Is there a way to run imagemagick or some other tool on s3 servers to resize the images.
The way I know is first downloading all the image files on my machine and then convert these files and reupload them on s3 server. The problem is the number of file is more than 10000. I don't want to download all the files on my local machine.
Is there a way to convert it on s3 server itself.
look at it: https://github.com/Turistforeningen/node-s3-uploader.
It is a library providing some features for s3 uploading including resizing as you want
Another option is NOT to change the resolution, but to use a service that can convert the images on-the-fly when they are accessed, such as:
Cloudinary
imgix
Also check out the following article on amazon's compute blog.. I found myself here because i had the same question. I think i'm going to implement this in Lambda so i can just specify the size and see if that helps. My problem is i have image files on s3 that are 2MB.. i dont want them at full resolution because I have an app that is retrieving them and it takes a while sometimes for a phone to pull down a 2MB image. But i dont mind storing them at full resolution if i can get a different size just by specifying it in the URL. easy!
https://aws.amazon.com/blogs/compute/resize-images-on-the-fly-with-amazon-s3-aws-lambda-and-amazon-api-gateway/
S3 does not, alone, enable arbitrary compute (such as resizing) on the data.
I would suggest looking into AWS-Lambda (available in the AWS console), which will allow you to setup a little program (which they call a Lambda) to run when certain events occur in a S3 bucket. You don't need to setup a VM, you only need to specify a few files, with a particular entry point. The program can be written in a few languages, namely node.js python and java. You'd be able to do it all from the console's web GUI.
Usually those are setup for computing things on new files being uploaded. To trigger the program for files that are already in place on S3, you have to "force" S3 to emit one of the events you can hook into for the files you already have. The list is here. Forcing a S3 copy might be sufficient (copy A to B, delete B), an S3 rename operation (rename A to A.tmp, rename A.tmp to A), and creation of new S3 objects would all work. You essentially just poke your existing files in a way that causes your Lambda to fire. You may also invoke your Lambda manually.
This example shows how to automatically generate a thumbnail out of an image on S3, which you could adapt to your resizing needs and reuse to create your Lambda:
http://docs.aws.amazon.com/lambda/latest/dg/walkthrough-s3-events-adminuser-create-test-function-create-function.html
Also, here is the walkthrough on how to configure your lambda with certain S3 events:
http://docs.aws.amazon.com/lambda/latest/dg/walkthrough-s3-events-adminuser.html
Related
I have a project in AWS to insert data from some files, which will be in S3, to Redshift. The point is that the ETL has to be scheduled each day to find new files in S3 and then check if those files are correct. However, this has to be done with custom code as the files can have different formats depending of their kind, provider, etc.
I see that AWS Glue allows to schedule, crawl and do the ETL. However I'm lost at how to one can create its own code for the ETL and parse the files to check the correctness before ending up doing the copy instruction from S3 to Redshift. Do you know if that can be done and how?
Another issue is that if the correctness is OK then, the system should upload the data from S3 to a web via some API. But if it's not the file should be left into an ftp email. Here again, do you know if that can be done as well with the AWS Glue and how?
many thanks!
You can write your glue/spark code, upload it to s3 and create a glue job referring to this script/library. Anything you want to write in python can be done in glue. its just a wrapper around spark which in turn uses python....
I have quite common situation, as I suppose. I have website that is lcoated on amazon EC2 and I'd like to move all dynamic files to amazon S3. Everything seems ok, except 2 points:
I'm using library PDFNet with their WebViewer. To display pdf files in browser Webviwer use special ".xod" format. PDFNet provide functionality to convert pdf files to xod format. Let's see an example, when PDF file was upload on S3 and no xod file was created (I'm going to use Lambda to avoid it in future, but still). So in this case I have to download file to my local machine, convert it to xod file and upload xod file on S3(I don't see any other opportunities to do it, but it can take a lot of traffic)?
Second problem is almost the same, but it's linked with thumbnails. Currently I'm dynamically resize thumbnails depending on the required resolution and I'd like to keep it. Amazon Lambda is not situable in this case, what is the best way to do it?
Why do you say that Lambda is not suitable here?
For pt#1 PDFNet gives a library for Java, you can write a lambda function in java (its possible now) and use that to get infinite scale.
For pt#2: Amazons tutorial (http://docs.aws.amazon.com/lambda/latest/dg/with-s3-example.html) gives a detailed example of how to resize images when uploaded to S3. The example is in nodeJs, you can write a java version as well if you like.
Note that if you want to have custom logic for decision making, you can add attributes while uploading the file in S3 (http://docs.aws.amazon.com/AmazonS3/latest/dev/UsingMetadata.html#User-Defined Metadata) which you can use in your lambda function to take decisions while resizing.
The documentation for the Redshift COPY command specifies two ways to choose files to load from S3, you either provide a base path and it loads all the files under that path, or you specify a manifest file with specific files to load.
However in our case, which I imagine is pretty common, the S3 bucket periodically receives new files with more recent data. We'd like to be able to load only the files that haven't already been loaded.
Given that there is a table stl_file_scan that logs all the files that have been loaded from S3, it would be nice to somehow exclude those that have successfully been loaded. This seems like a fairly obvious feature, but I can't find anything in the docs or online about how to do this.
Even the Redshift S3 loading template in AWS Data Pipeline appears to manage this scenario by loading all the data -- new and old -- to a staging table, and then comparing/upserting to the target table. This seems like an insane amount of overhead when we can tell up front from the filenames that a file has already been loaded.
I know we could probably move the files that have already been loaded out of the bucket, however we can't do that, this bucket is the final storage place for another process which is not our own.
The only alternative I can think of is to have some other process running that tracks files that have been successfully loaded to redshift, and then periodically compares that to the s3 bucket to determine the differences, and then writes the manifest file somewhere before triggering the copy process. But what a pain! We'd need a separate ec2 instance to run the process which would have it's own management and operational overhead.
There must be a better way!
This is how I solved the problem,
S3 -- (Lambda Trigger on newly created Logs) -- Lambda -- Firehose -- Redshift
It works at any scale. With more load, more calls to Lambda, more data to firehose and everything taken care automatically.
If there are issues with the format of the file, you can configure dead letter queues, events will be sent there and you can reprocess once you fix lambda.
Here I would like to mention some steps that includes process that how to load data in redshift.
Export local RDBMS data to flat files (Make sure you remove invalid
characters, apply escape sequence during export).
Split files into 10-15 MB each to get optimal performance during
upload and final Data load.
Compress files to *.gz format so you don’t end up with $1000
surprise bill :) .. In my case Text files were compressed 10-20
times.
List all file names to manifest file so when you issue COPY command
to Redshift its treated as one unit of load.
Upload manifest file to Amazon S3 bucket.
Upload local *.gz files to Amazon S3 bucket.
Issue Redshift COPY command with different options.
Schedule file archiving from on-premises and S3 Staging area on AWS.
Capturing Errors, setting up restart ability if something fails
Doing it easy way you can follow this link.
In general compare of loaded files to existing on S3 files is a bad but possible practice. The common "industrial" practice is to use message queue between data producer and data consumer that actually loads the data. Take a look on RabbitMQ vs Amazon SQS and etc..
We are experiencing problems with files produced by Java code which are written locally and then copied by the Data Pipeline to S3. The error mentions file size.
I would have thought that if multipart uploads is required, then the Pipeline would figure that out. I wonder if there is a way of configuring the Pipeline so that it indeed uses multipart uploading. Because otherwise the current Java code which is agnostic about S3 has to write directly to S3 or has to do what it used to and then use multipart uploading -- in fact, I would think the code would just directly write to S3 and not worry about uploading.
Can anyone tell me if Pipelines can use multipart uploading and if not, can you suggest whether the correct approach is to have the program write directly to S3 or to continue to write to local storage and then perhaps have a separate program be invoked within the same Pipeline which will do the multipart uploading?
The answer, based on AWS support, is that indeed 5 gig files can't be uploaded directly to S3. And there is no way currently for a Data Pipeline to say, "You are trying to upload a large file, so I will do something special to handle this." It simply fails.
This may change in the future.
Data Pipeline CopyActivity does not support files larger than 4GB. http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-object-copyactivity.html
This is below the 5GB limit imposed by S3 for each file-part put.
You need to write your own script wrapping AWS CLI or S3cmd (older). This script may be executed as a shell activity.
Writing directly to S3 may be an issue as S3 does not support append operations - unless you can somehow write multiple smaller objects in a folder.
Dropbox claims that during syncing only the portion of files that changes are transmitted back to main server, which is obviously a great functionality, but how do they perform changes to files stored in Amazon S3 cloud? So for example, lets say a 30 page document on user's desktop contains changes to only page 4. Dropbox now syncs the blocks representing the changes and what happens on the backend if they files that they store are in the cloud? Does that mean they have to download the 30 page document stored in S3 to their server, then perform replacement of blocks representing page 4, and then uploading back to the cloud? I doubt this would be the case because that would be somewhat inefficient. The other option I could think of is if Amazon S3 provides update of file stored in the cloud based on byte ranges, so for example, make a PUT request to file X from bytes 100-200 which will replace all the bytes from 100 to 200 with value of PUT request. So I was curious how companies that use other cloud services such as Amazon, implement this type of syncing.
Thanks
As S3 and similar storages don't offer filesystem capabilities, anything that pretends to store files and directories needs to emulate a file system. And when doing this files are often split to pages of certain size, where each page is stored in a separate file in the storage. This way the changed block requires uploading only one page (for example) and not the whole file. I should note, that with files like office documents this approach can be faulty if file size is changed - for example, if you insert a page at the beginning or delete a page, then the whole file will be changed and the complete file would need to be re-uploaded. We didn't analyze how Dropbox in particular does his job, and I just described the common scenario. There exist also different "patch algorithms", where a patch can be created locally (if Dropbox has an older local copy in the cache) and then applied to one or more blocks on the server.
There are several synchronizing tools which transfer deltas over the wire like rsync, rdiff, rdiff-backup, etc. For bi-directional synchronising with S3 there are paid services like s3rsync for example. For pure client-side synchronising, tools like zsync can be considered (which is what many people employ to roll-out app updates).
An alternative approach would be to tar-ball a directory, generate a delta file (using rdiff or xdelta3), and upload the delta file by using a timestamp as part of the key. In order to sync, all you need to do is to perform these 2 checks client-side:
You have all the delta files from S3. If not pull them and apply them to generate the latest backup state.
Your last backup state corresponds to your current directory. If not generate a new delta file and push to S3.
The concerning factor here would be the at least 100% additional space utilization, client-side. But this approach will help you revert changes if needed.