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DBMS > H2 vs. MarkLogic vs. Microsoft Azure Data Explorer vs. Milvus vs. Riak KV

System Properties Comparison H2 vs. MarkLogic vs. Microsoft Azure Data Explorer vs. Milvus vs. Riak KV

Editorial information provided by DB-Engines
NameH2  Xexclude from comparisonMarkLogic  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonMilvus  Xexclude from comparisonRiak KV  Xexclude from comparison
DescriptionFull-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.Operational and transactional Enterprise NoSQL databaseFully managed big data interactive analytics platformA DBMS designed for efficient storage of vector data and vector similarity searchesDistributed, fault tolerant key-value store
Primary database modelRelational DBMSDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMS infocolumn orientedVector DBMSKey-value store infowith links between data sets and object tags for the creation of secondary indexes
Secondary database modelsSpatial 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
Score8.13
Rank#49  Overall
#31  Relational DBMS
Score5.92
Rank#58  Overall
#10  Document stores
#1  Native XML DBMS
#1  RDF stores
#6  Search engines
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score2.31
Rank#113  Overall
#3  Vector DBMS
Score4.10
Rank#82  Overall
#9  Key-value stores
Websitewww.h2database.comwww.marklogic.comazure.microsoft.com/­services/­data-explorermilvus.io
Technical documentationwww.h2database.com/­html/­main.htmldocs.marklogic.comdocs.microsoft.com/­en-us/­azure/­data-explorermilvus.io/­docs/­overview.mdwww.tiot.jp/­riak-docs/­riak/­kv/­latest
DeveloperThomas MuellerMarkLogic Corp.MicrosoftOpenSource, formerly Basho Technologies
Initial release20052001201920192009
Current release2.2.220, July 202311.0, December 2022cloud service with continuous releases2.3.4, January 20243.2.0, December 2022
License infoCommercial or Open SourceOpen Source infodual-licence (Mozilla public license, Eclipse public license)commercial inforestricted free version is availablecommercialOpen Source infoApache Version 2.0Open Source infoApache version 2, commercial enterprise edition
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Zilliz Cloud – Cloud-native service for Milvus
Implementation languageJavaC++C++, GoErlang
Server operating systemsAll OS with a Java VMLinux
OS X
Windows
hostedLinux
macOS info10.14 or later
Windows infowith WSL 2 enabled
Linux
OS X
Data schemeyesschema-free infoSchema can be enforcedFixed schema with schema-less datatypes (dynamic)schema-free
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-typesVector, Numeric and Stringno
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.noyesyesnono
Secondary indexesyesyesall fields are automatically indexednorestricted
SQL infoSupport of SQLyesyes infoSQL92Kusto Query Language (KQL), SQL subsetnono
APIs and other access methodsJDBC
ODBC
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
RESTful HTTP APIHTTP API
Native Erlang Interface
Supported programming languagesJavaC
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C++
Go
Java
JavaScript (Node.js)
Python
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Server-side scripts infoStored proceduresJava Stored Procedures and User-Defined Functionsyes infovia XQuery or JavaScriptYes, possible languages: KQL, Python, RnoErlang
Triggersyesyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynoyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodesnoneShardingSharding infoImplicit feature of the cloud serviceShardingSharding infono "single point of failure"
Replication methods infoMethods for redundantly storing data on multiple nodesWith clustering: 2 database servers on different computers operate on identical copies of a databaseyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.selectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsSpark connector (open source): github.com/­Azure/­azure-kusto-sparknoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Eventual Consistency
Foreign keys infoReferential integrityyesnononono infolinks between data sets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infocan act as a resource manager in an XA/JTA transactionnonono
Concurrency infoSupport for concurrent manipulation of datayes, multi-version concurrency control (MVCC)yesyesyesyes
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.yesyes, with Range Indexesnoyes
User concepts infoAccess controlfine grained access rights according to SQL-standardRole-based access control at the document and subdocument levelsAzure Active Directory AuthenticationRole based access control and fine grained access rightsyes, using Riak Security
More information provided by the system vendor
H2MarkLogicMicrosoft Azure Data ExplorerMilvusRiak KV
Specific characteristicsMilvus is an open-source and cloud-native vector database built for production-ready...
» more
Competitive advantagesHighly available, versatile, and robust with millisecond latency. Supports batch...
» more
Typical application scenariosRAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more
Key customersMilvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more
Market metricsAs of January 2024, 25k+ GitHub stars 10M+ downloads and installations​ ​ 3k+ enterprise...
» more
Licensing and pricing modelsMilvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

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H2MarkLogicMicrosoft Azure Data ExplorerMilvusRiak KV
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