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DBMS > Google Cloud Datastore vs. Heroic vs. Percona Server for MongoDB vs. StarRocks vs. Tkrzw

System Properties Comparison Google Cloud Datastore vs. Heroic vs. Percona Server for MongoDB vs. StarRocks vs. Tkrzw

Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonHeroic  Xexclude from comparisonPercona Server for MongoDB  Xexclude from comparisonStarRocks  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchA drop-in replacement for MongoDB Community Edition with enterprise-grade features.An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics infoForked from Apache DorisA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelDocument storeTime Series DBMSDocument storeRelational DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.36
Rank#72  Overall
#12  Document stores
Score0.46
Rank#265  Overall
#22  Time Series DBMS
Score0.60
Rank#246  Overall
#39  Document stores
Score0.93
Rank#198  Overall
#93  Relational DBMS
Score0.07
Rank#372  Overall
#57  Key-value stores
Websitecloud.google.com/­datastoregithub.com/­spotify/­heroicwww.percona.com/­mongodb/­software/­percona-server-for-mongodbwww.starrocks.iodbmx.net/­tkrzw
Technical documentationcloud.google.com/­datastore/­docsspotify.github.io/­heroicdocs.percona.com/­percona-distribution-for-mongodbdocs.starrocks.io/­en-us/­latest/­introduction/­StarRocks_intro
DeveloperGoogleSpotifyPerconaThe Linux Foundation infosince Feb 2023Mikio Hirabayashi
Initial release20082014201520202020
Current release3.4.10-2.10, November 20172.5.3, March 20230.9.3, August 2020
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoGPL Version 2Open Source infoApache Version 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++C++, JavaC++
Server operating systemshostedLinuxLinuxLinux
macOS
Data schemeschema-freeschema-freeschema-freeyesschema-free
Typing infopredefined data types such as float or dateyes, details hereyesyesyesno
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 indexesyesyes infovia Elasticsearchyesyes
SQL infoSupport of SQLSQL-like query language (GQL)nonoyesno
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
HQL (Heroic Query Language, a JSON-based language)
HTTP API
proprietary protocol using JSONJDBC
MySQL protocol
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk
JavaC++
Java
Python
Ruby
Server-side scripts infoStored proceduresusing Google App EnginenoJavaScriptuser defined functionsno
TriggersCallbacks using the Google Apps Enginenononono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardinghorizontal partitioning (by range and hash)none
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosyesSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownoyesnono
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
Immediate Consistency
Eventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnonono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes infovia In-Memory Enginenoyes infousing specific database classes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users and rolesRole based access control and fine grained access rightsno

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More resources
Google Cloud DatastoreHeroicPercona Server for MongoDBStarRocksTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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