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DBMS > Bangdb vs. Lovefield vs. Microsoft Azure Table Storage vs. Solr

System Properties Comparison Bangdb vs. Lovefield vs. Microsoft Azure Table Storage vs. Solr

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Editorial information provided by DB-Engines
NameBangdb  Xexclude from comparisonLovefield  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionConverged and high performance database for device data, events, time series, document and graphEmbeddable relational database for web apps written in pure JavaScriptA Wide Column Store for rapid development using massive semi-structured datasetsA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelDocument store
Graph DBMS
Time Series DBMS
Relational DBMSWide column storeSearch engine
Secondary database modelsSpatial DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.16
Rank#338  Overall
#47  Document stores
#32  Graph DBMS
#31  Time Series DBMS
Score0.33
Rank#286  Overall
#131  Relational DBMS
Score4.04
Rank#77  Overall
#6  Wide column stores
Score41.02
Rank#24  Overall
#3  Search engines
Websitebangdb.comgoogle.github.io/­lovefieldazure.microsoft.com/­en-us/­services/­storage/­tablessolr.apache.org
Technical documentationdocs.bangdb.comgithub.com/­google/­lovefield/­blob/­master/­docs/­spec_index.mdsolr.apache.org/­resources.html
DeveloperSachin Sinha, BangDBGoogleMicrosoftApache Software Foundation
Initial release2012201420122006
Current releaseBangDB 2.0, October 20212.1.12, February 20179.6.1, May 2024
License infoCommercial or Open SourceOpen Source infoBSD 3Open Source infoApache 2.0commercialOpen 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 languageC, C++JavaScriptJava
Server operating systemsLinuxserver-less, requires a JavaScript environment (browser, Node.js) infotested with Chrome, Firefox, IE, SafarihostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeschema-freeyesschema-freeyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyes: string, long, double, int, geospatial, stream, eventsyesyesyes 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 indexesyes infosecondary, composite, nested, reverse, geospatialyesnoyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL like support with command line toolSQL-like query language infovia JavaScript builder patternnoSolr Parallel SQL Interface
APIs and other access methodsProprietary protocol
RESTful HTTP API
RESTful HTTP APIJava API
RESTful HTTP/JSON API
Supported programming languagesC
C#
C++
Java
Python
JavaScript.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresnononoJava plugins
Triggersyes, Notifications (with Streaming only)Using read-only observersnoyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesSharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithmnoneSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor, Knob for CAP (enterprise version only)noneyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemTunable consistency, set CAP knob accordinglyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDoptimistic lockingoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayes, optimistic concurrency controlyesyesyes
Durability infoSupport for making data persistentyes, implements WAL (Write ahead log) as wellyes, by using IndexedDB or the cloud service Firebase Realtime Databaseyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes, run db with in-memory only modeyes infousing MemoryDBnoyes
User concepts infoAccess controlyes (enterprise version only)noAccess rights based on private key authentication or shared access signaturesyes

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More resources
BangdbLovefieldMicrosoft Azure Table StorageSolr
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