- Setup CI / CD process to test and push to a staging server
- Debugged some JS warnings related to the Vuex state store, and a slightly malformed server response to an API endpoint
Not all cafes will make white, espresso or filter coffees equally, or equally well. Some might have a focus on espresso and bit a little average for the whites, or vice versa. Rather than combining all their ratings and losing that granularity I will let users specify what sort of drink they had when rating a coffee.
I think this will be important to help give better recommendations. A cafe might do stellar cappuccinos, but if they’re ordinary at a Long Black and that’s what you’re going to order the Cap won’t help you!
The feature does add quite a bit of complexity to the development though. I need to start tracking ratings separately for the drink categories, as well as in aggregate. The scoring algorithm will also ideally weight itself towards a users history and preferences too.
It also has frontend implications with extra UI, state management and icon design requirements. The image above is still, but I am working on building subtly animated SVG icons for the drink types to enhance the UX of choosing them. I’ve got a nice little stepped animation to show the selection action.
I love the flexibility of images-as-code and being able to add simple animation effects with CSS. I am a little concerned that bundling the SVG icons into Vue components will be adding more weight to the final JS build.
I think referencing a static SVG as an image will prevent me from being able to style and animate individual shapes with CSS. It needs to be an embedded object. When I’m already deep in a Vue component structure that means the icon also needs to be a Vue component (as opposed to server side injected SVG code).
In my defence though, I think most of the audience who are into visiting specialty coffee cafes for entertainment probably err towards having high end phones that are capable of dealing with the load. The JS will be cacheable and probably even service worker cached in the future, so it’s more a CPU/parsing issue.
Most of the backend adaptations are still to be done though, and the actual search weighting will happen later as I build that feature.
The growing amount of data stored for a rated coffee would get difficult to manage within a single parent component. I’m glad I’ve started the work of using Vuex to control the global state. Passing this many props and events up and down the component chain would be quite messy.
So much of the project framework is complete, but there are a few key features left to build.
Coded a basic cafe banner image handler today.
The code that renders the image on the frontend cafe page is suuuper rough. Just gets a random size, rather than using srcset. Sniffs webp support and does cloud disk access right in the view. Definitely needs tidying up, still a good start though.
Sourcing 2000px images (wide on retina) might be difficult. A lot of photos on the web seem to be lower quality. Might have to ask users or cafes to submit a quality one!
I’m trying to hold back on the scope creep and I’d planned not to PWA this thing, but so many people are asking if it’s a real app, or just a website.
Maybe it would be worth doing a 101 PWA with caching of the assets & home screen icon. Unless I do a lot more work none of the features would work offline though.
I’m not sure if that’s pointless, having an online-only PWA, or if in reality most users are online and they just need an icon launcher, fast loading, and no URL bar.
Maybe I’ll do that. If you’re offline I might be able to cache a list of Fav/To Try and at least render those?
Here’s how the web app is looking. These sections are a WIP and still include some of my visual to-dos on screen, but you get the idea.
Unfortunately not a lot of progress on HadCoffee last year.
I have made progress recently, but mostly on backend processes that I don’t think are super interesting to read about, and certainly not very visual. Stay tuned (if you want).
JS not included. What’s a good package to count that?
I made a nice chunk of progress on the ‘To Try’ feature of HadCoffee this weekend. This lets you bookmark cafés you’ve heard of and would like to try later. By default the list on HadCoffee will sort by proximity using your current location.
Most of the recent work for this feature was on some nice-to-use buttons for toggling a cafe in your Favourites or To Try list. I had mostly built the ‘Fav star’ earlier, but improved it with a visual :focus indicator for keyboard accessibility.
See the Pen SVG Favourite by Mike (@mike_hasarms) on CodePen.0
The coffee cup icons are SVG based Vue components that receive rating data from the page and fill the cup appropriately.
I also worked on some basic animation of the button to add a cafe to the To Try list to make the transition to the active state a bit more obvious. I think little touches of movement make the thing more engaging to use as well.
I spent a bit of time improving the A11Y of these (and other features). The Vue SVG components are keyboard accessible, labelled and indicate focus. I am planning to do a more thorough A11Y test before I release, but I’m certainly not leaving all of those concerns until the end of development.
Currently the Favourites and To Try lists require a user to be logged in. I had planned to allow anonymous users to begin keeping these lists for themselves in localStorage. The idea was to lower the barrier to entry to using the site. It does however add development complexity as the app needs to be able to use both local and server storage for those lists and load in data in different ways.
As this is a mostly server-rendered app all the initial page data is sent down with the page load, and populated into the Vue components. Supporting anon users will require separate logic to request their cafe data from an API endpoint.
That’s not hugely difficult, it’s just another thing to be done before launch.
I’m keen to make some solid progress on HadCoffee over the Christmas period to hopefully get to an early 2019 v1 launch. This week I was working on the main coffee rating interface which depends on users being able to select or enter the café they visited.
I’ll add the autocomplete lookups for typed cafe names, but I also wanted some basic quick suggestions based on where the user has been before and their current location.
This would save typing for common places and maybe also hint at other nearby cafés the user mightn’t know about.
The frontend part of these feature went pretty smoothly. The Vue component watches for the users location (HTML5 Geolocation) and calls the backend to load these quick suggestions as it’s known. There’s a little debounce action to slow things down as the geolocation API can return updated positions in quick succession as it locks on a more accurate location.
The smooth progress I was making through this feature hit a wall when it came to actually querying cafés by distance though. I was hoping to use MySQL’s ST_Distance_sphere function to let the DB do that work. I’m running MariaDB though, which although it’s advertised as a ‘drop in’ replacement for MySQL does not support this feature ????
I prefer a simpler dev environment (I’m not using Laravel Valet or Docker images) so I didn’t feel like swapping to MySQL for this project. Changing my workflow to use Valet also wasn’t very appealing when I’m otherwise happy with the setup. so I briefly tried migrating to Postgres. I know it’s a great DB, but I haven’t used it before and that’s a big change to have to make to run one type of query.
In the end I’m going with a raw SQL query to help with this. I’ll add a simple bounding box to its parameters first to avoid having to do a table scan of every cafe in the world (once my DB gets to that point ????)
Although it took a windy path this geo search will also provide the basis for the other cafe search features on the site such as the autocomplete (to improve relevance) and the location based search.
$query = "SELECT id, cafe_id, lat, lng, address, locality, city, ( 6371 * acos( cos( radians(:lat) ) * cos( radians( lat ) ) * cos( radians( lng ) - radians(:lng) ) + sin( radians(:lat2) ) * sin(radians(lat)) ) ) AS distance FROM cafe_locations ";
This is a good step towards being able to add café & coffee reviews, however the next big sticking point will be letting users add new cafés as they go.
Ideally I’d like to collect a bit of meta data such as roasters, menu and seating options to help users finding cafés, but I’ll have to see how much data entry users will tolerate. I also need to be aware of how or if I can verify this community sourced data.