NodeLearn
tarn.js
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| NodeLearn | tarn.js | |
|---|---|---|
| 2 | 1 | |
| 0 | 454 | |
| - | 2.4% | |
| 0.0 | 0.0 | |
| about 1 year ago | 7 months ago | |
| JavaScript | JavaScript | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
NodeLearn
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Piscina – The Node.js Worker Pool
If you can use redis, redlock can be a nice implementation.
Example: https://github.com/fendy3002/NodeLearn/blob/master/Redlock/s...
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After 1 year of learning how to code my biggest piece of advice to a newbie would be....
For example, my nodejs sandbox / testing ground: https://github.com/fendy3002/NodeLearn
tarn.js
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Piscina – The Node.js Worker Pool
Knex, the SQL query builder, uses Tarn.js[1] for connection pooling to the db.
I've been using Tarn a bunch at work recently. We're doing some batch jobs, and I'm queuing work at each stage in Tarn.js pools. I created my own enqueue function that waits until the pool is less than a high-water mark in size before enqueueing. Then the pool has however many workers running.
[1] https://github.com/vincit/tarn.js/
What are some alternatives?
camaro - camaro is an utility to transform XML to JSON, using Node.js binding to native XML parser pugixml, one of the fastest XML parser around.
piscina - A fast, efficient Node.js Worker Thread Pool implementation
poolifier - Fast and small Node.js worker_threads and cluster worker pool
bree - Bree is a Node.js and JavaScript job task scheduler with worker threads, cron, Date, and human syntax. Built for @ladjs, @forwardemail, @spamscanner, @cabinjs.
worm-scraper - Scrapes the web serial Worm and its sequel Ward into an eBook format
pambdajs - Parallelized Lambda, the wrapper to help run lambda/anonymous function in parallel. 🚀