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DBMS > Apache IoTDB vs. Firebase Realtime Database vs. Microsoft Azure Data Explorer vs. PostgreSQL vs. Sphinx

System Properties Comparison Apache IoTDB vs. Firebase Realtime Database vs. Microsoft Azure Data Explorer vs. PostgreSQL vs. Sphinx

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonPostgreSQL  Xexclude from comparisonSphinx  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 FlinkCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.Fully managed big data interactive analytics platformWidely used open source RDBMS infoDeveloped as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQLOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelTime Series DBMSDocument storeRelational DBMS infocolumn orientedRelational DBMS infowith object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.Search 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
Graph DBMS infowith Apache Age
Spatial DBMS
Vector DBMS infowith pgvector extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score14.29
Rank#39  Overall
#6  Document stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score645.54
Rank#4  Overall
#4  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Websiteiotdb.apache.orgfirebase.google.com/­products/­realtime-databaseazure.microsoft.com/­services/­data-explorerwww.postgresql.orgsphinxsearch.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlfirebase.google.com/­docs/­databasedocs.microsoft.com/­en-us/­azure/­data-explorerwww.postgresql.org/­docssphinxsearch.com/­docs
DeveloperApache Software FoundationGoogle infoacquired by Google 2014MicrosoftPostgreSQL Global Development Group infowww.postgresql.org/­developerSphinx Technologies Inc.
Initial release2018201220191989 info1989: Postgres, 1996: PostgreSQL2001
Current release1.1.0, April 2023cloud service with continuous releases16.3, May 20243.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infoBSDOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenoyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaCC++
Server operating systemsAll OS with a Java VM (>= 1.8)hostedhostedFreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesschema-freeFixed schema with schema-less datatypes (dynamic)yesyes
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesno
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 infospecific XML-type available, but no XML query functionality.
Secondary indexesyesyesall fields are automatically indexedyesyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query languagenoKusto Query Language (KQL), SQL subsetyes infostandard with numerous extensionsSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
Native API
Android
iOS
JavaScript API
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Proprietary protocol
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Java
JavaScript
Objective-C
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C
C++
Delphi
Java infoJDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresyeslimited functionality with using 'rules'Yes, possible languages: KQL, Python, Ruser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.no
TriggersyesCallbacks are triggered when data changesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Sharding infoImplicit feature of the cloud servicepartitioning by range, list and (since PostgreSQL 11) by hashSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replication infoother methods possible by using 3rd party extensionsnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparknoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Eventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Eventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoyesnoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnono
User concepts infoAccess controlyesyes, based on authentication and database rulesAzure Active Directory Authenticationfine grained access rights according to SQL-standardno

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More resources
Apache IoTDBFirebase Realtime DatabaseMicrosoft Azure Data ExplorerPostgreSQLSphinx
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