#460: Dropbase: Build Internal Tools with Python Transcript
00:00 Do you find yourself for your team building internal apps frequently for your company?
00:04 Are you familiar with the term forms over data?
00:07 They can be super empowering for your organization, but they can also be pretty repetitive
00:12 and you might find yourself spending more time than you like working on them rather than core products and services.
00:19 I've invited Jimmy Chan from DropBase to tell us about their services, whose tagline is build internal web apps with just Python.
00:27 It's a cool service and a fun conversation.
00:30 This is Talk Python to Me, episode 460 recorded March 12th, 2024.
00:36 - Are you ready for your host, here he is!
00:39 - You're listening to Michael Kennedy on Talk Python to Me.
00:42 Live from Portland, Oregon, and this segment was made with Python.
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01:41 Hey, Jimmy.
01:43 - Hey, Michael, how are you doing?
01:44 - I'm doing well.
01:45 Welcome to Talk Python to Me.
01:46 It's fantastic to have you here.
01:48 - Thank you, I'm really excited.
01:48 - Yeah, I am too.
01:49 I saw DropBase, I saw what you all were building.
01:52 I'm like, that is pretty darn interesting.
01:54 I think people will want to check it out.
01:56 So we'll dive into this app and this platform that you made for app building, basically.
02:03 We'll have a lot of fun.
02:04 Now, before we get into that, though, just give us a quick bit of background on yourself.
02:08 How'd you get into programming?
02:09 How'd you find yourself writing a Python building platform?
02:13 - Awesome, yeah.
02:14 I went to school for engineering.
02:15 This was actually back in Canada.
02:17 I studied in Montreal at a little school called McGill University.
02:20 - Nice.
02:21 - We started then.
02:22 I used to intern, by the way, at this company called Research In Motion that used to make BlackBerries way back in the time.
02:27 - Oh, yeah, awesome.
02:30 In the heyday?
02:30 - In the heyday, yeah.
02:32 I remember when they said we had 50% of the US smartphone market share.
02:37 But the platform was actually built on Java, not Python.
02:39 Python was something afterwards.
02:42 Actually, my co-founder is the Python programmer, and I picked up Python after we started working together.
02:47 - Cool.
02:48 You got any fun stories from your time at BlackBerry?
02:50 Did you enjoy it?
02:51 How long were you there?
02:51 - I was there as an intern only.
02:53 I spent about 16 to 20 months of internships.
02:57 - Yeah.
02:57 - At the time, I was, I think, in my third year of school.
02:59 And we were just working on these little Java apps for the smartphones, and just making sure everything was working.
03:06 The cool thing is when you work at a company that's making devices, devices have a very long development cycle.
03:14 So imagine, for example, you worked at Apple today.
03:16 The iPhone, what, 15, whichever one's coming next, they've been working on it for at least a year,
03:22 probably longer.
03:23 So you get access to all these prototypes that you get to use, 'cause you're building for it, you're testing it.
03:29 That was a really nice, nice, nice day.
03:30 But you actually couldn't tell anybody about it.
03:32 But it was really cool to know that you're on the last day.
03:35 - And you couldn't lose it in a bar or anything like that, right?
03:38 - You remember those stories, right?
03:40 - Yes, exactly.
03:40 - It was all referenced a few times.
03:41 - Somebody got a hold of a lost phone, and it was sold, and there was all sorts of drama.
03:46 It was a big deal.
03:47 That was around iPhone 4, 3, 4, 5, sort of timeframe.
03:51 - Yeah, I remember that very clearly.
03:53 - Yeah, so you didn't wanna be that.
03:55 You wanted to make sure you hung on to it.
03:57 - Oh yeah, no.
03:58 (laughing)
03:59 - Yeah, I think working for a company like that would have been awesome, at least in the heyday.
04:03 It's cool to work for a tech company that's got a lot of wind that it's back,
04:07 and a lot of people using it.
04:09 It's kinda neat.
04:10 - I was very happy.
04:11 We had a lot of other interns that they hired at the time.
04:14 They used that up lots to work in there over the summer.
04:17 - Yeah, a lot of excited people to do stuff.
04:19 Excellent, cool.
04:20 And so then you picked up Python, I guess, when you started this project, is that right?
04:25 - Just a little bit before.
04:26 So when we started our startup, this was back in 2020.
04:30 My co-founder had been using Python.
04:33 He did a master's degree, computer science, focused on machine learning, actually.
04:38 In that field, Python is really the main language you use.
04:42 - Yeah, it is.
04:43 - And so he was very familiar with it, and so he set our stack, basically, to be a Python backend.
04:49 And then just through that process, just looking at things and designing features,
04:55 we just all got involved with Python.
04:56 So it kinda just became the same.
04:58 And this is actually the reason why we're working on a Python builder, is we're kinda building a product for ourselves.
05:04 It's something where it fits our own needs, that works with our mental models.
05:09 So that's pretty much it.
05:10 So today, I spend a lot of time building apps with Dropbase, so internal tools,
05:16 and that's most of the app building I do now.
05:19 So building demos, testing the products, making sure everything is working,
05:23 and then just finding other ways to make the developer experience more convenient.
05:27 - It seems like something you could really take care of a bunch of internal automation type of apps.
05:33 You don't have to create full-on web apps to secretize this data or to get that dashboard or whatever.
05:40 You just fire up your own platform and point at your data sources.
05:43 - That's right, yeah.
05:44 So the big difference with other frameworks is that you're gonna use these frameworks
05:49 to build your customer-facing applications.
05:51 And customers, they have expectations about what they need.
05:56 Interactions sometimes make a big difference for them.
06:00 UI components, the way models come up.
06:03 It's all part of the experience.
06:04 And so for that, you really need a lot of extra components complexity.
06:09 For internal tools, it's not to say that internal users don't have high expectations,
06:14 but the kinds of things that you do with those tools tend to be very, very similar.
06:19 You search for data, you get context about that.
06:24 So for example, you search for a transaction or for a user, you get some more context to make sure
06:28 that that's the user or the transaction that you want to act on.
06:31 And then you just act on it, right?
06:32 Like you trigger an action on that item or that resource or that person.
06:37 It helps you simplify the kind of things you need.
06:39 And so for that, having a framework, a builder to make this happen is super convenient.
06:44 The subtitle catchphrase of DropBase is build internal web apps with just Python, which is awesome.
06:51 And when I think of internal web apps, or even traditionally, if you look back a ways,
06:57 there was VB6 and Windows Forms and those types of things that were kind of playing the same role.
07:02 So same basic idea.
07:04 There was a lot of people who referred to that as forms over data.
07:09 It kind of the, not quite a negative term, but kind of like we're sort of doing
07:13 the same basic architecture over and over, but it's a different problem every time.
07:18 And so we kind of got to write code, create these projects from scratch every time,
07:22 but they're the kind of, same kind of thing.
07:25 And so, I think that's partly why VB6 with its kind of visual drag and dropy experience
07:30 became real popular, 'cause you could drag a table on there, drag a data source, bind it to that,
07:34 make a button click when somebody touches this thing.
07:37 And that didn't create something that would rival, say, airbnb.com, or where you have these really dynamic design sites,
07:45 but that's not what they need.
07:47 The company says, look, we need, this week, I need our salespeople to be able to answer this question
07:54 and make this change.
07:55 That's what we need you to build this week.
07:57 And if it's from scratch every time, that's a ton of rework.
08:01 - It's a lot of work.
08:02 It's funny you mentioned the forms over data.
08:04 It is true that a lot of the, well, if you take it at a very high level,
08:09 all apps are forms over data.
08:11 - In some sense, yeah.
08:12 - All SaaS apps are forms over a database in some sense.
08:17 And then you start hearing also about the similar things with you've heard about the GPT wrappers, right?
08:25 It's the same concept where it's like an oversimplification of what's happening,
08:30 but at the same time, it is kind of core to what's happening.
08:33 And for internal tooling, at least, is exactly what you need, forms over data, but very quickly,
08:38 so that developers don't have to spend a lot of time basically doing the same things over and over again.
08:44 You wanna make their lives easier to support their internal users.
08:48 So people in customer support, operations, and I think sometimes even supporting
08:53 other engineers as well.
08:54 - Yeah, and it's not that people in companies don't have expectations of nice apps and nice interactions.
09:01 It's just that it's more utilitarian.
09:03 Like so many of these big enterprises, when you kind of think of them, maybe the right way to think of them is
09:08 there's a massive Postgres or Microsoft SQL Server at the center.
09:12 And like, it almost connects the business divisions, like the sales team and the marketing team
09:18 and the product team and all that.
09:21 And if you can get into the database and answer questions, expose data, expose relationships,
09:27 put a UI to some aspect of that, you're really right at the heart of the company
09:32 in some sense, you know?
09:33 - Yeah, it's definitely about effectiveness.
09:36 One way we think about what these kinds of tools should do, you have to enable developers to very quickly
09:44 build apps that have maximal value, that lets you get the job done as effectively as possible,
09:51 but also that's easy to use for the end user.
09:54 So, you know, if it's like a business operator or someone in the operations team,
09:58 they also need to be able to learn quickly how to use it and trigger actions.
10:02 And so we call that sort of effectiveness of the apps, some sort of like effectiveness score.
10:08 It just combines these components.
10:10 And that's really the, like, if you can get to the essence of it and create a framework or a tool builder
10:16 that maximizes for that, I think you're in a really good place.
10:19 And this is something we're trying to do with DropBase.
10:22 The components are simple.
10:24 The kinds of apps that you can build look roughly the same, but they look really nice.
10:29 And they're also quite simple to understand.
10:31 They're modeled after, but it's in a way where you have data, a grid or table in the middle,
10:37 and then there's a sidebar from where you can trigger actions.
10:40 And this ties back to this concept of when you ask people in operations,
10:45 what they do, like sales, marketing, business operations, essentially ties back to the previous comments,
10:51 which was you search for something, you get context about it and you trigger action.
10:56 And so the way we translated that insight about what people just end up doing over and over again,
11:00 we said, well, then you really just need some sort of search bar, you need a grid and you need a sidebar
11:07 with some buttons or some other drop downs and other components.
11:11 And then you realize, okay, this is kind of what stretches I've been trying to do for a long time.
11:15 - Yeah, that's true.
11:16 - Except we like to trigger actions.
11:17 - Yeah, people do oddly connect Excel to like external data sources and all sorts of weird things as well.
11:23 Yeah, or write VBA that makes them change.
11:25 They probably should just not be doing that.
11:27 - Yeah, there's a lot of, it's actually a big use case.
11:29 One of our previous product, we actually did exactly that.
11:32 We let you connect any database source to your Google Sheets and just kind of run that on a schedule.
11:39 So you'd imagine in a business setting, you have sort of these like weekly, daily reports
11:46 that analysts end up working on, right?
11:48 And what you want is for the data to be fresh, up to date.
11:52 And so people just wanted a way to on at 7 a.m, 8 a.m on Monday morning,
11:58 basically automatically pull data from the database, load it up to your spreadsheet,
12:02 and then all your tables and calculations, which is sort of updated because they're referencing a tab, a sheet,
12:09 basically that has fresh data.
12:11 It is very interesting, the evolution of all these tools.
12:13 - It is really interesting.
12:14 Maybe one of the big differences is the interactivity here versus what you might do in that story, right?
12:21 - That's read-only really.
12:23 And then here, what we're doing is letting you trigger actions, perform actions,
12:27 basically outside the apps.
12:29 And you might call an API or you might trigger an internal endpoints or just some triggers matching in the UI.
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12:58 I think we'll get to this more later, but I wanna maybe touch on it since there's a good question.
13:04 Also, it might be a question a lot of people have.
13:07 So what's the pricing model for this?
13:09 And basically more broadly, what is the business model around here?
13:13 Luis says, "Already amazed by it.
13:16 "Would like to compare it." First thought, there's nothing like this available
13:20 in open source, so very neat.
13:22 - The business model is we're gonna sell the product on a per user per month basis.
13:29 So it's a B2B SaaS product.
13:31 Now, developers need to be able to play around with products before they can decide whether that's something they want.
13:38 And so we have a sort of a free version of the products that you can set up locally, like in your own machine,
13:46 or you can just put it up on an EC2 and you can just build apps for yourself.
13:50 Like if you wanna automate personal tasks, you can totally use this for it.
13:54 And then the way that we, in a way, layer the pricing is like if you need very complex permission systems,
14:02 so like you build an app and you want it accessible by this person, by person A, but not person B,
14:08 then you'll need very granular permission systems.
14:10 And so that's an indicator that you're probably like a corporate user, you're a business user.
14:15 And so we do the pricing based on that.
14:17 - The source is open, at least.
14:19 People can go and check it out, right?
14:21 Is that true?
14:22 - Let me clarify that.
14:23 So you can self-host it.
14:24 - Yes.
14:25 - You can look at the repo.
14:27 Behind the scenes, it's a Docker image that you set.
14:30 So the code for drawbase is not immediately, you can't just browse it through the file system,
14:37 but the code is basically sitting in your machine when you set this up locally.
14:41 Now, the difference here is if you look at the directory, where you have the workspace.
14:46 So the workspace directory is where all your apps files would be generated as you build the apps.
14:52 - Okay.
14:53 - That's really nice because you can essentially version control this from day one.
14:58 You can build apps, you can check it in GitHub, you can just keep your code, you can version it.
15:03 And so it's really, it's open source, let's clarify that, but you can self-host it.
15:08 - It's on GitHub, but the licensing is really not like just straight up GPL open source type of thing, right?
15:16 - That's correct, yes.
15:17 - Okay.
15:18 - And it's on GitHub because you do need to clone the repo and then you need to send it out and install it.
15:23 - The free tier doesn't allow self-hosting, but if you pay for one of the versions,
15:27 then it does have a self-hosting option.
15:30 So when you, just to wrap this up on the price, when you say it's $10 per user per month,
15:35 is that per person that makes the project, that builds the apps, or is that per person that interacts with the web app?
15:43 - This is currently per member of your workspace.
15:46 So we're not yet differentiating between someone who is building the app or using the app,
15:51 although that might be something that we'll have to add later on.
15:54 - Sure, so if you had say four sales people and one "developer" in quotes maybe,
16:00 developer, that would be five users under this story.
16:03 - Correct, under the current pricing, yes.
16:05 - Okay, you know, even if it's not just pure open source, take it and run with it,
16:08 I do like the self-hosting aspect of it, because if you've got private data,
16:13 or maybe even more important than any of that, is I wanna connect it to data sources that are,
16:19 I don't wanna give your cloud access to my company database, for example.
16:22 - I can expand on that, actually.
16:23 So we built this specifically with that comment in mind.
16:30 So when you're using internal tools, it's very, well, let's say it's almost all the cases,
16:36 you are interacting with production, at least one production database, right?
16:41 Like if you're supporting customers and you're issuing them a refund, you don't do that in tests
16:46 and then take it to staging and then deploy it, right?
16:48 You basically, you act on it on your production data.
16:52 And so we thought, okay, people are probably going to be very hesitant to basically say, let me load up all my creds,
17:00 all my API keys into some new player, a new cloud, and then like hope for the best, right?
17:06 So when we designed Dropbase, we call it the worker or the worker server,
17:12 the way you connect to data sources is you basically go to your .env file
17:17 and you just add your database creds.
17:19 That env file sits in the same directory that that workspace directory sits,
17:25 the one that we saw on GitHub, and that's completely on your side and that's in your VPC or in your local machine.
17:33 And the same server is the one that triggers the task to query your database.
17:39 So, and that's also sitting on your side in your machine.
17:43 And so the entire part of the product that processes queries, touches your data,
17:49 your data sources, your credentials, that's completely on the user side.
17:54 And the only part that we manage is really just the permission system.
17:59 So that controlling the workspace and controlling the app permissions, that's the layer that we control.
18:06 And that's really like the part where we can do the pricing on, to begin with.
18:10 Like that's the feature set thing that helps us say, okay, this is gonna be paid customers
18:15 and these are gonna be developers who are just gonna learn how to code in Python,
18:19 just wanna build little apps for their own personal use.
18:22 - All right, that makes sense.
18:23 You know, let's maybe talk through an example.
18:27 If you go to dropbase.io, there's a, right at the top, there's a sort of tutorial
18:32 or a demo video of you using it.
18:35 Obviously, I don't wanna just play the video, but maybe just walk us through the steps
18:39 of what this app is and what are the steps to build it.
18:42 And just for people who are listening and haven't watched that video, maybe I think it's 12 minutes long
18:47 and you're like, let's create a new project.
18:49 And here it is published at the end, right?
18:52 Which is really nice.
18:53 So yeah, talk us through it.
18:53 - I'm gonna try to recreate the exact same one that you have on the screen, right?
18:58 So it looks like an app that has two tables and you can somehow perform an action on the sidebar.
19:05 Basically, it just notifies, either notifies a user or it performs some sort of permissions.
19:11 - I think what this one does is it has workspaces and then users are in a workspace
19:15 and you can make them admins or not admins of that workspace.
19:18 Is that what, does that help?
19:19 - Gotcha, perfect, okay.
19:21 Okay, so I'll describe how you build this app.
19:23 So you go to our app studio and it's gonna be a blank canvas.
19:26 In that canvas, you can add tables.
19:29 So the first thing you do is you add table one, you add the first table,
19:33 and then you would add table two, which is the second table.
19:36 And then you add a widget, so on the sidebar, you just click on add button
19:42 and then it just places a button on the screen for you.
19:44 So that will be sort of a shell of the application.
19:46 It'll be just the UI part.
19:49 At this point, it's just a few mouse clicks.
19:51 There's no Javascript code or Python code that you're writing.
19:54 The next step is to add the sort of the logic behind the scenes.
19:58 The first thing we need to do is to populate the first table with data about your workspaces.
20:04 And essentially what we do is you click on create new, you create a new function.
20:09 And in that function, it could be a SQL function or a Python function.
20:13 Because that's just a table, you can just create a new SQL function.
20:17 So you do create a new function, select everything from workspaces, let's say that's the table.
20:22 And then you can see a little preview of what that select statement will result in.
20:26 So you can verify that that's the data that you wanna show.
20:29 Once you've done that, you just save that file, that query, and you do something we call binding.
20:35 So in the UI, you can select table one, and it'll present you with the tables properties.
20:40 And one of the properties is which SQL file, which SQL script it's bound to.
20:46 So you just click bind to the SQL file that you just wrote, and then it just automatically populates it
20:52 with data for your workspace.
20:54 Now, the second table is a little bit more interesting because the second table will update
21:00 depending on what row you've selected in the first table.
21:02 When you click on a workspace in the first table, it should load all of the users
21:07 that are part of a particular workspace.
21:09 And so we do this, we have a feature called dependent tables, and that's exactly what it does.
21:14 So for the second table, you would still write a SQL query, and the query would just say,
21:19 select everything from users, where the workspace ID is the selected row
21:26 works as ID from the first table.
21:28 And we use that sort of a Jinja, I'm not sure, maybe the audience will be familiar with Jinja.
21:32 It's sort of like Jinja templating style where you say select everything from users
21:37 where workspace ID is double curly brace.
21:42 And then we have something called state.
21:44 They basically gives you the state of the UI at any point in time when you've clicked something.
21:48 And it's really, we made it into a class so that you can say state.tables.table1.workspaceID.
21:56 And we resolve that into the value that's the workspace ID from the first table.
22:02 And so that's, and then you do the same thing.
22:03 You save that SQL query, you bind it to the second table, and then we automatically infer
22:09 that whenever you need, whenever you select a different row in the first table,
22:13 the second table will update the workspace ID value, rerun the query, and then show you the data for it.
22:19 - That's cool.
22:20 I was wondering how that happened.
22:21 I thought you must have to wire some on change event, clicks over here, then it triggers that,
22:26 but you just have that all automatic, right?
22:27 - The UI actually knows that which tables are related.
22:31 The UI sends a payload to the backend saying, this table was clicked, and this other table is related to it.
22:38 And so we kind of know that we need to process those again.
22:42 And so that's how we do this dependent table.
22:44 For the getting the value of the workspace ID in the first table, we basically, we resolve the sort of the variables
22:53 in the backend and then pass a value so that we can update the query.
22:58 So let's say that you stop at that point, right?
23:00 And then you say, preview the app.
23:02 The app would look like two tables where it's really just lets you see all the users
23:07 for each workspace that you have in your database.
23:11 And then a few more things to make that very useful is you might want to search for a workspace
23:17 before you know who to support.
23:18 So let's say each workspace is associated with one of your customers.
23:22 So let's say company A is your customer and company A gives you a call, a support call and says,
23:27 hey, can you make John in our company an admin?
23:31 So the first thing you want to do is you want to find, you search for company A and then find John.
23:36 So we have this nice feature that basically automatically creates searches for you.
23:40 So let's say in the first table, you can click on a button to convert it to a smart table.
23:46 And when you do that, we basically generate filters for you.
23:50 So you can then just say, I'd like to search by company name and you can select a filter
23:56 and you can pin the filter into the table and that automatically performs a search for you.
24:01 I say, it's pretty neat that way.
24:03 - It reminds me a little bit of Notion, Notion tables.
24:05 I don't know if you play with Notion any.
24:07 - Yes.
24:08 - You know, you have got those pin sorts and those pin filters and different things
24:11 you can kind of put at the top of the, I guess their databases are called in Notion.
24:15 A little like that.
24:16 - It's very similar to that in the sense that you're not writing any code
24:20 to be able to search through tables, but you can still modify what you let people search
24:25 by pinning certain filters.
24:27 The really interesting thing that we've done here is that we let you do joins
24:33 and still provides you that feature.
24:34 So in Notion, for example, you would just have one database and you can filter and sort that database.
24:40 With DropBase, what we have is, let's say you have a table called users and a table called roles,
24:46 and then they're related by an ID where let's say ID one is users, ID two is developers, ID three is admins.
24:54 You might have to join those two tables first.
24:57 And so that same smart feature, smart table feature, you can just write that SQL statement,
25:01 that join statement, and we can still generate sort of the right filters for you.
25:06 And we'll actually let you edit those tables as well.
25:09 What we do is we take your SQL statement and then we infer all the SQL required
25:14 to search or edit any column within the SQL statement.
25:19 - That sounds awesome.
25:19 - That's a really neat feature, especially for the people who are used to using
25:25 something like Django admin, in the sense that it helps you very quickly
25:29 build an admin panel with very little developer work.
25:33 You can just write a SQL statement and then we give you an admin panel.
25:37 So that's really neat.
25:38 But the final thing with the app here in the demo is the little button on the right side.
25:42 That is a Python function.
25:44 So you create a new Python function and you essentially say, you essentially write a database update
25:51 to update the role from whatever it was, let's say from developer to an admin.
25:56 It's just really a select statement, update table, that's column something on this particular ID.
26:04 And so that's just a Python function.
26:05 And the final thing is just to connect that button to run this Python function.
26:10 You click on the button, we run the function and then we perform a database update
26:14 to update the role to whichever role you want.
26:17 And that really is just the gist of the demo.
26:19 - So a couple of things about that Python function that's pretty interesting.
26:23 When you create it, you can say, I wanna create a function and it says, do you want SQL or do you want Python?
26:27 Which is pretty interesting.
26:29 The Python function has access to the UI widgets.
26:32 I get in the actual video, not the screenshot of the video.
26:35 I think you've got a dropdown select box.
26:38 And so the Python function go, well, what did the user select?
26:41 And then we'll make the change to the person in the workspace along those lines.
26:46 So that's pretty cool, right?
26:47 That you interact with this UI in kind of a object oriented way, I suppose.
26:52 - That's exactly how it works.
26:53 So behind the scenes, we have two classes.
26:56 One is called state and one is called a context.
27:00 So the state class just lets us access all of the UI state at any point in time.
27:06 Anything you enter in an input component or any row you select in the tables,
27:11 you can see the currently selected values of it.
27:15 And so whenever you click on a button or you perform an action, that state is passed to the backend,
27:23 just as a payload, just like a dictionary.
27:25 And the backend, we generate all the classes, all the objects related to state and context.
27:31 So we just have to pass the values in and then execute the function with those values as the input.
27:37 And so that's how we're able to do the state thing with Python.
27:41 If you've seen how you would do it in Python, it is very much a dot notation, right?
27:45 So you would say to get the value of column one in table one, you basically just say,
27:51 state dot tables, which gives you all the tables, dot table one, dot column one.
27:57 And that basically resolves into the value of the table, of the row in that table for that column.
28:05 - Yeah, that's cool.
28:05 I can imagine the implementation of that thing.
28:08 It's like .. get adder.
28:11 And if you ask for the thing, you look in the dictionary and you traverse the thing.
28:16 But with that in mind, what I thought was really nice about this is you had pretty decent autocomplete
28:21 in your little embedded Python editor.
28:23 How'd you pull that off?
28:24 - I did that you noticed, yeah.
28:25 We spent a lot of time on it.
28:27 - I bet you did.
28:28 - Yeah. - How's that work?
28:29 - When we were designing this, we were thinking, damn, VS Code is really nice.
28:33 You have all the plugins, all the autocomplete and everything, but we had two choices, right?
28:38 Either we build this whole UI builder into VS Code, or we kind of take some of the things
28:44 that make VS Code great and then bring it into the DropBase Studio.
28:48 We don't know sort of the final, eventually where this will be, but the current version of this is we thought,
28:53 you know what, I think we need to give users an editor, like a built-in editor,
28:58 so they can just quickly, they can have the best experience building the app
29:01 and the access to UI and context and state really quickly.
29:06 We just did it that way, where we added in LSP, so like a language server.
29:12 So anytime you type code in your Python function, it's basically over a WebSocket connection,
29:18 basically streaming the code changes and saving them in your file system.
29:23 That's the only way we can do this, this like object-oriented Python.notation access.
29:29 And so we need to have access to all the files.
29:32 And so we set this up so that not only you can do autocomplete, but also that we can actually do the state and context
29:39 approach to the web framework.
29:41 It was a bit complicated because we hadn't been really exposed to LSPs before.
29:46 And so we had to learn how to use LSPs and we had to sort of hijack sort of the WebSocket connection in a way.
29:53 So like we intercept the code and then we save it in your file system.
29:56 So we had to customize it a little bit to do this.
29:59 And it's really nice because now you can do autocomplete for your files.
30:03 It's just a neat, it's so hard to develop now.
30:05 We're so used to these tools.
30:07 And now with things like Copilot, like we're used to even more than just autocomplete, right?
30:12 It's like autocomplete the function, not just the variable, right?
30:16 - I feel like autocomplete is just table stakes, you know, especially in this kind of thing.
30:21 You know, if there's somebody who's just done Python forever and they're just, they'd rather get it out of the way
30:26 and it's fine.
30:28 It's not my way of working, but it's fine for people who want to do that.
30:31 But if you're working with somebody who might not be a full-time developer,
30:34 but they want to use your framework, your service to build some app, it seems like this could really empower them.
30:41 But if you give them something with zero autocomplete, zero highlighting,
30:44 they're going to be having a rough time, you know?
30:46 - Yeah, even for a more experienced developer, I think it's still a hard time.
30:50 - Even if you can do it, it's just less productive to go like, oh yeah.
30:53 - Way less productive.
30:54 - Yeah, way less productive.
30:56 I think AI gets added to apps a lot just to kind of check a box.
31:01 But the result of it is not that great to get on my email client that has AI integrated.
31:07 But it almost always seems like, well, that's not something I'd, yeah, that's something I want to read.
31:11 Like, I don't really want to read that.
31:12 And I don't need to summarize it.
31:14 That's emails, you know, just seems like a lot of energy goes into it when it's not that big of a value.
31:19 But maybe something like Copilot could be here.
31:22 - And this is something we've talked about, me and my co-founder.
31:25 So one of the things that we didn't get to cover because we're not going to go through all the demos,
31:30 but the way that we render data in these tables is we basically format it as a pandas data frame.
31:37 So what that means is that, let's say that you query data from Stripe, right?
31:43 Like you get all your customers from Stripe.
31:46 Stripe is not going to return a data frame for you.
31:48 It's going to return like an object, right?
31:50 A dictionary.
31:51 And then you basically have to take that dictionary and then basically convert it into a data frame.
31:56 And then we automatically know how to render everything else from there.
31:58 So we've thought about this and like, wouldn't it be cool if as soon as you get the response
32:04 from an endpoints, let's say as a JSON dictionary, that we suggest that we can just take it
32:10 that next step for you, which is just to like convert it into a data frame.
32:14 And then you wouldn't have to hustle with it.
32:15 We've done that before, but in theory that sounds very, very helpful.
32:19 If you're proficient in Pandas and using data frames, it's actually not that hard.
32:24 It's a very quick thing for you.
32:25 But if you just know Python, but not exposed to the whole data frame ecosystem,
32:32 then maybe it would be really nice if you just said, okay, we just started to data frame for you
32:36 and then that's all you have to care about.
32:38 So we've thought about this.
32:39 We haven't added this yet.
32:40 - I think it's fine without, but it's kind of one of those things like you're in there
32:43 and you've got these data sources and maybe you're not super familiar with them.
32:47 Maybe you can pull them together somehow.
32:48 - One of the things that I find myself struggling with is, let's say I want to build a demo.
32:52 Let's say I haven't been exposed to like the SendGrid API or the HubSpot API.
32:57 Then I have to go to their documentation to figure out, how do I, what's the package name so I can install it?
33:05 How do I authenticate in?
33:06 How do I call a few endpoints to do what I need to do?
33:09 And I think in that case where it's like, it's not about your knowledge of Python.
33:13 It really is about your knowledge of this other thing that you're using.
33:17 In those cases, it seems that it could be extremely helpful for someone to build apps very quickly
33:22 if they somehow had access to either a quick documentation or it just suggested what you might want to do.
33:27 - Yeah, that's kind of what I was thinking as well.
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34:03 - So speaking of that sort of external resources, you've got this Python function.
34:09 What can I put in there?
34:11 Can I use external packages?
34:13 Can I call out on the network to talk to some API endpoints?
34:18 Can I spin up threads?
34:19 What can I do?
34:21 How much Python can I do there?
34:23 - Pretty much all of the above.
34:24 So I'll elaborate what I mean by that.
34:26 So the standard way that you would connect to some of these services, these third-party services,
34:32 is you'll make like an HTTP request, like GET request, POST request.
34:36 So you can just like import requests, basically pip install requests, and then just do it over the standard Python,
34:43 basically Python package.
34:45 Now, it is way more convenient when you use an SDK provided by one of these third parties.
34:50 So if you wanna use Stripe, if you're able to just import Stripe, it'll make your life a lot easier,
34:55 you know, very nicely Pydantic, everything set up.
34:58 So in DropBase, you can install anything that you can install via PyPI.
35:03 We have a file called custom requirements.
35:07 And if you just list them out, when you set up DropBase, we'll basically install all those for you.
35:12 And then when you write a function, you can just basically import Stripe directly in the function
35:17 without, you know, pip installing it again, and then you just use it.
35:21 So that's really nice, basically anything.
35:23 I think you were doing a previous, UV, you were talking--
35:26 - Yeah, yeah, I was talking to Charlie Marsh about UV earlier today, yeah.
35:29 - We haven't looked into it yet, but anything you can pip install from, you know, from PyPI,
35:34 you can install in DropBase, which makes it really, really powerful.
35:37 - Yeah, nice.
35:38 - There's a database connections.
35:40 I mean, you could use SQL, or you can import whatever else you want, but we make those a bit more native
35:46 because we know that you're gonna connect to databases.
35:48 So we wanna make it a bit more a convenience.
35:51 So--
35:52 - And your tables need sort of direct access to it as well, right?
35:55 Not just the output of some access to the database, but you've got to figure out the columns
36:00 and all that kind of stuff, right?
36:01 - That's correct.
36:02 So for, we created a little library, like a utility slash helper that lets you perform database operations very quickly.
36:11 So if you've added your database credentials to the .env file, then you can quickly perform, you know,
36:18 like a select statement, or you can make an update to that database via functions,
36:22 but without, you know, the standard thing where you do, if you use SQLAlchemy, for example,
36:27 where you like start a session, start a connection, you know, like all those steps,
36:31 we kind of abstract that for you, but you can totally use SQLAlchemy in its full glory
36:35 if you, you know, if that's what you're used to.
36:37 - Nice.
36:38 Can I write Python files, like separate Python scripts and stuff, and then just import and call them from there?
36:43 Or does it have to be done through the UI?
36:45 - Totally. - Yeah?
36:46 - This is actually one of the biggest advantages of DropBase is that if you have like a,
36:50 like a Python script lying around and you want to sort of use it in DropBase,
36:54 you can add it to that file system somewhere in the workspace directory,
36:58 and you can just import it in via your function.
37:01 So you just like, - Okay.
37:02 - From your file, you can import a specific function in that file, for example.
37:06 And so, so the implication of this is that you can set up DropBase in a way
37:11 that it just lives in your existing Python code base.
37:14 So imagine you already, you know, you've set up your company, you've got product market fit, you know,
37:19 things are running, right?
37:20 And you have a Python backend, and now you're starting to scale and you need to build tools.
37:26 So your first option would be to do all custom, obviously.
37:30 The second option is to use some sort of tools builder, but every single action you take
37:36 is done over an HTTP request with authentication, with everything.
37:40 So just like a standard internet communication.
37:42 But if you wanted to just interact with an existing functions or internal endpoints,
37:48 then you'd have to either set up an RPC server, or you might want to use some other way
37:53 to get in there, right?
37:54 With DropBase, you basically just set up DropBase in the same directory as your app,
38:00 and you can then import selectively what functions you need.
38:03 It's sort of like a window into your code base that it's like a plug into that code base
38:09 where you can now expand and build a lot of internal tools that leverages everything that you've already built.
38:14 - Yeah. - That's the way, by the way.
38:15 - Yeah, when you've got, oh yeah, there's the requirements like custom, right?
38:18 So you've got your Docker compose file.
38:20 I mean, could you set it up just so as you build, it may be even better,
38:24 just set up a volume so that it can directly find, I mean, I guess that's what you're kind of showing
38:29 right here, right?
38:30 Is that you can sort of map, mount the volume just directly into say your Git repo
38:36 for your big project code, and then you can just start importing that and doing stuff with it, right?
38:40 - We still need to do some tweaks because of relative cats, but the idea is that you can do that
38:46 with a little bit of customization.
38:49 But yeah, our Workspace Server, basically you mount files or directories,
38:53 and then we can look into those to execute.
38:56 - Yeah, manage those files with Git or something like that.
38:58 - A good example of this is, are you familiar with Django and Django admin?
39:02 - Not super as I do every day, but I'm aware, sure.
39:05 - Django, it's a great web framework.
39:07 It's used by a lot of companies.
39:08 It's like, if you wanna build web apps, Django, it comes with a lot of stuff, right?
39:13 And there's this thing called Django admin that basically automatically generates admin panels for you
39:19 based on your Django models.
39:22 And so if you have a model for like a book, right?
39:26 In the book as authors, it basically exposes that to the Django admin.
39:30 And then now you can edit a book without really doing anything.
39:33 Like it all kind of comes for free.
39:35 Like it's pretty neat.
39:36 - I think that's like half the appeal of Django right there.
39:38 - It is, yeah.
39:39 I think a lot of people use it for that.
39:41 - Yeah, they're like, I don't wanna write that stuff.
39:42 I'm using Django.
39:43 Yeah, for real.
39:44 - And it's great.
39:45 Now, at some point when your company hits a certain milestone or a certain number of customers,
39:53 you will need to customize those admin panels.
39:58 You might need to create admin panels for things that maybe you don't have models for yet
40:04 or for things that combine data from your database with data from like Stripe or some other source.
40:11 And so what we see when we talk to these people is that they kind of struggle to like,
40:16 either do I like keep using this or do I like completely rebuild everything?
40:21 For those users, when we talk to them about DropBase, they get very excited because with DropBase,
40:26 you can install Django and it can sit in your file system.
40:30 And so essentially all the models that you've already built in Django, you can basically call them via DropBase
40:36 with DropBase function 'cause it's all Python, right?
40:38 And so it gives you this like, okay, Django admins are great up to a certain point.
40:43 And then after that, what happens, right?
40:45 Like you might not wanna rebuild everything just so you can have internal tooling.
40:48 And so I think that's quite of a nice spot for us to say, look, DropBase can live in your code base,
40:55 uses everything you already have, but it gives you some extra things if you want to build internal tools very quickly.
41:02 So that's something we're obviously very, very excited about.
41:04 - That's really cool.
41:05 I like that aspect of it.
41:06 So when we talk about this code, I guess there's two scenarios, the cloud version or the self-hosted version,
41:12 but where and how does this Python run?
41:15 Is it, it must be full CPython?
41:17 Is it running in isolated Docker containers or in your cloud, what happens?
41:22 Is it functions like AWS functions, Lambda?
41:25 - It just runs either on your local machine, if you've set it up as a developer,
41:30 it's all running there.
41:31 Obviously, if you could turn off your machine, you can use your apps.
41:34 And then if you set it up in a production instance, let's say you deploy this on an EC2,
41:40 your Python functions are running on that EC2, but it's running via Docker container inside.
41:46 - So it gets a little isolation, a little bit of protection, that kind of stuff.
41:49 - And we're assuming that, I mean, you're building these apps to run on your own infrastructure,
41:54 is that you would, that you know what you're doing.
41:56 So this is as opposed to us having a full cloud version where all customers run untrusted code
42:03 in the same sort of backend system, which would be obviously very dangerous
42:08 'cause I'm sure there's ways a customer can somehow, you know, go through the back
42:12 and then access other customers' untrusted code.
42:16 And that would be a nightmare.
42:17 And so if we have isolation, first layer is, well, it's in your infrastructure,
42:22 you're gonna do, it's just your company's code, but then further, you know,
42:26 we run Python in containers in that instance.
42:30 - That sounds more like a durability thing than anything.
42:33 I mean, it is also for isolation and protection, but first and foremost,
42:36 just don't let bad Python code take down the whole thing, you know?
42:40 - The way we recommend people using Dropbix or setting up their dev sort of process with Dropbix
42:47 is that they would clone, it's like basically fork the work Dropbix, and then they will develop locally.
42:54 They will test changes.
42:56 And once they're happy with the new code, you know, they'll push it back
43:00 and then they will have CI/CD into the production instance.
43:04 And then they can version control everything.
43:05 So that also gives you that extra layer of, okay, let's make sure that whatever we're pushing is tested.
43:11 You can always add test databases that you develop locally with before you push to production,
43:17 where it's gonna have to act on your production data.
43:19 - Yeah, that's pretty interesting.
43:20 I like the version control aspect of it.
43:22 That gives it a much nicer touch to connection to real software, rather than just it's a text input area in an app
43:30 that who knows what happens.
43:32 - As developers, it sometimes feels a bit scary, right?
43:34 Like you're building something and it's in this other framework, in this other cloud,
43:39 and it just feels like you can't kind of get, like if the thing goes away,
43:43 like you can't even see your previous code.
43:45 That was another key concept that we wanted to have is in this tool that it feels friendly for developers,
43:51 that it matches your mental models and your sort of like your development process,
43:57 let's call it, like how you're used to making apps or developing code, that we keep that feeling
44:02 when you're building apps with Dropbase.
44:04 - I wanna maybe have a PR to understand if we accept this change to it, right?
44:09 - That's right, yeah.
44:10 - Yeah, you don't normally hear that kind of stuff around these kinds of tools.
44:14 - And if you do, I think it tends to be like, yeah, okay, it's like a huge enterprise contract,
44:19 pay us $50,000 a year, and then you get version control, which to me, it sounds scary.
44:26 I mean, it just, it will make me a little bit less comfortable.
44:28 Now, obviously, if I'm a big company and I just really need the tool, sure, maybe I'll just pay for it and I get version control,
44:35 but getting version control, it just feels so like, like table stakes for developers, especially today,
44:42 that yeah, it just wouldn't make sense otherwise for us.
44:44 I mean, it's the way we're approaching this product.
44:46 - Yeah, well, the other aspect is, what are you version controlling?
44:50 Do you end up with something like effectively a Jupyter notebook, like big blob of something,
44:57 and then there's an older blob of something, and then you diff it, you're like, what is this?
45:01 But since you end up with just these, your workspace files that just live there, right?
45:05 It's a real optimized, like show me the changes, show me what's happened here.
45:09 - Yeah, it all lives there, including the component properties.
45:13 Like we don't store this in like a separate database or anything like that.
45:18 So like, you know, like for some of their tools, they will store all the components in their database.
45:24 And so what they do is, you know, when you load up their tools, like their tools builder,
45:29 it'll fetch all the components from the provider's database, combine it with maybe your custom code,
45:35 and then it'll do it.
45:36 And so even that feels like, okay, but where's like config for my components?
45:41 - Yeah, where'd this button come from?
45:42 Why is it gone?
45:43 - That's tough, yeah.
45:44 And then like, imagine if you wanted to like share apps, or, you know, it's just like version control
45:49 makes a bit less sense.
45:50 I mean, you're just version controlling, just like the individual functions,
45:53 maybe like some custom part of the code that you write, which is still useful,
45:57 but it still feels like you don't have the full picture of what's happening.
46:00 - Yeah, you don't have a full record.
46:02 It could easily get out of sync.
46:03 - Yeah. - Quite interesting.
46:04 I guess one thing we touched on a little bit before I press go here in the show,
46:09 you said that at one point you were considering using Jupyter and this whole mechanism.
46:15 Yeah, what's the story around that?
46:17 - We love Jupyter, we love notebooks, and we love the experience of them.
46:21 The sense that you can write some code and you can quickly test and execute the code
46:26 without, basically without needing to write anything else.
46:30 So for prototyping, for getting quick feedback, it's great.
46:33 And we wanted to give our developers something like that, because when you build these apps,
46:39 you're kind of iterating, right?
46:41 You're like, you're testing it.
46:42 A lot of the times you don't even know the documentation for Vibe or HubSpot or,
46:46 and so like there's a lot of experimentation.
46:48 And so we wanted a quick way to do that.
46:51 So we considered maybe having something like a Jupyter kernel within DropBase
46:57 that allowed people to do it.
46:58 There were some issues with it, is in the sense that the kernel actually stores results
47:04 from like previous blocks.
47:05 And sometimes that gets in the way of you triggering an action in a later block.
47:10 And so we were like, okay, maybe that's a little bit more than we need.
47:14 Now we still kind of eventually want something like that.
47:17 We hacked it in a way, like, so the way that we do it is, you can write a Python function.
47:22 You basically get one block.
47:24 You can write a Python function and you can click on a play button to basically run that function,
47:30 but it's not like a kernel.
47:32 So what we do is behind the scenes, we have like a main function.
47:36 And when you trigger that function, we take the state from the client.
47:41 So whatever you've selected or input it, we take the underlying file for your Python function,
47:46 and then we call it from me.
47:48 And so we kind of simulate that experience of like instantly testing code that you just wrote,
47:54 but there is no, like there's no Jupyter kernel behind the scenes.
47:58 And then you can only do one block at a time.
48:00 And so we said, okay, for now, as we're getting started with it, it's good enough that you can write your functions.
48:05 You can run and test them immediately, which is really nice.
48:08 You can do this with VS Code, by the way, like you would have to open up the terminal
48:12 and then basically run your function.
48:13 And so we still wanted this feature where you can click and just see your outputs
48:17 and basically debug and troubleshoot, but it'd be really nice if we get out multiple blocks
48:22 and you can just build functions that way.
48:23 So maybe for the future.
48:25 - Interesting.
48:26 - Did you say that you were a core developer for Jupyter?
48:29 - No, no, I just spoke to them.
48:32 It sounds fun, but I was just, I interviewed a whole bunch of them recently.
48:35 And what I thought was interesting is, Jupyter is such an empowering thing for Python developers,
48:41 but many of the core developers are so focused on writing JavaScript and TypeScript
48:46 to sort of make these interactions possible.
48:49 So the rest of us don't have to write JavaScript and that kind of stuff.
48:53 - So I think we're getting about to the end of our time here.
48:55 I'm sure that actually this is somewhat similar.
48:59 It's really a dynamic interactive web app.
49:02 And in that case, it probably has to have some interesting front-end JavaScript stuff going on.
49:06 What's the JavaScript story?
49:07 - Yeah.
49:08 - What framework are you using and stuff like that?
49:09 - We use React to build our components.
49:12 Obviously we abstract that away so that you as a Python developer, you're just writing Python,
49:18 well, you're writing SQL as well, but let's call that Python.
49:21 And then we deal with the components for you.
49:25 And so it's not the kind of framework where you're writing Python and we're generating HTMLX or something like that.
49:33 So obviously we understand React and we know we can build some beautiful components,
49:38 customize them with it.
49:39 We weren't about to create a completely new framework where we somehow just generate HTML from Python.
49:46 I think there's a few people working on that, actually.
49:49 We already know React, so let's just build those components or customize those components
49:54 and then just let people declare them and then sort of access and modify them
49:59 via the context objects in your Python functions.
50:03 So our stack, we use FastAPI for our backend, which is great.
50:08 - It's excellent, yeah.
50:08 - I'm not sure if you've had the creator here on.
50:11 - Yeah, I've had Sebastian on, I think at least three times, Sebastian Ramirez.
50:16 - Yeah, he's great.
50:17 - Yeah, he is really great, yeah.
50:18 FastAPI is a fantastic app.
50:20 And so, yeah, we use them.
50:22 We use React on our client side and we are a lot of our infrastructure is on AWS.
50:28 - Yeah, sounds really interesting.
50:29 Sounds like a fun app to work on.
50:31 - Yeah, you should play around with it.
50:33 I can help you build some apps to manage your podcast library.
50:37 - All right, well, Jimmy, I think we are pretty much out of time.
50:41 Final thought for people listening, maybe they're excited to try this out.
50:44 What are you telling them?
50:45 - Yeah, just go to dropbase.io and sign up for a free account.
50:49 It's free for developers.
50:50 You can set it up locally.
50:52 If you have any issues setting it up, just reach out.
50:54 I'll personally help you get it up and running.
50:57 Yeah, you can look at the documentation for everything else.
50:59 - Yeah. - Let's try it.
51:00 Give us some feedback.
51:01 We'd love to hear from you.
51:02 - Awesome.
51:03 Congrats on building a cool project for people and yeah, good luck with it.
51:07 - Thanks, Michael.
51:08 - This has been another episode of Talk Python to Me.
51:12 Thank you to our sponsors.
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52:08 This is your host, Michael Kennedy.
52:10 Thanks so much for listening.
52:11 I really appreciate it.
52:12 Now get out there and write some Python code.
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