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DBMS > Google Cloud Datastore vs. Sphinx vs. Vitess vs. YDB

System Properties Comparison Google Cloud Datastore vs. Sphinx vs. Vitess vs. YDB

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonSphinx  Xexclude from comparisonVitess  Xexclude from comparisonYDB  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformOpen source search engine for searching in data from different sources, e.g. relational databasesScalable, distributed, cloud-native DBMS, extending MySQLA distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API
Primary database modelDocument storeSearch engineRelational DBMSDocument store
Relational DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score5.98
Rank#56  Overall
#5  Search engines
Score0.82
Rank#209  Overall
#97  Relational DBMS
Score0.35
Rank#278  Overall
#43  Document stores
#127  Relational DBMS
Websitecloud.google.com/­datastoresphinxsearch.comvitess.iogithub.com/­ydb-platform/­ydb
ydb.tech
Technical documentationcloud.google.com/­datastore/­docssphinxsearch.com/­docsvitess.io/­docsydb.tech/­en/­docs
DeveloperGoogleSphinx Technologies Inc.The Linux Foundation, PlanetScaleYandex
Initial release2008200120132019
Current release3.5.1, February 202315.0.2, December 2022
License infoCommercial or Open SourcecommercialOpen Source infoGPL version 2, commercial licence availableOpen Source infoApache Version 2.0, commercial licenses availableOpen Source infoApache 2.0; commercial license 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 languageC++Go
Server operating systemshostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Docker
Linux
macOS
Linux
Data schemeschema-freeyesyesFlexible Schema (defined schema, partial schema, schema free)
Typing infopredefined data types such as float or dateyes, details herenoyesyes
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 indexesyesyes infofull-text index on all search fieldsyesyes
SQL infoSupport of SQLSQL-like query language (GQL)SQL-like query language (SphinxQL)yes infowith proprietary extensionsSQL-like query language (YQL)
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Proprietary protocolADO.NET
JDBC
MySQL protocol
ODBC
RESTful HTTP API (DynamoDB compatible)
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Go
Java
JavaScript (Node.js)
PHP
Python
Server-side scripts infoStored proceduresusing Google App Enginenoyes infoproprietary syntaxno
TriggersCallbacks using the Google Apps Enginenoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosnoneMulti-source replication
Source-replica replication
Active-passive shard replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate 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.Eventual Consistency across shards
Immediate Consistency within a shard
Immediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnoyes infonot for MyISAM storage engineno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACID at shard levelACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes infotable locks or row locks depending on storage engineyes
Durability infoSupport for making data persistentyesyes infoThe original contents of fields are not stored in the Sphinx index.yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noUsers with fine-grained authorization concept infono user groups or rolesAccess rights defined for Yandex Cloud users

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More resources
Google Cloud DatastoreSphinxVitessYDB
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