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DBMS > Apache IoTDB vs. CouchDB vs. H2 vs. Microsoft Azure Data Explorer vs. Solr

System Properties Comparison Apache IoTDB vs. CouchDB vs. H2 vs. Microsoft Azure Data Explorer vs. Solr

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonCouchDB infostands for "Cluster Of Unreliable Commodity Hardware"  Xexclude from comparisonH2  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkA native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.Fully managed big data interactive analytics platformA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelTime Series DBMSDocument storeRelational DBMSRelational DBMS infocolumn orientedSearch engine
Secondary database modelsSpatial DBMS infousing the Geocouch extensionSpatial DBMSDocument 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#173  Overall
#15  Time Series DBMS
Score9.30
Rank#45  Overall
#7  Document stores
Score8.13
Rank#49  Overall
#31  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Websiteiotdb.apache.orgcouchdb.apache.orgwww.h2database.comazure.microsoft.com/­services/­data-explorersolr.apache.org
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.couchdb.org/­en/­stablewww.h2database.com/­html/­main.htmldocs.microsoft.com/­en-us/­azure/­data-explorersolr.apache.org/­resources.html
DeveloperApache Software FoundationApache Software Foundation infoApache top-level project, originally developed by Damien Katz, a former Lotus Notes developerThomas MuellerMicrosoftApache Software Foundation
Initial release20182005200520192006
Current release1.1.0, April 20233.3.3, December 20232.2.220, July 2023cloud service with continuous releases9.6.0, April 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache version 2Open Source infodual-licence (Mozilla public license, Eclipse public license)commercialOpen 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 languageJavaErlangJavaJava
Server operating systemsAll OS with a Java VM (>= 1.8)Android
BSD
Linux
OS X
Solaris
Windows
All OS with a Java VMhostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesschema-freeyesFixed 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 dateyesnoyesyes 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 infovia viewsyesall fields are automatically indexedyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like query languagenoyesKusto Query Language (KQL), SQL subsetSolr Parallel SQL Interface
APIs and other access methodsJDBC
Native API
RESTful HTTP/JSON APIJDBC
ODBC
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Java API
RESTful HTTP/JSON API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk
Java.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 proceduresyesView functions in JavaScriptJava Stored Procedures and User-Defined FunctionsYes, possible languages: KQL, Python, RJava plugins
Triggersyesyesyesyes 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 nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Sharding infoimproved architecture with release 2.0noneSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication
Source-replica replication
With clustering: 2 database servers on different computers operate on identical copies of a databaseyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparkyesnoSpark 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
Strong Consistency with Raft
Eventual ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Eventual Consistency
Foreign keys infoReferential integritynonoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono infoatomic operations within a single document possibleACIDnooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyes infostrategy: optimistic lockingyes, multi-version concurrency control (MVCC)yesyes
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.yesnoyesnoyes
User concepts infoAccess controlyesAccess rights for users can be defined per databasefine grained access rights according to SQL-standardAzure Active Directory Authenticationyes

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Apache IoTDBCouchDB infostands for "Cluster Of Unreliable Commodity Hardware"H2Microsoft Azure Data ExplorerSolr
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