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DBMS > AllegroGraph vs. GridGain vs. NSDb vs. Sadas Engine

System Properties Comparison AllegroGraph vs. GridGain vs. NSDb vs. Sadas Engine

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Editorial information provided by DB-Engines
NameAllegroGraph  Xexclude from comparisonGridGain  Xexclude from comparisonNSDb  Xexclude from comparisonSadas Engine  Xexclude from comparison
DescriptionHigh performance, persistent RDF store with additional support for Graph DBMSGridGain is an in-memory computing platform, built on Apache IgniteScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesSADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments
Primary database modelDocument store infowith version 6.5
Graph DBMS
RDF store
Vector DBMS
Key-value store
Relational DBMS
Time Series DBMSRelational DBMS
Secondary database modelsSpatial 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
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score0.00
Rank#383  Overall
#41  Time Series DBMS
Score0.00
Rank#383  Overall
#158  Relational DBMS
Websiteallegrograph.comwww.gridgain.comnsdb.iowww.sadasengine.com
Technical documentationfranz.com/­agraph/­support/­documentation/­current/­agraph-introduction.htmlwww.gridgain.com/­docs/­index.htmlnsdb.io/­Architecturewww.sadasengine.com/­en/­sadas-engine-download-free-trial-and-documentation/­#documentation
DeveloperFranz Inc.GridGain Systems, Inc.SADAS s.r.l.
Initial release2004200720172006
Current release8.0, December 2023GridGain 8.5.18.0
License infoCommercial or Open Sourcecommercial infoLimited community edition freecommercialOpen Source infoApache Version 2.0commercial infofree trial version available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, C++, .NetJava, ScalaC++
Server operating systemsLinux
OS X
Windows
Linux
OS X
Solaris
Windows
Linux
macOS
AIX
Linux
Windows
Data schemeyes infoRDF schemasyesyes
Typing infopredefined data types such as float or dateyesyesyes: int, bigint, decimal, 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 possibleyesnono
Secondary indexesyesyesall fields are automatically indexedyes
SQL infoSupport of SQLSPARQL is used as query languageANSI-99 for query and DML statements, subset of DDLSQL-like query languageyes
APIs and other access methodsRESTful HTTP API
SPARQL
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
gRPC
HTTP REST
WebSocket
JDBC
ODBC
Proprietary protocol
Supported programming languagesC#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala
C#
C++
Java
PHP
Python
Ruby
Scala
Java
Scala
.Net
C
C#
C++
Groovy
Java
PHP
Python
Server-side scripts infoStored proceduresyes infoJavaScript or Common Lispyes (compute grid and cache interceptors can be used instead)nono
Triggersyesyes (cache interceptors and events)no
Partitioning methods infoMethods for storing different data on different nodeswith FederationShardingShardinghorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
yes (replicated cache)none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes (compute grid and hadoop accelerator)nono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesUsing Apache Luceneyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes infomanaged by 'Learn by Usage'
User concepts infoAccess controlUsers with fine-grained authorization concept, user roles and pluggable authenticationSecurity Hooks for custom implementationsAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
AllegroGraphGridGainNSDbSadas Engine
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
News

Can Neuro-Symbolic AI Solve AI’s Weaknesses?
17 April 2024

100 Companies That Matter in KM – Franz Inc.
3 April 2024

Exploring AllegroGraph v8 – Unleashing the Power of Neuro-Symbolic AI (Recorded Webinar)
9 February 2024

What is Neuro-Symbolic AI?
23 January 2024

Allegro CL v11 – Now Available! – The Neuro-Symbolic AI Programming Platform
8 January 2024

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More resources
AllegroGraphGridGainNSDbSadas Engine
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Q&A: Can Neuro-Symbolic AI Solve AI’s Weaknesses?
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25 January 2024, Datanami

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22 August 2023, GlobeNewswire

GridGain Announces Call for Speakers for Virtual Apache Ignite Summit 2024
8 February 2024, PR Newswire

GridGain Releases Platform v8.9 for High-Speed Analytics Across Disparate Data Workloads
12 October 2023, Datanami

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