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DBMS > Amazon DocumentDB vs. Pinecone

System Properties Comparison Amazon DocumentDB vs. Pinecone

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Our visitors often compare Amazon DocumentDB and Pinecone with OpenSearch, MongoDB and Elasticsearch.

Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonPinecone  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceA managed, cloud-native vector database
Primary database modelDocument storeVector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.84
Rank#127  Overall
#23  Document stores
Score3.23
Rank#80  Overall
#4  Vector DBMS
Websiteaws.amazon.com/­documentdbwww.pinecone.io
Technical documentationaws.amazon.com/­documentdb/­resourcesdocs.pinecone.io/­docs/­overview
DeveloperPinecone Systems, Inc
Initial release20192019
License infoCommercial or Open Sourcecommercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Server operating systemshostedhosted
Data schemeschema-free
Typing infopredefined data types such as float or dateyesString, 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.nono
Secondary indexesyes
SQL infoSupport of SQLnono
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)RESTful HTTP API
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
Python
Server-side scripts infoStored proceduresno
Triggersno
Partitioning methods infoMethods for storing different data on different nodesnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicas
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)no
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possible
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operations
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.no
User concepts infoAccess controlAccess rights for users and roles

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More resources
Amazon DocumentDBPinecone
DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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Recent citations in the news

Unlock the power of parallel indexing in Amazon DocumentDB
19 June 2024, AWS Blog

Amazon DocumentDB zero-ETL integration with Amazon OpenSearch Service is now available
16 May 2024, AWS Blog

Reduce cost and improve performance by migrating to Amazon DocumentDB 5.0
15 April 2024, AWS Blog

Unlocking Semantic Search: Building Powerful Applications with Vector Search in Amazon DocumentDB
22 November 2024, AWS Blog

AWS announces Amazon DocumentDB zero-ETL integration with Amazon OpenSearch Service
16 May 2024, AWS Blog

provided by Google News

Pinecone expands vector database with cascading retrieval, boosting enterprise AI accuracy by up to 48%
2 December 2024, VentureBeat

Pinecone integrates AI inferencing with vector database
2 December 2024, Blocks and Files

Pinecone Revamps Retrieval Capabilities for Its Vector Database Platform
2 December 2024, The New Stack

Pinecone Spruces Up AI Knowledge Platform
2 December 2024, Forbes

First-of-its-kind Pinecone Knowledge Platform to Power Best-in-class Retrieval for Customers
3 December 2024, PR Newswire

provided by Google News



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