DDevelopment & Design

Idiomatic Peeking with Java Stream API

The peek() method from the Java Stream API is often misunderstood. Let’s try to clarify how it works and when should be used.

Stream

 

Introduction

Let’s start responsibly by RTFM inspecting peek()’s user’s manual.

Returns a stream consisting of the elements of this stream, additionally performing the provided action on each element as elements are consumed from the resulting stream.
Sounds pretty straightforward. We can use it for applying side-effects for every consumed Stream element:

Stream.of("one", "two", "three")
  .peek(e -> System.out.println(e + " was consumed by peek()."))
  .collect(Collectors.toList());

The result of such operation is not surprising:

one was consumed by peek().
two was consumed by peek().
three was consumed by peek().

Stream/peek Lazy Evaluation

but… what will happen if we replace the terminal collect() operation with a forEach()?

Stream.of("one", "two", "three")
  .peek(e -> System.out.println(e + " was consumed by peek()."))
  .forEach(System.out::println);

It might be tempting to think that we’ll see a series of peek() logs followed by a series of for-each logs but this is not the case:

one was consumed by peek().
one
two was consumed by peek().
two
three was consumed by peek().
three

It gets even more interesting if we get rid of the forEach call:

Stream.of("one", "two", "three")
  .peek(e -> System.out.println(e + " was consumed by peek()."));

the result would be nothing:



As JavaDoc states:


Intermediate operations return a new stream. They are always lazy; executing an intermediate operation such as filter() does not actually perform any filtering, but instead creates a new stream that, when traversed, contains the elements of the initial stream that match the given predicate. Traversal of the pipeline source does not begin until the terminal operation of the pipeline is executed.

So, because of the lazy evaluation Stream pipelines are always being traversed “vertically” and not “horizontally” which allows to avoid doing unnecessary calculations. Additionally, the traversal is triggered only when a terminal method is present. Hence, the observed behaviour.


This is why it’s possible to represent and manipulate infinite sequences using Streams. (Because where do you store an infinite sequence? In the cloud?):


Stream.iterate(0, i -> i + 1)
  .peek(System.out::println)
  .findFirst();


The above operation completes almost immediately because of the lazy character of Stream traversal. Of course, if you try to collect the whole infinite sequence to some data structure, even laziness will not save you.


So, we can see that peek() can’t be treated as an intermediate for-each replacement because it invokes the passed Consumer only on elements that are visited by the Stream.


Unfortunately, Streams do not always behave entirely lazily.


Proper Usage


Further inspection of the official docs reveals a note:


This method exists mainly to support debugging, where you want to see the elements as they flow past a certain point in a pipeline

The point of confusion is the “mainly” word. Let’s have a peek(pun intended) at the English dictionary:




We can see that non-debugging usages are not forbidden nor discouraged.


So, technically, we should be able to, e.g., modify the Stream elements on the fly which would not be possible in the immutable, functional world. Let’s use the infamous mutable java.util.Date:


Stream.of(Date.from(Instant.EPOCH))
  .peek(d -> d.setTime(Long.MAX_VALUE))
  .forEach(System.out::println);

// Sun Aug 17 08:12:55 CET 292278994


and we can observe that the result is far away from the standard epoch, which means the mutating operation was indeed applied.


The problem is that this behaviour is highly deceiving because certain Stream implementations can optimize out peek() calls.


This gets clarified in the early draft of JDK 9’s docs eventually:


In cases where the stream implementation is able to optimize away the production of some or all the elements (such as with short-circuiting operations like findFirst, or in the example described in count()), the action will not be invoked for those elements. (…) An implementation may choose to not execute the stream pipeline (either sequentially or in parallel) if it is capable of computing the count directly from the stream source.

So, now it’s clear that it might not be the best choice if we want to perform some side effects deterministically.


According to this, peek() might not be even that reliable debugging tool after all.


Key Takeaways


    

  • The peek() method works fine as a debugging tool when we want to see what is being consumed by a Stream
    • 

  • It seems to work fine when applying mutating operations but should not be used this way because this behaviour is non-deterministic due to the possibility of certain peek() calls being omitted due to internal optimization
    • 

  • The discussion, whether mutation operations should be allowed or not, would never take place if we were restricted to operate only on immutable values
    • 

  • We have around 292276977 years before we run out of java.util.Date range
    • 



		

		




	


			

			

				

					
				

				

					

						
							
						
					

					

									

			

		

	
	








	


				

			
	



					

						
					

						

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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.