From mboxrd@z Thu Jan 1 00:00:00 1970 Return-Path: X-Spam-Checker-Version: SpamAssassin 3.4.0 (2014-02-07) on dcvr.yhbt.net X-Spam-Level: X-Spam-ASN: X-Spam-Status: No, score=-4.0 required=3.0 tests=ALL_TRUSTED,AWL,BAYES_00 shortcircuit=no autolearn=ham autolearn_force=no version=3.4.0 Received: from localhost (dcvr.yhbt.net [127.0.0.1]) by dcvr.yhbt.net (Postfix) with ESMTP id D78BA1FF40; Mon, 12 Dec 2016 02:10:00 +0000 (UTC) Date: Mon, 12 Dec 2016 02:10:00 +0000 From: Eric Wong To: unicorn-public@bogomips.org Subject: WTF is up with memory usage nowadays? Message-ID: <20161212021000.GA15226@untitled> MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Disposition: inline List-Id: Came across this in my feeds today: https://about.gitlab.com/2016/12/11/proposed-server-purchase-for-gitlab-com/ ... Yeah, they cite 0.5 GB of memory usage per unicorn worker. I guess this is typical nowadays, but damn, it sucks :< This is not the future I had in mind or ever wanted unicorn to be associated with back in 2009 when I started. I don't think it's the fault of unicorn itself; unicorn recycles request buffers, uses pre-frozen hash keys, and even uses String#clear nowadays to discard heap memory, and never buffers more than it has to. Since day one, unicorn was built to handle multi-gigabyte uploads and responses; even from a crappy 256MB laptop. "curl -T-" is my co-pilot :) So... I guess the problem is up the stack in the app or framework. Maybe Rails? *shrug* I don't use that anymore... I remember using Rails over a decade ago and being shocked at 50MB (yes, fifty megabytes) of RSS usage. This was on 32-bit, but even in the worst case on 64-bit, it would be 100MB. Of course, nowadays Rails has grown to the point where I'm afraid to go near it; instead I work directly off Rack. And yes, I still freak out nowadays when my Rack processes exceed 100MB... So, what can and should we do about it? * First step: Limit ourselves. Use slower, older hardware, slower Internet connection so you force yourself to eke out every bit of performance out of what you have. It's utterly hilarious for me to hear about people complain about laptops which can "only" have 16GB RAM. I've definitely made transgressions in the past, and the worst code I've written was on powerful hardware. Disclaimer: Some of the following may not be very Ruby-ish :P And everything else is optional and the result of the first step above. * Recycle. Don't waste object slots: {Array,Hash,String}#clear can allow you to recycle heap memory for large objects and minimize GC pressure. Using thread-local variables in your app helps maintain compatibility with multi-threaded Rack servers; or perhaps go Rack env-local for compatibility with single-threaded non-blocking servers. * Can't recycle? Discard objects you don't need, ASAP, and continue #clear-ing what you can. Take advantage of streaming built into Rack. The Rack response body only needs to respond to #each. There should be no reason to build giant response documents in memory before sending them to a client. unicorn can't do the following for you automatically since we don't know how/if a Rack app will reuse a string; but upstack authors can String#clear after yielding in #each to ensure any malloced heap memory is immediately available for future use (but beware of downstream middlewares which do not expect this, too(**)): def each # .. do something to generate a giant string yield giant_string giant_string.clear # String#clear end A Rack response body may also respond to #close; it can be used to explicitly release any response-local resources. Rack::TempfileReaper + Rack::BodyProxy is an example of this for Tempfiles. Smaller functions and smaller code helps keep this manageable. * Avoid slurping. Large datasets do not need everything up front. For example, threading 10K messages entirely in memory is no problem: just don't load entire messages into memory up front, only what you need. JWZ's algorithm was doing this in the 90s: https://www.jwz.org/doc/threading.html Disclaimer: Some of these things may hurt throughput and performance in benchmarks, especially with smaller datasets; but I consider predictable and consistent performance more far more important than burst throughput. ** Know your entire stack; top to bottom. You ought to be able to track every single line of code in a high-level Rack app you maintain down through each and every layer of framework, middleware, Rack server, Ruby VM, C library, down to the OS kernel. Yes, this limits you to using smaller and simpler stacks :P *** Why stick with Ruby if you care about memory usage? I'm too impatient to wait on compilers, and don't like the extra storage of binaries. Scripting languages forces authors to distribute (hopefully non-obfuscated) code; reducing network and storage costs, and that also lowers the barrier from user to hacker. Fwiw, I actually prefer Perl5 with the predictability (and caveats of) refcounting over a GC like Ruby's.