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DBMS > Apache IoTDB vs. HarperDB vs. Microsoft Azure Data Explorer

System Properties Comparison Apache IoTDB vs. HarperDB vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonHarperDB  Xexclude from comparisonMicrosoft Azure Data Explorer  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 FlinkUltra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.Fully managed big data interactive analytics platform
Primary database modelTime Series DBMSDocument storeRelational DBMS infocolumn oriented
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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.19
Rank#176  Overall
#15  Time Series DBMS
Score0.55
Rank#252  Overall
#38  Document stores
Score5.16
Rank#69  Overall
#37  Relational DBMS
Websiteiotdb.apache.orgwww.harperdb.ioazure.microsoft.com/­services/­data-explorer
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.harperdb.io/­docsdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software FoundationHarperDBMicrosoft
Initial release201820172019
Current release1.1.0, April 20233.1, August 2021cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial infofree community edition availablecommercial
Cloud-based only infoOnly available as a cloud servicenonoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaNode.js
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
OS X
hosted
Data schemeyesdynamic schemaFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyes infoJSON data typesyes 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.nonoyes
Secondary indexesyesyesall fields are automatically indexed
SQL infoSupport of SQLSQL-like query languageSQL-like data manipulation statementsKusto Query Language (KQL), SQL subset
APIs and other access methodsJDBC
Native API
JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresyesCustom Functions infosince release 3.1Yes, possible languages: KQL, Python, R
Triggersyesnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)A table resides as a whole on one (or more) nodes in a clusterSharding infoImplicit feature of the cloud service
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 infothe nodes on which a table resides can be definedyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparknoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic execution of specific operationsno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyes, using LMDByes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
User concepts infoAccess controlyesAccess rights for users and rolesAzure Active Directory Authentication

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More resources
Apache IoTDBHarperDBMicrosoft Azure Data Explorer
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