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
You May Also Like

Scrum w Polsce – TouK

Nasi koledzy z Fluidcircle opublikowali właśnie studium przypadku opisujące doświadczenia TouK z wykorzystania metodyki Scrum. Wszystko w ramach programu “Scrum…

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.