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DBMS > AllegroGraph vs. LeanXcale vs. Microsoft Azure Data Explorer vs. MonetDB vs. Oracle Berkeley DB

System Properties Comparison AllegroGraph vs. LeanXcale vs. Microsoft Azure Data Explorer vs. MonetDB vs. Oracle Berkeley DB

Editorial information provided by DB-Engines
NameAllegroGraph  Xexclude from comparisonLeanXcale  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonMonetDB  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparison
DescriptionHigh performance, persistent RDF store with additional support for Graph DBMSA highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilitiesFully managed big data interactive analytics platformA relational database management system that stores data in columnsWidely used in-process key-value store
Primary database modelDocument store infowith version 6.5
Graph DBMS
RDF store
Vector DBMS
Key-value store
Relational DBMS
Relational DBMS infocolumn orientedRelational DBMSKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
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
Document store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.06
Rank#187  Overall
#30  Document stores
#17  Graph DBMS
#7  RDF stores
#7  Vector DBMS
Score0.29
Rank#291  Overall
#41  Key-value stores
#132  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score1.72
Rank#145  Overall
#67  Relational DBMS
Score2.21
Rank#117  Overall
#20  Key-value stores
#3  Native XML DBMS
Websiteallegrograph.comwww.leanxcale.comazure.microsoft.com/­services/­data-explorerwww.monetdb.orgwww.oracle.com/­database/­technologies/­related/­berkeleydb.html
Technical documentationfranz.com/­agraph/­support/­documentation/­current/­agraph-introduction.htmldocs.microsoft.com/­en-us/­azure/­data-explorerwww.monetdb.org/­Documentationdocs.oracle.com/­cd/­E17076_05/­html/­index.html
DeveloperFranz Inc.LeanXcaleMicrosoftMonetDB BVOracle infooriginally developed by Sleepycat, which was acquired by Oracle
Initial release20042015201920041994
Current release8.0, December 2023cloud service with continuous releasesDec2023 (11.49), December 202318.1.40, May 2020
License infoCommercial or Open Sourcecommercial infoLimited community edition freecommercialcommercialOpen Source infoMozilla Public License 2.0Open Source infocommercial license available
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.
Implementation languageCC, Java, C++ (depending on the Berkeley DB edition)
Server operating systemsLinux
OS X
Windows
hostedFreeBSD
Linux
OS X
Solaris
Windows
AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Data schemeyes infoRDF schemasyesFixed schema with schema-less datatypes (dynamic)yesschema-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-typesyesno
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.no infobulk load of XML files possibleyesyes infoonly with the Berkeley DB XML edition
Secondary indexesyesall fields are automatically indexedyesyes
SQL infoSupport of SQLSPARQL is used as query languageyes infothrough Apache DerbyKusto Query Language (KQL), SQL subsetyes infoSQL 2003 with some extensionsyes infoSQL interfaced based on SQLite is available
APIs and other access methodsRESTful HTTP API
SPARQL
JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
JDBC
native C library infoMAPI library (MonetDB application programming interface)
ODBC
Supported programming languagesC#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala
C
Java
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
.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
Server-side scripts infoStored proceduresyes infoJavaScript or Common LispYes, possible languages: KQL, Python, Ryes, in SQL, C, Rno
Triggersyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesyes infoonly for the SQL API
Partitioning methods infoMethods for storing different data on different nodeswith FederationSharding infoImplicit feature of the cloud serviceSharding via remote tablesnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none infoSource-replica replication available in experimental statusSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnoACIDACID
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.noyesnoyes
User concepts infoAccess controlUsers with fine-grained authorization concept, user roles and pluggable authenticationAzure Active Directory Authenticationfine grained access rights according to SQL-standardno
More information provided by the system vendor
AllegroGraphLeanXcaleMicrosoft Azure Data ExplorerMonetDBOracle Berkeley DB
Specific characteristicsKnowledge Graph Platform Leader FedShard - Designed for Entity-Event Knowledge Graph...
» more
Competitive advantagesAllegroGraph is uniquely suited to support adhoc queries through SPARQL, Prolog and...
» more
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