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DBMS > Fauna vs. Lovefield vs. Microsoft Azure Data Explorer vs. Riak TS vs. Sphinx

System Properties Comparison Fauna vs. Lovefield vs. Microsoft Azure Data Explorer vs. Riak TS vs. Sphinx

Editorial information provided by DB-Engines
NameFauna infopreviously named FaunaDB  Xexclude from comparisonLovefield  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonRiak TS  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionFauna 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.Embeddable relational database for web apps written in pure JavaScriptFully managed big data interactive analytics platformRiak TS is a distributed NoSQL database optimized for time series data and based on Riak KVOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelDocument store
Graph DBMS
Relational DBMS
Time Series DBMS
Relational DBMSRelational DBMS infocolumn orientedTime Series DBMSSearch engine
Secondary database modelsDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.52
Rank#153  Overall
#26  Document stores
#14  Graph DBMS
#71  Relational DBMS
#13  Time Series DBMS
Score0.29
Rank#293  Overall
#133  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.20
Rank#319  Overall
#27  Time Series DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Websitefauna.comgoogle.github.io/­lovefieldazure.microsoft.com/­services/­data-explorersphinxsearch.com
Technical documentationdocs.fauna.comgithub.com/­google/­lovefield/­blob/­master/­docs/­spec_index.mddocs.microsoft.com/­en-us/­azure/­data-explorerwww.tiot.jp/­riak-docs/­riak/­ts/­latestsphinxsearch.com/­docs
DeveloperFauna, Inc.GoogleMicrosoftOpen Source, formerly Basho TechnologiesSphinx Technologies Inc.
Initial release20142014201920152001
Current release2.1.12, February 2017cloud service with continuous releases3.0.0, September 20223.5.1, February 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialOpen SourceOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud serviceyesnoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaJavaScriptErlangC++
Server operating systemshostedserver-less, requires a JavaScript environment (browser, Node.js) infotested with Chrome, Firefox, IE, SafarihostedLinux
OS X
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeschema-freeyesFixed schema with schema-less datatypes (dynamic)schema-freeyes
Typing infopredefined data types such as float or datenoyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesnono
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 indexesyesyesall fields are automatically indexedrestrictedyes infofull-text index on all search fields
SQL infoSupport of SQLnoSQL-like query language infovia JavaScript builder patternKusto Query Language (KQL), SQL subsetyes, limitedSQL-like query language (SphinxQL)
APIs and other access methodsRESTful HTTP APIMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
HTTP API
Native Erlang Interface
Proprietary protocol
Supported programming languagesC#
Go
Java
JavaScript
Python
Ruby
Scala
Swift
JavaScript.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresuser defined functionsnoYes, possible languages: KQL, Python, RErlangno
TriggersnoUsing read-only observersyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes infopre-commit hooks and post-commit hooksno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning infoconsistent hashingnoneSharding infoImplicit feature of the cloud serviceShardingSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationnoneyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.selectable replication factornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Eventual Consistency
Foreign keys infoReferential integrityyesyesnono infolinks between datasets can be storedno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnonono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyes, by using IndexedDB or the cloud service Firebase Realtime Databaseyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infousing MemoryDBno
User concepts infoAccess controlIdentity management, authentication, and access controlnoAzure Active Directory Authenticationnono

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More resources
Fauna infopreviously named FaunaDBLovefieldMicrosoft Azure Data ExplorerRiak TSSphinx
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