I am uploading a single large file to S3 (> 1GB) and cannot get throughput over 250MB/sec regardless of instance class.
I've checked all the obvious things: I have a VPC endpoint, I am using multipart upload (via AWS CLI aws s3 cp and aws s3 sync), and the instance has ENA enabled.
What's more, I can upload multiple files in parallel and achieve an aggregate bandwidth > 500MB/sec, effectively saturating my EBS volume's throughput, but I cannot get the same bandwidth on a single file.
I do not see any evidence of throttling with --debug output.
Support hasn't found an answer for me yet either, so I'm wondering if there's something obvious I'm missing with a limit on how quickly you can upload to a single object.
Related
For an AI project I want to train a model over a dataset which is about 300 GB. I want to use the AWS SageMaker framework.
In SageMaker documentation, they write that SageMaker can import data from AWS S3 bucket. Since the dataset is huge, I zipped it (to several zip files) and uploaded it to a S3 bucket. It took several hours. However, in order to use it I need to unzip the dataset. There are several options:
Unzip directly in S3. This might be impossible to do. See refs below.
Upload the uncompressed data directly, I tried it but it takes too much time and stopped in the middle, uploading only 9% of the data.
Uploading the data to a AWS EC2 machine and unzip it there. But can I import the data to SageMaker from EC2?
Many solutions offer a Python script that downloading the data from S3, unzipping it locally (on the desktop) and then streaming it back to the S3 bucket (see references below). Since I have the original files I can simply upload them to S3, but this takes too long (see 2).
Added in Edit:
I am now trying to upload the uncompressed data using AWS CLI V2.
References:
How to extract files in S3 on the fly with boto3?
https://community.talend.com/s/question/0D53p00007vCjNSCA0/unzip-aws-s3?language=en_US
https://www.linkedin.com/pulse/extract-files-from-zip-archives-in-situ-aws-s3-using-python-tom-reid
https://repost.aws/questions/QUI8fTOgURT-ipoJmN7qI_mw/unzipping-files-from-s-3-bucket
https://dev.to/felipeleao18/how-to-unzip-zip-files-from-s3-bucket-back-to-s3-29o9
The main strategy most commonly used, and also least expensive (since space has its own cost * GB), is not to use the space of the EC2 instance used for the training job but rather to take advantage of the high transfer rate from bucket to instance memory.
This is on the basis that the bucket resides in the same region as the EC2 instance. Otherwise you have to increase the transmission performance, for a fee of course.
You can implement all the strategies for reading files in parallel in your script or reads by chunks, but my advice is to use automated frameworks such as dask/pyspark/pyarrow (in case you need to read dataframes) or review the nature of the storage of these zippers if it can be transformed into a more facilitative form (e.g., a csv transformed into parquet.gzip).
If the nature of the data is different (e.g., images or other), an appropriate lazy data-loading strategy must be identified.
For example, for your zipper problem, you can easily get the list of your files from an S3 folder and read them sequentially.
You already have the data in S3 zipped. What's left is:
Provision a SageMaker notebook instance, or an EC2 instance with enough EBS storage (say 800GB)
Login to the notebook instance, open a shell, copy the data from S3 to local disk.
Unzip the data.
Copy unzip data back to S3.
terminate the instance and the EBS to avoid extra cost.
This should be fast (no less than 250MB/sec) as both the instance has high bandwidth to S3 within the same AWS Region.
Assuming you refer to Training, when talking about using the dataset in SageMaker, read this guide on different storage options for large datasets.
My AWS S3 costs have been going up pretty quickly for usage type "DataTransfer-Out-Bytes". I have thousands of images in this one bucket and I can't seem to find a way to drill down into the bucket to see which individual bucket items might be causing the increase. Is there a way to see which individual files are attributing to the higher data transfer cost?
Use Cloudfront if you can - its cheaper(if you properly set your cache headers!) than directly hosting from S3 and Cloudfront includes a popular objects report - which would answer your question.
If your using S3 alone you need to enable logging on the bucket (more storage cost) and then crunch the data in the logs (more data transfer cost) to get your answer. You can use AWS Athena to process the s3 access logs or use unix command line tools like grep/wc/uniq/cut to operate on the log files locally/from a server to find the culprits.
I have an aws s3 bucket with 10,000 files totally around 1GB in size.
I call:
aws s3 sync <remote bucket> <local folder> --exact-timestamps
And no files are found to be changed, so no actual file downloads take place.
However, there must be data exchange for the sync - does anyone know how much?
The ListObjects() API call returns a maximum of 1000 objects.
Therefore, the AWS CLI would require at least 10 API calls to retrieve information about 10,000 objects to determine whether files need to by sync'd.
However, since the cost of requests is only $0.005 per 1,000 requests, the cost would be quite small.
I want to download million of files from S3 bucket which will take more than a week to be downloaded one by one - any way/ any command to download those files in parallel using shell script ?
Thanks,
AWS CLI
You can certainly issue GetObject requests in parallel. In fact, the AWS Command-Line Interface (CLI) does exactly that when transferring files, so that it can take advantage of available bandwidth. The aws s3 sync command will transfer the content in parallel.
See: AWS CLI S3 Configuration
If your bucket has a large number of objects, it can take a long time to list the contents of the bucket. Therefore, you might want to sync the bucket by prefix (folder) rather than trying it all at once.
AWS DataSync
You might instead want to use AWS DataSync:
AWS DataSync is an online data transfer service that simplifies, automates, and accelerates copying large amounts of data to and from AWS storage services over the internet or AWS Direct Connect... Move active datasets rapidly over the network into Amazon S3, Amazon EFS, or Amazon FSx for Windows File Server. DataSync includes automatic encryption and data integrity validation to help make sure that your data arrives securely, intact, and ready to use.
DataSync uses a protocol that takes full advantage of available bandwidth and will manage the parallel downloading of content. A fee of $0.0125 per GB applies.
AWS Snowball
Another option is to use AWS Snowcone (8TB) or AWS Snowball (50TB or 80TB), which are physical devices that you can pre-load with content from S3 and have it shipped to your location. You then connect it to your network and download the data. (It works in reverse too, for uploading bulk data to Amazon S3).
I've been trying to download these files all summer from the IRS AWS bucket, but it is so excruciatingly slow. Despite having a decent internet connection, the files start downloading at about 60 kbps and get progressively slower over time. That being said, there are literally millions of files, but each file is very small approx 10-50 kbs.
The code I use to download the bucket is:
aws s3 sync s3://irs-form-990/ ./ --exclude "*" --include "2018*" --include "2019*
Is there a better way to do this?
Here is also a link to the bucket itself.
My first attempt would be to provision an instance in us-east-1 with io type EBS volume of required size. From what I see there is about 14GB of data from 2018 and 15 GB from 2019. Thus an instance with 40-50 GB should be enough. Or as pointed out in the comments, you can have two instances, one for 2018 files, and the second for 2019 files. This way you can download the two sets in parallel.
Then you attach an IAM role to the instance which allows S3 access. With this, you execute your AWS S3 sync command on the instance. The traffic between S3 and your instance should be much faster then to your local workstation.
Once you have all the files, you zip them and then download the zip file. Zip should help a lot as the IRS files are txt-based XMLs. Alternatively, maybe you could just process the files on the instance itself, without the need to download them to your local workstation.
General recommendation on speeding up transfer between S3 and instances are listed in the AWS blog:
How can I improve the transfer speeds for copying data between my S3 bucket and EC2 instance?