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DBMS > GeoMesa vs. Google Cloud Datastore vs. Pinecone

System Properties Comparison GeoMesa vs. Google Cloud Datastore vs. Pinecone

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Editorial information provided by DB-Engines
NameGeoMesa  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonPinecone  Xexclude from comparison
DescriptionGeoMesa 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 PlatformA managed, cloud-native vector database
Primary database modelSpatial DBMSDocument storeVector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.78
Rank#213  Overall
#4  Spatial DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score3.16
Rank#95  Overall
#2  Vector DBMS
Websitewww.geomesa.orgcloud.google.com/­datastorewww.pinecone.io
Technical documentationwww.geomesa.org/­documentation/­stable/­user/­index.htmlcloud.google.com/­datastore/­docsdocs.pinecone.io/­docs/­overview
DeveloperCCRi and othersGooglePinecone Systems, Inc
Initial release201420082019
Current release4.0.5, February 2024
License infoCommercial or Open SourceOpen Source infoApache License 2.0commercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScala
Server operating systemshostedhosted
Data schemeyesschema-free
Typing infopredefined data types such as float or dateyesyes, details hereString, Number, Boolean
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 indexesyesyes
SQL infoSupport of SQLnoSQL-like query language (GQL)no
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
RESTful HTTP API
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Python
Server-side scripts infoStored proceduresnousing Google App Engine
TriggersnoCallbacks using the Google Apps Engine
Partitioning methods infoMethods for storing different data on different nodesdepending on storage layerSharding
Replication methods infoMethods for redundantly storing data on multiple nodesdepending on storage layerMulti-source replication using Paxos
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes 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.
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 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.depending on storage layernono
User concepts infoAccess controlyes infodepending on the DBMS used for storageAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources
GeoMesaGoogle Cloud DatastorePinecone
DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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Vector databases
2 June 2023, Matthias Gelbmann

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