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DBMS > Apache Doris vs. Apache Impala vs. Spark SQL

System Properties Comparison Apache Doris vs. Apache Impala vs. Spark SQL

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Editorial information provided by DB-Engines
NameApache Doris  Xexclude from comparisonApache Impala  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionAn MPP-based analytics DBMS embracing the MySQL protocolAnalytic DBMS for HadoopSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.57
Rank#244  Overall
#113  Relational DBMS
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitedoris.apache.org
github.com/­apache/­doris
impala.apache.orgspark.apache.org/­sql
Technical documentationgithub.com/­apache/­doris/­wikiimpala.apache.org/­impala-docs.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation, originally contributed from BaiduApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software Foundation
Initial release201720132014
Current release1.2.2, February 20234.1.0, June 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++Scala
Server operating systemsLinuxLinuxLinux
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.nonono
Secondary indexesyesyesno
SQL infoSupport of SQLyesSQL-like DML and DDL statementsSQL-like DML and DDL statements
APIs and other access methodsJDBC
MySQL client
JDBC
ODBC
JDBC
ODBC
Supported programming languagesJavaAll languages supporting JDBC/ODBCJava
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsyes infouser defined functions and integration of map-reduceno
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesnoneselectable replication factornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoquery execution via MapReduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono
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.nonono
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosno

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More resources
Apache DorisApache ImpalaSpark SQL
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