Fast drilldown dashboards from a single Parquet file
hamiltonulmer.com124 points by v3gas 8 hours ago
124 points by v3gas 8 hours ago
> 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 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.
And then you eventually just take the weekly files and combine them and you've reinvented data lakes with worse (no) metadata management
"static datasets with range payloads small enough to fit into a web response" fits a lot of workloads.
I expect that if your overall data is less than a GB this trick will work really well for you.
Most reporting I've ever worked on is based on live data and users expect updates. Range requests over parquet cubes are a cool party trick, but you outgrow it quickly once you need to start regularly updating the dataset such as to avoid full recompuation.
The next step in this journey is Iceberg (and a proper incremental pipeline), which can also be read directly in the browser via WASM either via DuckDB or without. This is from the same author as the parquet library mentioned in the OP https://github.com/hyparam/icebird
> 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.
What's the benefit of "data cubes" over caching?
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.
Interesting how noise complaints dwarf any other type of complaint in NYC.
Great read!