The Package Stream

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On 25.07.2020
Last modified:25.07.2020


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The Package Stream

Creates a new package part. Read)) { CopyStream(fileStream, packagePartDocument. CreatePart Initialisiert eine leere Stream für den neuen Teil. Spendenpackage mit Stream Schwoaze Helfen Live-Stream-Zugang, 1x Nordkurvenkalender , 1x Schwoaze Helfen Im Package inkludiert sind. The Twitch Package has everything a Twitch streamer needs to get started in the streaming. Gemerkt von: TacticalLionDesigns. 1.

Chapter 2. The AppStream repository

Wer streamt The Package? The Package online schauen auf Netflix, Prime, Maxdome, Sky und anderen Streaming-Diensten in Deutschland. The Package. The Twitch Package has everything a Twitch streamer needs to get started in the streaming. Gemerkt von: TacticalLionDesigns. 1. Definition of a stream data package. If all definitions of timeslot data are done, the stream data package is shown in the register Data.

The Package Stream Search StreamScheme Video

RHCSA RHEL 8 - Work with package module streams

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You will be able to edit some basic features about your overlay pack. This will include sine if the text, text colors, layouts, your socials, and your schedule.

Click on the screen you want to edit. You can also change the text-transform to uppercase, lowercase, or none depending on your personal preference.

You can also add a gradient to your name by clicking the checkbox and choosing a gradient color. Step 4: You can also change your alignment to vertical and move your name template to another location on the screen.

To change your schedule or your socials, you will follow the same sets. Double-click on each section and edit the text, colors, and information to what you need.

You may also want to add an alert box in case someone donates to you or follows you while you are on your starting or ending screen.

You will need to change your username, socials, and other information on every scene they are shown such as the ending screen.

Keep track of the colors and font styles you use on one so that you can maintain consistency on every page.

Many of the Nerd or Die packs will give you the option to have a 3x or 4x supporter bar. This simply will allow you to have either 3 or 4 overlays at the bottom of your screen where you can celebrate your recent or top donators, subscribers, and followers.

While many streamers show their top supporters, when you are just starting out, showing your latest followers, subscribers, bit donators, and PayPal donators is a great way to make sure that everyone in your chat is included and celebrated.

Step 1: Open one of the supporter folders either horizontal or vertical in the in-game folder. Step 3: Change the text to your desired label i.

You can also change the color of the text and the font if you wish. Step 4: Under label type, choose or search for the label you wish to display such as your most recent donation.

You can also choose between having your supporter bars be horizontal or vertical. You will need to set up both sets of bars if you wish to use both.

You can also choose to use both sets if you want to show 8 different metrics. Step 5: On your computer, open the alerts folder that you downloaded from Nerd or Die.

Step 6: Open the Streamlabs folder from inside the alerts folder and double-click on the weblink to go to the right page on your Streamlabs dashboard.

Step 9: Return to Streamlabs OBS and test your widget to ensure that it works properly. Step 1: From your computer, open the Overlay folder you received from Nerd or Die, then go into the Streamlabs folder.

Step 2: Double click on the Event List weblink to go the Streamlabs dashboard. Step 5: Return to Streamlabs OBS and move the Event List to your desired location on the screen.

Step 6: Double-click on the event list in your source section to choose which events you wish to show during your stream.

Step 2: Double click on the Chatbox weblink to go the Streamlabs dashboard. Step 5: Return to Streamlabs OBS and move the Chatbox to your desired location on the screen.

There are two common aspect ratios that come with the Nerd or Die packs. These are the and aspect ratios. Pipelines containing exclusively stateless intermediate operations can be processed in a single pass, whether sequential or parallel, with minimal data buffering.

Further, some operations are deemed short-circuiting operations. An intermediate operation is short-circuiting if, when presented with infinite input, it may produce a finite stream as a result.

A terminal operation is short-circuiting if, when presented with infinite input, it may terminate in finite time.

Having a short-circuiting operation in the pipeline is a necessary, but not sufficient, condition for the processing of an infinite stream to terminate normally in finite time.

Processing elements with an explicit for- loop is inherently serial. Streams facilitate parallel execution by reframing the computation as a pipeline of aggregate operations, rather than as imperative operations on each individual element.

All streams operations can execute either in serial or in parallel. The stream implementations in the JDK create serial streams unless parallelism is explicitly requested.

For example, Collection has methods Collection. The only difference between the serial and parallel versions of this example is the creation of the initial stream, using " parallelStream " instead of " stream ".

The stream pipeline is executed sequentially or in parallel depending on the mode of the stream on which the terminal operation is invoked.

The sequential or parallel mode of a stream can be determined with the BaseStream. The most recent sequential or parallel mode setting applies to the execution of the entire stream pipeline.

Except for operations identified as explicitly nondeterministic, such as findAny , whether a stream executes sequentially or in parallel should not change the result of the computation.

Most stream operations accept parameters that describe user-specified behavior, which are often lambda expressions. To preserve correct behavior, these behavioral parameters must be non-interfering , and in most cases must be stateless.

Such parameters are always instances of a functional interface such as Function , and are often lambda expressions or method references.

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.

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.

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; that different operations on the "same" element within the same stream pipeline are executed in the same thread; and that behavioral parameters are always invoked, since a stream implementation is free to elide operations or entire stages from a stream pipeline if it can prove that it would not affect the result of the computation.

The ordering of side-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.

The eliding of side-effects may also be surprising. With the exception of terminal operations forEach and forEachOrdered , side-effects of behavioral parameters may not always be executed when the stream implementation can optimize away the execution of behavioral parameters without affecting the result of the computation.

For a specific example see the API note documented on the count operation. 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.

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.

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.

Reduction parallellizes well because the implementation can operate on subsets of the data in parallel, and then combine the intermediate results to get the final correct answer.

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. We would get the desired result, and it would even work in parallel.

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.

As with reduce , a benefit of expressing collect in this abstract way is that it is directly amenable to parallelization: we can accumulate partial results in parallel and then combine them, so long as the accumulation and combining functions satisfy the appropriate requirements.

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.

Packaging mutable reductions into a Collector has another advantage: composability. The class Collectors contains a number of predefined factories for collectors, including combinators that transform one collector into another.

As with the regular reduction operation, collect operations can only be parallelized if appropriate conditions are met. 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. Here, equivalence generally means according to Object.

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.

The Package Stream


1 Kommentare

  1. Voodoojin

    Bemerkenswert, die sehr lustige Antwort

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