Backporting Ruby From Trusty to Oneiric
Goal
Create a backported Ruby 1.9.3p484 Ubuntu package. For the purposes of this post, we will assume that you are OK with not creating a signed package (required if uploading to launchpad). If you wish to upload to launchpad (for example to share your backport with the world), it may be useful for you to check out this site after reading through this guide.
Motivation
At my day job, I had to upgrade Ruby on very old production boxes that were running Oneiric AMD64. Upgrading the OS was a chore that would be happening later but we needed to start using the latest Ruby as soon as possible.
Unfortunately, there is no Ruby 1.9.3p484 Debian package readily available for Oneiric, not even as a backport.
Schwatcher v0.0.7 Released
Version 0.0.7 of Schwatcher has been released.
Changes:
- No longer has external logging library dependencies. Thanks crdueck for the suggestion.
- No longer uses any mutable members for extra thread-safety
Relevent info:
Schwatcher v0.0.4 Released
Version 0.0.4 of Schwatcher has been released.
Changes:
- No longer uses Akka Agent to hold CallbackRegistry (thanks crdueck). This should result in a small performance increase because of more ‘direct’ memory access inside MonitorActor.
- Refactored testing for better coverage and maintainability
- Scala 2.10.3 support in testing
Relevent info:
Schwatcher v0.0.3 Released
Version 0.0.3 of Schwatcher upgrades the Akka library used by the plugin to 2.2.1 so that devs already using Akka 2.2.x are able to use this library.
Those upgrading to 0.0.3 of Schwatcher who aren’t already using Akka 2.2.x should read the 2.1.x to 2.2.x Akka migration guide to make sure their projects don’t break.
Relevent info:
Resolving ‘Java OOM: Unable to Create New Native Thread’ Errors on Heroku
For the most part, I’m a very happy Heroku user. The platform allows me deploy my apps, be they Java, Scala or Ruby-based without having to think/worry about infrastructure, which is amazing. They also allow me to, for the most part, do this for free. For the most part, I love it, and so do many others. That said, sometimes you do run into problems that cause you to lose lots of time debugging … in the wrong direction.
Yesterday, after adding non-blocking I/O to my Metascraper library, I load tested my deployed application and found it fatally crashing with java.lang.OutOfMemoryError: unable to create new native thread
errors. Not Good
Metascraper v0.2.1 Released
Sorry for the quick version-up. Version 0.1.1 added non-blocking I/O, but was using Dispatch without configuring the threadpool used for HTTP connections. This caused issues on Heroku where there is a 256 combined thread + process limit for 1x dynos (512 for 2x dynos), whereby Java OOM “unable to create new native thread” errors would be thrown.
0.2.1 adds:
- Configuration of Actor HTTP client on ScraperActor instantiation
- Notably: HTTP client ExecutorService thread pool
Relevent info:
Metascraper v0.1.1 Released
Metascraper v0.1.1 has been released. Major changes include:
- Async / non-blocking I/O for page requests: Originally suggested by analytically, I’ve added asynchronous requesting of webpages via Dispatch
- ScraperActor now replies with
Either[Throwable, ScrapedData]
whereas before it replied withEither[Throwable, ScrapedData]
. This allows library users to access the full capabilities of thrown objects. This might break your app - Added URL validations
- Better guessing of metadata
- More relevant
User-Agent
out of the box - Better test coverage
Relevent info:
- Metascraper Github repo
- Add
libraryDependencies += "com.beachape.metascraper" %% "metascraper" % "0.1.1"
intobuild.sbt
to install
Introducing Metascraper - a Scala Library for Scraping Page Metadata
Scraping metadata (e.g. title
, description
, url
, etc.) from a URL is something that Facebook currently does for you when you paste a URL into the “Update Status” box. For a service that I’m currently building out, we wanted to do this as well for our users. Thus Metascraper was born.
There was already a Ruby solution called link_thumbnailer, but since this is a I/O heavy operation, I knew I wanted to build a solution using tools that supported non-blocking I/O and could be used without getting caught in callback spaghetti. Scala, Akka, and the Play framework immediately came to mind.
Introducing Schwatcher
The WatchService was added as part of Java 7 and introduced the ability to monitor files through the JVM without the use of external libraries like JNotify that require installing native libraries. Using this API for a project that requires monitoring files makes handling dependencies for both deployment and development much simpler.
Since Scala is able to directly invoke Java, I wanted to use this API when I was building Akanori-thrift, a trending-words detection service that is focused on the Japanese language. This post will not go over that service in detail (that will take up an entire post of its own if not more) but my use-case there was monitor a custom dictionary file for updates and then spawn a new instance of the Tokenizer
that uses the updated state of the file.
I quickly realised a few pain-points:
- There existed no file monitoring Scala library (at the time),
- Using the WatchService API requires the use of a blocking thread to get events,
- The WatchService API does not have recursive monitoring support built in
To address these, I set out to create Schwatcher, a Scala library that wraps the WatchService API of Java7 and allows callbacks to be registered and unregistered on both directories and files both as individual paths and recursively. Furthermore, I wanted to facilitate the use of the Java7 API in Scala in a simple way that is in line with the functional programming paradigm.
Scala: Processing Option-wrapped Collections
In Scala, there are a lot of cool things - too many to list. Among them is something borrowed from Haskell; the Maybe, spelt Option in Scala, which itself is based on the concept of computing via monads.
The reason why Option
is awesome is that, if used properly, it largely frees the programmer from having to worry about various variables being in states of nothingness (Nil, null, etc). Without fail, every programmer has at one point or another written things like thing.nil? ? do_nothing : do_something
…. all over the place. The point (in my mind, at least) of Option
is to free us from having to do this in as many places as possible.
Many libraries in Scala, such as Scala-Redis are made with the assumption that the programmer knows how to deal with Option
and return results wrapped in either Some[List[T]]
or None
. That said, how to work with these types of results is not exactly straight forward for someone coming from other languages that don’t have such constructs, so I’ve written down some of my thoughts.