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DBMS > Dolt vs. GeoMesa vs. Google Cloud Datastore vs. Spark SQL vs. Transbase

System Properties Comparison Dolt vs. GeoMesa vs. Google Cloud Datastore vs. Spark SQL vs. Transbase

Editorial information provided by DB-Engines
NameDolt  Xexclude from comparisonGeoMesa  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonSpark SQL  Xexclude from comparisonTransbase  Xexclude from comparison
DescriptionA MySQL compatible DBMS with Git-like versioning of data and schemaGeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.Automatically 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 resource-optimized, high-performance, universally applicable RDBMS
Primary database modelRelational DBMSSpatial DBMSDocument storeRelational DBMSRelational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.02
Rank#191  Overall
#89  Relational DBMS
Score0.86
Rank#205  Overall
#4  Spatial DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.17
Rank#334  Overall
#148  Relational DBMS
Websitegithub.com/­dolthub/­dolt
www.dolthub.com
www.geomesa.orgcloud.google.com/­datastorespark.apache.org/­sqlwww.transaction.de/­en/­products/­transbase.html
Technical documentationdocs.dolthub.comwww.geomesa.org/­documentation/­stable/­user/­index.htmlcloud.google.com/­datastore/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.transaction.de/­en/­products/­transbase/­features.html
DeveloperDoltHub IncCCRi and othersGoogleApache Software FoundationTransaction Software GmbH
Initial release20182014200820141987
Current release5.0.0, May 20243.5.0 ( 2.13), September 2023Transbase 8.3, 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache License 2.0commercialOpen Source infoApache 2.0commercial infofree development license
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoScalaScalaC and C++
Server operating systemsLinux
macOS
Windows
hostedLinux
OS X
Windows
FreeBSD
Linux
macOS
Solaris
Windows
Data schemeyesyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyes, details hereyesyes
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 indexesyesyesyesnoyes
SQL infoSupport of SQLyesnoSQL-like query language (GQL)SQL-like DML and DDL statementsyes
APIs and other access methodsCLI Client
HTTP REST
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
ADO.NET
JDBC
ODBC
Proprietary native 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
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python
Server-side scripts infoStored proceduresyes infocurrently in alpha releasenousing Google App Enginenoyes
TriggersyesnoCallbacks using the Google Apps Enginenoyes
Partitioning methods infoMethods for storing different data on different nodesnonedepending on storage layerShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesA database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.depending on storage layerMulti-source replication using PaxosnoneSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemdepending on storage layerImmediate 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 integrityyesnoyes infovia ReferenceProperties or Ancestor pathsnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnoyes
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.depending on storage layernonono
User concepts infoAccess controlOnly one user is configurable, and must be specified in the config file at startupyes infodepending on the DBMS used for storageAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nofine grained access rights according to SQL-standard

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More resources
DoltGeoMesaGoogle Cloud DatastoreSpark SQLTransbase
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