i am trying to test a Rest API PUT request. It is a spring boot application.
PUT request is used to do an update in the existing list of objects
traditional way of writing is working.
data is the data in the memory - which is a List<Bean> and
name (string type) is the key to find the object in the data and objectBean is the one to replace once we find with the key(that is name)
public void update(Bean objectBean, String name) {
for(int i = 0; i < data.size() ; i++) {
Bean l = data.get(i);
if(l.getName().equals(name)) {
data.set(i, objectBean);
return;
}
}
};
but i tried to write using Stream in java 8 . below is the code
Data.stream().map(p -> p.getName().equals(name) ? objectBean: p );
but this gives empty list.
Using streams here makes code only more complicated.
If you really wants you can introduce it to find the index i value. After that you can do the replacement.
IntStream.range(0, data.size())
.filter(i -> data.get(i).getName().equals(name)).findFirst()
.ifPresent(i -> data.set(i, objectBean));
Given that data is some List with Bean objects, you'd need to return your collected stream:
return data.stream()
.map(bean -> bean.getName().equals(name) ? objectBean : bean)
.collect(Collectors.toList());
If data is a non-empty Iterable then the output must be as well as map takes a Function object. However, this is not a good use case for the Stream API:
Firstly, streams are designed for side-effect-free purposes (i.e., creating new data structures rather updating them). The stream API supports forEach(Consumer<super T>) which is designed for side effects, but so do many other collections, in fact, all Iterables, whereas the immutable operations such as map and flatMap are not.
Second, I can't see the rest of your program, but at least in this snippet you seem to be updating your data structure based on the name, and you assume the name is unique because you stopped as soon as you reached the first Bean with the name you're looking for. Consider using Map<String, Bean> as your data structure.
Lastly, streams are lazy data structures, meaning that all the chained operations get computed when you collect. This provides incentive to chain a lot of computations together - chaining just a single map doesn't give you any performance advantages (tho it does give you referential transparency).
return data.stream()
.filter(bean -> bean.getName().equals(name))
.findAny()
I would like to know if there is any difference in the behavior between those both methods or if it's just a matter of style:
private Single<JsonObject> foo() {
return Single.just(new JsonObject()).flatMap(next -> Single.just(next));
}
private Single<JsonObject> bar() {
return Single.just(new JsonObject()).map(next -> next);
}
There is no difference in behavior as both are pointless operations. The first simply repeats wrapping the object into a Single, while the second maps it to itself. You would never have a reason to do either.
Read up on 'flatMap()' and 'map()': the first turns each value into an observable of different values, the second turns each value into a different value.
You can represent for your self a flatMap operator like a sequence of two other operator map and merge.
Map will convert your source item to Observable that emit a value based on the function inside of map.
At this point merge will help to put together every item that emitted by each of your new observables, not the source one.
There is a good illustration on that book https://www.manning.com/books/rxjava-for-android-developers
map with merge together
To simplify this code was introduced flatMap operator
only flatMap
I'm trying to learn the Java Set interface and have encountered the following code online, I understand the purpose of this code is to convert a Collection<Object> to a TreeSet, but I do not understand how the statement works because the syntax is complex and foreign to me. Could someone walk me through the process step by step?
Set<String> set = people.stream()
.map(Person::getName)
.collect(Collectors.toCollection(TreeSet::new));
And also, under what kind of circumstances should we prefer the above syntax over the one below?
Set<Integer> s1 = new TreeSet(c1); //where c1 is an instance of Collection interface type
people.stream()
Takes a set of people and obtains a stream.
.map(Person::getName)
Takes a stream of people and invokes the getName method on each one, returning a list with all the results. This would be "equivalent" to
for(Person person : people){
setOfNames.add(person.getName())
}
.collect(Collectors.toCollection(TreeSet::new));
Takes a stream of strings and converts it in a set.
The streams are very useful when you need to apply several transformations. They can also perform very well if you make use of parallel streams, since each transformation (in your case each getName) can be done in parallel instead of sequentially.
peopele.stream() create a stream of elements
.map(Person::getName) takes each object from people collection and calls getName , coverts to string then
.collect(Collectors.toCollection(TreeSet::new)) - Collects these String elements and create a TreeSet out of it.
Hope its clear
I'm still trying to fully grasp working with the Stream package in Java 8 and was hoping for some help.
I have a class, described below, instances of which I receive in a list as part of a database call.
class VisitSummary {
String source;
DateTime timestamp;
Integer errorCount;
Integer trafficCount;
//Other fields
}
To generate some possibly useful information about this, I have a class VisitSummaryBySource which holds the sum total of all visits (for a given timeframe):
class VisitSummaryBySource {
String sourceName;
Integer recordCount;
Integer errorCount;
}
I was hoping to construct a List<VisitSummaryBySource> collection which as the name sounds, holds the list of VisitSummaryBySource objects containing the sum total of records and errors encountered, for each different source.
Is there a way I can achieve this using streams in a single operation? Or do I need to necessarily break this down into multiple operations? The best I could come up with is:
Map<String, Integer> recordsBySrc = data.parallelStream().collect(Collectors.groupingBy(VisitSummaryBySource::getSource,
Collectors.summingInt(VisitSummaryBySource::getRecordCount)));
and to calculate the errors
Map<String, Integer> errorsBySrc = data.parallelStream().collect(Collectors.groupingBy(VisitSummaryBySource::getSource,
Collectors.summingInt(VisitSummaryBySource::getErrorCount)));
and merging the two maps to come up with the list I'm looking for.
You're on the right track. The uses of Collectors.summingInt are examples of downstream collectors of the outer groupingBy collector. This operation extracts one of the integer values from each VisitSummaryBySource instance in the same group, and sums them. This is essentially a reduction over integers.
The problem, as you note, is that you can extract/reduce only one of the integer values, so you have to perform a second pass to extract/reduce the other integer values.
The key is to consider reduction not over the individual integer values but over the entire VisitSummaryBySource object. Reduction takes a BinaryOperator, which takes two instances of the type in question and combines them into one. Here's how to do that, by adding a static method to VisitSummaryBySource:
static VisitSummaryBySource merge(VisitSummaryBySource a,
VisitSummaryBySource b) {
assert a.getSource().equals(b.getSource());
return new VisitSummaryBySource(a.getSource(),
a.getRecordCount() + b.getRecordCount(),
a.getErrorCount() + b.getErrorCount());
}
Note that we're not actually merging the source names. Since this reduction is only performed within a group, where the source names are the same, we assert that we can only merge two instances whose names are the same. We also assume the obvious constructor taking a name, record count, and error count, and call that to create the merged object, containing the sums of the counts.
Now our stream looks like this:
Map<String, Optional<VisitSummaryBySource>> map =
data.stream()
.collect(groupingBy(VisitSummaryBySource::getSource,
reducing(VisitSummaryBySource::merge)));
Note that this reduction produces map values of type Optional<VisitSummaryBySource>. This is somewhat odd; we'll deal with it below. We could avoid the Optional by using another form of the reducing collector that takes an identity value. This is possible but somewhat nonsensical, as there's no good value to use for the source name of the identity. (We could use something like the empty string, but we'd have to abandon our assertion that we merge only objects whose source names are equal.)
We don't really care about the map; it only needs to be kept around long enough to reduce the VisitSummaryBySource instances. Once that's done, we can just pull out the map values using values() and throw away the map.
We can also turn this back into a stream and unwrap the Optional by mapping them through Optional::get. This is safe, because a value never ends up in the map unless there's at least one member of the group.
Finally, we collect the results into a list.
The final code looks like this:
List<VisitSummaryBySource> output =
data.stream()
.collect(groupingBy(VisitSummaryBySource::getSource,
reducing(VisitSummaryBySource::merge)))
.values().stream()
.map(Optional::get)
.collect(toList());
When would you use collect() vs reduce()? Does anyone have good, concrete examples of when it's definitely better to go one way or the other?
Javadoc mentions that collect() is a mutable reduction.
Given that it's a mutable reduction, I assume it requires synchronization (internally) which, in turn, can be detrimental to performance. Presumably reduce() is more readily parallelizable at the cost of having to create a new data structure for return after every step in the reduce.
The above statements are guesswork however and I'd love an expert to chime in here.
reduce is a "fold" operation, it applies a binary operator to each element in the stream where the first argument to the operator is the return value of the previous application and the second argument is the current stream element.
collect is an aggregation operation where a "collection" is created and each element is "added" to that collection. Collections in different parts of the stream are then added together.
The document you linked gives the reason for having two different approaches:
If we wanted to take a stream of strings and concatenate them into a
single long string, we could achieve this with ordinary reduction:
String concatenated = strings.reduce("", String::concat)
We would get the desired result, and it would even work in parallel.
However, we might not be happy about the performance! Such an
implementation would do a great deal of string copying, and the run
time would be O(n^2) in the number of characters. A more performant
approach would be to accumulate the results into a StringBuilder,
which is a mutable container for accumulating strings. We can use the
same technique to parallelize mutable reduction as we do with ordinary
reduction.
So the point is that the parallelisation is the same in both cases but in the reduce case we apply the function to the stream elements themselves. In the collect case we apply the function to a mutable container.
The reason is simply that:
collect() can only work with mutable result objects.
reduce() is designed to work with immutable result objects.
"reduce() with immutable" example
public class Employee {
private Integer salary;
public Employee(String aSalary){
this.salary = new Integer(aSalary);
}
public Integer getSalary(){
return this.salary;
}
}
#Test
public void testReduceWithImmutable(){
List<Employee> list = new LinkedList<>();
list.add(new Employee("1"));
list.add(new Employee("2"));
list.add(new Employee("3"));
Integer sum = list
.stream()
.map(Employee::getSalary)
.reduce(0, (Integer a, Integer b) -> Integer.sum(a, b));
assertEquals(Integer.valueOf(6), sum);
}
"collect() with mutable" example
E.g. if you would like to manually calculate a sum using collect() it can not work with BigDecimal but only with MutableInt from org.apache.commons.lang.mutable for example. See:
public class Employee {
private MutableInt salary;
public Employee(String aSalary){
this.salary = new MutableInt(aSalary);
}
public MutableInt getSalary(){
return this.salary;
}
}
#Test
public void testCollectWithMutable(){
List<Employee> list = new LinkedList<>();
list.add(new Employee("1"));
list.add(new Employee("2"));
MutableInt sum = list.stream().collect(
MutableInt::new,
(MutableInt container, Employee employee) ->
container.add(employee.getSalary().intValue())
,
MutableInt::add);
assertEquals(new MutableInt(3), sum);
}
This works because the accumulator container.add(employee.getSalary().intValue()); is not supposed to return a new object with the result but to change the state of the mutable container of type MutableInt.
If you would like to use BigDecimal instead for the container you could not use the collect() method as container.add(employee.getSalary()); would not change the container because BigDecimal it is immutable.
(Apart from this BigDecimal::new would not work as BigDecimal has no empty constructor)
The normal reduction is meant to combine two immutable values such as int, double, etc. 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.
To illustrate the problem, let's suppose you want to achieve Collectors.toList() using a simple reduction like
List<Integer> numbers = stream.reduce(
new ArrayList<Integer>(),
(List<Integer> l, Integer e) -> {
l.add(e);
return l;
},
(List<Integer> l1, List<Integer> l2) -> {
l1.addAll(l2);
return l1;
});
This is the equivalent of Collectors.toList(). However, in this case you mutate the List<Integer>. As we know the ArrayList is not thread-safe, nor is safe to add/remove values from it while iterating so you will either get concurrent exception or ArrayIndexOutOfBoundsException or any kind of exception (especially when run in parallel) when you update the list or the combiner tries to merge the lists because you are mutating the list by accumulating (adding) the integers to it. If you want to make this thread-safe you need to pass a new list each time which would impair performance.
In contrast, the Collectors.toList() works in a similar fashion. However, it guarantees thread safety when you accumulate the values into the list. From the documentation for the collect method:
Performs a mutable reduction operation on the elements of this stream using a Collector. If the stream is parallel, and the Collector is concurrent, and either
the stream is unordered or the collector is unordered, then a
concurrent reduction will be performed. When executed in parallel, multiple intermediate results may be instantiated, populated, and merged so as to maintain isolation of mutable data structures. Therefore, even when executed in parallel with non-thread-safe data structures (such as ArrayList), no additional synchronization is needed for a parallel reduction.
So to answer your question:
When would you use collect() vs reduce()?
if you have immutable values such as ints, doubles, Strings then normal reduction works just fine. However, if you have to reduce your values into say a List (mutable data structure) then you need to use mutable reduction with the collect method.
Let the stream be a <- b <- c <- d
In reduction,
you will have ((a # b) # c) # d
where # is that interesting operation that you would like to do.
In collection,
your collector will have some kind of collecting structure K.
K consumes a.
K then consumes b.
K then consumes c.
K then consumes d.
At the end, you ask K what the final result is.
K then gives it to you.
They are very different in the potential memory footprint during the runtime. While collect() collects and puts all data into the collection, reduce() explicitly asks you to specify how to reduce the data that made it through the stream.
For example, if you want to read some data from a file, process it, and put it into some database, you might end up with java stream code similar to this:
streamDataFromFile(file)
.map(data -> processData(data))
.map(result -> database.save(result))
.collect(Collectors.toList());
In this case, we use collect() to force java to stream data through and make it save the result into the database. Without collect() the data is never read and never stored.
This code happily generates a java.lang.OutOfMemoryError: Java heap space runtime error, if the file size is large enough or the heap size is low enough. The obvious reason is that it tries to stack all the data that made it through the stream (and, in fact, has already been stored in the database) into the resulting collection and this blows up the heap.
However, if you replace collect() with reduce() -- it won't be a problem anymore as the latter will reduce and discard all the data that made it through.
In the presented example, just replace collect() with something with reduce:
.reduce(0L, (aLong, result) -> aLong, (aLong1, aLong2) -> aLong1);
You do not need even to care to make the calculation depend on the result as Java is not a pure FP (functional programming) language and cannot optimize out the data that is not being used at the bottom of the stream because of the possible side-effects.
Here is the code example
List<Integer> list = Arrays.asList(1,2,3,4,5,6,7);
int sum = list.stream().reduce((x,y) -> {
System.out.println(String.format("x=%d,y=%d",x,y));
return (x + y);
}).get();
System.out.println(sum);
Here is the execute result:
x=1,y=2
x=3,y=3
x=6,y=4
x=10,y=5
x=15,y=6
x=21,y=7
28
Reduce function handle two parameters, the first parameter is the previous return value int the stream, the second parameter is the current
calculate value in the stream, it sum the first value and current value as the first value in next caculation.
According to the docs
The reducing() collectors are most useful when used in a multi-level reduction, downstream of groupingBy or partitioningBy. To perform a simple reduction on a stream, use Stream.reduce(BinaryOperator) instead.
So basically you'd use reducing() only when forced within a collect.
Here's another example:
For example, given a stream of Person, to calculate the longest last name
of residents in each city:
Comparator<String> byLength = Comparator.comparing(String::length);
Map<String, String> longestLastNameByCity
= personList.stream().collect(groupingBy(Person::getCity,
reducing("", Person::getLastName, BinaryOperator.maxBy(byLength))));
According to this tutorial reduce is sometimes less efficient
The reduce operation always returns a new value. However, the accumulator function also returns a new value every time it processes an element of a stream. Suppose that you want to reduce the elements of a stream to a more complex object, such as a collection. This might hinder the performance of your application. If your reduce operation involves adding elements to a collection, then every time your accumulator function processes an element, it creates a new collection that includes the element, which is inefficient. It would be more efficient for you to update an existing collection instead. You can do this with the Stream.collect method, which the next section describes...
So the identity is "re-used" in a reduce scenario, so slightly more efficient to go with .reduce if possible.
There is a very good reason to always prefer collect() vs the reduce() method. Using collect() is much more performant, as explained here:
Java 8 tutorial
*A mutable reduction operation(such as Stream.collect()) collects the stream elements in a mutable result container(collection) as it processes them.
Mutable reduction operations provide much improved performance when compared to an immutable reduction operation(such as Stream.reduce()).
This is due to the fact that the collection holding the result at each step of reduction is mutable for a Collector and can be used again in the next step.
Stream.reduce() operation, on the other hand, uses immutable result containers and as a result needs to instantiate a new instance of the container at every intermediate step of reduction which degrades performance.*