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DBMS > AgensGraph vs. Bangdb vs. Google Cloud Firestore vs. Solr

System Properties Comparison AgensGraph vs. Bangdb vs. Google Cloud Firestore vs. Solr

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Editorial information provided by DB-Engines
NameAgensGraph  Xexclude from comparisonBangdb  Xexclude from comparisonGoogle Cloud Firestore  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionMulti-model database supporting relational and graph data models and built upon PostgreSQLConverged and high performance database for device data, events, time series, document and graphCloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.A widely used distributed, scalable search engine based on Apache Lucene
Primary database modelGraph DBMS
Relational DBMS
Document store
Graph DBMS
Time Series DBMS
Document storeSearch engine
Secondary database modelsSpatial DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.20
Rank#321  Overall
#27  Graph DBMS
#143  Relational DBMS
Score0.08
Rank#347  Overall
#47  Document stores
#34  Graph DBMS
#31  Time Series DBMS
Score7.85
Rank#51  Overall
#8  Document stores
Score42.91
Rank#24  Overall
#3  Search engines
Websitebitnine.net/­agensgraphbangdb.comfirebase.google.com/­products/­firestoresolr.apache.org
Technical documentationbitnine.net/­documentationdocs.bangdb.comfirebase.google.com/­docs/­firestoresolr.apache.org/­resources.html
DeveloperBitnine Global Inc.Sachin Sinha, BangDBGoogleApache Software Foundation
Initial release2016201220172006
Current release2.1, December 2018BangDB 2.0, October 20219.6.0, April 2024
License infoCommercial or Open SourceOpen Source infoApache License 2.0Open Source infoBSD 3commercialOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCC, C++Java
Server operating systemsLinux
OS X
Windows
LinuxhostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemedepending on used data modelschema-freeschema-freeyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyes: string, long, double, int, geospatial, stream, eventsyesyes infosupports customizable data types and automatic typing
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.nononoyes
Secondary indexesyesyes infosecondary, composite, nested, reverse, geospatialyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLyesSQL like support with command line toolnoSolr Parallel SQL Interface
APIs and other access methodsCypher Query Language
JDBC
Proprietary protocol
RESTful HTTP API
Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API
Java API
RESTful HTTP/JSON API
Supported programming languagesC
Java
JavaScript
Python
C
C#
C++
Java
Python
Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresyesnoyes, Firebase Rules & Cloud FunctionsJava plugins
Triggersnoyes, Notifications (with Streaming only)yes, with Cloud Functionsyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesno, but can be realized using table inheritanceSharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithmShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationselectable replication factor, Knob for CAP (enterprise version only)Multi-source replicationyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoUsing Cloud Dataflowspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyTunable consistency, set CAP knob accordinglyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDyesoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyes, optimistic concurrency controlyesyes
Durability infoSupport for making data persistentyesyes, implements WAL (Write ahead log) as wellyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes, run db with in-memory only modeyes
User concepts infoAccess controlfine grained access rights according to SQL-standardyes (enterprise version only)Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.yes

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More resources
AgensGraphBangdbGoogle Cloud FirestoreSolr
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