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DBMS > Badger vs. Fauna vs. Google Cloud Datastore vs. Lovefield vs. Microsoft Azure Search

System Properties Comparison Badger vs. Fauna vs. Google Cloud Datastore vs. Lovefield vs. Microsoft Azure Search

Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonFauna infopreviously named FaunaDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonLovefield  Xexclude from comparisonMicrosoft Azure Search  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformEmbeddable relational database for web apps written in pure JavaScriptSearch-as-a-service for web and mobile app development
Primary database modelKey-value storeDocument store
Graph DBMS
Relational DBMS
Time Series DBMS
Document storeRelational DBMSSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.14
Rank#340  Overall
#51  Key-value stores
Score1.70
Rank#146  Overall
#25  Document stores
#11  Graph DBMS
#71  Relational DBMS
#11  Time Series DBMS
Score6.94
Rank#63  Overall
#11  Document stores
Score0.38
Rank#279  Overall
#129  Relational DBMS
Score7.39
Rank#61  Overall
#7  Search engines
Websitegithub.com/­dgraph-io/­badgerfauna.comcloud.google.com/­datastoregoogle.github.io/­lovefieldazure.microsoft.com/­en-us/­services/­search
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerdocs.fauna.comcloud.google.com/­datastore/­docsgithub.com/­google/­lovefield/­blob/­master/­docs/­spec_index.mddocs.microsoft.com/­en-us/­azure/­search
DeveloperDGraph LabsFauna, Inc.GoogleGoogleMicrosoft
Initial release20172014200820142015
Current release2.1.12, February 2017V1
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercialOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenoyesyesnoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoScalaJavaScript
Server operating systemsBSD
Linux
OS X
Solaris
Windows
hostedhostedserver-less, requires a JavaScript environment (browser, Node.js) infotested with Chrome, Firefox, IE, Safarihosted
Data schemeschema-freeschema-freeschema-freeyesyes
Typing infopredefined data types such as float or datenonoyes, details hereyesyes
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.nonononono
Secondary indexesnoyesyesyesyes
SQL infoSupport of SQLnonoSQL-like query language (GQL)SQL-like query language infovia JavaScript builder patternno
APIs and other access methodsRESTful HTTP APIgRPC (using protocol buffers) API
RESTful HTTP/JSON API
RESTful HTTP API
Supported programming languagesGoC#
Go
Java
JavaScript
Python
Ruby
Scala
Swift
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
JavaScript.Net
C#
Server-side scripts infoStored proceduresnouser defined functionsusing Google App Enginenono
TriggersnonoCallbacks using the Google Apps EngineUsing read-only observersno
Partitioning methods infoMethods for storing different data on different nodesnonehorizontal partitioning infoconsistent hashingShardingnoneSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replicationMulti-source replication using Paxosnoneyes infoImplicit feature of the cloud service
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate Consistency
Foreign keys infoReferential integritynoyesyes infovia ReferenceProperties or Ancestor pathsyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes, by using IndexedDB or the cloud service Firebase Realtime Databaseyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nononoyes infousing MemoryDBno
User concepts infoAccess controlnoIdentity management, authentication, and access controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noyes infousing Azure authentication

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More resources
BadgerFauna infopreviously named FaunaDBGoogle Cloud DatastoreLovefieldMicrosoft Azure Search
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