ng-window | 基于angular和material-design设计的多窗口操作页面

There are 3 ways to lower costs:

• downscale your job, you will have higher latency but for a lower cost, up to a point where your job crashes because it runs out of memory over time and/or can't catch up with its backlog. Here you need to keep an eye on your metrics to make sure you can react before it's too late
• going further, you can regroup multiple queries into a single job. This job most likely won't be aligned in partitions, so it won't be able to scale linearly (adding SUs is not guaranteed to give you better performance). Same comment as above, plus you need to remember that when you need to scale back up, you probably will have to break down that job into multiple jobs to again be able to scale in a linear fashion
• finally you can auto-pause a job, one way to implement that being explained in the doc you linked. I wrote that doc, and what I meant by that comment is that here again you are taking the risk of overloading the job if it can't run long enough to process the backlog of events. This is a risky proposition for most production scenarios

But if you know what you are doing, and are monitoring closely the appropriate metrics (as explained in the doc), this is definitely something you should explore.

Finally, all of these approaches, including the auto-pause one, will deal with tumbling windows transparently for you.

Update: 2022-03-03 following comments here

Update: 2022-03-04 following comments there

There are 3 time dimensions here:

• When the job is running or not: the wall clock
• When the time window is expected to output results: Tumbling(day,1) -> 00:00AM every day, this is absolute (on the day, on the hour, on the minute...) and independent of the job start time below
• What output you want produced from the job, via the job start time

Let's say you have the job running 24/7 for multiple months, and decide to stop it at noon (12:00PM) on the 1st day of March.

It already has generated an output for the last day of February, at 00:00AM Mar1.

You won't see a difference in output until the following day, 00:00AM Mar2, when you expect to see the daily window of Mar1, but it's not output because the job is stopped.

Let's start the job at 01:00AM Mar2 wall clock time. If you want the missing time window, you should either pick a start time at 'when last stopped' (noon the day before), or a custom time any time before 23:59PM Mar1. What you are driving is the output window you want. Here you are telling ASA you want all the windows from that point onward.

ASA will then reload all the data it needs to generate that window (make sure the event hub has enough retention for that, we don't cache data between restarts in the job): Azure Stream Analytics will automatically look back at the data in the input source. For instance, if you start a job “Now” and if your query uses a 5-minutes Tumbling Window, Azure Stream Analytics will seek data from 5 minutes ago in the input. The first possible output event would have a timestamp equal to or greater than the current time, and ASA guarantees that all input events that may logically contribute to the output has been accounted for.

From the documentation here: https://docs.microsoft.com/en-us/stream-analytics-query/windowing-azure-stream-analytics#understanding-windows

Every window operation outputs event at the end of the window. The windows of Azure Stream Analytics are opened at the window start time and closed at the window end time. For example, if you have a 5 minute window from 12:00 AM to 12:05 AM all events with timestamp greater than 12:00 AM and up to timestamp 12:05 AM inclusive will be included within this window. The output of the window will be a single event based on the aggregate function used with a timestamp equal to the window end time. The timestamp of the output event of the window can be projected in the SELECT statement using the System.Timestamp() property using an alias. Every window automatically aligns itself to the zeroth hour. For example, a 5 minute tumbling window will align itself to (12:00-12:05] , (12:05-12:10], ..., and so on.

If you have historical data that you want to output, you can set a custom query start time either as any point up to the max cache of your streaming source (ususally 7 days) or as at the point the query was last stopped, so you don't lose any data during maintenance windows.

The query, however, will only output data with a timestamp that is after the query start time.

Therefore, imagine that your first data has a timestamp of 2022-02-20 01:23:00 and your second a timestamp of 2022-02-21 15:08:00. You start your streaming job as at 2022-02-21 14:00:00, so your 10 minute windows base themselves on the midnight of the 21st and then progress in 10 minute windows from there. The query does not output anything until the 15:00 - 15:10 window of the 21st, as this is the first window that is both post your query start time and contains data. In this scenario you can see how the windows work and why your data with the 2022-02-20 01:23:00 timestamp would not be output.

First let's mention two good practices when writing a query with a temporal element:

• If you're developing locally in VS Code, please use TIMESTAMP BY, or the whole file will be loaded on a single timestamp (query start time) which will make all temporal logic moot. If you don't have a event timestamp, or don't want to use one from the payload, you can always use TIMESTAMP BY EventEnqueuedUtcTime (that you will need to add to your local data sample) which is the default implicit behavior on event hub anyway
• In your query, make the window bounds visible by selecting both the WindowStart and WindowEnd like this:

Window creation succeeds (in theory, anyway). The issue is that the primary thread moves one to return, which causes the runtime to terminate the process.

To solve the issue you will have to keep the primary thread alive. A call to WaitForSingleObject, or a message loop are possible options.

This is mostly a result of following the conventions of C and C++. In either case returning from the main function is equivalent to calling the exit() function. This explains why returning from the primary thread tears down the entire process.

Bonus reading: If you return from the main thread, does the process exit?

In my answer, I have mentioned few lemmas. I have proved the first three in simple manners and provided only visual representation for the last 2. I have done so to avoid complexity and also because they seem trivial.

I have changed the variable names used in the algorithm. Throughout this discussion, I have assumed that k=2.

About the errors you get:

1. The first one means you can't pass a column to slice using DataFrame API function (unless you have Spark 3.1+). But you already got it as you tried using it within SQL expression.
2. Second error occurs because you pass column names quoted in your expr. It should be slice(qty_list, count, window_size) otherwise Spark is considering them as strings hence the error message.

That said, you almost got it, you need to change the expression for slicing to get the correct size of array, then use aggregate function to sum up the values of the resulting array. Try with this:

The process name for notepad.exe is notepad. Update

OK, so it was much simpler than I thought.

There is a keybinding for this, it is simply called "File: Open" under "Keyboard shortcuts", and its full name is workbench.action.files.openFileFolder.

It currently has the when-constraint isMacNative && openFolderWorkspaceSupport, but you can simply remove this by right-clicking the binding, and selecting "Edit When Expression".

Then simply bind it to CTRL + O or whatever you desire.