A clever repurposing of technologies but realistically only worthwhile for static datasets with range payloads small enough to fit into a web response.
> your pipeline has to rebuild each customer’s file fast enough to meet the update cadence. ... data that updates on a coarse schedule rather than in realtime
It doesn't have to all live in the same Parquet file. you can have a Parquet file for all your historical data, plus one for the current week which is updated often cheaply, and then when the week is over you merge that into your big parquet file.
You're making it seem like there's hard limits to what can be done but while there definitely is, you can do incredible stuff.
> The bytes pass through a small Cloudflare Worker on the way, because the free r2.dev URL is rate-limited.
For a 40MB file I suggest hosting it directly on GitHub Pages - that's effectively a free CORS-enabled CDN and supports HTTP range requests, so you should be able to get that demo working without needing to involve Cloudflare Workers at all.
> A dashboard like this one is designed to answer a bounded set of analytical questions ~ requests per day, requests per day for one agency, all-time totals by borough. Each question can be answered by GROUP BY queries, so we can precompute them all ahead of time and save each result as its own small table, called a grouping set. Stack all of the grouping sets in one Parquet file, one section per set, and you have a data cube. A grouping set is only useful if it either enables a question to be answered, or reduces the latency of pulling the data.
It's a good question. In a sense, the cube is caching, just materialized ahead of time instead of memoized on demand. A result cache still needs a live database behind it for misses; the cube has no misses, since every question the dashboard is designed to answer has data in the cube already. And for this experiment, the goal was to forgo a database to serve the data in the first place.
I provide caveats for when this would work vs. when it doesn't in the post. For a lot of customer-facing dashboards, I think it's probably pretty good.
On the surface (I haven't tested it) it looks like a great cost and runtime saver, but only for data sets that need a cadence above 5 or so minutes. You wouldn't be able to have a refresh button to get the "latest" data outside this window, depending on size and build time? Wondering if you can apply a hybrid approach, combining the historical parquet file with a live query.
How is this any different from the old OLAP cubes like IRL / Express? The cube is basically an array in memory and super fast, the dashboards based on Group By as well. I was an Express consultant at Oracle, great to see old concepts come back to life if it’s what I think it is.
Indeed, OLAP cubes keep finding new life. The critical difference here is that there isn't a heavy query engine (just a Parquet reader) and a few http requests to pull from the cube. So it's light on the backend and light on the frontend.
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[ 0.25 ms ] story [ 25.2 ms ] thread> your pipeline has to rebuild each customer’s file fast enough to meet the update cadence. ... data that updates on a coarse schedule rather than in realtime
You're making it seem like there's hard limits to what can be done but while there definitely is, you can do incredible stuff.
For a 40MB file I suggest hosting it directly on GitHub Pages - that's effectively a free CORS-enabled CDN and supports HTTP range requests, so you should be able to get that demo working without needing to involve Cloudflare Workers at all.
If you are okay with it being down regularly
What's the benefit of "data cubes" over caching?
I provide caveats for when this would work vs. when it doesn't in the post. For a lot of customer-facing dashboards, I think it's probably pretty good.
It will cost (slightly) more to write this frequently to R2 since you are charged per-write, but this is something you can tune.
https://duckdb.org/docs/current/sql/query_syntax/grouping_se...