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DBMS > Kdb vs. Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. Trafodion vs. YDB

System Properties Comparison Kdb vs. Microsoft Azure Data Explorer vs. Oracle Berkeley DB vs. Trafodion vs. YDB

Editorial information provided by DB-Engines
NameKdb  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonTrafodion  Xexclude from comparisonYDB  Xexclude from comparison
Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.
DescriptionHigh performance Time Series DBMSFully managed big data interactive analytics platformWidely used in-process key-value storeTransactional SQL-on-Hadoop DBMSA distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API
Primary database modelTime Series DBMS
Vector DBMS
Relational DBMS infocolumn orientedKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Relational DBMSDocument store
Relational DBMS
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
Score7.71
Rank#49  Overall
#2  Time Series DBMS
#1  Vector DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score2.01
Rank#126  Overall
#21  Key-value stores
#3  Native XML DBMS
Score0.33
Rank#287  Overall
#43  Document stores
#132  Relational DBMS
Websitekx.comazure.microsoft.com/­services/­data-explorerwww.oracle.com/­database/­technologies/­related/­berkeleydb.htmltrafodion.apache.orggithub.com/­ydb-platform/­ydb
ydb.tech
Technical documentationcode.kx.comdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.oracle.com/­cd/­E17076_05/­html/­index.htmltrafodion.apache.org/­documentation.htmlydb.tech/­en/­docs
DeveloperKx Systems, a division of First Derivatives plcMicrosoftOracle infooriginally developed by Sleepycat, which was acquired by OracleApache Software Foundation, originally developed by HPYandex
Initial release2000 infokdb was released 2000, kdb+ in 20032019199420142019
Current release3.6, May 2018cloud service with continuous releases18.1.40, May 20202.3.0, February 2019
License infoCommercial or Open Sourcecommercial infofree 32-bit versioncommercialOpen Source infocommercial license availableOpen Source infoApache 2.0Open Source infoApache 2.0; commercial license available
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageqC, Java, C++ (depending on the Berkeley DB edition)C++, Java
Server operating systemsLinux
OS X
Solaris
Windows
hostedAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
LinuxLinux
Data schemeyesFixed schema with schema-less datatypes (dynamic)schema-freeyesFlexible Schema (defined schema, partial schema, schema free)
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesnoyesyes
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.yesyesyes infoonly with the Berkeley DB XML editionnono
Secondary indexesyes infotable attribute 'grouped'all fields are automatically indexedyesyesyes
SQL infoSupport of SQLSQL-like query language (q)Kusto Query Language (KQL), SQL subsetyes infoSQL interfaced based on SQLite is availableyesSQL-like query language (YQL)
APIs and other access methodsHTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
ODBC
RESTful HTTP API (DynamoDB compatible)
Supported programming languagesC
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
All languages supporting JDBC/ODBC/ADO.NetGo
Java
JavaScript (Node.js)
PHP
Python
Server-side scripts infoStored proceduresuser defined functionsYes, possible languages: KQL, Python, RnoJava Stored Proceduresno
Triggersyes infowith viewsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infoonly for the SQL APIno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningSharding infoImplicit feature of the cloud servicenoneShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationyes, via HBaseActive-passive shard replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infosimilar paradigm used for internal processingSpark connector (open source): github.com/­Azure/­azure-kusto-sparknoyes infovia user defined functions and HBaseno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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 controlrights management via user accountsAzure Active Directory Authenticationnofine grained access rights according to SQL-standardAccess rights defined for Yandex Cloud users
More information provided by the system vendor
KdbMicrosoft Azure Data ExplorerOracle Berkeley DBTrafodionYDB
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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KdbMicrosoft Azure Data ExplorerOracle Berkeley DBTrafodionYDB
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