Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Transcript
This change includes the transcript for the podcast episode, created by Podwhisperer.
The summary below is generated by Episoder.
Episode Summary
In this episode, we discuss some tips and tricks for optimizing performance when working with Amazon S3 at scale. We start by giving an overview of how S3 works, highlighting the distributed nature of the service and how data is stored redundantly across multiple availability zones for durability. We then dive into specific tips like using multipart uploads and downloads, spreading load across key namespaces, enabling transfer acceleration, and using S3 byte-range fetches. Overall, we aim to provide developers building S3-intensive applications with practical guidance to squeeze the most performance out of the service.
Suggested Chapters
01:34 An overview of how S3 works at massive scale in terms of storage volume, request throughput, and data durability.
06:09 Tip 1 - Use multipart uploads and byte range fetches for large objects to improve throughput.
08:38 Tip 2 - Spread load across different key namespaces to avoid throttling.
Suggested Tags
S3, Amazon S3, AWS, performance, optimization, scale, throughput, latency, durability, availability, multipart upload, byte range fetch, concurrency, namespaces, transfer acceleration, cloud storage