Things I learned while writing high-performance JavaScript applications

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During the past months, I developed Lyra, an incredibly fast full-text search engine entirely written in TypeScript. It was surprising to me to see how it could compete with solutions written in Rust, Java, and Golang, all languages known for being typically "faster than JavaScript"... but is that even true? In this talk, I will share some lessons I learned while developing complex, performance-critical applications in JavaScript.

Michele Riva
Michele Riva
31 min
14 Apr, 2023

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Video Summary and Transcription

This talk explores the creation of a full-text search engine in JavaScript, highlighting the challenges with existing search engines like Algolia and the advantages of using JavaScript. The speaker emphasizes the importance of code optimization and performance enhancement techniques in JavaScript. The talk also discusses the evolution of the Lyra search engine into the open-source project Orama, which offers a feature-rich and highly performant full-text search engine for JavaScript. The speaker addresses questions about language choice, scalability, and deployment, and showcases the benefits of deploying an immutable database to a CDN.

Available in Español

1. Introduction to Full-Text Search

Short description:

Welcome to my talk on Disrupting Full-Text Search with JavaScript. I love Elasticsearch because of its performance and scalability. Elasticsearch is built on Apache Lucene, a powerful full-text search library. However, I also love other search engines like Algolia, MeliSearch, and MiniSearch. I decided to recreate a search engine with my team to learn more and address personal issues I had with existing software, such as deployment difficulties, upgrades, memory usage, and high costs.

Welcome everyone to my talk, Disrupting Full-Text Search with JavaScript. I've been already introduced, so I won't proceed any further with that.

And I'm here to talk about full-text search because it's a domain that I love and something that really keeps me awake at night because I love it so much that I can't just stop thinking about it. And there is a good reason why I love it so much, and it's mainly because of Elasticsearch.

How many of you knows Elasticsearch? Everyone. How many of you have used Elasticsearch? Again, almost everyone. And I gotta say I've been introduced to open source software mainly because of Elasticsearch. So I have a very passionate relationship with it and I had the pleasure and the honor to work on Apache You Know Me, which is a customer data platform that uses Elasticsearch as a leader database in its infrastructure. And when I was a bit more junior like, I don't know, almost 10 years ago now, I was impressed by the performances of such a complex and distributed system. I was impressed to see that I could throw like millions of millions of records against it and it wouldn't degrade the performances that much. That was seriously impressing to me, and this is where I decided to go into open source software and try and understand how Elasticsearch works.

So my first question as a curious junior engineer was how is that even possible? I mean, how can a software maintain such good performances even with a billion of records? So I later discovered that Elasticsearch is not actually a full-text search engine, but Apache Lucene is. So Apache Lucene is the full-text search library, which Elasticsearch wraps by providing a RESTful interface, disability system capabilities, sharding, data consistency, monitoring, cluster management and so on and so forth. So big shout out to Elasticsearch.

And before proceeding any further, let me please clarify that again I love Elasticsearch and I love Algolia. I love MeliSearch. I love MiniSearch. I love every single search engine out there. And the reason why, of course, I'd be talking about something that I recreated with my team. The reason why I did that in the first place is because I wanted to learn more and of course I wanted to solve some very personal issues that I had with such software. So nothing personal. Please, if you're using Elasticsearch, just continue using it, if you're comfortable with it. There's no problem with that, of course. I was talking about the fact that I had some personal issues with Elasticsearch. My first personal problem was that it's pretty hard to deploy, in my opinion. Could be simplified. Hard to upgrade. Has a big memory footprint. CPU consumption becomes terrible as soon as you add more data. It's really costly to manage and run.

2. Challenges with Java and Algolia

Short description:

I don't like Java. I prefer JavaScript. Algolia is expensive and hard to extend. Making simple software is extremely hard, but as engineers, we have to give it a try.

Hard to extend and customize. But most importantly, Java. I knew that people would have laughed at this one. But it's a real concern, actually. Like, I don't like Java. I've been coding in Java for a bit. I prefer JavaScript forever and always. Also, I tried different solutions, such as Algolia, which is, again, an extremely extraordinary software. And I'm not even exaggerating here. The problems I had with Algolia is that it's incredibly expensive at scale. It's a big black box, right? It's closed source. And therefore, it's hard to extend and try to understand what's going on with it. But again, as I said, these are my personal problems with them. And maybe when I had these problems in the first place, I was a bit too inexperienced in that domain. Elasticsearch and Algolia were a bit too much for me. Maybe it's worth it to have such problems, right? Because people are using them. So there must be a reason why. And I also do understand now that I'm a bit more experienced, that making simple software is extremely hard. But I feel like, as engineers, we have to give it a try.

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