Currently we are having a aws lambda (java based runtime) which takes a SNS as input and then perform business logic and generate 1 XML file , store it to S3.
The implementation now is create the XML at .tmp location which we know there is space limitation of aws lambda (500mb).
Do we have any way to still use lambda but can stream XML file to S3 without using .tmp folder?
I do research but still do not find solution for it.
Thank you.
You can directly load an object to s3 from memory without having to store it locally. You can use the put object API for this. However, keep in mind that you still have time and total memory limits with lambda as well. You may run out of those too if your object size is too big.
If you can split the file into chunks and don't require to update the beginning of the file while working with its end you can use multipart upload providing a ready to go chunk and then free the memory for the next chunk.
Otherwise you still need a temporary storage for form all the parts of the XML. You can use DynamoDB or Redis and when you collect there all the parts of the XML you can start uploading it part by part, then cleanup the db (or set TTL to automate the cleanup).
Related
I have a requirement where I need to write a long String log for each execution of my lambda function, this String is basically log having record of failed and success cases.
as this code is for lambda its not possible to create file in a physical location like local file directly and upload to s3. but I need to directly create a file and write the String in one go.
Thanks in advance.
AWS Lambda functions can write data to the /tmp/ directory. There is a 512MB limit, so delete any files before your Lambda function exits so that it is clear for the next function execution.
You can therefore create a 'local' file and upload it to Amazon S3.
Alternatively, you can use the Amazon S3 API to create an object (PutObject()) while specifying the Body content without creating an actual file. This might be easier for you.
By the way, if your Lambda function prints output, it will automatically be captured by the Lambda function and stored in CloudWatch Logs. It will be intermixed with other messages from Lambda (eg showing the amount of memory used), but it's an easy way to log information.
I want to store user action logs continuously to s3 file for that session.
Requirements:
for a session single file
continuous write operations to s3
should be able to download that file at the end of the session.
Dont want to create new file for single session, want to update same file. Please suggest only AWS solutions.
Do i need to create stream and use it with s3 or using mediator storage system and push once in while.
Objects in Amazon S3 are immutable -- they cannot be modified after they are created.
From your description, a good solution would be to use Amazon Kinesis Data Firehose. Your app can stream data to the Firehose and it will combine data together based on size or time. A long session might therefore produce multiple output files, so you would need a separate process that combines those files together into a single file.
Considering the fact that there is no data pipeline available in Singapore region, are there any alternatives available to efficiently push csv data to dynamodb?
If it was me, I would setup an s3 event notification on a bucket that fires a lambda function each time a CSV file was dropped into it.
The Notification would let Lambda know that a new file was available and a lambda function would be responsible for loading the data into dynamodb.
This would work better (because of the limits of lambda) if the CSV files were not huge, so they could be processed in a reasonable amount of time, and the bonus is the only worked that would need to be done once it was working would be to simply drop the new files into the right bucket - no server required.
Here is a github repository that has a CSV->Dynamodb loader written in java - it might help get you started.
We need to move our video file storage to AWS S3. The old location is a cdn, so I only have url for each file (1000+ files, > 1TB total file size). Running an upload tool directly on the storage server is not an option.
I already created a tool that downloads the file, uploads file to S3 bucket and updates the DB records with new HTTP url and works perfectly except it takes forever.
Downloading the file takes some time (considering each file close to a gigabyte) and uploading it takes longer.
Is it possible to upload the video file directly from cdn to S3, so I could reduce processing time into half? Something like reading chunk of file and then putting it to S3 while reading next chunk.
Currently I use System.Net.WebClient to download the file and AWSSDK to upload.
PS: I have no problem with internet speed, I run the app on a server with 1GBit network connection.
No, there isn't a way to direct S3 to fetch a resource, on your behalf, from a non-S3 URL and save it in a bucket.
The only "fetch"-like operation S3 supports is the PUT/COPY operation, where S3 supports fetching an object from one bucket and storing it in another bucket (or the same bucket), even across regions, even across accounts, as long as you have a user with sufficient permission for the necessary operations on both ends of the transaction. In that one case, S3 handles all the data transfer, internally.
Otherwise, the only way to take a remote object and store it in S3 is to download the resource and then upload it to S3 -- however, there's nothing preventing you from doing both things at the same time.
To do that, you'll need to write some code, using presumably either asynchronous I/O or threads, so that you can simultaneously be receiving a stream of downloaded data and uploading it, probably in symmetric chunks, using S3's Multipart Upload capability, which allows you to write individual chunks (minimum 5MB each) which, with a final request, S3 will validate and consolidate into a single object of up to 5TB. Multipart upload supports parallel upload of chunks, and allows your code to retry any failed chunks without restarting the whole job, since the individual chunks don't have to be uploaded or received by S3 in linear order.
If the origin supports HTTP range requests, you wouldn't necessarily even need to receive a "stream," you could discover the size of the object and then GET chunks by range and multipart-upload them. Do this operation with threads or asynch I/O handling multiple ranges in parallel, and you will likely be able to copy an entire object faster than you can download it in a single monolithic download, depending on the factors limiting your download speed.
I've achieved aggregate speeds in the range of 45 to 75 Mbits/sec while uploading multi-gigabyte files into S3 from outside of AWS using this technique.
This has been answered by me in this question, here's the gist:
object = Aws::S3::Object.new(bucket_name: 'target-bucket', key: 'target-key')
object.upload_stream do |write_stream|
IO.copy_stream(URI.open('http://example.com/file.ext'), write_stream)
end
This is no 'direct' pull-from-S3, though. At least this doesn't download each file and then uploads in serial, but streams 'through' the client. If you run the above on an EC2 instance in the same region as your bucket, I believe this is as 'direct' as it gets, and as fast as a direct pull would ever be.
if a proxy ( node express ) is suitable for you then the portions of code at these 2 routes could be combined to do a GET POST fetch chain, retreiving then re-posting the response body to your dest. S3 bucket.
step one creates response.body
step two
set the stream in 2nd link to response from the GET op in link 1 and you will upload to dest.bucket the stream ( arrayBuffer ) from the first fetch
I have some files that are being uploaded to S3 and processed for some Redshift task. After that task is complete these files need to be merged. Currently I am deleting these files and uploading merged files again.
These eats up a lot of bandwidth. Is there any way the files can be merged directly on S3?
I am using Apache Camel for routing.
S3 allows you to use an S3 file URI as the source for a copy operation. Combined with S3's Multi-Part Upload API, you can supply several S3 object URI's as the sources keys for a multi-part upload.
However, the devil is in the details. S3's multi-part upload API has a minimum file part size of 5MB. Thus, if any file in the series of files under concatenation is < 5MB, it will fail.
However, you can work around this by exploiting the loop hole which allows the final upload piece to be < 5MB (allowed because this happens in the real world when uploading remainder pieces).
My production code does this by:
Interrogating the manifest of files to be uploaded
If first part is
under 5MB, download pieces* and buffer to disk until 5MB is buffered.
Append parts sequentially until file concatenation complete
If a non-terminus file is < 5MB, append it, then finish the upload and create a new upload and continue.
Finally, there is a bug in the S3 API. The ETag (which is really any MD5 file checksum on S3, is not properly recalculated at the completion of a multi-part upload. To fix this, copy the fine on completion. If you use a temp location during concatenation, this will be resolved on the final copy operation.
* Note that you can download a byte range of a file. This way, if part 1 is 10K, and part 2 is 5GB, you only need to read in 5110K to get meet the 5MB size needed to continue.
** You could also have a 5MB block of zeros on S3 and use it as your default starting piece. Then, when the upload is complete, do a file copy using byte range of 5MB+1 to EOF-1
P.S. When I have time to make a Gist of this code I'll post the link here.
You can use Multipart Upload with Copy to merge objects on S3 without downloading and uploading them again.
You can find some examples in Java, .NET or with the REST API here.