Amazon Web Services | Optimise Amazon DocumentDB Queries for Nested Array Objects | Amazon Web Services @amazonwebservices | Uploaded October 2024 | Updated October 2024, 1 hour ago.
In Amazon DocumentDB ,for fields that have an array value, a multi-key in for fields that have an array value, a multi-key index allows you to create an index key for each element in the array. The array can be scalar (strings or numbers) and nested documents. When using multiple $elemMatch along with $and operator on indexed arrays containing nested objects as elements , Amazon DocumentDB query optimizer can utilize multikey indexes more efficiently when we arrange the query conditions on multikey index columns in increasing order of number of documents they match. This enables the narrowing down of the search scope of the first pass of the query execution to a fewer number of documents, leading to faster execution times.
You can elevate performance for workloads that query arrays by tailoring queries to the cardinality of your data. This project aims to facilitate the replication of test setups for Amazon DocumentDB, demonstrating performance enhancements through optimal rewriting of array queries. With insights into your dataset, crafting array queries tailored to DocumentDB's cloud native engine can significantly optimize execution.
Learn more about Amazon DocumentDB Queries: https://go.aws/4eQNvB6
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In Amazon DocumentDB ,for fields that have an array value, a multi-key in for fields that have an array value, a multi-key index allows you to create an index key for each element in the array. The array can be scalar (strings or numbers) and nested documents. When using multiple $elemMatch along with $and operator on indexed arrays containing nested objects as elements , Amazon DocumentDB query optimizer can utilize multikey indexes more efficiently when we arrange the query conditions on multikey index columns in increasing order of number of documents they match. This enables the narrowing down of the search scope of the first pass of the query execution to a fewer number of documents, leading to faster execution times.
You can elevate performance for workloads that query arrays by tailoring queries to the cardinality of your data. This project aims to facilitate the replication of test setups for Amazon DocumentDB, demonstrating performance enhancements through optimal rewriting of array queries. With insights into your dataset, crafting array queries tailored to DocumentDB's cloud native engine can significantly optimize execution.
Learn more about Amazon DocumentDB Queries: https://go.aws/4eQNvB6
Subscribe to AWS: https://go.aws/subscribe
Sign up for AWS: https://go.aws/signup
AWS free tier: https://go.aws/free
Explore more: https://go.aws/home
Contact AWS: https://go.aws/contact
Next steps
Explore on AWS in Analyst Research: https://go.aws/reports
Discover, deploy, and manage software that runs on AWS: https://go.aws/marketplace
Join the AWS Partner Network: https://go.aws/partners
Learn more on how Amazon builds and operates software: https://go.aws/library
Do you have technical AWS questions?
Ask the community of experts on AWS re:Post: https://go.aws/3lPaoPb
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—use AWS to be more agile, lower costs, and innovate faster.
#AWSTutorial #AmazonDocumentDB #AWS #AmazonWebServices #CloudComputing
