partition | Partitioning

To really "mount" the filesystem (ie. to be able to just "fopen" something inside that filesystem) you need support from the kernel. Either you let your operating system take care of it completely (e.g the VHD commands on windows; example use on stackoverflow). Alternatively can use libfuse/winfsp to interact with the kernel more directly, but afaik that requires a custom kernel driver.

If you have an image file with some filesystem inside and you just want to look whats inside, but don't need the C/C++ standard library commands (fopen, ifstream, etc.) to work, then something that can specifically read/write that combination of image file format and filesystem would also suffice.

For C# there would be the DiscUtils library which might do what you want, but I'm not aware of a similar c/c++ library with NTFS support.

CTEs can break this down a little easier. In the code below, the payment_streak CTE is the key bit; the start_of_streak field is first marking rows that count as the start of a streak, and then taking the maximum over all previous rows (to find the start of this streak).

The last SELECT is only comparing these two dates, computing how many months are between them (excluding June/July), and then finding the best streak per customer.

You should use an object store to save and serve your user uploaded files. django-storages makes the implementation really simple.

If you don’t want to use cloud based AWS S3 or equivalent, you can host your own on-prem S3 compatible object store with minio.

On your current setup I don’t see any easy way to fix where the number of vm s are dynamic depending on load.

If you have deployment automation then maybe try out rsync so that the vm takes care of syncing files with other vms.

Since you have everything containerized, it's way simpler.

Just set up a small healthcheck endpoint with Spring Web which serves to see if the server is still running, something like:

I modified your query a little, and then got group ids using RANK():

I found and tested in shorter toggle mechanism.

The configuration for Windows Feature:

• Windows Subsystem for Linux is installed.
• Windows Hypervisor Platform is installed.
• Hyper-V is installed.

If you need the Emulator, you only need to turn off Hypervisor + Restart. Run: bcdedit /set hypervisorlaunchtype off

If you need the Docker back, you can run the hypervisor hence disabling Emulator. Run: bcdedit /set hypervisorlaunchtype auto

You need to restart after setting Hypervisor

You cannot run both at the same time. Another forum worth checking in How about running docker? in my older answer below.

I think I solved this issue, tested to run from CMD / Android Studio and ran perfectly as before installing WSL. There are several step we go:

1. Removed Windows Subsystem for Linux
2. Removed Windows Hypervisor Platform
3. Removed Hyper-V

Here is my current setup:

I know after removing there are some odds because the AVD still get the same error as before and expected to get into WSL. I stumbled and found something when ran:

C:\Users\[NAME]\AppData\Local\Android\Sdk\emulator\emulator-check.exe accel

That command will check the current accel. It explains that the Hypervisor need to be set off and give specific help: run bcdedit /set hypervisorlaunchtype off.

After running the bcdedit, I restarted and all is reverted. Now I can run emulator both from CMD and Android Studio perfectly.

Sad truth, yeah you cannot run both pararel. There are several workaround in this forum:

How can I run both Docker and Android Studio Emulator on Windows?

Several option ranging from changing emulator, add & remove docker when in need using above step, created nested vm, etc. My personal choice right now is using another Emulator for the time being and removed docker for the latter.

Yes, we can read all the json files without partition columns. Directly use the parent folder path and it will load all partitions data into the data frame.

After reading the data frame, you can use withColumn() function to rename the date field.

Something like the following should work

I see two problems with this program. One of them I know how to fix, but the other apparently requires more knowledge of recursion schemes than I have.

The one I can fix is that it's looking up the wrong values in its cache. When given = 10, of course validCoins = [10,5,1], and so we find (zeroes, toProcess) = ([0], [5,9]). So far so good: we can give a dime directly, or give a nickel and then make change for the remaining five cents, or we can give a penny and change the remaining nine cents. But then when we write lookup 9 attr, we're saying "look 9 steps in history to when curr = 1", where what we meant was "look 1 step into history to when curr = 9". As a result we drastically undercount in pretty much all cases: even change 100 is only 16, while a Google search claims the right result is 292 (I haven't verified this today by implementing it myself).

There are a few equivalent ways to fix this; the smallest diff would be to replace

TLDR: Spark triggers a sort when you call partitionBy, and not a hash re-partitioning. This is why it is much slower in your case.

We can check that with a toy example: