From the sample code, I can upload 64MB, without any problem:
myblob = open(r'task1.txt', 'r').read()
blob_service.put_blob('mycontainer', 'myblob', myblob, x_ms_blob_type='BlockBlob')
What if I want to upload bigger size?
Thank you
I ran into the same problem a few days ago, and was lucky enough to find this. It breaks up the file into chunks and uploads it for you.
I hope this helps. Cheers!
I'm not a Python programmer. But a few extra tips I can offer (my stuff is all in C):
Use HTTP PUT operations(comp=block option) for as many Blocks (4MB each) as required for your file, and then use a final PUT Block List (comp=blocklist option) that coalesces the Blocks. If your Block uploads fail or you need to abort, the cleanup for deleting the partial set of Blocks previously uploaded is a DELETE command for the file you are looking to create, but this appears supported by the 2013-08-15 version only (Someone from the Azure support should confirm this).
If you need to add Meta information, an additional PUT operation (with the comp=metadata prefix) is what I do when using the Block List method. There might be a more efficient way to tag the meta information without requiring an additional PUT, but I'm not aware of it.
This is good question. Unfortunately I don't see a real implementation for uploading arbitrary large files. So, from what I see there is much more work to do on the Python SDK, unless I am missing something really crucial.
The sample code provided in the documentation indeed uses just a single text file and uploads at once. There is no real code that is yet implemented (from what I see in the SDK Source code) to support upload of larger files.
So, for you, to work with Blobs from Python you need to understand how Azure Blob Storage works. Start here.
Then take a quick look at the REST API documentation for PutBlob operation. It is mentioned in the remarks:
The maximum upload size for a block blob is 64 MB. If your blob is
larger than 64 MB, you must upload it as a set of blocks. For more
information, see the Put Block (REST API) and Put Block List (REST
API) operations. It's not necessary to call Put Blob if you upload the
blob as a set of blocks.
The good news is that PutBlock and PutBlockList is implemented in the Python SDK, but with no sample provided for how to use it. What you have to do is to manually split your file into chunks (blocks) of up to 4 MB each. and then use put_block(self, container_name, blob_name, block, blockid, content_md5=None, x_ms_lease_id=None): function from the python SDK to upload the blocks. Ultimately you will upload the blocks in parallel. Do not forget however that you have to execute also put_block_list(self, container_name, blob_name, block_list, content_md5=None, x_ms_blob_cache_control=None... at the end to commit all blocks uploaded.
Unfortunately I'm not Python expert to help you further, but at least I give you a good picture of the situation.
Related
I believe I have a very simple requirement for which a solution has befuddled me. I am new to the azure-python-sdk and have had little success with its new blob streaming functionality.
Some context
I have used the Java SDK for several years now. Each CloudBlockBlob object has a BlobInputStream and a BlobOutputStream object. When a BlobInputStream is opened, one can invoke its many functions (most notably its read() function) to retrieve data in a true-streaming fashion. A BlobOutputStream, once retrieved, has a write(byte[] data) function where one can continuously write data as frequently as they want until the close() function is invoked. So, it was very easy for me to:
Get a CloudBlockBlob object, open it's BlobInputStream and essentially get back an InputStream that was 'tied' to the CloudBlockBlob. It usually maintained 4MB of data - at least, that's what I understood. When some amount of data is read from its buffer, a new (same amount) of data is introduced, so it always has approximately 4MB of new data (until all data is retrieved).
Perform some operations on that data.
Retrieve the CloudBlockBlob object that I am uploading to, get it's BlobOutputStream, and write to it the data I did some operations on.
A good example of this is if I wanted to compress a file. I had a GzipStreamReader class that would accept an BlobInputStream and an BlobOutputStream. It would read data from the BlobInputStream and, whenever it has compressed some amount of data, write to the BlobOutputStream. It could call write() as many times as it wished; when it finishes reading all the daya, it would close both Input and Output streams, and all was good.
Now for Python
Now, the Python SDK is a little different, and obviously for good reason; the io module works differently than Java's InputStream and OutputStream classes (which the Blob{Input/Output}Stream classes inherit from. I have been struggling to understand how streaming truly works in Azure's python SDK. To start out, I am just trying to see how the StorageStreamDownloader class works. It seems like the StorageStreamDownloader is what holds the 'connection' to the BlockBlob object I am reading data from. If I want to put the data in a stream, I would make a new io.BytesIO() and pass that stream to the StorageStreamDownloader's readinto method.
For uploads, I would call the BlobClient's upload method. The upload method accepts a data parameter that is of type Union[Iterable[AnyStr], IO[AnyStr]].
I don't want to go into too much detail about what I understand, because what I understand and what I have done have gotten me nowhere. I am suspicious that I am expecting something that only the Java SDK offers. But, overall, here are the problems I am having:
When I call download_blob, I get back a StorageStreamDownloader with all the data in the blob. Some investigation has shown that I can use the offset and length to download the amount of data I want. Perhaps I can call it once with a download_blob(offset=0, length=4MB), process the data I get back, then again call download_bloc(offset=4MB, length=4MB), process the data, etc. This is unfavorable. The other thing I could do is utilize the max_chunk_get_size parameter for the BlobClient and turn on the validate_content flag (make it true) so that the StorageStreamDownloader only downloads 4mb. But this all results in several problems: that's not really streaming from a stream object. I'll still have to call download and readinto several times. And fine, I would do that, if it weren't for the second problem:
How the heck do I stream an upload? The upload can take a stream. But if the stream doesn't auto-update itself, then I can only upload once, because all the blobs I deal with must be BlockBlobs. The docs for the upload_function function say that I can provide a param overwrite that does:
keyword bool overwrite: Whether the blob to be uploaded should overwrite the current data.
If True, upload_blob will overwrite the existing data. If set to False, the
operation will fail with ResourceExistsError. The exception to the above is with Append
blob types: if set to False and the data already exists, an error will not be raised
and the data will be appended to the existing blob. If set overwrite=True, then the existing
append blob will be deleted, and a new one created. Defaults to False.
And this makes sense because BlockBlobs, once written to, cannot be written to again. So AFAIK, you can't 'stream' an upload. If I can't have a stream object that is directly tied to the blob, or holds all the data, then the upload() function will terminate as soon as it finishes, right?
Okay. I am certain I am missing something important. I am also somewhat ignorant when it comes to the io module in Python. Though I have developed in Python for a long time, I never really had to deal with that module too closely. I am sure I am missing something, because this functionality is very basic and exists in all the other azure SDKs I know about.
To recap
Everything I said above can honestly be ignored, and only this portion read; I am just trying to show I've done some due diligence. I want to know how to stream data from a blob, process the data I get in a stream, then upload that data. I cannot be receiving all the data in a blob at once. Blobs are likely to be over 1GB and all that pretty stuff. I would honestly love some example code that shows:
Retrieving some data from a blob (the data received in one call should not be more than 10MB) in a stream.
Compressing the data in that stream.
Upload the data to a blob.
This should work for blobs of all sizes; whether its 1MB or 10MB or 10GB should not matter. Step 2 can be anything really; it can also be nothing. Just as long as long as data is being downloaded, inserted into a stream, then uploaded, that would be great. Of course, the other extremely important constraint is that the data per 'download' shouldn't be an amount more than 10MB.
I hope this makes sense! I just want to stream data. This shouldn't be that hard.
Edit:
Some people may want to close this and claim the question is a duplicate. I have forgotten to include something very important: I am currently using the newest, mot up-to-date azure-sdk version. My azure-storage-blob package's version is 12.5.0. There have been other questions similar to what I have asked for severely outdated versions. I have searched for other answers, but haven't found any for 12+ versions.
If you want to download azure blob in chunk, process every chunk data and upload every chunk data to azure blob, please refer to the follwing code
import io
import os
from azure.storage.blob import BlobClient, BlobBlock
import uuid
key = '<account key>'
source_blob_client = BlobClient(account_url='https://andyprivate.blob.core.windows.net',
container_name='',
blob_name='',
credential=key,
max_chunk_get_size=4*1024*1024, # the size of chunk is 4M
max_single_get_size=4*1024*1024)
des_blob_client = BlobClient(account_url='https://<account name>.blob.core.windows.net',
container_name='',
blob_name='',
credential=key)
stream = source_blob_client.download_blob()
block_list = []
#read data in chunk
for chunk in stream.chunks():
#process your data
# use the put block rest api to upload the chunk to azure storage
blk_id = str(uuid.uuid4())
des_blob_client.stage_block(block_id=blk_id, data=<the data after you process>)
block_list.append(BlobBlock(block_id=blk_id))
#use the put blobk list rest api to ulpoad the whole chunk to azure storage and make up one blob
des_blob_client.commit_block_list(block_list)
Besides, if you just want to copy one blob from storage place to anoter storage place, you can directly use the method start_copy_from_url
I am working for a company which is currently storing PDF files into a remote drive and subsequently manually inserting values found within these files into an Excel document. I would like to automate the process using Zapier, and make the process scalable (we receive a large amount of PDF files). Would anyone know any applications useful and possibly free for converting PDFs into Excel docs and which integrate with Zapier? Alternatively, would it be possible to create a Python script in Zapier to access the information and store it into an Excel file?
This option came to mind. I'm using google drive as an example, you didn't say what you where using as storage, but Zapier should have an option for it.
Use cloud convert, doc parser (depends on what you want to pay, cloud convert at least gives you some free time per month, so that may be the closest you can get).
Create a zap with this step:
Trigger on new file in drive (Name: Convert new Google Drive files with CloudConvert)
Convert file with CloudConvert
Those are two options by Zapier that I can find. But you could also do it in python from your desktop by following something like this idea. Then set an event controller in windows event manager to trigger an upload/download.
Unfortunately it doesn't seem that you can import JS/Python libraries into zapier, however I may be wrong on that. If you could, or find a way to do so, then just use PDFminer and "Code by Zapier". A technician might have to confirm this though, I've never gotten libraries to work in zaps.
Hope that helps!
I'm trying to upload a set of pd.DataFrames as CSV to a folder in Dropbox using the Dropbox Python SDK (v2). The set of files is not particularly big, but it's numerous. Using batches will help to reduce the API calls and comply with the developer recommendations outlined in the documentation:
"The idea is to group concurrent file uploads into batches, where files
in each batch are uploaded in parallel via multiple API requests to
maximize throughput, but the whole batch is committed in a single,
asynchronous API call to allow Dropbox to coordinate the acquisition
and release of namespace locks for all files in the batch as
efficiently as possible."
Following several answers in SO (see the most relevant to my problem here), and this answer from the SDK maintainers in the Dropbox Forum I tried the following code:
commit_info = []
for df in list_pandas_df:
df_raw_str = df.to_csv(index=False)
upload_session = dbx.upload_session_start(df_raw_str.encode())
commit_info.append(
dbx.files.CommitInfo(path=/path/to/db/folder.csv
)
dbx.files_upload_finish_batch(commit_info)
Nonetheless, when reading the files_upload_finish_batch docstring I noticed that the function only takes a list of CommitInfo as an argument (documentation), which is confusing since the non-batch version (files_upload_session_finish) does take a CommitInfo object with a path, and a cursor object with data about the session.
I'm fairly lost in the documentation, and even the source code is not so helpful to understand how the batch works to upload several files (and not as a case for uploading heavy files). What I am missing here?
I can't seem to get around this(below mentioned) error while trying to upload a function onto AWS lambda:
The Code tab failed to save. Reason: Unzipped size must be smaller than 262144000 bytes
I've zipped the function and all of it's dependencies and uploaded the zipped file to S3, and pasted the file's S3 URL at the lambda's prompt (upload a file from Amazon S3).
Any leads would be appreciated. Thanks
Adding to Entropic's answer, what about using something like pyminifier? This could be a very simple solution if the minification it performs is sufficient to reach the limit of 250 MB.
Also, if you are using the AWS SDK, you do not need to include it in your package as it is included in the Lambda execution environment. This could also save some space if it is the case.
As kosa mentioned there is a hard limit at 250MB. This reddit thread had a few good ideas:
https://www.reddit.com/r/aws/comments/4qrw9m/how_to_work_around_aws_lambdas_250mb_limit/
Most solutions along the lines of 1) Loading more code later, thus getting around the 250 limit 2) Split up the code into smaller pieces, which is more aws-lambda-ish anyway, and 3) use strip command like this guy: https://serverlesscode.com/post/scikitlearn-with-amazon-linux-container/
2 is probably the best way to go, if you can split it up.
So, i'm trying to create an google app engine (python) app that allows people to share files. I have file uploads working well, but my concern is about checking the file extension and making sure, primarily, that the files are read only, and secondly, that they are of the filetype that is specified. These will not be image files, as a know they are a lot of image resources already. Specifically, .stl mesh files, but i'd like to be able to do this more generally.
I know there are modules that can do this, python-magic seems to be able to do this for example, but i can't seem to find any that i'm able to import without LoadModuleRestricted. I'm considering writing my own parser, but that would be a lot of work for such a common (i'm assuming) issue.
Anyway, i'm totally stumped so this is my first stackoverflow question, so hope i'm doing well etiquette wise. Let me know, and thanks!
It sounds like you want to read the first few bytes of the uploaded file to verify that its signature matches the purported mime type. Assuming that you're uploading to blobstore (i.e., via a url obtained from blobstore.get_upload_url(), then once you're redirected to the upload handler whose path you gave to get_upload_url, you can open blob using a BlobReader, then read and verify the signature.
The Blobstore sample app lays out the framework. You'd glue in code in UploadHandler once you have blob_info (using blob_info.key() to open the blob).