DB-EnginesExtremeDB: mitigate connectivity issues in a DBMSEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > Amazon CloudSearch vs. Heroic vs. SWC-DB vs. Vitess

System Properties Comparison Amazon CloudSearch vs. Heroic vs. SWC-DB vs. Vitess

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameAmazon CloudSearch  Xexclude from comparisonHeroic  Xexclude from comparisonSWC-DB infoSuper Wide Column Database  Xexclude from comparisonVitess  Xexclude from comparison
DescriptionA hosted search engine service by Amazon with the data stored in Amazons cloudTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchA high performance, scalable Wide Column DBMSScalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelSearch engineTime Series DBMSWide column storeRelational DBMS
Secondary database modelsTime Series DBMSDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.85
Rank#137  Overall
#12  Search engines
Score0.51
Rank#255  Overall
#21  Time Series DBMS
Score0.01
Rank#376  Overall
#13  Wide column stores
Score0.82
Rank#209  Overall
#97  Relational DBMS
Websiteaws.amazon.com/­cloudsearchgithub.com/­spotify/­heroicgithub.com/­kashirin-alex/­swc-db
www.swcdb.org
vitess.io
Technical documentationdocs.aws.amazon.com/­cloudsearchspotify.github.io/­heroicvitess.io/­docs
DeveloperAmazonSpotifyAlex KashirinThe Linux Foundation, PlanetScale
Initial release2012201420202013
Current release0.5, April 202115.0.2, December 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoGPL V3Open Source infoApache Version 2.0, commercial licenses available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++Go
Server operating systemshostedLinuxDocker
Linux
macOS
Data schemeyesschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyesyes
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.nono
Secondary indexesyes infoall search fields are automatically indexedyes infovia Elasticsearchyes
SQL infoSupport of SQLnonoSQL-like query languageyes infowith proprietary extensions
APIs and other access methodsHTTP APIHQL (Heroic Query Language, a JSON-based language)
HTTP API
Proprietary protocol
Thrift
ADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languagesC++Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresnononoyes infoproprietary syntax
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesyes infoautomatic partitioning across Amazon Search Instance as requiredShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes infomanaged transparently by AWSyesMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate ConsistencyEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integritynonoyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infotable locks or row locks depending on storage engine
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlauthentication via encrypted signaturesUsers with fine-grained authorization concept infono user groups or roles

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
Amazon CloudSearchHeroicSWC-DB infoSuper Wide Column DatabaseVitess
DB-Engines blog posts

Amazon - the rising star in the DBMS market
3 August 2015, Matthias Gelbmann

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

show all

Recent citations in the news

Amazon CloudSearch – Start Searching in One Hour for Less Than $100 / Month | Amazon Web Services
12 April 2012, AWS Blog

Is Amazon CloudSearch superior to do-it-yourself search tools?
24 January 2014, TechTarget

Searching CloudTrail Logs Easily with Amazon CloudSearch | AWS Startups Blog
21 October 2014, AWS Blog

Amazon Takes On Google And Microsoft With CloudSearch
16 April 2012, Forbes

Amazon CloudSearch – Even Better Searching for Less Than $100/Month | Amazon Web Services
24 March 2014, AWS Blog

provided by Google News

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

provided by Google News

2022 All O-Zone Football Team
17 December 2022, Ozarks Sports Zone

provided by Google News

Vitess, the database clustering system powering YouTube, graduates CNCF incubation
5 November 2019, SiliconANGLE News

PlanetScale Unveils Distributed MySQL Database Service Based on Vitess
18 May 2021, Datanami

PlanetScale grabs YouTube-developed open-source tech, promises Vitess DBaaS with on-the-fly schema changes
18 May 2021, The Register

They scaled YouTube — now they’ll shard everyone with PlanetScale
13 December 2018, TechCrunch

Massively Scaling MySQL Using Vitess
19 February 2019, InfoQ.com

provided by Google News



Share this page

Featured Products

AllegroGraph logo

Graph Database Leader for AI Knowledge Graph Applications - The Most Secure Graph Database Available.
Free Download

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

RaimaDB logo

RaimaDB, embedded database for mission-critical applications. When performance, footprint and reliability matters.
Try RaimaDB for free.

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Present your product here