Unable to Upload Video Generated From Galaxy Note 8 to S3 Bucket

Go the Nearly out of AWS S3 With these FAQs

Well-nigh everyone who's used Amazon Web Services has used AWS S3. In the decade since it was first released, S3 storage has become essential to thousands of companies for file storage. While using S3 in simple ways is like shooting fish in a barrel, at larger scale it involves a lot of subtleties and potentially costly mistakes, specially when your data or squad are scaling up.

Sadly, as with much of AWS, we oft learn some of these tips the hard way, when nosotros've made mistakes or wish we'd done things differently. Without farther ado, here are the x things near AWS S3 that volition help you avoid plush mistakes. Nosotros've assembled these tips from our own experience and the collective wisdom of several engineering friends and colleagues.

Operational visibility from AWS

Machine data holds hidden secrets that deliver truthful insights about the operational health of your AWS infrastructure. Acquire more nearly operational visibility from AWS today!

How to amend S3 performance by getting log data into and out of S3 faster

Getting data into and out of AWS S3 takes time. If yous're moving data on a frequent basis, at that place'southward a practiced gamble yous tin speed it upwards. Cutting downward time you lot spend uploading and downloading files can be remarkably valuable in indirect ways — for example, if your squad saves 10 minutes every time y'all deploy a staging build, you are improving engineering productivity significantly.

S3 is highly scalable, so in principle, with a big enough pipe or enough instances, y'all can get arbitrarily loftier throughput. A good case is S3DistCp, which uses many workers and instances. But nearly ever you're hit with ane of two bottlenecks:

The size of the pipage betwixt the source (typically a server on premises or EC2 instance) and S3.
The level of concurrency used for requests when uploading or downloading (including multipart uploads).

How to improve S3 latency by paying attention to regions and connectivity

The get-go takeaway from this is that regions and connectivity thing. Obviously, if you lot're moving data within AWS via an EC2 example or through various buckets, such equally off of an EBS volume, y'all're meliorate off if your EC2 example and S3 region represent. More surprisingly, even when moving information within the same region, Oregon (a newer region) comes in faster than Virginia on some benchmarks.

If your servers are in a major data heart simply not in EC2, you lot might consider using DirectConnect ports to get significantly higher bandwidth (you pay per port). Alternately, y'all can use S3 Transfer Acceleration to go data into AWS faster simply by changing your API endpoints. You take to pay for that too, the equivalent of 1-ii months of storage price for the transfer in either direction. For distributing content quickly to users worldwide, call back you can use BitTorrent support, CloudFront, or another CDN with S3 as its origin.

How to Amend S3 performance by using higher bandwidth networks

Secondly, case types matter. If you're using EC2 servers, some case types have higher bandwidth network connectivity than others. You can see this if you sort by "Network Performance" on the excellent ec2instances.info list.

How to use concurrency to meliorate AWS S3 latency and performance

Thirdly, and critically if you are dealing with lots of items, concurrency matters. Each S3 operation is an API asking with pregnant latency — tens to hundreds of milliseconds, which adds up to pretty much forever if you lot have millions of objects and try to work with them one at a time. And then what determines your overall throughput in moving many objects is the concurrency level of the transfer: How many worker threads (connections) on 1 instance and how many instances are used.

Many common AWS S3 libraries (including the widely used s3cmd) do not by default brand many connections at in one case to transfer data. Both s4cmd and AWS' ain aws-cli do make concurrent connections, and are much faster for many files or large transfers (since multipart uploads allow parallelism).

Another approach is with EMR, using Hadoop to parallelize the problem. For multipart syncs or uploads on a college-bandwidth network, a reasonable role size is 25–50MB. Information technology's also possible to listing objects much faster, too, if you traverse a folder hierarchy or other prefix bureaucracy in parallel.

Finally, if yous really have a ton of information to move in batches, just transport it.

What is AWS S3 information optimization and how to improve lifecycles up front

Okay, we might have gotten ahead of ourselves. Before you put something in AWS S3 in the commencement identify, there are several things to call up most. I of the most important is a uncomplicated question:

When and how should this object be deleted?

Remember, large data will probably elapse — that is, the price of paying Amazon to store it in its current form will go college than the expected value it offers your business organization. You might re-process or amass information from long agone, but it's unlikely you desire raw unprocessed logs or builds or athenaeum forever.

At the time you are saving a piece of data, information technology may seem like you can but decide afterwards. Most files are put in S3 by a regular process via a server, a data pipeline, a script, or fifty-fifty repeated human processes — but you've got to remember through what's going to happen to that data over fourth dimension.

In our experience, about AWS S3 users don't consider lifecycle up front, which means mixing files that take short lifecycles together with ones that have longer ones. By doing this y'all incur meaning technical debt effectually data system (or equivalently, monthly debt to Amazon!).

Once you lot know the answers, you'll observe managed lifecycles and AWS S3 object tagging are your friends. In particular, you want to delete or annal based on object tags, then it's wise to tag your objects appropriately so that it is easier to utilise lifecycle policies. Information technology is important to mention that S3 tagging has maximum limit of x tags per object and 128 unicode character. (We'll return to this in Tip four and Tip 5.)

Y'all'll also desire to consider compression schemes. For large information that isn't already compressed, you virtually certainly desire to — S3 bandwidth and cost constraints generally brand compression worth it. (Likewise consider what tools volition read it. EMR supports specific formats like gzip, bzip2, and LZO, so information technology helps to pick a compatible convention.)

When and how is AWS S3 object modified?

Every bit with many applied science problems, adopt immutability when possible — design so objects are never modified, but merely created and later deleted. Still, sometimes mutability is necessary. If S3 is your sole copy of mutable log data, you should seriously consider some sort of backup — or locate the data in a bucket with versioning enabled.

If all this seems similar it's a headache and hard to certificate, information technology'south a good sign no one on the squad understands it. By the time y'all scale to terabytes or petabytes of information and dozens of engineers, it'll exist more painful to sort out.

How to prioritize access command, encryption, and compliance with AWS S3

This AWS S3 FAQ is the least sexy, and possibly the most important 1 here. Before yous put something into S3, ask yourself the post-obit questions:

Are there people who should not be able to modify this data?
Are there people who should not be able to read this data?
How are the latter admission rules likely to alter in the futurity?
Should the information be encrypted? (And if so, where and how will we manage the encryption keys?)
Are at that place specific compliance requirements?

At that place'southward a good risk your answers are, "I'grand not sure. Am I actually supposed to know that?"

Well … yeah, you have to.

Some data is completely non-sensitive and can exist shared with any employee. For these scenarios the answers are easy: Just put information technology into S3 without encryption or complex admission policies. However, every business has sensitive data — it'southward merely a affair of which data, and how sensitive information technology is. Determine whether the answers to any of these questions are "yes."

The compliance question can besides exist confusing. Ask yourself the following:

Does the data you're storing incorporate fiscal, PII, cardholder, or patient information?
Do y'all have PCI, HIPAA, SOX, or EU Safe Harbor compliance requirements? (The latter has get rather circuitous recently.)
Practice you accept customer data with restrictive agreements in identify — for example, are y'all promising customers that their data is encrypted in at residue and in transit? If the answer is yes, you may need to piece of work with (or become!) an proficient on the relevant type of compliance and bring in services or consultants to help if necessary.

Minimally, you'll probably want to store information with different needs in separate S3 buckets, regions, and/or AWS accounts, and ready documented processes around encryption and admission command for that data.

It'due south not fun earthworks through all this when all you want to do is save a fiddling bit of data, but trust u.s., it'll save in the long run to think about it early.

How to use nested S3 folder organization and common problems

Newcomers to S3 are always surprised to learn that latency on S3 operations depends on key names since prefix similarities go a clogging at more than about 100 requests per second. If you have need for high volumes of operations, information technology is essential to consider naming schemes with more variability at the beginning of the cardinal names, similar alphanumeric or hex hash codes in the first 6 to eight characters, to avoid internal "hot spots" within S3 infrastructure.

This used to be in conflict with Tip 2 before declaration of new S3 storage direction features such as object tagging. If you've thought through your lifecycles, yous probably desire to tag objects and then you can automatically delete or transition objects based on tags, for instance setting a policy similar "archive everything with object tag raw to Glacier later 3 months."

In that location's no magic bullet here, other than to decide upwardly front which you lot care most more for each type of data: Easy-to-manage policies or high-volume random-access operations?

A related consideration for how you organize your data is that it'due south extremely slow to crawl through millions of objects without parallelism. Say you want to tally up your usage on a bucket with x million objects. Well, if you don't have whatsoever idea of the structure of the information, adept luck! If you have a sane tagging, or if you have uniformly distributed hashes with a known alphabet, it's also possible to parallelize.

Go up to 70% cost savings

Try Sumo Logic today to begin optimizing your AWS costs.

How to save money with Reduced Redundancy, Exceptional Access, or Glacier

S3's "Standard" storage course offers very high durability (it advertises 99.999999999% immovability, or "11 9s"), high availability, depression latency admission, and relatively cheap admission cost.

There are three ways you tin store data with lower toll per gigabyte:

S3's Reduced Back-up Storage (RRS) has lower durability (99.99%, so just four nines). That is, there'southward a good chance you'll lose a small amount of data. For some datasets where information has value in a statistical way (losing say half a percent of your objects isn't a big deal), this is a reasonable merchandise-off.
S3's Infrequent Access (IA) (confusingly as well called "Standard – Infrequent Access") lets you get cheaper storage in exchange for more expensive access. This is groovy for athenaeum like logs yous already processed only might desire to look at afterwards.
Glacier gives you much cheaper storage with much slower and more expensive admission. It is intended for archival usage.

Amazon's pricing pages are far from effortless to read, so hither'due south an illustrative comparing as of Baronial 2016 for the Virginia region:

A mutual policy that saves money is to prepare upwardly managed lifecycles that migrate Standard storage to IA and and then from IA to Glacier.

How to organize S3 information along the right axes

I of the most common oversights is to organize information in a way that causes concern risks or costs subsequently. You might initially assume data should exist stored according to the type of data, or the production, or by team, merely often that'southward not plenty.

Information technology's usually best to organize your information into different buckets and paths at the highest level not on what the data is itself, simply rather by considering these axes:

Sensitivity: Who can and cannot access information technology? (East.g. is it helpful for all engineers or just a few admins?)
Compliance: What are necessary controls and processes? (East.g. is information technology PII?)
Lifecycle: How will it be expired or archived? (East.g. is it verbose logs just needed for a month, or of import financial information?)
Realm: Is it for internal or external apply? For development, testing, staging, product?
Visibility: Practice I need to rails usage for this category of data exactly?

Nosotros've already discussed the first three. The concept of a realm is just that you lot ofttimes want to partition things in terms of process: For example, to brand sure no 1 puts test data into a production location. It'south all-time to assign buckets and prefixes by realm upwardly front.

The final signal is a technical one: If you lot desire to track usage, AWS offers piece of cake usage reporting at the bucket level. If you put millions of objects in one bucket, tallying usage by prefix or other ways can exist cumbersome at all-time, then consider individual buckets where y'all desire to rail meaning S3 usage or you can use a log analytics solution like Sumo Logic to analyze your S3 logs.

Why you should avert using AWS S3 locations into your code

This is pretty simple, but information technology comes upward a lot. Don't difficult-code S3 locations in your code. This is tying your code to deployment details, which is almost guaranteed to injure you later on. You might want to deploy multiple production or staging environments. Or you might desire to migrate all of ane kind of information to a new location, or audit which pieces of lawmaking admission certain data.

Decouple code and S3 locations. Especially if you lot follow Tip half-dozen, this will also assistance with exam releases, or unit or integration tests then they use unlike buckets, paths, or mocked S3 services. Ready upwardly some sort of configuration file or service, and read S3 locations similar buckets and prefixes from that.

How to deploy your own testing or production alternatives to S3

There are many services that are (more or less) compatible with S3 APIs. This is helpful both for testing and for migration to local storage. Unremarkably used tools for small examination deployments are S3Proxy (Coffee) and FakeS3 (Blood-red), which can brand it far easier and faster to examination S3-dependent code in isolation. More full-featured object storage servers with S3 compatibility include Minio (in Get), Ceph (C++/Terra), and Riak CS (Erlang).

Many big enterprises have private cloud needs and deploy AWS-compatible cloud components, including layers corresponding to AWS S3, in their own private clouds, using Eucalyptus and OpenStack. These are non quick and piece of cake to set but are mature open-source private cloud systems.

Why should evaluate newer tools for mapping filesystem and AWS S3 information

1 tool that's been around a long time is s3fs, the FUSE filesystem that lets you lot mount S3 equally a regular filesystem in Linux and Mac OS. Disappointingly, it turns out this is oftentimes more than of a novelty than a good idea, as S3 doesn't offer all the right features to arrive a robust filesystem: Appending to a file requires rewriting the whole file, which cripples performance, there is no atomic rename of directories or mutual exclusion on opening files, and a few other problems.

That said, in that location are some other solutions that use a unlike object format and let filesystem-similar access. Riofs (C) and Goofys (Become) are more than recent implementations that are generally improvements on s3fs. S3QL is a Python implementation that offers data de-duplication, snap-shotting, and encryption. It only supports 1 client at a time, however. A commercial solution that offers lots of filesystem features and concurrent clients is ObjectiveFS.

Some other utilize case is filesystem backups to S3. The standard approach is to utilize EBS volumes and employ snapshots for incremental backups, but this does not fit every use instance. Open source backup and sync tools include zbackup (deduplicating backups, inspired by rsync, in C++), restic (deduplicating backups, in Go), borg (deduplicating backups, in Python), and rclone (data syncing to deject) can be used in conjunction with S3.

Don't employ S3 if some other solution is better

Consider that S3 may not be the optimal choice for your use instance. Equally discussed, Glacier and cheaper S3 variants are peachy for cheaper pricing. EBS and EFS can be much more suitable for random-access data, only price 3 to 10 times more per gigabyte (come across the table above).

Traditionally, EBS (with regular snapshots) is the option of pick if y'all need a filesystem brainchild in AWS. Call up EBS has a very high failure rate compared to S3 (0.1-0.2% per yr), so you lot need to use regular snapshots. Y'all can only attach 1 instance to an EBS volume at a time. Withal, with the release of EFS, AWS' new network file service (NFS v4.1) there is another option that allows up to thousands of EC2 instances to connect to the aforementioned drive meantime — if you can beget information technology.

Of course, if you lot're willing to shop data exterior AWS, the directly competitive cloud options include Google Cloud Storage, Azure Blob Storage, Rackspace Cloud Files, EMC Atmos, and BackBlaze B2. (Note: BackBlaze has a unlike architecture that offloads some work to the client, and is significantly cheaper.)

Bonus Tips: Two AWS S3 bug yous no longer need to worry virtually

A few AWS "gotchas" are significant enough people remember them years afterwards, even though they are no longer relevant. Two long-hated AWS S3 limitations you might remember or have heard rumors of accept (finally!) gone away:

For many years, there was a hard 100-saucepan limit per account, which acquired many companies significant pain. Yous'd blithely exist calculation buckets, and and so slam into this limit, and exist stuck until you created a new business relationship or consolidated buckets. Every bit of 2015, it can at present be raised if yous ask Amazon nicely. (Up to k per business relationship; it'south nevertheless not unlimited every bit buckets are in a global namespace.)
For a long time, the data consistency model in the original 'united states-standard' region was dissimilar and more lax than in the other (newer) S3 regions. Since 2015, this is no longer the case. All regions have read-afterward-write consistency.

AWS S3 fabricated like shooting fish in a barrel with Sumo Logic

With Sumo Logic, y'all tin can finally become a 360 degree view of all of your AWS S3 data. Leveraging these powerful AWS S3 monitoring tools y'all can alphabetize, search, and perform deeper and more comprehensive analysis of performance and access/audit log information. To learn more than, sign upward for a free trial of Sumo Logic.

S3 log management made easy

Effort Sumo Logic today and run across how yous tin can hands track meaning S3 usage

phillipslegreasing.blogspot.com

Source: https://www.sumologic.com/insight/10-things-might-not-know-using-s3/