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DBMS > Apache Impala vs. Citus vs. Databricks vs. InfluxDB

System Properties Comparison Apache Impala vs. Citus vs. Databricks vs. InfluxDB

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonCitus  Xexclude from comparisonDatabricks  Xexclude from comparisonInfluxDB  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.DBMS for storing time series, events and metrics
Primary database modelRelational DBMSRelational DBMSDocument store
Relational DBMS
Time Series DBMS
Secondary database modelsDocument storeDocument storeSpatial DBMS infowith GEO package
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score2.21
Rank#118  Overall
#56  Relational DBMS
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score25.83
Rank#28  Overall
#1  Time Series DBMS
Websiteimpala.apache.orgwww.citusdata.comwww.databricks.comwww.influxdata.com/­products/­influxdb-overview
Technical documentationimpala.apache.org/­impala-docs.htmldocs.citusdata.comdocs.databricks.comdocs.influxdata.com/­influxdb
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaDatabricks
Initial release2013201020132013
Current release4.1.0, June 20228.1, December 20182.7.6, April 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoAGPL, commercial license also availablecommercialOpen Source infoMIT-License; commercial enterprise version available
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++CGo
Server operating systemsLinuxLinuxhostedLinux
OS X infothrough Homebrew
Data schemeyesyesFlexible Schema (defined schema, partial schema, schema free)schema-free
Typing infopredefined data types such as float or dateyesyesNumeric data and Strings
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.noyes infospecific XML type available, but no XML query functionalityyesno
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsyes infostandard, with numerous extensionswith Databricks SQLSQL-like query language
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
RESTful HTTP API
HTTP API
JSON over UDP
Supported programming languagesAll languages supporting JDBC/ODBC.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Python
R
Scala
.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.user defined functions and aggregatesno
Triggersnoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoin enterprise version only
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replication infoother methods possible by using 3rd party extensionsyesselectable replication factor infoin enterprise version only
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDno
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.nononoyes infoDepending on used storage engine
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosfine grained access rights according to SQL-standardsimple rights management via user accounts
More information provided by the system vendor
Apache ImpalaCitusDatabricksInfluxDB
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
» more
InfluxData is the creator of InfluxDB , the open source time series database. It...
» more
Competitive advantagesTime to Value InfluxDB is available in all the popular languages and frameworks,...
» more
Typical application scenariosIoT & Sensor Monitoring Developers are witnessing the instrumentation of every available...
» more
Key customersInfluxData has more than 1,900 paying customers, including customers include MuleSoft,...
» more
Market metricsFastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances
» more
Licensing and pricing modelsOpen source core with closed source clustering available either on-premise or on...
» more
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