npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

viaduct

v1.0.0

Published

Topologic is a cloud native framework for building real time processing graphs on Kubernetes.

Downloads

7

Readme

topologic

Topologic is a cloud native framework for building real time processing graphs on Kubernetes.

The best way to understand how it works is with an example. Let's say you were running a bus system and wanted to provide bus location and stop arrival information. We want to log the bus' current location in a database, fetch the current route timetable for this bus, predict its estimated time of arrival at its remaining stops given this timetable, and emit a message to notify all of our waiting passengers with the current location of the bus and its expected time of arrival.

Looking at this as a graph it would look like this

        ------------------------- Locations -------------------
        V (location)              V (location)                V (location)
        writeLocation       fetchBusTimetable                 notifyLocation
                                  V (location, timetable)
                            predictArrivals
                                  V (timetable, arrivals)
                            notifyArrivals

We express this graph in topologic like this:

let locations = new KafkaConnection({
    topic: 'locations'
});

let timeTableConnector = new InProcessConnection();
let arrivalsConnector = new InProcessConnection();

let topology = new Topology([
    new Vertex({
        inputs: [locations],
        processor: writeLocation,
    }),

    new Vertex({
        inputs: [locations],
        processor: fetchBusTimetable,
        outputs: [timeTableConnector]
    }),
    new Vertex({
        inputs: [timeTableConnector],
        processor: predictArrivals,
        outputs: [arrivalsConnector]
    }),
    new Vertex({
        inputs: [locations],
        processor: notifyArrivals
    })

    new Vertex({
        inputs: [locations],
        processor: notifyLocation
    })
]);

connection handles pressure - vertex should check for pressure indicator before taking on new work.

You did not need to build the logic for scaling all of the processing pods, nor all of the queueing and dequeuing logic for your location messages, nor code to monitor your pipeline. Topologic provides all of that for you out of the box.