DDevelopment & Design

The hidden benefit of writing short methods

A method should do only one thingQuoting Uncle Bob:The first rule of functions is that they should be small. The second rule of functions is that they should be smaller than that. Functions should not be 100 lines long. Functions should hardly ever be …

A method should do only one thing

Quoting Uncle Bob:

The first rule of functions is that they should be small. The second rule of functions is that they should be smaller than that. Functions should not be 100 lines long. Functions should hardly ever be 20 lines long.

From one of Martin Thompson’s presentations I have learned another – more easily available and intuitive – method of judging wether a method is small enough. What you are supposed to do is to physically cover the part of your screen on which the method is displayed with your hand. If you can’t do that, then it means that your method probably is not small enough and you should consider refactoring it.

The obvious advantages that you gain by following these rules are improved readability and maintainability of your code.

But there is one – less obvious – benefit of writing short methods

And that is that Java HotSpot VM’s JIT compiler uses a compilation technique which is called inlining. 
What the compiler does is it substitutes the body of a method into places where this method is invoked thus saving the cost of calling the method. 
Current default for HotSpot is set at 35 bytes, which means that the compiler will inline a method if it contains less than 35 bytes of bytecode.

How do I know the bytecode size of a given method?

The easiest way is to dump the class file containing your method with:
javap -c mypackage.MyClass

which returns bytecode of decompiled class and the size of each method (well actually the size is equal to the byte offset of the last instruction – you can read more about javap HERE).

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DDevelopment & Design

Recently at storm-users

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()

  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.

I've been reading through storm-users Google Group recently. This resolution was heavily inspired by Adam Kawa's post "Football zero, Apache Pig hero". Since I've encountered a lot of insightful and very interesting information I've decided to describe some of those in this post.

  • nimbus will work in HA mode - There's a pull request open for it already... but some recent work (distributing topology files via Bittorrent) will greatly simplify the implementation. Once the Bittorrent work is done we'll look at reworking the HA pull request. (storm’s pull request)

  • pig on storm - Pig on Trident would be a cool and welcome project. Join and groupBy have very clear semantics there, as those concepts exist directly in Trident. The extensions needed to Pig are the concept of incremental, persistent state across batches (mirroring those concepts in Trident). You can read a complete proposal.

  • implementing topologies in pure python with petrel looks like this:

class Bolt(storm.BasicBolt):
    def initialize(self, conf, context):
       ''' This method executed only once '''
        storm.log('initializing bolt')

    def process(self, tup):
       ''' This method executed every time a new tuple arrived '''       
       msg = tup.values[0]
       storm.log('Got tuple %s' %msg)

if __name__ == "__main__":
    Bolt().run()

  • Fliptop is happy with storm - see their presentation here

  • topology metrics in 0.9.0: The new metrics feature allows you to collect arbitrarily custom metrics over fixed windows. Those metrics are exported to a metrics stream that you can consume by implementing IMetricsConsumer and configure with Config.java#L473. Use TopologyContext#registerMetric to register new metrics.

  • storm vs flume - some users' point of view: I use Storm and Flume and find that they are better at different things - it really depends on your use case as to which one is better suited. First and foremost, they were originally designed to do different things: Flume is a reliable service for collecting, aggregating, and moving large amounts of data from source to destination (e.g. log data from many web servers to HDFS). Storm is more for real-time computation (e.g. streaming analytics) where you analyse data in flight and don't necessarily land it anywhere. Having said that, Storm is also fault-tolerant and can write to external data stores (e.g. HBase) and you can do real-time computation in Flume (using interceptors)

That's all for this day - however, I'll keep on reading through storm-users, so watch this space for more info on storm development.

DDevelopment & Design

4Developers 2010 Review

I've been to 4Developers in 2009 in Cracow, together with Tomasz Przybysz and we had very nice impressions, no wonder then I wanted to signed up for 2010 edition in Poznań as well. Tomasz was sick, but Jakub Kurlenda decided to come with me. This time...I've been to 4Developers in 2009 in Cracow, together with Tomasz Przybysz and we had very nice impressions, no wonder then I wanted to signed up for 2010 edition in Poznań as well. Tomasz was sick, but Jakub Kurlenda decided to come with me. This time...
DDevelopment & Design

Simple HBase ORM

When dealing with data stored in HBase, you are quick to come to a conclusion, that it is extremaly inconvenient to reach to it via HBase native API. It is very verbose and you always need to convert between bytes and simple types - a pain. While I wa...When dealing with data stored in HBase, you are quick to come to a conclusion, that it is extremaly inconvenient to reach to it via HBase native API. It is very verbose and you always need to convert between bytes and simple types - a pain. While I wa...