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DBMS > Google Cloud Datastore vs. Hypertable vs. Manticore Search vs. Vitess

System Properties Comparison Google Cloud Datastore vs. Hypertable vs. Manticore Search vs. Vitess

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonHypertable  Xexclude from comparisonManticore Search  Xexclude from comparisonVitess  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformAn open source BigTable implementation based on distributed file systems such as HadoopMulti-storage database for search, including full-text search.Scalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelDocument storeWide column storeSearch engineRelational DBMS
Secondary database modelsTime Series DBMS infousing the Manticore Columnar LibraryDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score0.22
Rank#312  Overall
#21  Search engines
Score0.82
Rank#209  Overall
#97  Relational DBMS
Websitecloud.google.com/­datastoremanticoresearch.comvitess.io
Technical documentationcloud.google.com/­datastore/­docsmanual.manticoresearch.comvitess.io/­docs
DeveloperGoogleHypertable Inc.Manticore SoftwareThe Linux Foundation, PlanetScale
Initial release2008200920172013
Current release0.9.8.11, March 20166.0, February 202315.0.2, December 2022
License infoCommercial or Open SourcecommercialOpen Source infoGNU version 3. Commercial license availableOpen Source infoGPL version 2Open 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 languageC++C++Go
Server operating systemshostedLinux
OS X
Windows infoan inofficial Windows port is available
FreeBSD
Linux
macOS
Windows
Docker
Linux
macOS
Data schemeschema-freeschema-freeFixed schemayes
Typing infopredefined data types such as float or dateyes, details herenoInt, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Booleanyes
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.noCan index from XML
Secondary indexesyesrestricted infoonly exact value or prefix value scansyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLSQL-like query language (GQL)noSQL-like query languageyes infowith proprietary extensions
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
C++ API
Thrift
Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL
ADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Java
Perl
PHP
Python
Ruby
Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
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 proceduresusing Google App Enginenouser defined functionsyes infoproprietary syntax
TriggersCallbacks using the Google Apps Enginenonoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosselectable replication factor on file system levelSynchronous replication based on Galera libraryMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowyesnono
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.Immediate ConsistencyEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnonoyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnoyes infoisolated transactions for atomic changes and binary logging for safe writesACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infotable locks or row locks depending on storage engine
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Manticore index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nonoUsers with fine-grained authorization concept infono user groups or roles

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More resources
Google Cloud DatastoreHypertableManticore SearchVitess
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