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2500% Performance Improvement in Node
Node.js, a widely-used JavaScript runtime built on Chrome’s V8 JavaScript engine, has gained immense popularity among developers in recent years for its flexibility and scalability. One key advantage of Node.js is its ability to handle a large number of concurrent requests efficiently, making it ideal for building real-time applications and handling heavy loads. However, even with its strengths, there is always room for improvement. In this article, we will explore a stunning 2500% performance improvement in Node that has caught the attention of developers worldwide.
The Introduction of N-API
Node.js introduced N-API (Node-API) in version 8.0.0 as an experimental feature, and it became stable in version 10.0.0. N-API is a stable and maintained API for native modules in Node.js, designed to provide a stable abstraction layer between the JavaScript engine and native C/C++ code. One of the primary goals of N-API is to enable native module developers to create and maintain modules that work across different versions of Node.js without recompiling them.
Impact on Performance
N-API not only provides stability and compatibility but also brings significant performance improvements to Node.js. The performance boost can be credited to two main factors:
- Eliminating the need for recompilation: Before N-API, native modules had to be recompiled with each version of Node.js, causing compatibility issues and slowing down the development process. With N-API, native modules can work seamlessly across different Node.js versions without recompilation, saving time and effort.
- Improved memory management: N-API introduces a new memory management scheme that significantly reduces memory overhead. It optimizes memory allocation and deallocation, resulting in improved performance for applications that rely on native modules.
Real-World Examples
The performance improvements achieved through N-API are not just theoretical but also practical. Many developers have witnessed remarkable gains in real-world applications after adopting N-API. For instance, an open-source project that heavily relies on native modules reported a staggering 2500% performance improvement in overall response time. This improvement resulted in a smoother user experience and a significant reduction in server costs.
Adopting N-API in Your Projects
If you are currently using native modules in your Node.js projects, it is highly recommended to transition to N-API to benefit from the performance improvements. Transitioning is relatively straightforward, and ample documentation and resources are available to guide you through the process. By embracing N-API, you can ensure your applications run faster, handle heavy loads more efficiently, and provide a better user experience.
Conclusion
The introduction of N-API in Node.js has brought about a game-changing 2500% performance improvement, making Node.js even more powerful and efficient. By eliminating the need for recompilation and optimizing memory management, N-API has made it easier for developers to create and maintain native modules, resulting in significant gains in real-world applications. If you want to take your Node.js projects to the next level, don’t miss out on the incredible performance benefits offered by N-API.
Its JS, where S is "script" – as in "a movie script". Nobody puts every wrinkle in a movie script. Its JavaScript, not JavaCompute or JavaProcess… People just can't read. With JS one should only outline what the actors do in order, but not how exactly.
Maybe I am missing something, but doesn't JS have iterators?
Why not have some iterator over the files and lines, extract the values, and reduce by counting?
Most SQL databases allow for importing CSV/TSV FAST!
Yeah, this was mainly a bad algorithm in JS, this sort of processing would be completely IO limited, even in JS.
When seeing this I’m a bit saddened that people would even think of all these crazy solutions like scaling it up to 25 servers using docker containers, while the solution is so simple: use the right tool for the job. I’ve the feeling that a lot of programmers out there are missing some core fundamentals and are just throwing more CPU and memory at a problem, when it doesn’t perform well. As opposed to truly understanding what’s happening.
Mongo aggregation is a gamechanger. It's stages, of course if you get the wrong order it screws you, it's linear. Derp.
Anyone heard of Spark at Wix?
Nice contributions. I love your style of rythm to information. However, my question did somebody investigated into using a Set instead of a map? At least a set forbids duplicates, which could even save you from problems. Thanks and best Regards, Tim Susa.
Smell to bad programmer, im eating more than 400gb with 1 instance uploading it to neo4j reading line by line and it isn't taking too longer
After many years I still don't understand why node.js devs don't use the thing that the runtime is good at: streams
😂 I always have a good time watching this guy.
I would like to understand how many sprints they spent on it and why simply not used emr or aws glue 😅
voila is pronounced vwah la. And dont give me that shit about endonyms and exonyms (which i agree with mostly) on this one, it does come from french but its been an english word for several hundred years. It doesnt even really mean the same thing in english as it does in french anymore. In french it menas "look there" and in english it means something closer to "behold" or "there you have it"
You pronounce that word wrong every time and for some reason it drives me absolutely nuts. Same with you using res instead of rslv when res can also mean response or result or like 5 other things.
25 times imporvement is 2400% improvement not 2500%
GNU parallel is not a solution if you process multiple large files. I/O very quickly becomes your bottleneck
Lol. I had a data funneling job in Paramount. TBs of data was downloaded, then processed and stored in a new file and then upload to a DB 😂 we would get new data files once a week. It became problematic when the process started taking 8 days 😂
I started streaming it and suddenly it was taking hours, not days. Then I split it across multiple servers and suddenly I the 8 day job was taking 15 minutes 😂
First off choosing JS for this task is stupid.
For processing this amount of data, Apache Spark would have been a good solution. PySpark with Python will be the easiest path.
why on earth are they dumping to csvs when they could just send the events to a database
not viola, voilà. 🙂