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DBMS > Elasticsearch vs. GeoSpock vs. IBM Db2 vs. Microsoft Azure Data Explorer vs. Solr

System Properties Comparison Elasticsearch vs. GeoSpock vs. IBM Db2 vs. Microsoft Azure Data Explorer vs. Solr

Editorial information provided by DB-Engines
NameElasticsearch  Xexclude from comparisonGeoSpock  Xexclude from comparisonIBM Db2 infoformerly named DB2 or IBM Database 2  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSolr  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.
DescriptionA distributed, RESTful modern search and analytics engine based on Apache Lucene infoElasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metricSpatial and temporal data processing engine for extreme data scaleCommon 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 modelSearch engineRelational DBMSRelational DBMS infoSince Version 10.5 support for JSON/BSON documents compatible with MongoDBRelational DBMS infocolumn orientedSearch engine
Secondary database modelsDocument store
Spatial DBMS
Vector DBMS
Time Series DBMSDocument 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
Score135.35
Rank#7  Overall
#1  Search engines
Score128.46
Rank#8  Overall
#5  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Websitewww.elastic.co/­elasticsearchgeospock.comwww.ibm.com/­products/­db2azure.microsoft.com/­services/­data-explorersolr.apache.org
Technical documentationwww.elastic.co/­guide/­en/­elasticsearch/­reference/­current/­index.htmlwww.ibm.com/­docs/­en/­db2docs.microsoft.com/­en-us/­azure/­data-explorersolr.apache.org/­resources.html
DeveloperElasticGeoSpockIBMMicrosoftApache Software Foundation
Initial release20101983 infohost version20192006
Current release8.6, January 20232.0, September 201912.1, October 2016cloud service with continuous releases9.6.0, April 2024
License infoCommercial or Open SourceOpen Source infoElastic Licensecommercialcommercial infofree version is availablecommercialOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenoyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJava, JavascriptC and C++Java
Server operating systemsAll OS with a Java VMhostedAIX
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-free infoFlexible type definitions. Once a type is defined, it is persistentyesyesFixed 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 dateyesyesyesyes 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 infoAll search fields are automatically indexedtemporal, categoricalyesall fields are automatically indexedyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like query languageANSI SQL for query only (using Presto)yesKusto Query Language (KQL), SQL subsetSolr Parallel SQL Interface
APIs and other access methodsJava API
RESTful HTTP/JSON API
JDBCADO.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 languages.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby
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 proceduresyesnoyesYes, possible languages: KQL, Python, RJava plugins
Triggersyes infoby using the 'percolation' featurenoyesyes 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 nodesShardingAutomatic shardingSharding infoonly with Windows/Unix/Linux VersionSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes 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 methodsES-Hadoop ConnectornonoSpark 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 Consistency infoSynchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, allImmediate ConsistencyEventual Consistency
Immediate Consistency
Eventual Consistency
Foreign keys infoReferential integritynonoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDnooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
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.Memcached and Redis integrationnonoyes
User concepts infoAccess controlAccess rights for users can be defined per tablefine grained access rights according to SQL-standardAzure Active Directory Authenticationyes

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More resources
ElasticsearchGeoSpockIBM Db2 infoformerly named DB2 or IBM Database 2Microsoft Azure Data ExplorerSolr
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