Lambda.pdf

Behavior
Parameterization:Example-1
Parameterize
what? -> Filtering on a List
how? -> Multiple Test Conditions
Behaviour - > Function
Parameterization -> Configureable

Behavior
Value Parameterization

Behavior

Behavior
Parameterization:Predicate

Behavior
Parameterization:Predicate:Example-3

Behavior Parameterization:Predicate

Behavior
Parameterization:Predicate:Multiple
Behavior one parameter

Behavior
Parameterization:Predicate:Anonymous
Classes

Behavior
Parameterization:Predicate:Lambda

Lambda:Introduction
● A lambda expression can be understood as a concise representation of an
anonymous function that can be passed around: it doesn’t have a name, but it
has a list of parameters, a body, a return type, and also possibly a list of
exceptions that can be thrown. That’s one big definition;
– Anonymous— We say anonymous because it doesn’t have an explicit name
like a method would normally have: less to write and think about!
– Function— We say function because a lambda isn’t associated with a
particular class like a method is. But like a method, a lambda has a list of
parameters, a body, a return type, and a possible list of exceptions that can
be thrown.
– Passed around— A lambda expression can be passed as argument to a
method or stored in a variable.
– Concise— You don’t need to write a lot of boilerplate like you do for
anonymous classes.

Funtional Interfaces
● What can you do with functional interfaces?
– Lambda expressions let you provide the implementation of
the abstract method of a functional interface directly inline
and treat the whole expression as an instance of a functional
interface (more technically speaking, an instance of a
concrete implementation of the functional interface).
– You can achieve the same thing with an anonymous inner
class, although it’s clumsier

Streams:Software Engineering Benefits
● The code is written in a declarative way:
– you specify what you want to achieve (that is, filter dishes that are low in
calories) as opposed to specifying how to implement an operation (using
control-flow blocks such as loops and if conditions).
– Together with behavior parameterization, enables you to cope with
changing requirements: you could easily create an additional version of
your code to filter high-calorie dishes using a lambda expression, without
having to copy and paste code.
● You chain together several building-block operations to express a
complicated data processing pipeline while keeping your code readable and
its intent clear.
– The result of the filter is passed to the sorted method, which is then
passed to the map method and then to the collect method.

● Because operations such as filter (or sorted, map, and collect)
are available as high-level building blocks that don’t depend on
a specific threading model, their internal implementation could
be single-threaded or potentially maximize your multicore
architecture transparently!

Java 8 Streams API
● Declarative— More concise and readable
● Composable— Greater flexibility
● Parallelizable— Better performance

Java 8 Streams API
● A short definition is “a sequence of elements from a source that
supports data processing operations.”
– Sequence of elements—
● Like a collection, a stream provides an interface to a
sequenced set of values of a specific element type.
● Because collections are data structures, they’re mostly about
storing and accessing elements with specific time/space
complexities (for example, an ArrayList vs. a LinkedList).
● But streams are about expressing computations such as filter,
sorted, and map .
● Collections are about data; streams are about
computations.

Java 8 Streams API
– Source—
● Streams consume from a data-providing source such as
collections, arrays, or I/O resources.
● Note that generating a stream from an ordered collection
preserves the ordering.
● The elements of a stream coming from a list will have the
same order as the list.

Java 8 Streams API
– Data processing operations—
● Streams support database-like operations and common
operations from functional programming languages to
manipulate data, such as filter, map, reduce, find, match,
sort, and so on.
● Stream operations can be executed either sequentially or
in parallel.

Java 8 Streams API
● A short definition is “a sequence of elements from a source that supports
data processing operations.”
– Pipelining—
● Many stream operations return a stream themselves, allowing
operations to be chained and form a larger pipeline.
● This enables certain optimizations , such as laziness and short-
circuiting.
● A pipeline of operations can be viewed as a database-like query
on the data source.
– Internal iteration—
● In contrast to collections, which are iterated explicitly using an
iterator, stream operations do the iteration behind the scenes.

Java 8 Streams API
The result is [chole,
bhature,chicken

Java 8 Streams API:Streams vs
Collection

Collection:Collection Iteration

Collection:Stream Iteration

Java 8 Streams API:Intermediate
Operations

Java 8 Streams API:Intermediate
Operations
● notice several optimizations due to the lazy nature of streams.
– First, despite the fact that many dishes have more than 300
calories, only the first three are selected! This is because of
the limit operation and a technique called short-circuiting.
– Second, despite the fact that filter and map are two separate
operations, they were merged into the same pass (we call
this technique loop fusion).

Java 8 Streams API:Terminal
Operations
● Terminal operations produce a result from a stream pipeline. A
result is any nonstream value such as a List, an Integer, or even
void.

Java 8 Streams API:Operations:distinct

Java 8 Streams API:Operations:limit

Java 8 Streams API:Operations:map

Java 8 Streams API:Operations:flatmap

Java 8 Streams
API:Operations:matches

Java 8 Streams API:Operations:findFirst
vs FindAny
● The answer is parallelism. Finding the first element is more
constraining in parallel. If you don’t care about which element is
returned, use findAny because it’s less constraining when using
parallel streams.

Java 8 Streams API:Operations:reduce

Java 8 Streams API:Operations:reduce
vs step-by-step iterative summation
● The benefit of using reduce compared to the step-by-step iteration summation
is that the iteration is abstracted using internal iteration, which enables the
internal implementation to choose to perform the reduce operation in parallel.
– The iterative summation example involves shared updates to a sum
variable, which doesn’t parallelize gracefully. With needed synchronization,
the thread contention robs you of all the performance that parallelism was
supposed to give you!
– Parallelizing this computation requires a different approach: partition the
input, sum the partitions, and combine the sums.
– int sum = numbers.parallelStream().reduce(0, Integer::sum);
– But there’s a price to pay to execute this code in parallel, the lambda
passed to reduce can’t change state (for example, instance variables), and
the operation needs to be associative so it can be executed in any order.

Java 8 Streams API:Operations:max
and min

Java 8 Streams
API:Operations:Stateless vs Stateful
● Operations like map and filter take each element from the input
stream and produce zero or one result in the output stream.
These operations are thus in general stateless: they don’t have
an internal state (assuming the user-supplied lambda or method
reference has no internal mutable state).
● But operations like reduce, sum, and max need to have
internal state to accumulate the result. In this case the internal
state is small. The internal state is of bounded size no matter
how many elements are in the stream being processed.

Java 8 Streams
API:Operations:Stateless vs Stateful
● Some operations such as Sorted or Distinct seem at first to
behave like filter or map—all take a stream and produce
another stream (an intermediate operation), but there’s a crucial
difference.
– Both sorting and removing duplicates from a stream require
knowing the previous history to do their job.
– For example, sorting requires all the elements to be buffered
before a single item can be added to the output stream; the
storage requirement of the operation is unbounded.
– This can be problematic if the data stream is large or infinite.
These are stateful operations.

Java 8 Streams API:Operations:Details

Java 8 Streams
API:Operations:Example

Java 8 Streams API:Primitive Stream
Behind the scenes
Each Integer needs
to be unboxed to a
primitive before performing
the summation.

Java 8 Streams API:Primitive Stream

Java 8 Streams API:Primitive
Stream:OptionalInt/Long/Double

Stream:Numeric Ranges

Stream:Pythagorean Triples

Java 8 Streams API:Building Streams

Java 8 Streams API:Infinite
Stream:Iterate

Java 8 Streams API:Infinite
Stream:Generate
Not good for parallel
Computation as it has
mutable state. The one with
iterate is immutable.

Java 8 Streams
API:Collector:Predefined
● Predefined collectors provide 3 main functionalities
– Reducing and summarizing stream elements to a single
value
– Grouping elements
– Partitioning elements

Java 8 Streams
API:Collector:Predefined:maxBy
Same implementation.
MaxBy,predefined, built on
top of generic reduce.

Java 8 Streams
API:Collector:Predefined:counting
Count calls counting,
counting built on top
of reduce

Java 8 Streams
API:Collector:Predefined:summingInt

Java 8 Streams
API:Collector:Predefined:summingInt:tra
nsforming

Java 8 Streams
API:Collector:Predefined:summingInt:A
ggregation

Java 8 Streams
API:Collector:Predefined:summarizingIn
t

Java 8 Streams
API:Collector:Predefined:joining(uses
StringBuilder)

Java 8 Streams
API:Collector:Predefined:Collectors.red
ucing(generic factory)
● It takes three arguments:
– The first argument is the starting value of the reduction
operation and will also be the value returned in the case of a
stream with no elements, so clearly 0 is the appropriate
value in the case of a numeric sum.
– The second argument is the same function to transform a
dish into an int representing its calorie content.
– The third argument is a BinaryOperator that aggregates two
items into a single value of the same type. Here, it just sums
two ints.

Java 8 Streams API:Collector:Collect vs
Reduce
● This solution has two problems:
– a semantic one and a practical one. The semantic problem lies in the
fact that the reduce method is meant to combine two values and produce
a new one; it’s an immutable reduction. In contrast, the collect method is
designed to mutate a container to accumulate the result it’s supposed to
produce.
– using the reduce method with the wrong semantic is also the cause of a
practical problem: this reduction process can’t work in parallel because
theconcurrent modification of the same data structure operated by
multiple threads can corrupt the List itself.

Java 8 Streams API:Collector:Grouping

Java 8 Streams
API:Collector:Grouping:Multilevel

Java 8 Streams
API:Collector:Grouping:collectingAndthe
n(Adapting to a different type)

Java 8 Streams
API:Collector:Grouping:collectingAndthen(Adapting to a different
type)

Java 8 Streams API:Collector:Grouping:reduction within a
reduction

Java 8 Streams API:Collector:Partitioning
Partitioning has the advantage of
keeping both lists of the stream
elements, for which the application
of the partitioning function returns
true or false.

Java 8 Streams API:Collector:Static factories

Java 8 Streams API:Collector:Custom Collectors

● supplier() returns a function that creates an instance of
accumulator - mutable data structure that we will use to
accumulate input elements of type T
● accumulator() returns a function that will take accumulator and
one item of type T, mutating accumulator
● combiner() is used to join two accumulators together into one. It
is used when collector is executed in parallel, splitting input
Stream<T> and collecting parts independently first
● finisher() takes an accumulator A and turns it into a result value,
e.g. collection, of type R. All of this sounds quite abstract, so
let's do a simple example

One possible optimization is to test
only if the candidate number is
divisible by prime numbers.The problem
with the predefined collectors is that
during the collecting process you don’t
have access to the partial result.

Java 8 Streams API:Parallel Stream

Java 8 Streams API:Parallel Stream:parallel reduction

Java 8 Streams API:Parallel Stream
Not good for parallel
Computation as it has
mutable state.

Java 8 Streams API:Parallel Stream:Fork/Join

Java 8 Streams API:Parallel Stream:SplitIterator

Java 8 Streams API:Parallel Stream:SplitIterator:recursive splitting

Java 8 Streams API:Parallel Stream:SplitIterator:characteristics

Java 8 Streams API:Parallel Stream:SplitIterator:wordcount in
parallel

Java 8 Streams API:CompletableFuture

Java 8 Streams API:CompletableFuture:cascade
thenApply - Synchronous
thenCompose- Asynchronous

Java 8 Streams API:CompletableFuture:cascade

Java 8 Streams API:CompletableFuture:parallel

Java 8 Streams API:CompletableFuture:callback

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