How we made Jupyter notebooks load faster
singlestore.com
singlestore.com
(If you're not familiar with SingleStore's Jupyter Notebooks, they're similar to Databricks Notebooks[1] or Azure Synapse Notebooks[2]).
[1]: https://docs.databricks.com/en/notebooks/index.html
[2]: https://learn.microsoft.com/en-us/azure/synapse-analytics/sp...
1. Contributing to an open source project, especially one the size and complexity of jupyter-lab, is generally going to be a slower process than finding a solution "in house". Improving the load times became a priority once we realized our notebooks were bringing value to users, and we wanted to deliver a better experience as soon as possible; 2. It's not always apparent if the changes you are looking for from an open sourced project are useful to a more general audience or if it's very specific to the way you are using the project. A lot of the requests that were mocked could only be so because we either didn't use them in our implementation (for example, users and workspaces) or because we know the response won't change (for example, some extension settings which we don't allow users to change). Is this a common situation for others or is it a niche circumstance of how we are using jupyter-lab? If it's not common, then adding these options in jupyter-lab itself could just increase its complexity while not bringing that much benefit (not saying this is necessarily the case here);
To your point though, a good example of this is the checkpoints feature. There is an open issue requesting the option to disable checkpoints[1] as it is not always useful for people. We had the same issue, since we are not using checkpoints, but the requests were always being made. Ultimately, we just mocked the checkpoints requests, but it's probably the case that making the changes to jupyter-lab to disable this would benefit us and other people as well.
Its much easier to organize .py files into a proper python module, and then share and collaborate with others. Instead, groups will collect jumbles of slightly different versions of the same jupyter notebooks that progressively become more complex and less manageable over time. It's not a hypothetical unfortunately, I've seen this happen at major university labs. I'm not blaming anyone because I understand -- the funding is there to do science and not rewrite code to build convenient software libraries. Yet, I can't help but wish jupyter notebooks could be removed from academic workflows.
Everytime I search my Slack, I have to run two searches because DS can't agree on how to spell the damn thing.
It's great for exploring code and data too, especially situations where I'm really trying to feel my way towards a solution. I get to merrily intermingle rich text narrative and code so I explain how I got to where I got to and can walk people through it (I did that with some experimenting with an SMT solver several months ago, meant that people that had no experience with an SMT solver could understand the model I built).
I'd never use it to share code though. If we get to that stage, it's time to export from jupyter (which it natively supports), and then tidy up the code and productionise it. There's no way jupyter should be the deployed thing.
Also, while many practices out there is questionable, in alternative scenarios where ipynb doesn’t exist, they might have been using something like matlab for example. Eg, in my field (physics), often time there are experimentalists doing some coding. Ipynb can be very enabling for them.
I think a piece of research should be broken down and worked by multiple people to improve the state of the project. Some scientists might be passing you the initial prototype in the form of a notebook, and some others should be refactoring to something more suitable for deployment and archival purpose. Properly funding these roles is important, and is lacking but improving (eg hiring RSE.)
In my field, the most prominent way when ipynb is shared a lot is for training. It’s a great application as that becomes literate programming. In this sense notebook is highly underused as literate programming still hasn’t got mainstream.
I think the notebooks are a fine learning tool to introduce people to programming initially, but I'm afraid it doesn't allow for growth beyond a certain level. You have a good point about funding for those software roles. Perhaps this may not be as big of a concern if there were more software talent in these labs to handle the issues that arise.
While in an ideal world scientists who write softwares should write professionally, the same goes for anything they do, including math and stats used in their research, writing and typesetting and generates publishing quality visualization… That rarely happens because of how the academic world is financed, and the incentives associated with it. I can certainly complain about that all days, but in short a researcher hired by a research university, especially with a tenured track position in the US, will not be successful to get such position, let alone getting tenured, if they had not focused their scarce resource of time to maximize their “research output” (publications, grant, etc.), where software engineering is not part of. (Sorry, sentence too complicated.)
There isn't a single install tool that "just works" for this at the moment. If editors came with more robust support for it by default, I think the notebook format wouldn't be needed at that point and people could use regular python and interactive cell based python more interchangeably. I've seen important code get buried under collections of jupyter notebooks across different users so I have a good reason for this. Notebooks simply dont scale beyond a certain complexity.
The tool and the tool maker are supposed to serve the user. The user is not supposed to conform to the whims of the tool maker.
Probably the solution is that things like Jupyter notebooks and spreadsheets should be views into some better source of truth rather than the source of truth themselves.
[0] https://phys.org/news/2024-08-business-spreadsheets-critical.... I remember a similar figure from studies a decade or so ago.
Spreadsheets are the nonprogrammers programming / modeling tool in business.
It does presentation, data filtering / sorting, modeling and more.
No AI needed (and you can now plug AI in in some cases).
Suppose you have some formula that computes a financial metric for your company. Someone you've shared it with drunkenly fat-fingers the formula 3/4 of the way down a long row, and that causes all entries below it to recompute with the wrong formula. Unless the change is really drastic, you may never know it happened.
And this sort of mistake -- basically a typo or a bad mouse movement -- happens daily in every company in the world in some spreadsheet. Often people will notice the mistake, but not with probability 1.
Software engineers have mechanisms to guard against some of these mistakes, and even we have a hard time getting people to take code review or tests seriously. What is the guard in the spreadsheet world?
If the code is the end product, sure, use a python package.
But does your .py with `# %%` in it also store the outputs? If not, why even bring this up? A .py output without the plots tied to the code doesn't meet the basic use case.
If the end product is the plot, I want to see how that plot was generated. And a Jupyter notebook is a much much better artifact than a Python package, unless that Python package hard codes the inputs and execution path like a notebook would.
Over the past 20 years of my career I have run into this divergence of use cases a lot. Software engineers seem to not understand the end goals, how it should be performed, and the learnings of the practitioners that have been generating results for a long time. It's hard to protect data scientists from these inflexible software engineers that see "aha that's code, I know this!" without bothering to understand the actual use case at hand.
But since most uses of Jupyter notebooks I've seen don't version control them much at all, it's not as useful in practice often.
Something like `if check_versions(pandas__gt="2.0.0", pandas__lt="3.0.0"):`
What a strange thing to assert, especially as a general overarching truth.
The best reports I have ever seen have matched code and output in the same file. There's never a question of what code generated a plot or a table with a notebook.
With .py files and separate outputs there's far more change for unreproducibke science, it's far messier, and for someone who doesn't appear to respect the organizational capabilities of academic labs, you are condemning them to far more poorly organized outputs.
> Having multiple copies of code
That doesn't have anything to do with notebooks. It's as silly as saying that a Python package is a poor idea because you say somebody repeat code across multiple places.
> non-version controlled files
Notebooks are no less version controllable than .py files.
> outputs with timestamps and run information
Jupyter notebooks are perfect for this, far superior to a directory of cryptically named outputs that need to be strung together in some order
> documentation dispersed with questionable organization
Using separate Python files rather than a notebook means that documentation can never be where it needs to be: next to the output. This is one of the ways that Python files are strictly inferior for generating results.
There are roughly two modes for notebooks: exploration with a REPL, and well-documented reports. The best scientific reports I have ever seen are notebooks (or R Markdown output) that are the full report text plus code plus figures.
This is not a great way to make your argument, though you are not the not only one here making a personal judgement without even knowing about my background. These are all issues I have seen first hard. With most academic labs being funding limited, the "organizational capabilities of academic labs" seems irrelevant to me. In our field, no one is getting grants to manage code of any kind .py or .ipynb and I suspect its the same at most university labs. It's effort wasted that ultimately does take time away from the actual research that's fundable and publishable. As someone who has been responsible for wrangling people's notebooks in the past, it's enough of a problem that I would encourage to remove all .ipynb.
> That doesn't have anything to do with notebooks. It's as silly as saying that a Python package is a poor idea because you say somebody repeat code across multiple places.
Human factors make jupyter notebooks lead to the problems I have listed. The issues are most apparent with large groups and over long periods of time. Python and other programming languages already solved most of these problems with git. There isn't a tool that is as elegant and scales from individuals to massive organizations.
> There are roughly two modes for notebooks: exploration with a REPL, and well-documented reports. The best scientific reports I have ever seen are notebooks (or R Markdown output) that are the full report text plus code plus figures.
The REPL functionality is handled by .py cell execution, as I’ve mentioned in other comments. It baffles me how the minimal effort saved by not using separate tools -- one for code, one for documentation -- justifies the issues it introduces.
Like you I see the appeal, but they're a usability nightmare beyond a few lines. Part of the problem, I think, is that you can't really incrementally improve them. Who wants to refactor a notebook and deal with all the cell dependency breakage?
So they start off okay and then slowly become terrible until they're either irreplaceable or too terrible to work with and a new one is started.
What mix of JS packages do you see? Could that be built into one uber package?
You can take a look at the notebooks entrypoint network request: https://portal-notebooks.singlestore.com/
After cursory googling I couldn't find one, do you have a public notebook gallery?
Not much of a worry on the tiny HTML file, but the 11.5MB JS file should compress to a much smaller file on the wire.
`CloudFront compresses objects that are between 1,000 bytes and 10,000,000 bytes in size.` - since the file became bigger than 10mb, cloudfront stopped compressing it...
It's just that your tools make you look that way... and cost you more money in extra bandwidth as well. I hope that's the last of the surprises in that area.