Autocomplete hangs the kernel for interactive window

[stevenlis]

Version: 2020.1.58038 (13 January 2020)

I have a dataframe with shape of (810103, 26). If I type code and run a cell from the editor, everything is fine. But it will be extremely slow if I run code from the interactive window directly such as df.head(), df.columns. I can even make a screenshot without shortcut😂

[rchiodo]

That's really weird. We have reports of the opposite. Running from the editor being really slow, but directly from the interactive window being fast.

The problem is we can't repro in house. Do you have some code and data you can share?

[stevenlis]

I found the issue when cleaning this CDC dataset. Just read the csv file and then df.head()

I also tried with the toy dataset tips in seaborn, which didn't cause any problem. I'm using 2019.11.50794 now, which has no issues.

[rchiodo]

Hmm. I'm reproing. Only goes slow from the interactive window directly. And it's the kernel that's taking a super long time (not our code). But if I run it from the editor, df.head() is instant.

[rchiodo]

And we submit the same code in both cases. I'm guessing this is a kernel issue.

In the editor case, we run two executions actually:

*** REQUEST EXECUTE : {"code":"__file__ = 'd:\\\\Training\\\\SnakePython\\\\foo.py'","stop_on_error":false,"allow_stdin":true,"store_history":false} 
*** REQUEST EXECUTE : {"code":"df.head()\n","stop_on_error":false,"allow_stdin":true,"store_history":true}

whereas in the interactive window we only run the second. And running both of those in the interactive window has no effect on the execution, it still takes a long time.

[rchiodo]

And this is not a regression. It repros with 2019.11.50794

[rchiodo]

I take that back. Only sometimes with 2019.11.50794.

[stevenlis]

Hi, @rchiodo. just wondering... will this be fixed any time soon?

[rchiodo]

Not likely. We have no idea what causes it. It's jupyter that's taking all the time (not our code), so we'd have to debug the jupyter kernel.

[stevenlis]

@rchiodo I don't know what the advantage of this jupyter kernel thing but it adds some env I don't want:

I feel like I prefer the old extension approach better.

[rchiodo]

Those two new 'kernels' represent the environment you're running your python code in. They are an exact match for an environment generated by conda.

The advantage is that they will load all of the same packages that the associated conda environment loads.

There is no disadvantage other than they are listed there. They have nothing to do with the slowdown you're seeing.

The slowdown is in the IPython code itself (which is what I meant by we had to debug the jupyter kernel). The slowdown is not in the code we ship with the ms-python extension.

[stevenlis]

@rchiodo any process on this buddy? This happens so frequently and even with small datasets from time to time.

[DonJayamanne]

Do you have the variables list open when this issue occurs?

[stevenlis]

not at all I never used that thing

[rchiodo]

Sorry but we are not actively investigating this. I believe this is an issue in the IPython code itself and not our own. We don't do anything special to send code to the kernel.

[stevenlis]

Could you guys please take a look. This is so weird and really interrupt my workflow.