DDevelopment & Design

Multi phased processing in scala

Last time in our project we had to add progress bar for visualization of long time running process. Process was made of a few phases and we had to print in which phase we currently are. In first step we conclude that we need to create a class of Progre…

Last time in our project we had to add progress bar for visualization of long time running process. Process was made of a few phases and we had to print in which phase we currently are. In first step we conclude that we need to create a class of Progress which will be passed as an implicit parameter to our service. Then we will wrap method calls be inProgress method which will notify some e.g. akka actor about phase begin and phase end.

But this approach has some disadvantages. Firstly before we start service’s operation we need to init progress with count of all phases to get know ratio of progress finish. With this approach we had to add some extra counting before operation start.

If we want to keep real progress notifications the numbers of phases had to fit count of inPhase blocks. Some of phases were dynamically computed and some where omitted in case of failure validations results. This code become to be unmaintained.

We found that we need to join computation of phases with real phase processing. In this case we need to change approach from building process to building chain of phases that will run the process. Each phase will take the result of previous phase and transform it to new output. So example process will look like this:

Code giving this chain functionality looks like this:

We’ve used right associative operator :: for building chain of phases. “Body” of phases is piped by andThen: processPrevWrapped andThen processNext. For nil-tail we need to have a factory creating empty chain with identity “body” function.

Also if we have this kind of tool, we can modify piping code according to nature of our flow. For example if we are using scalaz.Validation we can do validating chain which will extract a success from n-step output and pass it to input of next step (like flatMap). In the other hand if n-step will return Failure, we will skip all remaining phases of validating chain.

To make building of chain more production-ready we add some extra features:

  • Chaining of chains (sth like ::: in scala Lists)
  • Transforming of input/output – for adding some “glue” code for simpler phases chaining
  • Wrapping of chains – also some “glue” code doing both input and output transformations
  • Sequencing of chains – sequenced processing of multiple phases with the same input

If you are interested in using similar approach, take a look at my github project: scala-phases-chain. If you want to integrate this tool with akka actors, simply change MultiPhasedProgress.notifyAboutStatus method to look like this:

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DDevelopment & Design

Log4j and MDC in Grails

Log4j provides very useful feature: MDC - mapped diagnostic context. It can be used to store data in context of current thread. It may sound scary a bit but idea is simple.

My post is based on post http://burtbeckwith.com/blog/?p=521 from Burt Beckwith's excellent blog, it's definitely worth checking if you are interested in Grails.

Short background story...


Suppose we want to do logging our brand new shopping system and we want to have in each log customer's shopping basket number. And our system can be used at once by many users who can perform many transactions, actions like adding items and so on. How can we achieve that? Of course we can add basket number in every place where we do some logging but this task would be boring and error-prone. 

Instead of this we can use MDC to store variable with basket number in map. 

In fact MDC can be treated as map of custom values for current thread that can be used by logger. 


How to do that with Grails?


Using MDC with Grails is quite simple. All we need to do is to create our own custom filter which works for given urls and puts our data in MDC.

Filters in Grails are classes in directory grails-app/conf/* which names end with *Filters.groovy postfix. We can create this class manually or use Grails command: 
grails create-filters info.rnowak.App.Basket

In result class named BasketFilters will be created in grails-app/conf/info/rnowak/UberApp.

Initially filter class looks a little bit empty:
class BasketFilters {
    def filters = {
        all(controller:'*', action:'*') {
            before = {

            }
            after = { Map model ->

            }
            afterView = { Exception e ->

            }
        }
    }
}
All we need to do is fill empty closures, modify filter properties and put some data into MDC.

all is the general name of our filter, as class BasketFilters (plural!) can contain many various filters. You can name it whatever you want, for this post let assume it will be named basketFilter. 

Another thing is change of filter parameters. According to official documentation (link) we can customize our filter in many ways. You can specify controller to be filtered, its actions, filtered urls and so on. In our example you can stay with default option where filter is applied to every action of every controller. If you are interested in filtering only some urls, use uri parameter with expression describing desired urls to be filtered.

Three closures that are already defined in template have their function and they are started in these conditions:

  • before - as name says, it is executed before filtered action takes place
  • after - similarly, it is called after the action
  • afterView - called after rendering of the actions view
Ok, so now we know what are these mysterious methods and when they are called. But what can be done within them? In official Grails docs (link again) under section 7.6.3 there is a list of properties that are available to use in filter.

With that knowledge, we can proceed to implementing filter.

Putting something into MDC in filter


What we want to do is quite easy: we want to retrieve basket number from parameters and put it into MDC in our filter:
class BasketFilters {
    def filters = {
        basketFilter(controller:'*', action:'*') {
            before = {
                MDC.put("basketNumber", params.basketNumber ?: "")
            }
            after = { Map model ->
                MDC.remove("basketNumber")
            }
        }
    }
}

We retrieve basket number from Grails params map and then we put in map under specified key ("basketNumber" in this case), which will be later used in logger conversion pattern. It is important to remove custom value after processing of action to avoid leaks.

So we are putting something into MDC. But how make use of it in logs?


We can refer to custom data in MDC in conversion patter using syntax: %X{key}, where key is our key we used in filter to put data, like:
def conversionPattern = "%d{yyyy-MM-dd HH:mm:ss} %-5p %t [%c{1}] %X{basketNumber} - %m%n"


And that's it :) We've put custom data in log4j MDC and successfully used it in logs to display interesting values.
DDevelopment & Design

Micro services on the JVM part 1 – Clojure

Micro services could be a buzzword of 2014 for me. Few months ago I was curious to try Dropwizard framework as a separate backend, but didn’t get the whole idea yet. But then I watched a mind-blowing “Micro-Services Architecture” talk by Fred George. Also, the 4.0 release notes of Spring covers microservices as an important rising trend as well. After 10 years of having SOA in mind, but still developing monoliths, it’s a really tempting idea to try to decouple systems into a set of independently developed and deployed RESTful services.

Micro services could be a buzzword of 2014 for me. Few months ago I was curious to try Dropwizard framework as a separate backend, but didn’t get the whole idea yet. But then I watched a mind-blowing “Micro-Services Architecture” talk by Fred George. Also, the 4.0 release notes of Spring covers microservices as an important rising trend as well. After 10 years of having SOA in mind, but still developing monoliths, it’s a really tempting idea to try to decouple systems into a set of independently developed and deployed RESTful services.