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What’s wrong with computational notebooks?

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Re: What’s wrong with computational notebooks?

#11

Post by ktpsns »

Obviously it's pretty hard to make general criticism of the Notebook GUI. This is especially without comparing to a specific other user interface for data scientists, such as a traditional REPL terminal, or some other command line tools?

The Python world gives a good example about the sheer complexity of a notebook infrastructure. The is IPython, there is Jupyter Notebook, JupyterLab. There is even stuff like the SageMathCloud (nowadays called CoCalc) which is basically a web GUI to a VPS combining command lines and various notebooks. And hell, most of these web based interfaces try to make sharing easy.

Mabye we should start comparing these (mostly OSS) tools to the traditional notebook GUIs of Matlab and Mathematica, something we used in the 90s and 2000s. From my feeling, they were more robust, could handle large data better, but they lack all the tooling we get for free in the web.

Re: What’s wrong with computational notebooks?

#12

Post by rcar »

I'm someone who has been programming for a very long time and has been using notebooks for a reasonably long time (and almost always starts projects with them), my feeling is that they are a bit like C in that they make it easy to accidentally shoot yourself in the foot if you aren't careful. I always strive to end up with a notebook that can be "Run All" from a fresh clone, and I'd say that I'm successful with that maybe 60-70% of the time, and am close enough that I can fix it in the remainder.

As the article (and the many others like it that have frequently cropped up as soon as IPython Notebooks first started ramping up in popularity) points out though, a lot of newer users don't have the discipline to ensure that they're not jumping around too much. It's not a problem for them in the immediate term since they know how the state ought to work, but then it becomes a mess when they try to share it with someone else (or to run it themselves again 3 months later).

The challenge though is that the data analysis workflows that it allows are unbeatable by any other tools I've tried. In the end, it may just be that it's the worst form of data programming except for all of the others that have been tried.

Re: What’s wrong with computational notebooks?

#13

Post by tbenst »

I think Atom’s hydrogen and VSCode’s python are best-in-class Jupyter clients that achieve everything Jupyter Lab set out to do with more and better features. I develop scripts that function top to bottom with a notebook side-by-side that on a keyboard stroke executes code blocks from my script in the notebook.

Re: What’s wrong with computational notebooks?

#15

Post by omarhaneef »

Good list.

Their observations bring to mind the benefits of watching people program on YouTube or video where you learn a style of working you may not even have considered.

However there is one other issue that is not on the list: because a notebook is meant to be read or shared, I always feel like my work is public and feel less inclined to play around and just take a look at things. When I do “transfer” my work to a notebook, it’s only surprising or interesting things that suppress the discovery process.

Re: What’s wrong with computational notebooks?

#16

Post by johnc1231 »

moultano wrote: post #5

I want a notebook where causality can only flow forward through the cells. I hate notebook time-loops where a variable from a deleted cell can still be in scope. 1. Checkpoint the interpreter state after every cell execution. 2. If I edit a cell, roll back to the previous checkpoint and let execution follow from there. I can't tell you how many times I've seen accidental persistence of dead state waste hours of peopl…

I haven't dug into it myself, but Netflix makes something called Polynote that is supposed to add some awareness of the sequence of the cells to combat this

Re: What’s wrong with computational notebooks?

#17

Post by ivalm »

moultano wrote: post #5

I want a notebook where causality can only flow forward through the cells. I hate notebook time-loops where a variable from a deleted cell can still be in scope. 1. Checkpoint the interpreter state after every cell execution. 2. If I edit a cell, roll back to the previous checkpoint and let execution follow from there. I can't tell you how many times I've seen accidental persistence of dead state waste hours of peopl…

If the interpreter state contains large variables checkpointing might not be viable (eg I have dataframes that are 100s of GB/large fractions of total available memory, reading/writing from hard drive all the time would be relatively slow. If you can save deltas I guess it wouldn't be too space inefficient but I imagine still slow).

At the same time, I do like the idea of an append only notebook where you can:

1. Only run cells in sequential order

2. Only edit cells that are below the most recently ran cell.

Thankfully you can enforce it through code practice and the notebook is relatively guaranteed to be "run all"-able. You will need to refactor it after the initial dirty run, but at least it's easy to reason about.

Re: What’s wrong with computational notebooks?

#18

Post by thomasballinger »

moultano wrote: post #5

I want a notebook where causality can only flow forward through the cells. I hate notebook time-loops where a variable from a deleted cell can still be in scope. 1. Checkpoint the interpreter state after every cell execution. 2. If I edit a cell, roll back to the previous checkpoint and let execution follow from there. I can't tell you how many times I've seen accidental persistence of dead state waste hours of peopl…

I've played with prototypes of this by calling fork on IPython to take snapshots of interpreter state https://github.com/thomasballinger/rlundo/blob/master/readme... but if you can't serialize state fully, rerunning from the top (bpython's approach) can work, or rerunning as a dependency dag shows is necessary (my current employer Observable's approach) works nicely.

Re: What’s wrong with computational notebooks?

#19

Post by IanCal »

moultano wrote: post #5

I want a notebook where causality can only flow forward through the cells. I hate notebook time-loops where a variable from a deleted cell can still be in scope. 1. Checkpoint the interpreter state after every cell execution. 2. If I edit a cell, roll back to the previous checkpoint and let execution follow from there. I can't tell you how many times I've seen accidental persistence of dead state waste hours of peopl…

If people are wondering about cases that can cause this - a common one (for me) is a mis-spelled variable name. If you go back and change it, the old one is still there and if you make the same mistake twice you will have code that runs but doesn't work. It's then really not obvious why it doesn't work.

Re: What’s wrong with computational notebooks?

#20

Post by nl »

ktpsns wrote: post #11

Obviously it's pretty hard to make general criticism of the Notebook GUI. This is especially without comparing to a specific other user interface for data scientists, such as a traditional REPL terminal, or some other command line tools? The Python world gives a good example about the sheer complexity of a notebook infrastructure. The is IPython, there is Jupyter Notebook, JupyterLab. There is even stuff like the Sag…

Jupyter Notebook/Jupyter Lab has replaced IPython as the notebook front end.

I suspect 90%+ of Python Notebook work is done in Jupyter/Jupyter Lab (or things built on it like Google Collab/Kaggle Kernels).

traditional notebook GUIs of Matlab and Mathematica, something we used in the 90s and 2000s. From my feeling, they were more robust, could handle large data better

I've done 10s of terabyte analysis on Jupyter (Spark backend) and I personally know people doing petabyte work on it so this seems doubtful.

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