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DBMS > AnzoGraph DB vs. ObjectBox vs. Sphinx vs. Splice Machine

System Properties Comparison AnzoGraph DB vs. ObjectBox vs. Sphinx vs. Splice Machine

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Editorial information provided by DB-Engines
NameAnzoGraph DB  Xexclude from comparisonObjectBox  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionScalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualizationExtremely fast embedded database for small devices, IoT and MobileOpen source search engine for searching in data from different sources, e.g. relational databasesOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelGraph DBMS
RDF store
Object oriented DBMSSearch engineRelational DBMS
Secondary database modelsTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.17
Rank#283  Overall
#25  Graph DBMS
#13  RDF stores
Score0.65
Rank#208  Overall
#11  Object oriented DBMS
Score6.47
Rank#59  Overall
#7  Search engines
Score0.79
Rank#186  Overall
#91  Relational DBMS
Websitewww.cambridgesemantics.com/­anzographobjectbox.iosphinxsearch.comsplicemachine.com
Technical documentationdocs.cambridgesemantics.com/­anzograph/­userdoc/­home.htmdocs.objectbox.iosphinxsearch.com/­docsdoc.splicemachine.com
DeveloperCambridge SemanticsObjectBox LimitedSphinx Technologies Inc.Splice Machine
Initial release2018201720012014
Current release2.2, August 20203.2.1, January 20202.0
License infoCommercial or Open Sourcecommercial infofree trial version availableOpen Source infoApache License 2.0Open Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC and C++C++Java
Server operating systemsLinuxAndroid
iOS
Linux
macOS
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeSchema-free and OWL/RDFS-schema supportyesyesyes
Typing infopredefined data types such as float or dateyesnoyes
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.nono
Secondary indexesnoyesyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLSPARQL and SPARQL* as primary query language. Cypher preview.noSQL-like query language (SphinxQL)yes
APIs and other access methodsApache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL
Proprietary native APIProprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languagesC++
Java
Python
C
C++
Dart
Go
Java
JavaScript infoplanned (as of Jan 2019)
Kotlin
Python infoplanned (as of Jan 2019)
Swift
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions and aggregatesnonoyes infoJava
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesAutomatic shardingnoneSharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication in MPP-Clusteronline/offline synchronization between client and servernoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsKerberos/HDFS data loadingnonoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency in MPP-ClusterImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityno infonot needed in graphsyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes
User concepts infoAccess controlAccess rights for users and rolesyesnoAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
AnzoGraph DBObjectBoxSphinxSplice Machine
Specific characteristicsAnzoGraph DB is a massively parallel processing (MPP) native graph database built...
» more
THE ONLY SCALE-OUT SQL RDBMS WITH BUILT-IN MACHINE LEARNING For data-rich, real-time...
» more
Competitive advantagesAnzoGraph DB’s flexible data model and analytical capabilities not only let you load...
» more
Operational Workloads: Full ACID support with high performance at scale Multi-row...
» more
Typical application scenariosDevelopers can use AnzoGraph DB to build and execute data warehouse-style BI analytics...
» more
New AI/ML and IoT Applications Data Warehouse Offloads Application Modernization...
» more
Key customersAnzograph DB has customers in biotech, pharma, financial services and other verticals.
» more
Licensing and pricing modelsAnzoGraph DB is offered in a free edition , free for commercial use for workloads...
» more
Kubernetes Community Edition: https://hub.helm.sh/charts/splicemachine/splice-helm...
» more
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