distributed | A distributed task scheduler for Dask | Machine Learning library

you could make Workflow implement Serializeble and SparkSession as @transient

The error implies that it can't find a range to work with. As we are working with a table, the .Columns(2) wont work.

This part hints that you have a table that your are trying to sort.

There's two approaches that I can think of now, to solve this:

1. Sort a regular range by custom list

We can remove the table by:

1. Click on the table
2. Go to design tab
3. Convert to Range

Then your originally code will work (Changed Key1:=.Columns(2)):

Okay, so you want an app registration to manage an App Service through Azure Resource Management API as itself with client credentials flow? In that case you don't need to assign any application permissions to your app. You need to create the app, and then go to e.g. the App Service resource's Access Control (IAM) tab, and add the needed role to your app there.

The reason that the app permissions tab there is grey is because the Azure Service Management app registration (which you can't edit) does not define any app permissions. When you define an app permission in the manifest, that becomes a permission that other applications could use to call your API, not Azure Resource Management API.

Jason, I'm sorry, hashbytes() is not supported against external tables.

If I were asked this, my first thought would be to use promises/futures. The idea behind them is that you can execute time-consuming operations asynchronously and they will somehow notify you when they've completed, either successfully or unsuccessfully, typically by calling a callback function. So the first step is to spawn five asynchronous tasks and get five promises.

Then I would join the five promises together, creating a unified promise that represents the five separate tasks. In JavaScript I might call Promise.all(); in Java I would use CompletableFuture.allOf().

I would want to make sure to handle both success and failure. The combined promise should succeed if all of the API calls succeed and fail if any of them fail. If any fail there should be appropriate error handling/reporting. What happens if multiple calls fail? How would a mix of successes and failures be reported? These would be design points to mention, though not necessarily solve during the interview.

Promises and futures typically have modular layering system that would allow edge cases like timeouts to be handled by chaining handlers together. If done right, timeouts could become just another error condition that would be naturally handled by the error handling already in place.

This solution would not require any state to be shared across threads, so I would not have to worry about mutexes or deadlocks or other thread synchronization problems.

She said she would use a flag variable to keep track of the number of API calls have returned.

One thing that makes great interviewees stand out is their ability to anticipate follow-up questions and explain details before they are asked. The best answers are fully fleshed out. They demonstrate that one has thought through one's answer in detail, and they have minimal handwaving.

When I read the above I have a slew of follow-up questions:

• How will she know when each API call has returned? Is she waiting for a function call to return, a callback to be called, an event to be fired, or a promise to complete?
• How is she causing all of the API calls to be executed concurrently? Is there multithreading, a fork-join pool, multiprocessing, or asynchronous execution?
• Flag variables are booleans. Is she really using a flag, or does she mean a counter?
• What is the variable tracking and what code is updating it?
• What is monitoring the variable, what condition is it checking, and what's it doing when the condition is reached?
• If using multithreading, how is she handling synchronization?
• How will she handle edge cases such API calls failing, or timing out?

A flag variable might lead to a workable solution or it might lead nowhere. The only way an interviewer will know which it is is if she thinks about and proactively discusses these various questions. Otherwise, the interviewer will have to pepper her with follow-up questions, and will likely lower their evaluation of her.

When I interview people, my mental grades are something like:

• S — Solution works and they addressed all issues without prompting.
• A — Solution works, follow-up questions answered satisfactorily.
• B — Solution works, explained well, but there's a better solution that more experienced devs would find.
• C — What they said is okay, but their depth of knowledge is lacking.
• F — Their answer is flat out incorrect, or getting them to explain their answer was like pulling teeth.

If I understand your question the right way, you could use dplyr and tidyr:

By running .compute on the dask dataframe you are converting it into a pandas dataframe in memory. If you want a future object, then you can run:

Every time you call .compute() on Dask dataframe/series, it converts it into pandas. So what is happening in this line

artists["name"] = artists["name"].astype(str).compute()

is that you are computing the string column and then assigning pandas series to a dask series (without ensuring alignment of partitions). The solution is to call .compute() only on the final result, while intermediate steps can use regular pandas syntax:

Ok, I found the answer myself and want to post it for the case that anyone else has the same question.

The answer lies in the config file for each database, i.e. the DB2Config.java file mentioned in the tutorial mentioned in the question.

While I'm at it, I'll inadvertedly also answer the question "how do I manipulate any of the spring.jpa properties for several databases independently".

In the example, the following method gets called: