Back in November 2009 I was working on a project to port Scribd.com code base to Rails 2.2 and noticed that some old plugins we were using in 2.1 were abandoned by their authors. Some of them were just removed from the code base, but one needed a replacement – that was an old plugin called acts_as_readonlyable that helped us to distribute our queries among a cluster of MySQL slaves. There were some alternatives but we didn’t like them for one or another reasons so we’ve decided to go with creating our own ActiveRecord plugin, that would help us scale our databases out. That’s the story behind the first release of DbCharmer.
Today, six months after the first release of the gem and we’ve moved it to gemcutter (which is now the official gems hosting) and we’re already at version 1.6.11. The gem was downloaded more than 2000 times. There are (at least) 10+ large users that rely on this gem to scale their products out. And (this is the most exciting) we’ve added tons of new features to the product.
Here are the main features added since the first release:
- Much better multi-database migrations support including default migrations connection changing.
- We’ve added ActiveRecord associations preload support that makes it possible to move eager loading queries to the same connection where your finder queries go to.
- We’ve improved ActiveRecord’s query logging feature and now you can see what connections your queries executed on (and yes, all those improvements are colorized
).
- We’ve added an ability to temporary remap any ActiveRecord connections to any other connections for a block of code (really useful when you need to make sure all your queries would go to some non-default slave and you do not want to mess with all your models).
- The most interesting change: we’ve implemented some basic sharding functionality in ActiveRecord which currently is being used in production in our application.
As you can see now DbCharmer helps you to do three major scalability tasks in your Rails projects:
- Master-Slave clusters to scale out your Rails models reads.
- Vertical sharding by moving some of your models to a separate (maybe even dedicated) servers and still keep using AR associations
- Horizontal sharding by slicing your models data to pieces and placing those pieces into different databases and/or servers.
So, If you didn’t check DbCharmer out yet and you’re working on some large rails project that is (or going to be) facing scalability problems, go read the docs, download/install the gem and prove them that Rails CAN scale!
Today I’m proud to announce the first public release of our ActiveRecord database connection magic plugin: DbCharmer.
DB Charmer – ActiveRecord Connection Magic Plugin
DbCharmer is a simple yet powerful plugin for ActiveRecord that does a few things:
- Allows you to easily manage AR models’ connections (
switch_connection_to method)
- Allows you to switch AR models’ default connections to a separate servers/databases
- Allows you to easily choose where your query should go (
on_* methods family)
- Allows you to automatically send read queries to your slaves while masters would handle all the updates.
- Adds multiple databases migrations to ActiveRecord
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loops is a small and lightweight framework for Ruby on Rails and Merb created to support simple background loops in your application which are usually used to do some background data processing on your servers (queue workers, batch tasks processors, etc).
Originally loops plugin was created to make our (Scribd.com) own loops code more organized. We used to have tens of different modules with methods that were called with script/runner and then used with nohup and other not so convenient backgrounding techniques. When you have such a number of loops/workers to run in background it becomes a nightmare to manage them on a regular basis (restarts, code upgrades, status/health checking, etc).
After a short time of writing our loops in more organized ways we were able to generalize most of the loops code so now our loops look like a classes with a single mandatory public method called run. Everything else (spawning many workers, managing them, logging, backgrounding, pid-files management, etc) is handled by the plugin itself.
The major idea behind this small project was to create a deadly simple and yet robust framework to be able to run some tasks in background and do not think about spawning many workers, restarting them when they die, etc. So, if you need to be able to run either one or many copies of your worker or you do not want to think about re-spawning dead workers and do not want to spend megabytes of RAM on separate copies of Ruby interpreter (when you run each copy of your loop as a separate process controlled by monit/god/etc), then I’d recommend you to try this framework — you’ll like it.
For more information, visit the project site and, of course, read the sources
It’s been a few months since we‘ve started actively using ActiveMQ queue server in our project. For some time we had pretty weird problems with it and even started thinking about switching to something else or even writing our own queue server which would comply with our requirements. The most annoying problem was the following: some time after activemq restart everything worked really well and then activemq started lagging, queue started growing and all producer processes were stalling on push() operations. We rewrote our producers from Ruby to JRuby, then to Java and still – after some time everything was in a bad shape until we restarted the queue server.
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Few months ago I’ve switched one of our internal projects from doing synchronous database saves of analytics data to an asynchronous processing using starling + a pool of workers. This was the day when I really understood the power of specialized queue servers. I was using database (mostly, MySQL) for this kind of tasks for years and sometimes (especially under a highly concurrent load) it worked not so fast… Few times I worked with some queue servers, but those were either some small tasks or I didn’t have a time to really get the idea, that specialized queue servers were created just to do these tasks quickly and efficiently.
All this time (few months now) I was using starling noticed really bad thing in how it works: if workers die (really die, or lock on something for a long time, or just start lagging) and queue start growing, the thing could kill your server and you won’t be able to do something about it – it just eats all your memory and this is it. Since then I’ve started looking for a better solution for our queuing, the technology was too cool to give up. I’ve tried 5 or 6 different popular solutions and all of them sucked… They ALL had the same problem – if your queue grows, this is your problem and not queue broker’s :-/ The last solution I’ve tested was ActiveMQ and either I wasn’t able to push it to its limits or it is really so cool, but looks like it does not have this memory problem. So, we’ve started using it recently.
In this small post I’d like to describe a few things that took me pretty long to figure out in ruby Stomp client: how to make queues persistent (really!) and how to process elements one by one with clients’ acknowledgments.
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Today I’ve managed to finish initial version of our bounces-handler package we use for mailing-related stuff in Scribd.
Bounces-handler package is a simple set of scripts to automatically process email bounces and ISP‘s feedback loops emails, maintain your mailing blacklists and a Rails plugin to use those blacklists in your RoR applications.
This piece of software has been developed as a part of more global work on mailing quality improvement in Scribd.com, but it was one of the most critical steps after setting up reverse DNS records, DKIM and SPF.
The package itself consists of two parts:
- Perl scripts to process incoming email:
- bounces processor — could be assigned to process all your bounce emails
- feedback loops messages processor — more specific for Scribd, but still – could be modified for your needs (will be released soon).
- Rails plugin to work with mailing blacklists
For more information, please check our README file. If you have any questions, comments or suggestions, please leave them here as a comments and I’ll try to reply as soon as possible.
How often do you think about the reasons why your favorite RDBMS sucks? Last few months I was doing this quite often and yes, my favorite RDBMS is MySQL. The reason why I was thinking so because one of my recent tasks at Scribd was fixing scalability problems in documents browsing.
The problem with browsing was pretty simple to describe and as hard to fix – we have large data set which consists of a few tables with many fields with really bad selectivity (flag fields like is_deleted, is_private, etc; file_type, language_id , category_id and others). As the result of this situation it becomes really hard (if possible at all) to display documents lists like “most popular 1-10 pages PDF documents in Italian language from the category “Business” (of course, non-deleted, non-private, etc). If you’ll try to create appropriate indexes for each possible filters combination, you’ll end up having tens or hundreds of indexes and every INSERT query in your tables will take ages.
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Hello my dear readers.
Today I have a question for all of you. What platforms (32bit or 64 bit) do you use for your servers with more than 4Gb RAM? I’m asking because recently we‘ve hit few really weird bugs in Linux kernels 2.6.18 to 2.6.22 and all those bugs were PAE-related. Now I’d really love to move all machines to 64-bit, but I’m in doubt because we don’t know too much about Rails stack (ruby, mongrel, haproxy) on 64-bit platforms (all our DB boxes are 64-bit of course).
So, please drop me a line if you have any experience (negative or positive) with Rails platform on 64-bit machines. I’d really appreciate your help.
We were using memcache in our application for a long time and it helped a lot to reduce DB servers load on some huge queries. But there was a problem (sometimes called a “dog-pile effect”) – when some cached value was expired and we had a huge traffic, sometimes too many threads in our application were trying to calculate new value to cache it.
For example, if you have some simple but really bad query like
1
| SELECT COUNT(*) FROM some_table WHERE some_flag = X |
which could be really slow on a huge tables, and your cache expires, then ALL your clients calling a page with this counter will end up waiting for this counter to be updated. Sometimes there could be tens or even hundreds of such a queries running on your DB killing your server and breaking an entire application (number of application instances is constant, but more and more instances are locked waiting for a counter).
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How often do we think about our http sessions implementation? I mean, do you know, how your currently used sessions-related code will behave when sessions number in your database will grow up to millions (or, even, hundreds of millions) of records? This is one of the things we do not think about. But if you’ll think about it, you’ll notice, that 99% of your session-related operations are read-only and 99% of your sessions writes are not needed. Almost all your sessions table records have the same information: session_id and serialized empty session in the data field.
Looking at this sessions-related situation we have created really simple (and, at the same time, really useful for large Rails projects) plugin, which replaces ActiveRecord-based session store and makes sessions much more effective. Below you can find some information about implementation details and decisions we’ve made in this plugin, but if you just want to try it, then check out our project site.
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