Comment by mantraxC | Hacker News Reader

Comment by mantraxC | Hacker News Reader

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They say that, but there's one giant missing ", because..." in that paragraph.

They never explain why they render in 10 sizes (why don't they know which sizes they need), and why is loading one of those sizes on a mobile app not feasible.

It's stated like that and left hanging in the air.

Either their use case is very weird, or they're deliberately vague for some reason only they know about.

Also to clarify why 10 sizes is weird, normally what you'd do is double the width/height of your images with every size (so quadruple the pixels), same as one does with mip-maps. Or with icons.

Let's say the smallest sensible size is 128x128 (minimum needed to discern a product, and easy to downsize from there, won't get much smaller at good quality).

So we have 128, 256, 512, 1024, 2048, that's 5 sizes. And I'm pretty sure the images they need aren't over 2048x.

So 10 sizes is just pointless.

And with OpenRoss, doing cropping and adding whitespace at the server, producing pointless image duplicates, is senseless. Even the most crippled client-side technique can do the cropping and whitespace for you (yeah, even html).

This screams "we didn't think this through much".

When the site was originally designed, there were only 4 or 5 sizes, one for the feed, one for the product page, one for related products, one for thumbnails, etc.

As the design changed, extra sizes got added to the image processing, and as with most early stage start-ups, it worked therefore there was no need to fix it.

We eventually ended up in the position of having 2.5M products all with preprocessed images of certain sizes from our design history. As we wanted more flexibility with design, but also knew that a lot of our images would have a very low likelihood of being accessed (fashion items have single runs and are never remade in future seasons), a big batch process didn't seem appropriate. Additionally, it would mean storing several different copies of images in our S3 bucket, even if we knew the product would not likely be seen again.

A more attractive solution (at least to us) was to do the hybrid approach, where we would resize on demand, and then cache for a long time. This way, we only do the processing for images that need it, in almost a functionally identical way to large scale batch processing, but the process is demand-led.

But the nice thing is - using a storage solution where you don't pay for what you don't use.

It's a chicken/egg scenario. How does the resource cost of resizing on-demand compare to storing all sizes? Also, like @mantraxC said, are all those image sizes really needed?

I have a system I'm trying to sunset that has a 160 and 130 size. Totally redundant, and doesn't really save that much space/bandwidth.

Still, OpenRoss is a cool project to learn from. It might not fit many use-cases, but apparently it works for Lyst.

"This screams "we didn't think this through much"."

That's actually quite rude.

I have had this exact problem/need overtime on several projects, and it doesn't mean "we didn't think this through much". Products change/grow and there becomes a need for tens of different images. First it starts with a thumbnail or two, then composite images, then, etc. Not to mention you need 2x images for retina devices.

Bottom line, overtime you tend to understand that there is great flexibility in this type of architecture.

"Even the most crippled client-side technique can do the cropping and whitespace for you (yeah, even html)."

Facebook, Twitter and Google Plus all have a feature where a URL gets expanded out to a "rich preview" (Twitter Product Cards for example are particularly relevant to a site like Lyst: https://dev.twitter.com/docs/cards/types/product-card )

These all work best with an image that has been pre-cropped and resized.

As an added bonus, a new one of these emerges every now and then - with a different size requirement.

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