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DBMS > AllegroGraph vs. Microsoft Azure Table Storage vs. Milvus vs. Spark SQL

System Properties Comparison AllegroGraph vs. Microsoft Azure Table Storage vs. Milvus vs. Spark SQL

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Editorial information provided by DB-Engines
NameAllegroGraph  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonMilvus  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionHigh performance, persistent RDF store with additional support for Graph DBMSA Wide Column Store for rapid development using massive semi-structured datasetsA DBMS designed for efficient storage of vector data and vector similarity searchesSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument store infowith version 6.5
Graph DBMS
RDF store
Vector DBMS
Wide column storeVector DBMSRelational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.13
Rank#179  Overall
#30  Document stores
#17  Graph DBMS
#7  RDF stores
#9  Vector DBMS
Score4.04
Rank#77  Overall
#6  Wide column stores
Score2.78
Rank#103  Overall
#4  Vector DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websiteallegrograph.comazure.microsoft.com/­en-us/­services/­storage/­tablesmilvus.iospark.apache.org/­sql
Technical documentationfranz.com/­agraph/­support/­documentation/­current/­agraph-introduction.htmlmilvus.io/­docs/­overview.mdspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperFranz Inc.MicrosoftApache Software Foundation
Initial release2004201220192014
Current release8.0, December 20232.4.4, May 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercial infoLimited community edition freecommercialOpen Source infoApache Version 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
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 languageC++, GoScala
Server operating systemsLinux
OS X
Windows
hostedLinux
macOS info10.14 or later
Windows infowith WSL 2 enabled
Linux
OS X
Windows
Data schemeyes infoRDF schemasschema-freeyes
Typing infopredefined data types such as float or dateyesyesVector, Numeric and Stringyes
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 possiblenonono
Secondary indexesyesnonono
SQL infoSupport of SQLSPARQL is used as query languagenonoSQL-like DML and DDL statements
APIs and other access methodsRESTful HTTP API
SPARQL
RESTful HTTP APIRESTful HTTP APIJDBC
ODBC
Supported programming languagesC#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Go
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresyes infoJavaScript or Common Lispnonono
Triggersyesnonono
Partitioning methods infoMethods for storing different data on different nodeswith FederationSharding infoImplicit feature of the cloud serviceShardingyes, utilizing Spark Core
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
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDoptimistic lockingnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesno
User concepts infoAccess controlUsers with fine-grained authorization concept, user roles and pluggable authenticationAccess rights based on private key authentication or shared access signaturesRole based access control and fine grained access rightsno
More information provided by the system vendor
AllegroGraphMicrosoft Azure Table StorageMilvusSpark SQL
Specific characteristicsKnowledge Graph Platform Leader FedShard - Designed for Entity-Event Knowledge Graph...
» more
Milvus is an open-source and cloud-native vector database built for production-ready...
» more
Competitive advantagesAllegroGraph is uniquely suited to support adhoc queries through SPARQL, Prolog and...
» more
Highly 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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