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DBMS > Google Cloud Datastore vs. Hive vs. KeyDB vs. TimesTen

System Properties Comparison Google Cloud Datastore vs. Hive vs. KeyDB vs. TimesTen

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonHive  Xexclude from comparisonKeyDB  Xexclude from comparisonTimesTen  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platformdata warehouse software for querying and managing large distributed datasets, built on HadoopAn ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocolsIn-Memory RDBMS compatible to Oracle
Primary database modelDocument storeRelational DBMSKey-value storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score0.71
Rank#226  Overall
#33  Key-value stores
Score1.31
Rank#163  Overall
#74  Relational DBMS
Websitecloud.google.com/­datastorehive.apache.orggithub.com/­Snapchat/­KeyDB
keydb.dev
www.oracle.com/­database/­technologies/­related/­timesten.html
Technical documentationcloud.google.com/­datastore/­docscwiki.apache.org/­confluence/­display/­Hive/­Homedocs.keydb.devdocs.oracle.com/­database/­timesten-18.1
DeveloperGoogleApache Software Foundation infoinitially developed by FacebookEQ Alpha Technology Ltd.Oracle, TimesTen Performance Software, HP infooriginally founded in HP Labs it was acquired by Oracle in 2005
Initial release2008201220191998
Current release3.1.3, April 202211 Release 2 (11.2.2.8.0)
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2Open Source infoBSD-3commercial
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++
Server operating systemshostedAll OS with a Java VMLinuxAIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows
Data schemeschema-freeyesschema-freeyes
Typing infopredefined data types such as float or dateyes, details hereyespartial infoSupported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexesyes
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.nonono
Secondary indexesyesyesyes infoby using the Redis Search moduleyes
SQL infoSupport of SQLSQL-like query language (GQL)SQL-like DML and DDL statementsnoyes
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
Thrift
Proprietary protocol infoRESP - REdis Serialization ProtocoJDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Java
PHP
Python
C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic
C
C++
Java
PL/SQL
Server-side scripts infoStored proceduresusing Google App Engineyes infouser defined functions and integration of map-reduceLuaPL/SQL
TriggersCallbacks using the Google Apps Enginenonono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosselectable replication factorMulti-source replication
Source-replica replication
Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowyes infoquery execution via MapReducenono
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 ConsistencyEventual Consistency
Strong eventual consistency with CRDTs
Immediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoOptimistic locking, atomic execution of commands blocks and scriptsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoConfigurable mechanisms for persistency via snapshots and/or operations logsyes infoby means of logfiles and checkpoints
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and rolessimple password-based access control and ACLfine grained access rights according to SQL-standard

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More resources
Google Cloud DatastoreHiveKeyDBTimesTen
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