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DBMS > CouchDB vs. GeoMesa vs. IBM Db2 vs. Microsoft Azure Data Explorer vs. Solr

System Properties Comparison CouchDB vs. GeoMesa vs. IBM Db2 vs. Microsoft Azure Data Explorer vs. Solr

Editorial information provided by DB-Engines
NameCouchDB infostands for "Cluster Of Unreliable Commodity Hardware"  Xexclude from comparisonGeoMesa  Xexclude from comparisonIBM Db2 infoformerly named DB2 or IBM Database 2  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionA native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.Common in IBM host environments, 2 different versions for host and Windows/LinuxFully managed big data interactive analytics platformA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelDocument storeSpatial DBMSRelational DBMS infoSince Version 10.5 support for JSON/BSON documents compatible with MongoDBRelational DBMS infocolumn orientedSearch engine
Secondary database modelsSpatial DBMS infousing the Geocouch extensionDocument store
RDF store infoin Db2 LUW (Linux, Unix, Windows)
Spatial DBMS infowith Db2 Spatial Extender
Document store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score9.30
Rank#45  Overall
#7  Document stores
Score0.78
Rank#213  Overall
#4  Spatial DBMS
Score128.46
Rank#8  Overall
#5  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Websitecouchdb.apache.orgwww.geomesa.orgwww.ibm.com/­products/­db2azure.microsoft.com/­services/­data-explorersolr.apache.org
Technical documentationdocs.couchdb.org/­en/­stablewww.geomesa.org/­documentation/­stable/­user/­index.htmlwww.ibm.com/­docs/­en/­db2docs.microsoft.com/­en-us/­azure/­data-explorersolr.apache.org/­resources.html
DeveloperApache Software Foundation infoApache top-level project, originally developed by Damien Katz, a former Lotus Notes developerCCRi and othersIBMMicrosoftApache Software Foundation
Initial release200520141983 infohost version20192006
Current release3.3.3, December 20234.0.5, February 202412.1, October 2016cloud service with continuous releases9.6.0, April 2024
License infoCommercial or Open SourceOpen Source infoApache version 2Open Source infoApache License 2.0commercial infofree version is availablecommercialOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageErlangScalaC and C++Java
Server operating systemsAndroid
BSD
Linux
OS X
Solaris
Windows
AIX
HP-UX
Linux
Solaris
Windows
z/OS
hostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeschema-freeyesyesFixed schema with schema-less datatypes (dynamic)yes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or datenoyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes 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.nonoyesyes
Secondary indexesyes infovia viewsyesyesall fields are automatically indexedyes infoAll search fields are automatically indexed
SQL infoSupport of SQLnonoyesKusto Query Language (KQL), SQL subsetSolr Parallel SQL Interface
APIs and other access methodsRESTful HTTP/JSON APIADO.NET
JDBC
JSON style queries infoMongoDB compatible
ODBC
XQuery
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Java API
RESTful HTTP/JSON API
Supported programming languagesC
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk
C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresView functions in JavaScriptnoyesYes, possible languages: KQL, Python, RJava plugins
Triggersyesnoyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesSharding infoimproved architecture with release 2.0depending on storage layerSharding infoonly with Windows/Unix/Linux VersionSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
depending on storage layeryes infowith separate tools (MQ, InfoSphere)yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyesnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistencydepending on storage layerEventual Consistency
Immediate Consistency
Eventual Consistency
Foreign keys infoReferential integritynonoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infoatomic operations within a single document possiblenoACIDnooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayes infostrategy: optimistic lockingyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nodepending on storage layernoyes
User concepts infoAccess controlAccess rights for users can be defined per databaseyes infodepending on the DBMS used for storagefine grained access rights according to SQL-standardAzure Active Directory Authenticationyes

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CouchDB infostands for "Cluster Of Unreliable Commodity Hardware"GeoMesaIBM Db2 infoformerly named DB2 or IBM Database 2Microsoft Azure Data ExplorerSolr
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