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DBMS > Apache IoTDB vs. Google Cloud Spanner vs. Kdb vs. Microsoft Azure Data Explorer vs. searchxml

System Properties Comparison Apache IoTDB vs. Google Cloud Spanner vs. Kdb vs. Microsoft Azure Data Explorer vs. searchxml

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonGoogle Cloud Spanner  Xexclude from comparisonKdb  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonsearchxml  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 horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.High performance Time Series DBMSFully managed big data interactive analytics platformDBMS for structured and unstructured content wrapped with an application server
Primary database modelTime Series DBMSRelational DBMSTime Series DBMS
Vector DBMS
Relational DBMS infocolumn orientedNative XML DBMS
Search engine
Secondary database modelsRelational 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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score2.89
Rank#103  Overall
#52  Relational DBMS
Score7.55
Rank#53  Overall
#3  Time Series DBMS
#1  Vector DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.00
Rank#383  Overall
#7  Native XML DBMS
#25  Search engines
Websiteiotdb.apache.orgcloud.google.com/­spannerkx.comazure.microsoft.com/­services/­data-explorerwww.searchxml.net/­category/­products
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlcloud.google.com/­spanner/­docscode.kx.comdocs.microsoft.com/­en-us/­azure/­data-explorerwww.searchxml.net/­support/­handouts
DeveloperApache Software FoundationGoogleKx Systems, a division of First Derivatives plcMicrosoftinformationpartners gmbh
Initial release201820172000 infokdb was released 2000, kdb+ in 200320192015
Current release1.1.0, April 20233.6, May 2018cloud service with continuous releases1.0
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercial infofree 32-bit versioncommercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaqC++
Server operating systemsAll OS with a Java VM (>= 1.8)hostedLinux
OS X
Solaris
Windows
hostedWindows
Data schemeyesyesyesFixed schema with schema-less datatypes (dynamic)schema-free
Typing infopredefined data types such as float or dateyesyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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.nonoyesyesyes
Secondary indexesyesyesyes infotable attribute 'grouped'all fields are automatically indexedyes
SQL infoSupport of SQLSQL-like query languageyes infoQuery statements complying to ANSI 2011SQL-like query language (q)Kusto Query Language (KQL), SQL subsetno
APIs and other access methodsJDBC
Native API
gRPC (using protocol buffers) API
JDBC infoAt present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API
HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
RESTful HTTP API
WebDAV
XQuery
XSLT
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Go
Java
JavaScript (Node.js)
Python
C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C++ infomost other programming languages supported via APIs
Server-side scripts infoStored proceduresyesnouser defined functionsYes, possible languages: KQL, Python, Ryes infoon the application server
Triggersyesnoyes infowith viewsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Shardinghorizontal partitioningSharding infoImplicit feature of the cloud servicenone
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 with 3 replicas for regional instances.Source-replica replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes infosychronisation to multiple collections
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyes infousing Google Cloud Dataflowno infosimilar paradigm used for internal processingSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyes infoby using interleaved tables, this features focuses more on performance improvements than on referential integrityyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoStrict serializable isolationnonomultiple readers, single writer
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
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.yesnoyesnono
User concepts infoAccess controlyesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)rights management via user accountsAzure Active Directory AuthenticationDomain, group and role-based access control at the document level and for application services
More information provided by the system vendor
Apache IoTDBGoogle Cloud SpannerKdbMicrosoft Azure Data Explorersearchxml
Specific characteristicsIntegrated columnar database & programming system for streaming, real time and historical...
» more
Competitive advantagesprovides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more
Typical application scenariostick database streaming sensor data massive intelligence applications oil and gas...
» more
Key customersGoldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more
Market metricskdb+ performance and reliability proven by our customers in critical infrastructure...
» more
Licensing and pricing modelsupon request
» more

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More resources
Apache IoTDBGoogle Cloud SpannerKdbMicrosoft Azure Data Explorersearchxml
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