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Maximizing Amazon S3 Performance

Craig Carl, AWS

November 15, 2013

Trillions Of Unique Customer Objects

Q4 2006

Q1 2007

Q2 2007

Q3 2007

Q4 2007

Q1 2008

Q2 2008

Q3 2008

Q4 2008

Q1 2009

Q2 2009

Q3 2009

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Q1 2010

Q2 2010

Q3 2010

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Q1 2011

Q2 2011

Q3 2011

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Q1 2012

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Q3 2012

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Q1 2013

Q2 2013

Q3 2013

1.5 Million+ Peak Transactions Per Second

Architecture Optimizing PUTs Optimizing GETs

Choosing a region

Building a naming scheme

Considering LISTs

Multipart upload Using CloudFront

Range-based GETs

Choosing a Region

• Performance – Proximity to your users

– Co-locating with compute, other AWS resources

• Other things to think about – Legal and regulatory requirements

– Costs vary by region

Pay Attention to Your Naming Scheme If:

• You want consistent performance from a bucket

• You want a bucket capable of routinely

exceeding 100 TPS

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Transactions Per Second (TPS)

1 2

5 8

100/8 = 12.5 events/sec

100,000 users @ 10 events an hour = 224 TPS

Distributing Key Names

• Don’t do this

<my_bucket>/2013_11_13-164533125.jpg <my_bucket>/2013_11_13-051033564.jpg <my_bucket>/2013_11_13-061133789.jpg <my_bucket>/2013_11_13-051033458.jpg <my_bucket>/2013_11_12-063433125.jpg <my_bucket>/2013_11_12-021033564.jpg <my_bucket>/2013_11_12-065533789.jpg <my_bucket>/2013_11_12-011033458.jpg <my_bucket>/2013_11_11-022333125.jpg <my_bucket>/2013_11_11-153433564.jpg <my_bucket>/2013_11_11-065233789.jpg <my_bucket>/2013_11_11-065633458.jpg

Distributing Key Names

• Add randomness to the beginning of the key name

<my_bucket>/521335461-2013_11_13.jpg <my_bucket>/465330151-2013_11_13.jpg <my_bucket>/987331160-2013_11_13.jpg <my_bucket>/465765461-2013_11_13.jpg <my_bucket>/125631151-2013_11_13.jpg <my_bucket>/934563160-2013_11_13.jpg <my_bucket>/532132341-2013_11_13.jpg <my_bucket>/565437681-2013_11_13.jpg <my_bucket>/234567460-2013_11_13.jpg <my_bucket>/456767561-2013_11_13.jpg <my_bucket>/345565651-2013_11_13.jpg <my_bucket>/431345660-2013_11_13.jpg

Other Techniques for Distributing Key Names

• Store objects as a hash of their name – add the original name as metadata

• “deadmau5_mix.mp3” 0aa316fb000eae52921aab1b4697424958a53ad9

– watch for duplicate names!

– prepend keyname with short hash

• 0aa3-deadmau5_mix.mp3

• Epoch time (reverse) – 5321354831-deadmau5_mix.mp3

Randomness in a Key Name Can Be an Anti-Pattern

• Lifecycle policies

• LISTs with prefix filters

• Maintaining thumbnails of images – craig.jpg -> stored as orig-09329jed0fc

– thumb-09329jed0fc

• When you need to recover a file with its original

name

Solving for the Anti-Pattern

• Add additional prefixes to help sorting

• Amazon S3 maintains keys lexicographically in its

internal indices

<my_bucket>/images/521335461-2013_11_13.jpg <my_bucket>/images/465330151-2013_11_13.jpg <my_bucket>/movies/293924440-2013_11_13.jpg <my_bucket>/movies/987331160-2013_11_13.jpg <my_bucket>/thumbs-small/838434842-2013_11_13.jpg <my_bucket>/thumbs-small/342532454-2013_11_13.jpg <my_bucket>/thumbs-small/345233453-2013_11_13.jpg <my_bucket>/thumbs-small/345453454-2013_11_13.jpg

Distributing Your Key Names Is Always a Good Idea!

It can take some time for improvements to manifest

Open a support case if you need an immediate bump

or if you’ve got any questions!

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Amazon CloudFront

Using Amazon CloudFront for Distribution

• Caches objects from Amazon S3

• Reduces the number of Amazon S3 GETs

• Low latency with multiple endpoints

• High transfer rate

• Two flavors: – Web distribution (static content)

– RTMP distribution (on-demand streaming of media)

Multipart Upload Provides Parallelism

• Allows faster, more flexible uploads

• Allows you to upload a single object as a set of parts

• Upon upload, Amazon S3 then presents all parts as a single object

• Enables parallel uploads, pausing and resuming an object upload, and beginning uploads before you know the total object size

Choose the Right Part Size

• Strike a balance between part size and number of parts

– Lots of small parts increase connection overhead, invalidating the benefits

of parallelism

– Too few large parts don’t get you enough benefits of multipart; don’t get you

resiliency to network errors

• We recommend parts of 25–50 MB on higher-bandwidth networks and parts of 10 MB on mobile networks

You Can Parallelize Your GETs, Too

• Use range-based GETs to get multithreaded performance when downloading objects

• Compensates for unreliable networks

• Benefits of multithreaded parallelism

• Align your ranges with your parts!

If you’re using SSL and parallelizing…

• You’re likely to become CPU-constrained because encryption is CPU-intensive

• Amazon S3 recommends using AES-256 to optimize for security and performance

• You can leverage AES-NI hardware on your host to improve your performance

If Your Application Relies on LIST…

• Getting the objects your customers have stored

• Seeing sets of files (all animations, videos)

• Getting logs

• Viewing inventories

• Sorting keys based on metadata

What Should You Do?

• Parallelize LIST when you need a sequential list of your keys

• You should build a secondary index of your keys, such as with Amazon DynamoDB, to get a faster alternative to LIST when a sequential list isn’t sufficient – Sorting by metadata

– Looking up by category

– Objects by time stamp

LIST Operations with Amazon DynamoDB

• Maintain metadata in DynamoDB – Keep data about what’s in your buckets in DynamoDB

• On PUTs, enter data about your objects in DynamoDB

• On GETs, use DynamoDB to assist in your search for specific objects

• You can use DynamoDB to give you “LIST” based on specific criteria

Wrap up: Maximizing Amazon S3 Performance

Architecture Optimizing PUTs Optimizing GETs

Choosing a region

Building a naming scheme

Considering LISTs

Multipart upload Using CloudFront

Range-based GETs

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