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DBMS > GeoSpock vs. PlanetScale vs. Spark SQL vs. Yanza

System Properties Comparison GeoSpock vs. PlanetScale vs. Spark SQL vs. Yanza

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Editorial information provided by DB-Engines
NameGeoSpock  Xexclude from comparisonPlanetScale  Xexclude from comparisonSpark SQL  Xexclude from comparisonYanza  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionSpatial and temporal data processing engine for extreme data scaleScalable, distributed, serverless MySQL database platform built on top of VitessSpark SQL is a component on top of 'Spark Core' for structured data processingTime Series DBMS for IoT Applications
Primary database modelRelational DBMSRelational DBMSRelational DBMSTime Series DBMS
Secondary database modelsTime Series DBMSDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.49
Rank#155  Overall
#72  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitegeospock.complanetscale.comspark.apache.org/­sqlyanza.com
Technical documentationplanetscale.com/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperGeoSpockPlanetScaleApache Software FoundationYanza
Initial release202020142015
Current release2.0, September 20193.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0commercial infofree version available
Cloud-based only infoOnly available as a cloud serviceyesyesnono infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, JavascriptGoScala
Server operating systemshostedDocker
Linux
macOS
Linux
OS X
Windows
Windows
Data schemeyesyesyesschema-free
Typing infopredefined data types such as float or dateyesyesyesno
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 indexestemporal, categoricalyesnono
SQL infoSupport of SQLANSI SQL for query only (using Presto)yes infowith proprietary extensionsSQL-like DML and DDL statementsno
APIs and other access methodsJDBCADO.NET
JDBC
MySQL protocol
ODBC
JDBC
ODBC
HTTP API
Supported programming languagesAda
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Java
Python
R
Scala
any language that supports HTTP calls
Server-side scripts infoStored proceduresnoyes infoproprietary syntaxnono
Triggersnoyesnoyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodesAutomatic shardingShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
nonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency across shards
Immediate Consistency within a shard
Immediate Consistency
Foreign keys infoReferential integritynoyes infonot for MyISAM storage enginenono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID at shard levelnono
Concurrency infoSupport for concurrent manipulation of datayesyes infotable locks or row locks depending on storage engineyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlAccess rights for users can be defined per tableUsers with fine-grained authorization concept infono user groups or rolesnono

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More resources
GeoSpockPlanetScaleSpark SQLYanza
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