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DBMS > AnzoGraph DB vs. Faircom DB vs. Solr vs. Spark SQL

System Properties Comparison AnzoGraph DB vs. Faircom DB vs. Solr vs. Spark SQL

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Editorial information provided by DB-Engines
NameAnzoGraph DB  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonSolr  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionScalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualizationNative high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.A widely used distributed, scalable search engine based on Apache LuceneSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelGraph DBMS
RDF store
Key-value store
Relational DBMS
Search engineRelational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.23
Rank#307  Overall
#24  Graph DBMS
#13  RDF stores
Score0.20
Rank#318  Overall
#48  Key-value stores
#141  Relational DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitecambridgesemantics.com/­anzographwww.faircom.com/­products/­faircom-dbsolr.apache.orgspark.apache.org/­sql
Technical documentationdocs.cambridgesemantics.com/­anzograph/­userdoc/­home.htmdocs.faircom.com/­docs/­en/­UUID-7446ae34-a1a7-c843-c894-d5322e395184.htmlsolr.apache.org/­resources.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperCambridge SemanticsFairCom CorporationApache Software FoundationApache Software Foundation
Initial release2018197920062014
Current release2.3, January 2021V12, November 20209.6.0, April 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercial infofree trial version availablecommercial infoRestricted, free version availableOpen Source infoApache Version 2Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageANSI C, C++JavaScala
Server operating systemsLinuxAIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
OS X
Windows
Data schemeSchema-free and OWL/RDFS-schema supportschema free, schema optional, schema required, partial schema,yes infoDynamic Fields enables on-the-fly addition of new fieldsyes
Typing infopredefined data types such as float or dateyes, ANSI SQL Types, JSON, typed binary structuresyes infosupports customizable data types and automatic typingyes
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.nonoyesno
Secondary indexesnoyesyes infoAll search fields are automatically indexedno
SQL infoSupport of SQLSPARQL and SPARQL* as primary query language. Cypher preview.yes, ANSI SQL with proprietary extensionsSolr Parallel SQL InterfaceSQL-like DML and DDL statements
APIs and other access methodsApache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL
ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
Java API
RESTful HTTP/JSON API
JDBC
ODBC
Supported programming languagesC++
Java
Python
.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions and aggregatesyes info.Net, JavaScript, C/C++Java pluginsno
Triggersnoyesyes infoUser configurable commands triggered on index changesno
Partitioning methods infoMethods for storing different data on different nodesAutomatic shardingFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioningShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication in MPP-Clusteryes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).yesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsKerberos/HDFS data loadingnospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency in MPP-ClusterEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Eventual Consistency
Foreign keys infoReferential integrityno infonot needed in graphsyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDtunable from ACID to Eventually Consistentoptimistic lockingno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesYes, tunable from durable to delayed durability to in-memoryyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyesno
User concepts infoAccess controlAccess rights for users and rolesFine grained access rights according to SQL-standard with additional protections for filesyesno

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More resources
AnzoGraph DBFaircom DB infoformerly c-treeACESolrSpark SQL
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