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vega
falcon
Jupyter Notebook2019–2023
vega / falcon

falcon

Brushing and linking for big data

Data & Databases
Explore on GitHubRead Hacker News discussions
6Distinct people
6Mentions
0Explicit recommenders
974GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Brushing and linking for big data

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year6 dated mentions

Observed mentions: 2019-02-20 14:05:34 UTC to 2023-10-24 17:26:20 UTC.

01232019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2023

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC110
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC303
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC101
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC101

What Hacker News says

6 sources
paddy_m·Oct 24, 2023
Somewhat related, who has played with performant JS dataframe libraries? I mostly want to serialize from pandas/polars to JS in a fast way, with some very minor selection and indexing operations JS side. Ideally I could find a library that does dataframe like operations from reading base64 encoded Buffers/Arrays into their JS objects, then doing the right things. I'm investigating https://arrow.apache.org/docs/js/ https://github.com/vega/falcon https://github.com/pola-rs/nodejs-polars https://github.com/uwdata/arquero
In Goodbye, Node.js Buffer
paulgb·Feb 28, 2022
Good questions! > Why do you need one process per user? / Wouldn't this "event loop" actually be more efficient that one user/process, as there would be less context switching cost from the OS? We're particularly interested in apps that are often CPU-bound, so a traditional event-loop would be blocked for long periods of time. A typical solution is to put the work into a thread, so there would still be a context switch, albeit a smaller one. The process-per-user approach makes the most sense when a significant amount of the data used by each user does not overlap with other users. VS Code (in client/server mode) is a good example of this -- the overhead of siloing each process is relatively low compared to the benefits it gives. We think more data-heavy apps will make the same trade-offs. > Can I just keep a map of (connection, thread_id)
Read the rest
on my server, and spawn one thread per user on my own server? If you don't have to scale beyond one server, this approach works fine, but it makes scaling horizontally complicated because you suddenly can't just use a plain old load balancer. It's not just about routing requests to the right server; deciding which server to run the threads on becomes complicated because you ideally want to decide based on the server load of each. We started going down this path, realized we'd end up re-inventing Kubernetes, so decided to embrace it instead. > Could I just load up my server with many cores, and give each user a SQLite database which runs each query in its own thread? This way a multi GB database would not be loaded into RAM, the query would filter it down to a result set. If, for a particular use case, it's economical to keep the data ready in a database that supports the query pattern users will make, it's probably not a good fit for a session-lived backend. In database terms, where our architecture makes sense is when you need to create an index on a dataset (or subset of a dataset) during the runtime of an application. For example, if you have thousands of large parquet files in blob storage and you want a user to be able to load one and run Falcon-type[1] analysis on it. [1] https://github.com/vega/falcon
In Launch HN: Drifting in Space (YC W22) – A server process for every user
tracyhenry·Nov 20, 2020
We are building a data vis tool (https://github.com/tracyhenry/kyrix) to solve this exact problem many web vis tools have. The idea is not surprising: using database indexes and a backend to send the frontend necessary data to render on demand. DB and the backend are containerized so the installation, data loading and authoring are all one command. For creators, we offer D3 and JSON-based declarative primitives that enable creation of pan/zoom like visualizations very easily (e.g. 10s of lines of JSON for a 15-zoom-level vis). You can check out an interactive demo here, which visualizes 1.88 million wildfires: http://wildfire.kyrixdemo.live/ Since big data vis is very use case dependent, I'd like to reference two other tools that might be useful: Falcon: https://github.com/uwdata/falcon NanoCubes: https://nanocubes.net/ Disclaimer: I'm st
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ill in academia so I'm apparently biased towards academic works. But fortunately they are all open source.
In Why We're Building Observable
domoritz·Oct 30, 2020
Vega-Lite is built on Vega, which is fully reactive and can do partial updates. In order to use it, you need to update the data via the Vega view api. Check out https://vega.github.io/vega/docs/api/view/#view_data. One other piece of advice I have it to reduce the number of marks you need to draw with aggregation or sampling. I used this approach in https://github.com/uwdata/falcon to visualize billions of points and interact with them in real time.
In Vega-Lite: A Grammar of Interactive Graphics

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