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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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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Since the day one when I joined Scribd, I was thinking about the fact that 90+% of our traffic is going to the document view pages, which is a single action in our documents controller. I was wondering how could we improve this action responsiveness and make our users happier.
Few times I was creating a git branches and hacking this action trying to implement some sort of page-level caching to make things faster. But all the time results weren’t as good as I’d like them to be. So, branches were sitting there and waiting for a better idea.
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Today I was doing some work on one of our database servers (each of them has 4 SAS disks in RAID10 on an Adaptec controller) and it required huge multi-thread I/O-bound read load. Basically it was a set of parallel full-scan reads from a 300Gb compressed innodb table (yes, we use innodb plugin). Looking at the iostat I saw pretty expected results: 90-100% disk utilization and lots of read operations per second. Then I decided to play around with linux I/O schedulers and try to increase disk subsystem throughput. Here are the results:
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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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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
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| 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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Last few days one of our customers (one of the largest Ruby on Rails sites on the Net) was struggling to solve some really strange problem – once upon a time they were getting an error from ActiveRecord on their site:
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| (ActiveRecord::StatementInvalid) "Mysql::Error: Lock wait timeout exceeded; try restarting transaction: UPDATE some_table..... |
They have innodb_lock_wait_timeout set to 20 seconds. After a few hours of looking for strange transactions we were decided to create s script to dump SHOW INNODB STATUS and SHOW FULL PROCESSLIST commands output to a file every 10 seconds to catch one of those moments when this error occurred.
Today we’ve got next error and started digging in our logs…
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Thanks to MySQL community members we’ve got really great collection of media files from the recent mysqluc. Thanks to Seeri for all that work he’s done to collect everything in one place so we could watch/listen/read information from this great event.
So, if you did not attended mysqluc07, then you definitely should visit Technocation page, dedicated to this conference.
P.S. I’m going to post links to these videos and descriptions for the sessions on the Best Tech Videos soon, so If you are not sure to watch some video or not, just wait while I’m merging these links with information from mysqluc site.