wish | node assertion library without special syntax | Assertion library

Although they 'don't exist', can Format.ps1xml files be created/used in versions of PowerShell greater than 5.1?

• Yes; in fact any third-party code must use them to define custom formatting.

  • That *.ps1xml files are invariably needed for such definitions is unfortunate; GitHub issue #7845 asks for an in-memory, API-based alternative (which for type data already exists, via the Update-TypeData cmdlet).

• It is only the formatting data that ships with PowerShell that is now hardcoded into the PowerShell (Core) executable, presumably for performance reasons.

is there some better practice for suggesting to PowerShell how a certain function should format returned data?

The lack of an API-based way to define formatting data requires the following approach:

• Determine the full name of the .NET type(s) to which the formatting should apply.

  • If it is [pscustomobject] instances that the formatting should apply to, you need to (a) choose a unique (virtual) type name and (b) assign it to the [pscustomobject] instances via PowerShell's ETS (Extended Type System); e.g.:

    • For [pscustomobject] instances created by the Select-Object cmdlet:

Get data based on its id (Server Side)

Using REGEXEXTRACT should achieve your wanted data.

=REGEXEXTRACT(A1, """splashtopname"":""([^""]*)""")

Simply extract from pattern "splashtopname":"" via regex.

You can get a List of CarModel for each car company in the CarCompany entity through the oneToMany annotation like this:

You can use sort -V command to consider the proper versioning of files and then invoke copy command on each file one by one or a list of files at a time.

ls | sort -V

If you're on a GNU system, you can also use ls -v. This won't work in MacOS.

How about using TaggedOutput.

Pythons multiprocessing package is limited to shared memory parallelization. It spawns new processes that all have access to the main memory of a single machine.

You cannot simply scale out such a software onto multiple nodes. As the different machines do not have a shared memory that they can access.

To run your program on multiple nodes at once, you should have a look into MPI (Message Passing Interface). There is also a python package for that.

Depending on your task, it may also be suitable to run the program 4 times (so one job per node) and have it work on a subset of the data. It is often the simpler approach, but not always possible.

a) Even if obviously it could be better, is this solution is acceptable or not at all ?

I think it has some problems you want to address, but the general idea of using the filesystem and storing just filenames can be acceptable, depending on how many users you need to serve and what guarantees regarding data consistency and concurrent accesses you want to make.

I would consider the uploaded file temporary data that may be lost on system failure. If you want to provide any guarantees of not losing the data, you want to store it in a database instead of on the filesystem.

b) I pass 'uploaded_file' from one view to another using "request.session" is it a good practice? Is there another way to do it without using GET variables?

There are up- and downsides to using request.session.

• attackers can not change the filename and thus retrieve data of other users. This is also the reason why you should not use a GET parameter here: If you used one, attackers could simpy change that parameter and get access to files of other users.
• users can upload a file, go and do other stuff, and later come back to actually import the file, however:
• if users end their session, you lose the filename. Also, users can not upload the file on one device, change to another device, and then go on with the import, since the other device will have a different session.

The last point correlates with the leftover files problem: If you lose your information about which files are still needed, it makes cleaning up harder (although, in theory, you can retrieve which files are still needed from the session store).

If it is a problem that sessions might end or change because users clear their cookies or change devices, you could consider adding the filename to the UserProfile in the database. This way, it is not bound to sessions.

c) At first my wish was to avoid to save the csv-file. But I could not figure out how to do it? Reading all the file to request.session seems not a good idea for me. Is there some possibility to upload the file into memory in Django?

You want to store state. The go-to ways of storing state are the database or a session store. You could load the whole CSVFile and put it into the database as text. Whether this is acceptable depends on your databases ability to handle large, unstructured data. Traditional databases were not originally built for that, however, most of them can handle small binary files pretty well nowadays. A database could give you advantages like ACID guarantees where concurrent writes to the same file on the file system will likely break the file. See this discussion on the dba stackexchange

Your database likely has documentation on the topic, e.g. there is this page about binary data in postgres.

d) If I have to use the tmp-file. How should I handle the situation if user abandon upload at the middle (for example, he sees the confirmation page, but does not click "yes" and decide to re-write his file). How to remove the tmp-file?

Some ideas:

• Limit the count of uploaded files per user to one by design. Currently, your filename is based on a timestamp. This breaks if two users simultaneously decide to upload a file: They will both get the same timestamp, and the file on disk may be corrupted. If you instead use the user's primary key, this guarantees that you have at most one file per user. If they later upload another file, their old file will be overwritten. If your user count is small enough that you can store one leftover file per user, you don't need additional cleaning. However, if the same user simultaneusly uploads two files, this still breaks.
• Use a unique identifier, like a UUID, and delete the old stored file whenever the user uploads a new file. This requires you to still have the old filename, so session storage can not be used with this. You will still always have the last file of the user in the filesystem.
• Use a unique identifier for the filename and set some arbitrary maximum storage duration. Set up a cronjob or similar that regularly goes through the files and deletes all files that have been stored longer than your specified maximum duration. If a user uploads a file, but does not do the actual import soon enough, their data is deleted, and they would have to do the upload again. Here, your code has to handle the case that the file with the stored filename does not exist anymore (and may even be deleted while you are reading the file).

You probably want to limit your server to one file stored per user so that attackers can not fill your filesystem.

e) Small additional question : what kind of checks there are in Django about uploaded file? For example, how could I check that the file is at least a text-file? Should I do it?

You definitely want to set up some maximum file size for the file, as described e.g. here. You could limit the allowed file extensions, but that would only be a usability thing. Attackers could also give you garbage data with any accepted extension.

Keep in mind: If you only store the csv as text data that you load and parse everytime a certain view is accessed, this can be an easy way for attackers to exhaust your servers, giving them an easy DoS attack.

Overall, it depends on what guarantees you want to make, how many users you have and how trustworthy they are. If users might be malicious, you want to keep all possible kinds of data extraction and resource exhaustion attacks in mind. The filesystem will not scale out (at least not as easily as a database).

I know of a similar setup in a project where only a handful of priviliged users are allowed to upload stuff, and we can tolerate deletion of all temporary files on failure. Users will simply have to reupload their files. This works fine.

Faster is use native pandas functions, which are vectorized, not apply solutions (because apply are loops under the hood) with accessor .dt for apply function for Series/ column: