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DBMS > Apache Impala vs. Citus vs. IBM Db2 Event Store vs. Solr

System Properties Comparison Apache Impala vs. Citus vs. IBM Db2 Event Store vs. Solr

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonCitus  Xexclude from comparisonIBM Db2 Event Store  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLDistributed Event Store optimized for Internet of Things use casesA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSRelational DBMSEvent Store
Time Series DBMS
Search engine
Secondary database modelsDocument storeDocument storeSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score2.15
Rank#117  Overall
#56  Relational DBMS
Score0.27
Rank#309  Overall
#2  Event Stores
#28  Time Series DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Websiteimpala.apache.orgwww.citusdata.comwww.ibm.com/­products/­db2-event-storesolr.apache.org
Technical documentationimpala.apache.org/­impala-docs.htmldocs.citusdata.comwww.ibm.com/­docs/­en/­db2-event-storesolr.apache.org/­resources.html
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaIBMApache Software Foundation
Initial release2013201020172006
Current release4.1.0, June 20228.1, December 20182.09.6.1, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoAGPL, commercial license also availablecommercial infofree developer edition 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++CC and C++Java
Server operating systemsLinuxLinuxLinux infoLinux, macOS, Windows for the developer additionAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesyesyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyesyes infosupports customizable data types and automatic typing
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 functionalitynoyes
Secondary indexesyesyesnoyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like DML and DDL statementsyes infostandard, with numerous extensionsyes infothrough the embedded Spark runtimeSolr Parallel SQL Interface
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP/JSON API
Supported programming languagesAll languages supporting JDBC/ODBC.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
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.yesJava plugins
Triggersnoyesnoyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replication infoother methods possible by using 3rd party extensionsActive-active shard replicationyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyEventual ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesNo - written data is immutableyes
Durability infoSupport for making data persistentyesyesYes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storageyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosfine grained access rights according to SQL-standardfine grained access rights according to SQL-standardyes

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