All posts by Dan Croak

Opt-In, Project-Specific Vim Spell-Checking and Word Completion

- Dan Croak
- December 1, 2015
- Uncategorized

Improve your project team’s spelling abilities with these Vim tips.

Read more at the source

Create a Retention Curve with Mixpanel and Google Sheets

- Dan Croak
- July 1, 2015
- Uncategorized

Better visualize product retention with a curve instead of a tabular cohort report.

Read more at the source

Optimizing Full Text Search with Postgres tsvector Columns and Triggers

- Dan Croak
- June 25, 2015
- Uncategorized

Improve the speed of Postgres full-text search by introducing a tsvector column to cache lexemes and using a trigger to keep the lexemes up-to-date.

Read more at the source

How to Process Twitter Lead Cards with FormKeep

- Dan Croak
- March 23, 2015
- Uncategorized

Generate leads for your product with Twitter Ads and FormKeep.

Read more at the source

A Closer Look at Test Spies

- Dan Croak
- March 19, 2015
- Uncategorized

What benefits do test spies have compared to mocks in our isolated unit tests?

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Hound Reviews SCSS for Style Violations

- Dan Croak
- February 8, 2015
- Uncategorized

Hound reviews Ruby, JavaScript, CoffeeScript, and SCSS code for style violations.

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Multiple CI Services on a GitHub Pull Request

- Dan Croak
- January 28, 2015
- Uncategorized

Review your GitHub pull requests with multiple CI services such as TravisCI, Code Climate, and Hound.

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Hound Reviews CoffeeScript For Style Violations

In addition to Ruby files,
Hound can now review CoffeeScript code for style violations using CoffeeLint.

Read more at the source

Paid Private Repos for Hound

- Dan Croak
- August 19, 2014
- Uncategorized

Hound is now reviewing 7,000+ GitHub pull requests each week. We are now
charging for private repos. Public repos are still free.

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DNS to CDN to Origin

- Dan Croak
- August 8, 2014
- Uncategorized

<p>Content Distribution Networks (CDNs) such as <a href="http://aws.amazon.com/cloudfront/">Amazon CloudFront</a> and
<a href="http://www.fastly.com/">Fastly</a> have the ability to “pull” content from their <a href="http://www.w3.org/Protocols/rfc2616/rfc2616-sec1.html#sec1.3">origin server</a>
during HTTP requests in order to cache them. They can also proxy POST, PUT,
PATCH, DELETE, and OPTION HTTP requests…</p>

Read more at the source

EOL for Ruby 1.8.7 and 1.9.2

July 1, 2014

Extended maintenance of Ruby versions 1.8.7 and 1.9.2 will end on July 31, 2014.

Further information

Last December, we extended the supported lifetime of Ruby versions 1.8.7 and 1.9.2 by six months. Heroku sponsored this extension to support customers on their Bamboo and Cedar stacks.

We’re now coming to the close of this time window. Ruby 1.8.7 came out in June 2008, making it just over six years old. Ruby 1.9.2 was released in August of 2010, making it just under four years old. With every passing day, backporting and maintaining security patches to these code bases is becoming increasingly difficult. Ruby 2.1.2 has just been released and Ruby 2.2.0 is scheduled to come out in six months. We recommend you upgrade to a more modern Ruby immediately. This will get you all the improvements we’ve made to the Ruby language and runtime. In addition, this will ensure your apps continue to receive bug and security updates in the future.

For any parties concerned with continuing maintenance of Ruby 1.8.7 or 1.9.2, please contact us at hone@ruby-lang.org and zzak@ruby-lang.org.

Posted by zzak and hone on 1 Jul 2014

[…]

Makara: a read-write splitting adapter for Active Record

January 2, 2013

Makara is a ruby gem which allows your Rails 3.x application to split its database queries based on their contents. The features of Makara include:

Read/Write splitting across multiple databases
Failover om slave errors/loss
Automatic reconnection attempts to lost slaves
Optional “sticky” connections to master and slaves
Works with many database types (mysql, postgres, etc)
Provides a middleware for releasing stuck connections
Weighted connection pooling for slave priority

Development History

The main www.taskrabbit.com site is a Rails3 application. As we have grown, we have learned a few things about our load profile:

We have far more reads than writes (over 20x)
As we have grown, the ratio of reads to writes has remained consistent
As most of our traffic is read-heavy, it can be a few seconds out of date, but there are some pages/use-cases which require up-to-date information. This includes pages you have just created (Task posting) or content you just edited (profile updates)
There are only so many interactions any database can handle before it gets SLOW

As TaskRabbit grew, we quickly realized the need to scale our database tier. We don’t yet have the volume of data which would require traditional sharding (and it’s always nicer to have all of your data in one place for analysis if you can), so we wanted to approach scaling our database tier from a replication point-of-view. As noted above, we aren’t write-heavy, especially since we make use of many temporary stores (like on-disk, memcache, riak, and redis), so the added complexity of master-master replication didn’t seem worth the hassle either. We also have a new bus system current being phased in which will further limit writes. This left traditional master-slave replication and scaling.

By default, the mysql and mysql2 adaptors for Ruby don’t have any support for more than one database, so we went exploring for other options. Our first stop was the (SoundCloud-specific fork) of the master_slave_adaptor. We used this in production for some time, but we eventually learned that it had a few bugs regarding the way it checked if a slave was up-to-date, and the majority of the time we ended up reading from our master database.

Next we moved on to the Octopus gem (here’s our fork of it). While this gem did allow us to do master-slave splitting, it didn’t handle errors so well. In fact, if any of your slaves went down or timed out, the error bubbled up to your application and presented as a normal database error. While we didn’t solve that within Octopus, we at least were able to introduce the notion of a blacklist (i.e., slaves that went bad) and didn’t use them for subsequent requests after the first user saw the 500 error. After some amount of time had passed, we would check the slave again to see if it came back.

After using Octopus for a while we noticed that, in some rare cases, our web app could be faster than our database replication. For example, if you just posted a Task, the next page we rendered for you is the public Task page so you can confirm that everything looks as you expected. At this point, the INSERT statement that just ran on the master database may not finsihed replicating to the SLAVE(S).If you then query the slave there’s a chance you’ll get a RecordNotFound error. It was this type of error that prompted us to develop the ‘sticky’ notion of choosing a database. In a nutshell, once you have modified a record (INSERT, DELETE, or UPDATE), you should continue to use whichever database you performed that action on for the remainder of your request. This ensures that the data you are using is consistent throughout the request. This notion of sticking was also very important in our Delayed Job workers, which often performed more requests faster than our web servers. Keeping a consistent database is also important when traversing any belongs_to relationships for obvious reasons.

Unfortunately, our logic for this while using the Octopus gem was fairly hacky:

ActiveRecord::ConnectionAdapters::AbstractAdapter.class_eval do
  attr_reader :last_query
  def log_with_last_query(sql, name, &block)
    @last_query = [sql, name]
    Octopus::Proxy.master_lock?(sql)
    log_without_last_query(sql, name, &block)
  end
  alias_method_chain :log, :last_query
end

You will notice that because the Octopus gem didn’t expose the actual query Active Record created, we hijacked the logger to get the final query. Octopus made its choices of which type of database to use by inspecting the method called from Active Record rather than query inspection. The way we handled “un-sticking” from a database was to reset it after the request ended in our Unicorn configuration.

We had some trouble upgrading from Rails 2 to Rails 3 with Octopus so we thought it was time to write our own solution.

Features

First and foremost, Makara needed to be able to allow us to scale our database capacity in a way that didn’t require us to change any code within our application (other than a database.yml update). We needed the application to be able to perform on a development laptop running vanilla mySQL and in production with n-geography-specific replication shards. The gem also needed to be able to handle the assignment of roles to these databases. Thus, the structure of our ideal database.yml was born:

production:
  sticky_slave: true
  sticky_master: true
  adapter: makara
  db_adapter: mysql2
  host: xxx
  user: xxx
  password: xxx
  blacklist_duration: 5
  databases:
    - name: master
      role: master
    - name: slave1
      role: slave
      host: xxx
      user: xxx
      password: xxx
      weight: 3
    - name: slave2
      role: slave
      host: xxx
      user: xxx
      weight: 2

You will note that you can define “common” connection parameters (like a database name), and overwrite them or provide specifics for each replica (for example in production we have a read+write user for master, and a read+only user for the slaves).

The database.yml is structured the same as the underlying connection infrastructure. We have one top-level makara adapter which serves a single purpose: delegating the execution of sql to the best underlying adapter. The underlying adapters are your standard adapters (mysql2, sqlite3, postgresql, etc) with some ruby magic sprinkled on top. The ruby magic isn’t that magical, it’s merely some instance extending which overrides the execute() method, giving the top-level makara adapter the chance to re-route the execution.

On to blacklisting. Because we have inserted ourselves as the Active Record adaptor directly, we have the luxury of actually catching errors from the “real” database adaptors we hold connections to. This made the creation of the blacklist a lot simpler. We can simply hold an array of all the connection pools, and choose which types of errors to catch, and which types to pass though. This also allows us to retry a failed query before passing the results back up to the rest of the Rails stack. Did your read from SLAVE2 just fail because it is under heavy load? It’s cool, lets try it on SLAVE1 and then MASTER. Now if your master databases fails, you have problems, but no gem can save you from that :D, unless you are running in master-master mode, then we have your back again. There are types of errors (like duplicate key warnings etc) which you do want to bubble up, so we have methods to pass those back to the stack.

We made the choice to be a Rails-only gem, that allowed us to make use of a middleware that helped us enforce our ‘stickiness’ across requests. We ensured that only one slave was used for all queries in a request by default, adn f your wrote to master, you stayed there for the subsequent request. What about the case where you create a new record in one request, and then instantly want to view it in the next? How can you be sure that whichever slave you hit next has the data you need? With a cookie! We use the rails middleware to drop down a cookie if you have been stuck to master, and on the next request, Makara will ensure that you come back to that same database, just for that next request.

Makara is available now. Enjoy!

[…]

The Ruby Rails Aggregator