/­sql
Technical documentationimpala.apache.org/­impala-docs.htmldoc.chdb.iocwiki.apache.org/­confluence/­display/­Hive/­Homespark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software Foundation infoinitially developed by FacebookApache Software Foundation
Initial release2013202320122014
Current release4.1.0, June 20223.1.3, April 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2.0Open Source infoApache Version 2Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaScala
Server operating systemsLinuxserver-lessAll OS with a Java VMLinux
OS X
Windows
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesyesyes
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 indexesyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsClose to ANSI SQL (SQL/JSON + extensions)SQL-like DML and DDL statementsSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
Thrift
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCBun
C
C++
Go
JavaScript (Node.js)
Python
Rust
C++
Java
PHP
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyes infouser defined functions and integration of map-reduceno
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorselectable replication factornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyes infoquery execution via MapReduce
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonono
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 controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess rights for users, groups and rolesno

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More resources
Apache ImpalachDBHiveSpark SQL
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