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DBMS > Apache Druid vs. Kdb vs. Manticore Search vs. Microsoft Azure AI Search

System Properties Comparison Apache Druid vs. Kdb vs. Manticore Search vs. Microsoft Azure AI Search

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonKdb  Xexclude from comparisonManticore Search  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataHigh performance Time Series DBMSMulti-storage database for search, including full-text search.Search-as-a-service for web and mobile app development
Primary database modelRelational DBMS
Time Series DBMS
Time Series DBMS
Vector DBMS
Search engineSearch engine
Secondary database modelsRelational DBMSTime Series DBMS infousing the Manticore Columnar LibraryVector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.34
Rank#88  Overall
#48  Relational DBMS
#7  Time Series DBMS
Score7.55
Rank#53  Overall
#3  Time Series DBMS
#1  Vector DBMS
Score0.22
Rank#312  Overall
#21  Search engines
Score5.59
Rank#63  Overall
#7  Search engines
Websitedruid.apache.orgkx.commanticoresearch.comazure.microsoft.com/­en-us/­services/­search
Technical documentationdruid.apache.org/­docs/­latest/­designcode.kx.commanual.manticoresearch.comlearn.microsoft.com/­en-us/­azure/­search
DeveloperApache Software Foundation and contributorsKx Systems, a division of First Derivatives plcManticore SoftwareMicrosoft
Initial release20122000 infokdb was released 2000, kdb+ in 200320172015
Current release29.0.1, April 20243.6, May 20186.0, February 2023V1
License infoCommercial or Open SourceOpen Source infoApache license v2commercial infofree 32-bit versionOpen Source infoGPL version 2commercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaqC++
Server operating systemsLinux
OS X
Unix
Linux
OS X
Solaris
Windows
FreeBSD
Linux
macOS
Windows
hosted
Data schemeyes infoschema-less columns are supportedyesFixed schemayes
Typing infopredefined data types such as float or dateyesyesInt, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Booleanyes
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.noyesCan index from XMLno
Secondary indexesyesyes infotable attribute 'grouped'yes infofull-text index on all search fieldsyes
SQL infoSupport of SQLSQL for queryingSQL-like query language (q)SQL-like query languageno
APIs and other access methodsJDBC
RESTful HTTP/JSON API
HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket
Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala
Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
C#
Java
JavaScript
Python
Server-side scripts infoStored proceduresnouser defined functionsuser defined functionsno
Triggersnoyes infowith viewsnono
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedhorizontal partitioningSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesSource-replica replicationSynchronous replication based on Galera libraryyes infoImplicit feature of the cloud service
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono infosimilar paradigm used for internal processingnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoyes infoisolated transactions for atomic changes and binary logging for safe writesno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Manticore index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemrights management via user accountsnoyes infousing Azure authentication
More information provided by the system vendor
Apache DruidKdbManticore SearchMicrosoft Azure AI Search
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 DruidKdbManticore SearchMicrosoft Azure AI Search
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