I have django inline to uploads files, so
it can upload multiple files at same time. (one file for a one file field)
And my nginx limits upload size to 20MB.
Now I want to check the total size of all files, and give proper error massage if it exceeds 20MB before nginx does.
Any helps? please.
Related
I am attempting to limit the size of an image that can be uploaded. To do this in the docs I found DATA_UPLOAD_MAX_MEMORY_SIZE I set the value of it to 3mb (3145728 bytes) in my settings.py file but I am still able to upload files larger than 3 mb. I also tried FILE_UPLOAD_MAX_MEMORY_SIZE and the same thing occurred. The only way i can get it to trigger is if i set it to a very low value such as 1 or 2. Any ideas on what I'm doing wrong.
From the docs for DATA_UPLOAD_MAX_MEMORY_SIZE, it does not include uploaded files.
The check is done when accessing request.body or request.POST and is calculated against the total request size excluding any file upload data.
FILE_UPLOAD_MAX_MEMORY_SIZE defines when an uploaded file is saved to the filesystem instead of staying in memory, it does not impose any limits on how large the uploaded file can be
Your best bet is to configure your webserver to limit upload size, client_max_body_size if you are using nginx for example
I am working with Django 2.2 and got stuck with file upload size validation. I have read django documentation:
DATA_UPLOAD_MAX_MEMORY_SIZE
FILE_UPLOAD_MAX_MEMORY_SIZE
I only set DATA_UPLOAD_MAX_MEMORY (to 20 MB), as mentioned in documentation:
The check is done when accessing request.body or request.POST and is
calculated against the total request size excluding any file upload
data.
But in my project it also checks my uploading file size in request.FILES.
Can someone explain differences between FILE_UPLOAD_MAX_MEMORY and DATA_UPLOAD_MAX_MEMORY? And how to use them properly?
DATA_UPLOAD_MAX_MEMORY
The check is done when accessing request.body or request.POST and is calculated against the total request size excluding any file upload data.
FILE_UPLOAD_MAX_MEMORY_SIZE
The maximum size (in bytes) that an upload will be before it gets streamed to the file system. If uploading files are larger than FILE_UPLOAD_MAX_MEMORY_SIZE, the data of files will be streamed to FILE_UPLOAD_TEMP_DIR
https://docs.djangoproject.com/en/2.2/ref/settings/#file-upload-temp-dir
I don't think you have to set FILE_UPLOAD_MAX_MEMORY_SIZE, because it's the size of memory cache.
FILE_UPLOAD_MAX_MEMORY will load the file contents into the RAM, if the request body or the file which is uploaded is more than FILE_UPLOAD_MAX_MEMORY then the file will be stored in the /tmp directory.
You can restrict the file size in the webserver (Nginx) by adding client_max_body_size 20M;
Here 20 megabytes is the maximum data that request body can have. So if you are uploading a file more than 20 MB, the webserver will not accept it.
We upload many small (1kb) text files one at a time to the Data Management API, and the latency becomes a real issue as the number increases.
Is it possible to upload a zipped folder containing several text files, and have the individual files appear inside a single folder in BIM 360?
Ideally we could compress the files into a single zip folder, upload this package once and have the Data Management API extract all the files into a BIM 360 folder.
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.
I am building a site that requires the user to upload images that will be around 70MB each to my server. Currently I am running a Linode with 512MB of RAM. There isn't much extra memory to spare due to other sites being on this server, so is it possible to upload those images to the server without taking up any RAM by dumping the image directly to the filesystem, or does any file uploaded via POST have to be loaded into memory first before it can be dumped to the filesystem? Does the nature of this problem require a server with a lot of RAM?
Would there be a way to somehow integrate an ftp client into an html form? I'm using Django if that makes a difference.
In your project settings, set FILE_UPLOAD_MAX_MEMORY_SIZE to something small (eg 1024 bytes). That will make Django spool request.FILES to disk sooner, not use up RAM
Docs are here if you want more detail: https://docs.djangoproject.com/en/dev/ref/settings/#file-upload-max-memory-size
As per your requirement .... django files upload have two types of uploading.
1 - InMemory Upload
2. Temporary Upload
In case of InMemoryUpload the files you uploaded is in ram only through request.FILES ,
But can set that upload to covert it from InMemoryUpload to TemporaryUpload which ultimately use /tmp folder to store it .. which saves for RAM ...
In settings.py :-
FILE_UPLOAD_MAX_MEMORY_SIZE = #something
The maximum size, in bytes, for files that will be uploaded into memory.
Files larger than FILE_UPLOAD_MAX_MEMORY_SIZE will be streamed to disk.
Defaults to 2.5 megabytes.
FILE_UPLOAD_TEMP_DIR = #to sme path
The directory where uploaded files larger than FILE_UPLOAD_MAX_MEMORY_SIZE will be stored.
Defaults to your system’s standard temporary directory (i.e. /tmp on most Unix-like systems).
Then you can write that file in chunks to your required directory as /tmp deletes all files once system is down.
Follow this link :
https://docs.djangoproject.com/en/dev/topics/http/file-uploads/#changing-upload-handler-behavior