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DBMS > Apache Kylin vs. Elasticsearch vs. Faircom DB vs. Microsoft Azure Data Explorer vs. Solr

System Properties Comparison Apache Kylin vs. Elasticsearch vs. Faircom DB vs. Microsoft Azure Data Explorer vs. Solr

Editorial information provided by DB-Engines
NameApache Kylin  Xexclude from comparisonElasticsearch  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionA distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stackA 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 metricNative high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.Fully managed big data interactive analytics platformA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSSearch engineKey-value store
Relational DBMS
Relational DBMS infocolumn orientedSearch engine
Secondary database modelsDocument store
Spatial DBMS
Vector DBMS
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
Score1.18
Rank#172  Overall
#79  Relational DBMS
Score135.35
Rank#7  Overall
#1  Search engines
Score0.20
Rank#318  Overall
#48  Key-value stores
#141  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Websitekylin.apache.orgwww.elastic.co/­elasticsearchwww.faircom.com/­products/­faircom-dbazure.microsoft.com/­services/­data-explorersolr.apache.org
Technical documentationkylin.apache.org/­docswww.elastic.co/­guide/­en/­elasticsearch/­reference/­current/­index.htmldocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmldocs.microsoft.com/­en-us/­azure/­data-explorersolr.apache.org/­resources.html
DeveloperApache Software Foundation, originally contributed from eBay IncElasticFairCom CorporationMicrosoftApache Software Foundation
Initial release20152010197920192006
Current release3.1.0, July 20208.6, January 2023V12, November 2020cloud service with continuous releases9.6.0, April 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoElastic Licensecommercial infoRestricted, free version 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 languageJavaJavaANSI C, C++Java
Server operating systemsLinuxAll OS with a Java VMAIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
hostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesschema-free infoFlexible type definitions. Once a type is defined, it is persistentschema free, schema optional, schema required, partial schema,Fixed 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 dateyesyesyes, ANSI SQL Types, JSON, typed binary structuresyes 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.nononoyesyes
Secondary indexesyesyes infoAll search fields are automatically indexedyesall fields are automatically indexedyes infoAll search fields are automatically indexed
SQL infoSupport of SQLANSI SQL for queries (using Apache Calcite)SQL-like query languageyes, ANSI SQL with proprietary extensionsKusto Query Language (KQL), SQL subsetSolr Parallel SQL Interface
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP/JSON API
ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
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
.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
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 proceduresyesyes info.Net, JavaScript, C/C++Yes, possible languages: KQL, Python, RJava plugins
Triggersyes infoby using the 'percolation' featureyesyes 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 nodesShardingFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioningSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).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 methodsyesES-Hadoop ConnectornoSpark 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 systemImmediate ConsistencyEventual Consistency infoSynchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, allEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Eventual Consistency
Immediate Consistency
Eventual Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanotunable from ACID to Eventually Consistentnooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesYes, tunable from durable to delayed durability to in-memoryyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noMemcached and Redis integrationyesnoyes
User concepts infoAccess controlFine grained access rights according to SQL-standard with additional protections for filesAzure Active Directory Authenticationyes

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Apache KylinElasticsearchFaircom DB infoformerly c-treeACEMicrosoft Azure Data ExplorerSolr
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