I created a new Lambda based on a 2MB zip file (it has a heavy dependency). After that, my S3 costs really increased (from $12.27 to $31).
Question 1: As this is uploaded from a CI/CD pipeline, could it be that it's storing every version and then increasing costs?
Question 2: Is this storage alternative more expensive than choosing directly an owned s3 bucket instead of the private one owned by Amazon where this zip goes? Looking at the S3 prices list, only 2MB can't result in 19 Dollars.
Thanks!
few things you can do to mitigate cost:
Use Lambda Layers for dependencies
Use S3 Infrequent access for your lambda archive
Being that I don't have your full configuration of S3, its hard to tell what can be causing cost...things like S3 versioning would do it.
The reason was that Object versioning was enabled, and after some stress tests those versions were accumulated and stored. Costs went back to $12 after they were removed.
It's key to keep the "Show" enabled (see image) to keep track of those files.
Related
I need a cloud service for saving file objects for my project.
Requirements:
1. Pushing the files would be less compared to reading files from the storage.
2. Need to maintain versions of the file objects
3. File objects must be indexed for fast retrieval.
Note:
We have already considered using Amazon S3 bucket, but considering our project requirement, as reading the file objects would be 1 million times more than writing a file into storage.
As Amazon charges S3 usage more on the number of reads that writes it really is the last option for us to use.
Can anybody kindly provide suggestions on what can we use here?
Thanks!
We have a ton of S3 buckets and are in the process of cleaning things up. We identified Glacier as a good way to archive their data. The plan is to store the content of those buckets and then remove them.
It would be a one-shot operation, we don't need something automated.
I know that:
a bucket name may not be available anymore if one day we want to restore it
there's an indexing overhead of about 40kb per file which makes it a not so cost-efficient solution for small files and better to use an Infrequent access storage class or to zip the content
I gave it a try and created a vault. But I couldn't run the aws glacier command. I get some SSL error which is apparently related to a Python library, wether I run it on my Mac or from some dedicated container.
Also, it seems that it's a pain to use the Glacier API directly (and to keep the right file information), and that it's simpler to use it via a dedicated bucket.
What about that? Is there something to do what I want in AWS? Or any advice to do it in a not too fastidious way? What tool would you recommend?
Whoa, so many questions!
There are two ways to use Amazon Glacier:
Create a Lifecycle Policy on an Amazon S3 bucket to archive data to Glacier. The objects will still appear to be in S3, including their security, size, metadata, etc. However, their contents are stored in Glacier. Data stored in Glacier via this method must be restored back to S3 to access the contents.
Send data directly to Amazon Glacier via the AWS API. Data sent this way must be restored via the API.
Amazon Glacier charges for storage volumes, plus per request. It is less-efficient to store many, small files in Glacier. Instead, it is recommended to create archives (eg zip files) that make fewer, larger files. This can make it harder to retrieve specific files.
If you are going to use Glacier directly, it is much easier to use a utility, such as Cloudberry Backup, however these utilities are designed to backup from a computer to Glacier. They probably won't backup S3 to Glacier.
If data is already in Amazon S3, the simplest option is to create a lifecycle policy. You can then use the S3 management console and standard S3 tools to access and restore the data.
Using a S3 archiving bucket did the job.
Here is how I proceeded:
First, I created a S3 bucket called mycompany-archive, with a lifecycle rule that turns the Storage class into Glacier 1 day after the file creation.
Then, (with the aws tool installed on my Mac) I ran the following aws command to obtain the buckets list: aws s3 ls
I then pasted the output into an editor that can do regexp relacements, and I did the following one:
Replace ^\S*\s\S*\s(.*)$ by aws s3 cp --recursive s3://$1 s3://mycompany-archive/$1 && \
It gave me a big command, from which I removed the trailing && \ at the end, and the lines corresponding the buckets I didn't want to copy (mainly mycompany-archive had to be removed from there), and I had what I needed to do the transfers.
That command could be executed directly, but I prefer to run such commands using the screen util, to make sure the process wouldn't stop if I close my session by accident.
To launch it, I ran screen, launched the command, and then pressed CTRL+A then D to detach it. I can then come back to it by running screen -r.
Finally, under MacOS, I ran cafeinate to make sure the computer wouldn't sleep before it's over. To run it, issued ps|grep aws to locate the process id of the command. And then caffeinate -w 31299 (the process id) to ensure my Mac wouldn't allow sleep before the process is done.
It did the job (well, it's still running), I have now a bucket containing a folder for each archived bucket. Next step will be to remove the undesired S3 buckets.
Of course this way of doing could be improved in many ways, mainly by turning everything into a fault-tolerant replayable script. In this case, I have to be pragmatic and thinking about how to improve it would take far more time for almost no gain.
I have flat files in which I store data and retrieve it instead of storing to database. This is temporary and may last for couple of months.I was wondering If I should be using EBS or S3. EBS is mainly used for I/O , S3 for content delivery , but S3 is on use you go model and EBS is you have to pay for the volume purchased ?
Pls guide, which one is better ?
S3 sounds like it's more appropriate for your use case.
S3 is object storage. Think of it as an Amazon-run file server. (Objects are not exactly equal to files, but it's close enough here.) You tell S3 to put a file, it'll store it. You tell S3 to get a file, it'll get return it. You tell S3 to delete it, it's gone. This is easy to work with and very scalable.
EBS is block storage. Think of it as an Amazon-run external hard drive. You can plug an EBS volume into an EC2 virtual machine, or you access it over the Internet via AWS Storage Gateway. Like an external hard drive, you can only plug it into one computer at a time. The size is set up front, and while there are ways to grow and shrink it, you're paying for all the bits all the time. It's also much more complex than S3, since it has to provide strong consistency guarantees for the entire volume, instead of just on a file-by-file basis.
To build on the good answer from willglynn. If you are interacting with the data regularly, or need more file-system-like access you might consider EBS more strongly.
If the amount of data is relatively small and you read and write to the data store regularly, you might consider something like elasticache for in-memory storage which would likely be superior performance-wise then using s3 or EBS.
Similarly, you might look at DynamoDb for document type storage, especially if you need to be able to search/filter across your data objects.
Point 1) You can use both S3 and EBS for this option. If you want reduced latency and file sizes are bigger then EBS is better option.
Point 2) If you want lower costs, then S3 is a better option.
From what you describe, S3 will be the most cost-effective and likely easiest solution.
Pros to S3:
1. You can access the data from anywhere. You don't need to spin up an EC2 instance.
2. Crazy data durability numbers.
3. Nice versioning story around buckets.
4. Cheaper than EBS
Pros to EBS
1. Handy to have the data on a file system in EC2. That let you do normal processing with the Unix pipeline.
2. Random Access patterns work as you would expect.
3. It's a drive. Everyone knows how to deal with files on drives.
If you want to get away from a flat file, DynamoDB provides a nice set of interfaces for putting lots and lots of rows into a table, then running operations against those rows.
I am migrating my Java,Tomcat, Mysql server to AWS EC2.
I have already attached EBS volume for storing MySql data. In my web application people may upload images. So I should persist them. There are 2 alternatives in my mind:
Save uploaded images to EBS volume.
Use the S3 service.
The followings are my notes, please be skeptic about them, as my expertise is not on servers, but software development.
EBS plus: S3 storage is more expensive. (0.15 $/Gb > 0.1$/Gb)
S3 plus: Serving statics from EBS may influence my web server's performance negatively. Is this true? Does Serving images affect server performance notably? For S3 my server will not be responsible for serving statics.
S3 plus: Serving statics from EBS may result I/O cost, probably it will be minor.
EBS plus: People say EBS is faster.
S3 plus: People say S3 is more safe for persistence.
EBS plus: No need to learn API, it is straight forward to save the images to EBS volume.
Namely I can not decide, will be happy if you guide.
Thanks
The price comparison is not quite right:
S3 charges are $0.14 per GB USED, whereas EBS charges are $0.10 per GB PROVISIONED (the size of your EBS volume), whether you use it or not. As a result, S3 may or may not be cheaper than EBS.
I'm currently using S3 for a project and it's working extremely well.
EBS means you need to manage a volume + machines to attach it to. You need to add space as it's filling up and perform backups (not saying you shouldn't back up your S3 data, just that it's not as critical).
It also makes it harder to scale: when you want to add additional machines, you either need to pull off the images to a separate machine or clone the images across all. This also means you're adding a bottleneck: you'll have to manage your own upload process that will either upload to all machines or have a single machine managing it.
I recommend S3: it's set and forget. Any number of machines can be performing uploads in parallel and you don't really need to notify other machines about the upload.
In addition, you can use Amazon Cloudfront as a cheap CDN in front of the images instead of directly downloading from S3.
I have architected solutions on AWS for Stock photography sites which stores millions of images spanning TB's of data, I would like to share some of the best practice in AWS for your requirement:
P1) Store the Original Image file in S3 Standard option
P2) Store the reproducible images like thumbs etc in the S3 Reduced Redundancy option (RRS) to save costs
P3) Meta data about images including the S3 URL can be stored in Amazon RDS or Amazon DynamoDB depending upon the query complexity. Query the entries from Amazon RDS. If your query is complex it is also common practice to Store the meta data in Amazon CloudSearch or Apache Solr.
P4) Deliver your thumbs to users with low latency using Amazon CloudFront.
P5) Queue your image conversion either thru SQS or RabbitMQ on Amazon EC2
P6) If you are planning to use EBS, then they are not scalable with your EC2. So ideally you can use GlusterFS as your common storage pool for all your images. Multiple Amazon EC2 in Auto Scaled mode can still connect to it and access/write images.
You already outlined the advantages and disadvantages of both.
If you are planning to store terabytes of images, with storage requirements increasing day after day, S3 will probably be your best bet as it is built especially for these kinds of situations. You get unlimited storage space, without having to worry about sharding your data over many EBS volumes.
The recurrent cost of S3 is that it comes 50% more expensive than EBS. You will also have to learn the API and implement it in your application, but that is a one-off expense which I think you should be able to absorb very quickly.
Do you expect the images to last indefinitely?
The Amazon EBS FAQ is pretty clear; the annual failure rate is not "essentially zero"; they quote 0.1% to 0.5%. It's better than the disk under your desk, but it would need some kind of backup.
What is the easiest way to duplicate an entire Amazon S3 bucket to a bucket in a different account?
Ideally, we'd like to duplicate the bucket nightly to a different account in Amazon's European data center for backup purposes.
One thing to consider is that you might want to have whatever is doing this running in an Amazon EC2 VM. If you have your backup running outside of Amazon's cloud then you pay for the data transfer both ways. If you run in an EC2 VM, you pay no bandwidth fees (although I'm not sure if this is true when going between the North American and European stores) - only for the wall time that the EC2 instance is running (and whatever it costs to store the EC2 VM, which should be minimal I think).
Cool, I may look into writing a script to host on Ec2. The main purpose of the backup is to guard against human error on our side -- if a user accidentally deletes a bucket or something like that.
If you're worried about deletion, you should probably look at S3's new Versioning feature.
I suspect there is no "automatic" way to do this. You'll just have to write a simple app that moves the files over. Depending on how you track the files in S3 you could move just the "changes" as well.
On a related note, I'm pretty sure Amazon does a darn good job backup up the data so I don't think you necessarily need to worry about data loss, unless your back up for archival purposes, or you want to safeguard against accidentally deleting files.
You can make an application or service that responsible to create two instances of AmazonS3Client one for the source and the other for the destination, then the source AmazonS3Client start looping in the source bucket and streaming objects in, and the destination AmazonS3Client streaming them out to the destination bucket.
Note: this doesn't work for cross-account syncing, but this works for cross-region on the same account.
For simply copying everything from one bucket to another, you can use the AWS CLI (https://aws.amazon.com/premiumsupport/knowledge-center/move-objects-s3-bucket/): aws s3 sync s3://SOURCE_BUCKET_NAME s3://NEW_BUCKET_NAME
In your case, you'll need the --source-region flag: https://docs.aws.amazon.com/cli/latest/reference/s3/sync.html
If you are moving an enormous amount of data, you can optimize how quickly it happens by finding ways to split the transfers into different groups: https://aws.amazon.com/premiumsupport/knowledge-center/s3-large-transfer-between-buckets/
There are a variety of ways to run this nightly. One is example is the AWS instance-schedule (personally unverified) https://docs.aws.amazon.com/solutions/latest/instance-scheduler/appendix-a.html