I have a use case where I have to back up a 200+TB, 18M object S3 bucket to another account that changes often (used in batch processing of critical data). I need to add a verification step, but due to the large size of both bucket, object count, and frequency of change this is tricky.
My current thoughts are to pull the eTags from the original bucket and archive bucket, and the write a streaming diff tool to compare the values. Has anyone here had to approach this problem and if so did you come up with a better answer?
Firstly, if you wish to keep two buckets in sync (once you've done the initial sync), you can use Cross-Region Replication (CRR).
To do the initial sync, you could try using the AWS Command-Line Interface (CLI), which has a aws s3 sync command. However, it might have some difficulties with a large number of files -- I suggest you give it a try. It uses keys, dates and filesize to determine which files to sync.
If you do wish to create your own sync app, then eTag is definitely a definitive way to compare files.
To make things simple, activate Amazon S3 Inventory, which can provide a daily listing of all files in a bucket, including eTag. You could then do a comparison between the Inventory files to discover which remaining files require synchronization.
For anyone looking for a way to solve this problem in an automated way (as was I),
I created a small python script that leverages S3 Inventories and Athena to do the comparison somewhat efficiently. (This is basically automation of John Rosenstein's suggestion)
You can find it here https://github.com/forter/s3-compare
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I have a TB sized S3 bucket with pdf files. I need to migrate the old files to glacier. I know that I can create a life cycle rule to migrate files which are older than certain number of days. But in my case currently the bucket consists of both old and new pdf files and they were added at a same time. So they may have same uploaded date. In this case a life cycle rule won't be useful.
In the pdf files there is a field called capture_date. So i need to migrate those files based on the capture_date. (ie: migrate all pdf files if the capture_date < 2015-05-21 likewise).
Will a Fargate job will be useful here? if so, please give a brief idea.
Please suggest your ideas. Thanks in advance
S3 by itself will not read your pdf files. Thus you have to read them yourself, extract data that determine which ones are old and new, and using AWS SDK (or CLI) to move them to Glacier.
Since the files are not too big, you could use S3 Batch along with lambda function which would do the change of the class to glacier.
Alternatively, you could do this on an EC2 instance, using S3 Inventory's CSV list of your objects (assuming large number of them).
And the most traditional way is to just list your bucket, and iterate over each object.
I have a single bucket with a large number of very small text files (betwen 500 bytes to 1.2k). This bucket currently contains over 1.7 Million files and will be ever increasing.
The way that I add data to this bucket is by generating batches of files (in the order 50.000 files) and transfering those files into the bucket.
Now the problem is this. If I transfer the files one by one in a loop it takes an unbareably long amount of time. So if all the files a in a directory origin_directory I would do
aws s3 cp origin_directory/filename_i s3://my_bucket/filename_i
I would do this command 50000 times.
Right now I'm testing this on a set of about 280K files. Doing this would take approximately 68 hours according to my calculations. However I found out that I can sync:
aws s3 sync origin_directory s3://my_bucket/
Now this, works much much faster. (Will take about 5 hours, according to my calculations). However, the sync needs to figure out what to copy (files present in the directory and not present in the bucket). Since the files in the bucket will be ever increasing, I'm thinking that this will take longer and longer as times moves on.
However, since I delete the information after every sync, I know that the sync operation needs to transfer all files in that directory.
So my question is, is there a way to start a "batch copy" similar to the sync, without actually doing the sync?
You can use:
aws s3 cp --recursive origin_directory/ s3://my_bucket/
This is the same as a sync, but it will not check whether the files already exist.
Also, see Use of Exclude and Include Filters to learn how to specify wildcards (eg all *.txt files).
When copying a large number of files using aws s3 sync or aws s3 cp --recursive, the AWS CLI will parallelize the copying, making it much faster. You can also play with the AWS CLI S3 Configuration to potentially optimize it for your typical types of files (eg copy more files simultaneously).
try using https://github.com/mondain/jets3t
it does this same function but works in parallel, so it will complete the job much faster.
I am trying to find possible orphans in an S3 bucket. What I mean is that we might delete something out of the DB, and for whatever reason, it doesn't get cleared from S3. This can be a bug in our system or something of that nature. I want to double check against our API that the object in S3 maps to something that exists - the naming convention let's us map things together like that.
Scraping an entire bucket every X days seems unscalable. I was thinking that for each object in the bucket, it can add itself to an SQS queue for the relevant checking to happen, every 30 days or so.
I've only found events around uploads and specific modifications over at https://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html. Is there anything more generalized I can't find? Any creative solutions to this problem?
You should activate Amazon S3 Inventory, which can provide a regular CSV file (as often as daily) that contains a list of every object in the Amazon S3 bucket.
You could then trigger some code that compares the contents of the CSV file against the database to find 'orphan' objects.
I have 2 million zipped HTML files (100-150KB) being added each day that I need to store for a long time.
Hot data (70-150 million) is accessed semi regularly, anything older than that is barely ever accessed.
This means each day I'm storing an additional 200-300GB worth of files.
Now, Standard storage costs $0.023 per GB and $0.004 for Glacier.
While Glacier is cheap, the problem with it is that it has additional costs, so it would be a bad idea to dump 2 million files into Glacier:
PUT requests to Glacier $0.05 per 1,000 requests
Lifecycle Transition Requests into Glacier $0.05 per 1,000 requests
Is there a way of gluing the files together, but keeping them accessible individually?
An important point, that if you need to provide quick access to these files, then Glacier can give you access to the file in up to 12 hours. So the best you can do is to use S3 Standard – Infrequent Access (0,0125 USD per GB with millisecond access) instead of S3 Standard. And maybe for some really not using data Glacier. But it still depends on how fast do you need that data.
Having that I'd suggest following:
as html (text) files have a good level of compression, you can compress historical data in big zip files (daily, weekly or monthly) as together they can have even better compression;
make some index file or database to know where each html-file is stored;
read only desired html-files from archives without unpacking whole zip-file. See example in python how to implement that.
Glacier would be extremely cost sensitive when it comes to the number of files. The best method would be to create a Lambda function that handles zip, unzip operations for you.
Consider this approach:
Lambda creates archive_date_hour.zip of the 2 Million files from that day by hour, this solves the "per object" cost problem by creating 24 giant archival files.
Set a policy on the s3 bucket to move expired objects to glacier over 1 day old.
Use an unzipping Lambda function to fetch and extract potential hot items from the glacier bucket from within the zip files.
Keep the main s3 bucket for hot files with high frequent access, as a working directory for the zip/unzip operations, and for collecting new files daily
Your files are just too small. You will need to combine them probably in an ETL pipeline such as glue. You can also use the Range header i.e. -range bytes=1000-2000 to download part of an object on S3.
If you do that you'll need to figure out the best way to track the bytes ranges, such as after combining the files recording the range for each one, and changing the clients to use the range as well.
The right approach though depends on how this data is accessed and figuring out the patterns. If somebody who looks at TinyFileA also looks at TinyFileB you could combine them together and just send them both along with other files they are likely to use. I would be figuring out logical groupings of files which make sense to consumers and will reduce the number of requests they need, without sending too much irrelevant data.
Lately, we've noticed that our AWS bill has been higher than usual. It's due to adding an aws s3 sync task to our regular build process. The build process generates something around 3,000 files. After the build, we run aws s3 sync to upload them en masse into a bucket. The problem is that this is monetarily expensive. Each upload is costing us a ~$2 (we think) and this adds up to a monthly bill that raises the eyebrow.
All but maybe 1 or 2 of those files actually change from build to build. The rest are always the same. Yet aws s3 sync sees that they all changed and uploads the whole lot.
The documentation says that aws s3 sync compares the file's last modified date and byte size to determine if it should upload. The build server creates all those files brand-new every time, so the last modified date is always changed.
What I'd like to do is get it to compute a checksum or a hash on each file and then use that hash to compare the files. Amazon s3 already has the etag field which is can be an MD5 hash of the file. But the aws s3 sync command doesn't use etag.
Is there a way to use etag? Is there some other way to do this?
The end result is that I'd only like to upload the 1 or 2 files that are actually different (and save tremendous cost)
The aws s3 sync command has a --size-only parameter.
From aws s3 sync options:
--size-only (boolean) Makes the size of each key the only criteria used to decide whether to sync from source to destination.
This will likely avoid copying all files if they are updated with the same content.
As an alternative to s3 sync or cp you could use s5cmd
https://github.com/peak/s5cmd
This is able to sync files on the size and date if different, and also has speeds of up to 4.6gb/s
Example of the sync command:
AWS_REGION=eu-west-1 /usr/local/bin/s5cmd -stats cp -u -s --parents s3://bucket/folder/* /home/ubuntu
S3 charges $0.005 per 1,000 PUT requests (doc), so it's extremely unlikely that uploading 3,000 files is costing you $2 per build. Maybe $2 per day if you're running 50-100 builds a day, but that's still not much.
If you really are paying that much per build, you should enable CloudTrail events and see what is actually writing that much (for that matter, maybe you've created some sort of recursive CloudTrail event log).
The end result is that I'd only like to upload the 1 or 2 files that are actually different
Are these files the artifacts produced by your build? If yes, why not just add a build step that copies them explicitly?
The issue that I got was using wildcard * in the --include option. Using one wildcard was fine but when I added the second * such as /log., it looked like sync tried to download everything to compare, which took a lot of CPU and network bandwidth.