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DokuWiki should at least support ogv , webm and mp4 format, but see supported media formats for a more up to date list.
Unfortunately not all browsers understand all video and audio formats. To mitigate the problem, you can upload your file in different formats for maximum browser compatibility.
When you upload a video. That image needs to have the same filename as the video and be either a jpg or png file. In the example above a video.
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DokuWiki and your browser will take care of the details. DokuWiki supports all kinds defined by the W3C , but user experience may vary, depending on browser and installed software.
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User Tools Log In. The notable exception to this are streams whose sources are concurrent collections, which are specifically designed to handle concurrent modification.
Accordingly, behavioral parameters in stream pipelines whose source might not be concurrent should never modify the stream's data source.
A behavioral parameter is said to interfere with a non-concurrent data source if it modifies, or causes to be modified, the stream's data source.
The need for non-interference applies to all pipelines, not just parallel ones. Unless the stream source is concurrent, modifying a stream's data source during execution of a stream pipeline can cause exceptions, incorrect answers, or nonconformant behavior.
For well-behaved stream sources, the source can be modified before the terminal operation commences and those modifications will be reflected in the covered elements.
Then a stream is created from that list. Next the list is modified by adding a third string: "three". Finally the elements of the stream are collected and joined together.
Since the list was modified before the terminal collect operation commenced the result will be a string of "one two three".
All the streams returned from JDK collections, and most other JDK classes, are well-behaved in this manner; for streams generated by other libraries, see Low-level stream construction for requirements for building well-behaved streams.
Stateless behaviors Stream pipeline results may be nondeterministic or incorrect if the behavioral parameters to the stream operations are stateful.
A stateful lambda or other object implementing the appropriate functional interface is one whose result depends on any state which might change during the execution of the stream pipeline.
Here, if the mapping operation is performed in parallel, the results for the same input could vary from run to run, due to thread scheduling differences, whereas, with a stateless lambda expression the results would always be the same.
Note also that attempting to access mutable state from behavioral parameters presents you with a bad choice with respect to safety and performance; if you do not synchronize access to that state, you have a data race and therefore your code is broken, but if you do synchronize access to that state, you risk having contention undermine the parallelism you are seeking to benefit from.
The best approach is to avoid stateful behavioral parameters to stream operations entirely; there is usually a way to restructure the stream pipeline to avoid statefulness.
Side-effects Side-effects in behavioral parameters to stream operations are, in general, discouraged, as they can often lead to unwitting violations of the statelessness requirement, as well as other thread-safety hazards.
If the behavioral parameters do have side-effects, unless explicitly stated, there are no guarantees as to the visibility of those side-effects to other threads, nor are there any guarantees that different operations on the "same" element within the same stream pipeline are executed in the same thread.
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. The Stream. You can ensure the stream is unordered by using the BaseStream.
Note that if it is important that the elements for a given key appear in the order they appear in the source, then we cannot use a concurrent reduction, as ordering is one of the casualties of concurrent insertion.
We would then be constrained to implement either a sequential reduction or a merge-based parallel reduction.
Examples of associative operations include numeric addition, min, and max, and string concatenation.
Low-level stream construction So far, all the stream examples have used methods like Collection. How are those stream-bearing methods implemented?
The class StreamSupport has a number of low-level methods for creating a stream, all using some form of a Spliterator.
A spliterator is the parallel analogue of an Iterator ; it describes a possibly infinite collection of elements, with support for sequentially advancing, bulk traversal, and splitting off some portion of the input into another spliterator which can be processed in parallel.
At the lowest level, all streams are driven by a spliterator.
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