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DBMS > Apache Spark (SQL) vs. Google Cloud Datastore

System Properties Comparison Apache Spark (SQL) vs. Google Cloud Datastore

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Editorial information provided by DB-Engines
NameApache Spark (SQL)  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparison
DescriptionApache Spark SQL is a component on top of 'Spark Core' for structured data processingAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform
Primary database modelRelational DBMSDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score19.76
Rank#30  Overall
#18  Relational DBMS
Score3.78
Rank#70  Overall
#12  Document stores
Websitespark.apache.org/­sqlcloud.google.com/­datastore
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmlcloud.google.com/­datastore/­docs
DeveloperApache Software FoundationGoogle
Initial release20142008
Current release3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageScala
Server operating systemsLinux
OS X
Windows
hosted
Data schemeyesschema-free
Typing infopredefined data types such as float or dateyesyes, details here
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 indexesnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query language (GQL)
APIs and other access methodsJDBC
ODBC
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Supported programming languagesJava
Python
R
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresnousing Google App Engine
TriggersnoCallbacks using the Google Apps Engine
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replication using Paxos
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflow
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.
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor paths
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactions
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources
Apache Spark (SQL)Google Cloud Datastore
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