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DBMS > EsgynDB vs. Microsoft Azure Data Explorer vs. Postgres-XL vs. Riak KV vs. Solr

System Properties Comparison EsgynDB vs. Microsoft Azure Data Explorer vs. Postgres-XL vs. Riak KV vs. Solr

Editorial information provided by DB-Engines
NameEsgynDB  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonPostgres-XL  Xexclude from comparisonRiak KV  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionFully managed big data interactive analytics platformBased on PostgreSQL enhanced with MPP and write-scale-out cluster featuresDistributed, fault tolerant key-value storeA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSRelational DBMS infocolumn orientedRelational DBMSKey-value store infowith links between data sets and object tags for the creation of secondary indexesSearch engine
Secondary database modelsDocument 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
Document store
Spatial DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.25
Rank#312  Overall
#138  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.53
Rank#254  Overall
#117  Relational DBMS
Score4.01
Rank#79  Overall
#9  Key-value stores
Score41.02
Rank#24  Overall
#3  Search engines
Websitewww.esgyn.cnazure.microsoft.com/­services/­data-explorerwww.postgres-xl.orgsolr.apache.org
Technical documentationdocs.microsoft.com/­en-us/­azure/­data-explorerwww.postgres-xl.org/­documentationwww.tiot.jp/­riak-docs/­riak/­kv/­latestsolr.apache.org/­resources.html
DeveloperEsgynMicrosoftOpenSource, formerly Basho TechnologiesApache Software Foundation
Initial release201520192014 infosince 2012, originally named StormDB20092006
Current releasecloud service with continuous releases10 R1, October 20183.2.0, December 20229.6.1, May 2024
License infoCommercial or Open SourcecommercialcommercialOpen Source infoMozilla public licenseOpen Source infoApache version 2, commercial enterprise editionOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++, JavaCErlangJava
Server operating systemsLinuxhostedLinux
macOS
Linux
OS X
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesFixed schema with schema-less datatypes (dynamic)yesschema-freeyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesnoyes 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.noyesyes infoXML type, but no XML query functionalitynoyes
Secondary indexesyesall fields are automatically indexedyesrestrictedyes infoAll search fields are automatically indexed
SQL infoSupport of SQLyesKusto Query Language (KQL), SQL subsetyes infodistributed, parallel query executionnoSolr Parallel SQL Interface
APIs and other access methodsADO.NET
JDBC
ODBC
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
HTTP API
Native Erlang Interface
Java API
RESTful HTTP/JSON API
Supported programming languagesAll languages supporting JDBC/ODBC/ADO.Net.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresJava Stored ProceduresYes, possible languages: KQL, Python, Ruser defined functionsErlangJava plugins
Triggersnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesyes infopre-commit hooks and post-commit hooksyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud servicehorizontal partitioningSharding infono "single point of failure"Sharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication between multi datacentersyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.selectable replication factoryes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparknoyesspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate ConsistencyEventual ConsistencyEventual Consistency
Foreign keys infoReferential integrityyesnoyesno infolinks between data sets can be storedno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID infoMVCCnooptimistic 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.nononoyes
User concepts infoAccess controlfine grained access rights according to SQL-standardAzure Active Directory Authenticationfine grained access rights according to SQL-standardyes, using Riak Securityyes

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More resources
EsgynDBMicrosoft Azure Data ExplorerPostgres-XLRiak KVSolr
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