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Lwan is a high-performance & scalable web server.

Requests per second vs. number of concurrent connections?

performance-chart

Hello, World! (C)
100B file

Hello, World! (LuaJIT)
32KiB file

Lwan was until recently just a personal research effort that focused mostly on building a solid infrastructure for a lightweight and speedy web server.

With its low disk and memory footprints, it's suitable to be used from embedded devices to robust servers. Both static and dynamic content can be served, as it can also be used as a library. Dynamic content can be generated by code written in either C or Lua.

Its simple architecture and tiny source code guarantees the entire code base can be easily grokked.

Connections are handled individually by coroutines, which are transparently and efficiently juggled by a per-CPU cooperative scheduler, giving the illusion of blocking I/O to handlers.

Resource management is also greatly simplified with coroutines: whenever a client connection is closed, memory is automatically reclaimed, files are automatically closed, references are automatically decremented. This provides more predictability than most garbage collectors, helping keep latencies low while being almost as easy to use.

Homepage : https://lwan.ws/
Github : https://github.com/lpereira/lwan
2018/07/27 15:34 2018/07/27 15:34
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https://blogs.dropbox.com/tech/
2018/01/03 12:57 2018/01/03 12:57
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This is an expanded version of my talk at NginxConf 2017 on September 6, 2017. As an SRE on the Dropbox Traffic Team, I’m responsible for our Edge network: its reliability, performance, and efficiency. The Dropbox edge network is an nginx-based proxy tier designed to handle both latency-sensitive metadata transactions and high-throughput data transfers. In a system that is handling tens of gigabits per second while simultaneously processing tens of thousands latency-sensitive transactions, there are efficiency/performance optimizations throughout the proxy stack, from drivers and interrupts, through TCP/IP and kernel, to library, and application level tunings.

https://blogs.dropbox.com/tech/2017/09/optimizing-web-servers-for-high-throughput-and-low-latency/
2018/01/03 12:56 2018/01/03 12:56
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Despite being a voice and text chat app, Discord sees over a hundred million images passing through its tubes every day. While we wish it was as simple as sending them out across the tubes to your friends, delivering these images creates some pretty large technical problems. Linking directly to images would leak users’ IP addresses to image hosts, and large images use up lots of bandwidth. To circumvent these problems, Discord needs an intermediary service to fetch images on behalf of users and then resize them to reduce bandwidth usage.


https://blog.discordapp.com/how-discord-resizes-150-million-images-every-day-with-go-and-c-c9e98731c65d
2018/01/03 12:48 2018/01/03 12:48
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In information retrieval, Okapi BM25 (BM stands for Best Matching) is a ranking function used by search engines to rank matching documents according to their relevance to a given search query. It is based on the probabilistic retrieval framework developed in the 1970s and 1980s by Stephen E. Robertson, Karen Spärck Jones, and others.

The name of the actual ranking function is BM25. To set the right context, however, it is usually referred to as "Okapi BM25", since the Okapi information retrieval system, implemented at London's City University in the 1980s and 1990s, was the first system to implement this function.

BM25 and its newer variants, e.g. BM25F (a version of BM25 that can take document structure and anchor text into account), represent state-of-the-art TF-IDF-like retrieval functions used in document retrieval.


https://en.wikipedia.org/wiki/Okapi_BM25
https://inyl.github.io/search_engine/2017/04/01/bm25.html
https://www.slideshare.net/kjmorc/ss-80803233
2018/01/03 12:09 2018/01/03 12:09
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