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Inizia la ricerca. Further, the ordering of those effects may be surprising. Even when a pipeline is constrained to produce a result that is consistent with the encounter order of the stream source for example, IntStream.
Many computations where one might be tempted to use side effects can be more safely and efficiently expressed without side-effects, such as using reduction instead of mutable accumulators.
However, side-effects such as using println for debugging purposes are usually harmless. A small number of stream operations, such as forEach and peek , can operate only via side-effects; these should be used with care.
As an example of how to transform a stream pipeline that inappropriately uses side-effects to one that does not, the following code searches a stream of strings for those matching a given regular expression, and puts the matches in a list.
This code unnecessarily uses side-effects. If executed in parallel, the non-thread-safety of ArrayList would cause incorrect results, and adding needed synchronization would cause contention, undermining the benefit of parallelism.
Ordering Streams may or may not have a defined encounter order. Whether or not a stream has an encounter order depends on the source and the intermediate operations.
Certain stream sources such as List or arrays are intrinsically ordered, whereas others such as HashSet are not. Some intermediate operations, such as sorted , may impose an encounter order on an otherwise unordered stream, and others may render an ordered stream unordered, such as BaseStream.
Further, some terminal operations may ignore encounter order, such as forEach. However, if the source has no defined encounter order, then any permutation of the values [2, 4, 6] would be a valid result.
For sequential streams, the presence or absence of an encounter order does not affect performance, only determinism.
If a stream is ordered, repeated execution of identical stream pipelines on an identical source will produce an identical result; if it is not ordered, repeated execution might produce different results.
For parallel streams, relaxing the ordering constraint can sometimes enable more efficient execution. Certain aggregate operations, such as filtering duplicates distinct or grouped reductions Collectors.
Similarly, operations that are intrinsically tied to encounter order, such as limit , may require buffering to ensure proper ordering, undermining the benefit of parallelism.
In cases where the stream has an encounter order, but the user does not particularly care about that encounter order, explicitly de-ordering the stream with unordered may improve parallel performance for some stateful or terminal operations.
However, most stream pipelines, such as the "sum of weight of blocks" example above, still parallelize efficiently even under ordering constraints.
Reduction operations A reduction operation also called a fold takes a sequence of input elements and combines them into a single summary result by repeated application of a combining operation, such as finding the sum or maximum of a set of numbers, or accumulating elements into a list.
The streams classes have multiple forms of general reduction operations, called reduce and collect , as well as multiple specialized reduction forms such as sum , max , or count.
Not only is a reduction "more abstract" -- it operates on the stream as a whole rather than individual elements -- but a properly constructed reduce operation is inherently parallelizable, so long as the function s used to process the elements are associative and stateless.
Even if the language had a "parallel for-each" construct, the mutative accumulation approach would still required the developer to provide thread-safe updates to the shared accumulating variable sum , and the required synchronization would then likely eliminate any performance gain from parallelism.
Using reduce instead removes all of the burden of parallelizing the reduction operation, and the library can provide an efficient parallel implementation with no additional synchronization required.
The "widgets" examples shown earlier shows how reduction combines with other operations to replace for loops with bulk operations. The accumulator function takes a partial result and the next element, and produces a new partial result.
The combiner function combines two partial results to produce a new partial result. The combiner is necessary in parallel reductions, where the input is partitioned, a partial accumulation computed for each partition, and then the partial results are combined to produce a final result.
More formally, the identity value must be an identity for the combiner function. This means that for all u , combiner.
Additionally, the combiner function must be associative and must be compatible with the accumulator function: for all u and t , combiner. The three-argument form is a generalization of the two-argument form, incorporating a mapping step into the accumulation step.
The generalized form is provided for cases where significant work can be optimized away by combining mapping and reducing into a single function.
Mutable reduction A mutable reduction operation accumulates input elements into a mutable result container, such as a Collection or StringBuilder , as it processes the elements in the stream.
However, we might not be happy about the performance! 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. The mutable reduction operation is called collect , as it collects together the desired results into a result container such as a Collection.
A collect operation requires three functions: a supplier function to construct new instances of the result container, an accumulator function to incorporate an input element into a result container, and a combining function to merge the contents of one result container into another.
The three aspects of collect -- supplier, accumulator, and combiner -- are tightly coupled. We can use the abstraction of a Collector to capture all three aspects.
The class Collectors contains a number of predefined factories for collectors, including combinators that transform one collector into another.
For any partially accumulated result, combining it with an empty result container must produce an equivalent result. That is, for a partially accumulated result p that is the result of any series of accumulator and combiner invocations, p must be equivalent to combiner.
Further, however the computation is split, it must produce an equivalent result. This is because the combining step merging one Map into another by key can be expensive for some Map implementations.
Suppose, however, that the result container used in this reduction was a concurrently modifiable collection -- such as a ConcurrentHashMap.
In that case, the parallel invocations of the accumulator could actually deposit their results concurrently into the same shared result container, eliminating the need for the combiner to merge distinct result containers.
This potentially provides a boost to the parallel execution performance. We call this a concurrent reduction.
A Collector that supports concurrent reduction is marked with the Collector. However, a concurrent collection also has a downside. If multiple threads are depositing results concurrently into a shared container, the order in which results are deposited is non-deterministic.
Consequently, a concurrent reduction is only possible if ordering is not important for the stream being processed. It stated that Netflix is continuing their DVD serMaiden s with 5.
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