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DBMS > Apache Impala vs. Cachelot.io vs. OrigoDB vs. Spark SQL

System Properties Comparison Apache Impala vs. Cachelot.io vs. OrigoDB vs. Spark SQL

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonCachelot.io  Xexclude from comparisonOrigoDB  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopIn-memory caching systemA fully ACID in-memory object graph databaseSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSKey-value storeDocument store
Object oriented DBMS
Relational DBMS
Secondary database modelsDocument store
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
#60  Key-value stores
Score0.00
Rank#383  Overall
#53  Document stores
#20  Object oriented DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websiteimpala.apache.orgcachelot.ioorigodb.comspark.apache.org/­sql
Technical documentationimpala.apache.org/­impala-docs.htmlorigodb.com/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaRobert Friberg et alApache Software Foundation
Initial release201320152009 infounder the name LiveDB2014
Current release4.1.0, June 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoSimplified BSD LicenseOpen SourceOpen 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++C++C#Scala
Server operating systemsLinuxFreeBSD
Linux
OS X
Linux
Windows
Linux
OS X
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesnoUser defined using .NET types and collectionsyes
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 infocan be achieved using .NETno
Secondary indexesyesnoyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsnonoSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
Memcached protocol.NET Client API
HTTP API
LINQ
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby
.NetJava
Python
R
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoyesno
Triggersnonoyes infoDomain Eventsno
Partitioning methods infoMethods for storing different data on different nodesShardingnonehorizontal partitioning infoclient side managed; servers are not synchronizedyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornoneSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistencynone
Foreign keys infoReferential integritynonodepending on modelno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesnoyes infoWrite ahead logyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosnoRole based authorizationno

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More resources
Apache ImpalaCachelot.ioOrigoDBSpark SQL
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