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DBMS > Google Cloud Datastore vs. Spark SQL vs. Tkrzw

System Properties Comparison Google Cloud Datastore vs. Spark SQL vs. Tkrzw

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonSpark SQL  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 PlatformSpark SQL is a component on top of 'Spark Core' for structured data processingA 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 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
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.07
Rank#372  Overall
#57  Key-value stores
Websitecloud.google.com/­datastorespark.apache.org/­sqldbmx.net/­tkrzw
Technical documentationcloud.google.com/­datastore/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperGoogleApache Software FoundationMikio Hirabayashi
Initial release200820142020
Current release3.5.0 ( 2.13), September 20230.9.3, August 2020
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaC++
Server operating systemshostedLinux
OS X
Windows
Linux
macOS
Data schemeschema-freeyesschema-free
Typing infopredefined data types such as float or dateyes, details hereyesno
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 indexesyesno
SQL infoSupport of SQLSQL-like query language (GQL)SQL-like DML and DDL statementsno
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresusing Google App Enginenono
TriggersCallbacks using the Google Apps Enginenono
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowno
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 Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes infousing specific database classes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nono

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More resources
Google Cloud DatastoreSpark SQLTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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