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DBMS > Apache Phoenix vs. CouchDB vs. InfinityDB vs. MarkLogic vs. Microsoft Azure Data Explorer

System Properties Comparison Apache Phoenix vs. CouchDB vs. InfinityDB vs. MarkLogic vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonCouchDB infostands for "Cluster Of Unreliable Commodity Hardware"  Xexclude from comparisonInfinityDB  Xexclude from comparisonMarkLogic  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseA native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.A Java embedded Key-Value Store which extends the Java Map interfaceOperational and transactional Enterprise NoSQL databaseFully managed big data interactive analytics platform
Primary database modelRelational DBMSDocument storeKey-value storeDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMS infocolumn oriented
Secondary database modelsSpatial DBMS infousing the Geocouch extensionDocument 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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score8.30
Rank#47  Overall
#7  Document stores
Score0.08
Rank#365  Overall
#55  Key-value stores
Score5.18
Rank#63  Overall
#11  Document stores
#1  Native XML DBMS
#1  RDF stores
#7  Search engines
Score3.80
Rank#81  Overall
#43  Relational DBMS
Websitephoenix.apache.orgcouchdb.apache.orgboilerbay.comwww.progress.com/­marklogicazure.microsoft.com/­services/­data-explorer
Technical documentationphoenix.apache.orgdocs.couchdb.org/­en/­stableboilerbay.com/­infinitydb/­manualwww.progress.com/­marklogic/­documentationdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software FoundationApache Software Foundation infoApache top-level project, originally developed by Damien Katz, a former Lotus Notes developerBoiler Bay Inc.MarkLogic Corp.Microsoft
Initial release20142005200220012019
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20193.3.3, December 20234.011.0, December 2022cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache version 2commercialcommercial inforestricted free version is availablecommercial
Cloud-based only infoOnly available as a cloud servicenonononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaErlangJavaC++
Server operating systemsLinux
Unix
Windows
Android
BSD
Linux
OS X
Solaris
Windows
All OS with a Java VMLinux
OS X
Windows
hosted
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeyes infonested virtual Java Maps, multi-value, logical ‘tuple space’ runtime Schema upgradeschema-free infoSchema can be enforcedFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesnoyes infoall Java primitives, Date, CLOB, BLOB, huge sparse arraysyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types
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 infovia viewsno infomanual creation possible, using inversions based on multi-value capabilityyesall fields are automatically indexed
SQL infoSupport of SQLyesnonoyes infoSQL92Kusto Query Language (KQL), SQL subset
APIs and other access methodsJDBCRESTful HTTP/JSON APIAccess via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/­docs/­ItemSpaceDataStructures.htm)
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk
JavaC
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresuser defined functionsView functions in JavaScriptnoyes infovia XQuery or JavaScriptYes, possible languages: KQL, Python, R
Triggersnoyesnoyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoimproved architecture with release 2.0noneShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replication
Source-replica replication
noneyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationyesnoyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyEventual ConsistencyImmediate Consistency infoREAD-COMMITTED or SERIALIZEDImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynonono infomanual creation possible, using inversions based on multi-value capabilitynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno infoatomic operations within a single document possibleACID infoOptimistic locking for transactions; no isolation for bulk loadsACID infocan act as a resource manager in an XA/JTA transactionno
Concurrency infoSupport for concurrent manipulation of datayesyes infostrategy: optimistic lockingyesyesyes
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.yesnonoyes, with Range Indexesno
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users can be defined per databasenoRole-based access control at the document and subdocument levelsAzure Active Directory Authentication

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Apache PhoenixCouchDB infostands for "Cluster Of Unreliable Commodity Hardware"InfinityDBMarkLogicMicrosoft Azure Data Explorer
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