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Prüfungen finden unter Einhaltung der hygienischen Verhaltensregeln weitestgehend statt. However, TU Graz will not abandon the principle of the "digital semester". From mid-May onwards, the library services will be expanded in stages. Textbook collections can now also be created for digital media and are now accessible via TU Graz LibrarySearch under the category Collections.

We already saw few reduction operations like findFirst , min and max. Here, we start with the initial value of 0 and repeated apply Double::sum on elements of the stream.

We already saw how we used Collectors. It internally uses a java. StringJoiner to perform the joining operation.

We can use Collectors. We can also use a constructor reference for the Supplier :. Here, an empty collection is created internally, and its add method is called on each element of the stream.

Notice how we can analyze the salary of each employee and get statistical information on that data — such as min, max, average etc.

We can partition a stream into two — based on whether the elements satisfy certain criteria or not. It takes a classification function as its parameter.

This classification function is applied to each element of the stream. The value returned by the function is used as a key to the map that we get from the groupingBy collector:.

In this quick example, we grouped the employees based on the initial character of their first name.

Here mapping maps the stream element Employee into just the employee id — which is an Integer — using the getId mapping function.

These ids are still grouped based on the initial character of employee first name. It simply returns a collector which performs a reduction of its input elements:.

To perform a simple reduction on a stream, use reduce instead. Here we group the employees based on the initial character of their first name.

Within each group, we find the employee with the longest name. Using the support for parallel streams, we can perform stream operations in parallel without having to write any boilerplate code; we just have to designate the stream as parallel:.

Here salaryIncrement would get executed in parallel on multiple elements of the stream, by simply adding the parallel syntax. This functionality can, of course, be tuned and configured further , if you need more control over the performance characteristics of the operation.

As is the case with writing multi-threaded code, we need to be aware of few things while using parallel streams:. Sometimes, we might want to perform operations while the elements are still getting generated.

Unlike using list or map , where all the elements are already populated, we can use infinite streams, also called as unbounded streams.

We provide a Supplier to generate which gets called whenever new stream elements need to be generated:. Here, we pass Math: :random as a Supplier , which returns the next random number.

With infinite streams, we need to provide a condition to eventually terminate the processing. One common way of doing this is using limit.

In above example, we limit the stream to 5 random numbers and print them as they get generated. Please note that the Supplier passed to generate could be stateful and such stream may not produce the same result when used in parallel.

Here, we pass 2 as the seed value, which becomes the first element of our stream. This value is passed as input to the lambda, which returns 4.

This value, in turn, is passed as input in the next iteration. This continues until we generate the number of elements specified by limit which acts as the terminating condition.

Here we use forEach to write each element of the stream into the file by calling PrintWriter. Here Files.

This also increases code reusability and simplifies unit testing. Java 8 brought Java streams to the world. However, the following version of the language also contributed to the feature.

It does what its name implies: it takes elements from a stream while a given condition is true. The moment the condition becomes false, it quits and returns a new stream with just the elements that matched the predicate.

In the code above we obtain an infinite stream and then use the takeWhile method to select the numbers that are less than or equals to After that, we calculate their squares and print those.

After all, you could accomplish the same result with the following code:. Here, we have two identical streams, which we filter using takeWhile and filter , respectively.

As you can see, filter applies the predicate throughout the whole sequence. On the other hand, takeWhile stops evaluating as soon as it finds the first occurrence where the condition is false.

The dropWhile method does pretty much the same thing the takewhile does but in reverse. That is to say: the previous method uses the predicate the condition to select the elements to preserve in the new stream it returns.

This method does the opposite, using the condition to select the items not to include in the resulting stream. The resulting items are:.

As you can see, there are numbers less than or equals to five in the latter half of the sequence. Java 9 brings an override of the method.

In Java 9 we have the new version of iterate , which adds a new parameter, which is a predicate used to decide when the loop should terminate.

As long as the condition remains true, we keep going. We could say that the new iterate method is a replacement for the good-old for statement.

In fact, the code above is equivalent to the following excerpt:. The last item in this list of additions to the Stream APIs is a powerful way not only to avoid the dreaded null pointer exception but also to write cleaner code.

Check out the following example:. Assume that number refers to some integer obtained through the UI, the network, filesystem or another external untrusted source.

So, it could be null. To avoid that that we can check for null and return an empty stream. The example above is a contrived example, sure.

In real life, code in similar scenarios could become really messy, really fast. We could employ ofNullable instead:.

The new method returns empty Optionals in it receives null, avoiding runtime errors in scenarios that would normally cause one, like in the following example:.

In this article, we focused on the details of the new Stream functionality in Java 8. We saw various operations supported and how lambdas and pipelines can be used to write concise code.

We also saw some characteristics of streams like lazy evaluation, parallel and infinite streams. Start free. Deliver powerful insights from your streaming data with ease, in real time.

Read report. Build streaming pipelines in minutes Run complex analytics with no need to learn new processing frameworks or provision virtual machines VMs or clusters.

Get started with real-time analytics on Azure. Deploy in the cloud and on the edge Bring real-time insights and analytics capabilities closer to where your data originates.

Learn more about real-time analytics on the edge. Power real-time analytics with artificial intelligence Take advantage of built-in machine learning ML models to shorten time to insights.

Learn more about built-in machine learning for streaming jobs. Build a secure streaming solution with enhanced security and compliance from Azure.

Learn more about security with Azure. Scale up rapidly and pay only for what you use Pay only for the virtual machines and the associated storage and networking resources consumed Take advantage of no-charge cluster management.

See Azure Stream Analytics pricing. Training resources for developers. Documentation Explore tutorials, samples, and other documentation to learn how to use Azure Stream Analytics.

Stream Analytics documentation overview Stream Analytics quickstart documentation Stream Analytics query pattern documentation.

Videos See the scenarios enabled by Azure Stream Analytics with video deep dives and demos. Machine learning models in the cloud and on the intelligent edge Anomaly detection using machine learning Managing timelines and coding on IoT Edge.

Learning modules Expand your knowledge and earn certifications with hands-on online courses from Microsoft Learn. Browse training at Microsoft Learn.

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Explore Azure Stream Analytics tutorials and other documentation. Start quickly with a few clicks and create a Stream Analytics job using the Azure portal.

Popular templates and labs Discover self-paced labs and popular quickstart templates for common configurations made by Microsoft and the community.

See what companies are achieving with Azure Stream Analytics. Global energy company optimizes operations and drives real-time insights ExxonMobil overcomes the challenges of monitoring and optimizing a vast number of widely dispersed field assets with Azure analytics solutions.

World-famous brewers capture and process real-time data on a global scale Find out how Anheuser Busch InBev uses Azure data services including Azure Stream Analytics to optimize inventory, reduce out-of-stock scenarios, and better forecast future retailer consumption trends.

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Energy provider enhances storm recovery and customer service with real-time asset tracking Learn how Southern Company uses Stream Analytics and IoT Hub to transform sensor data into actionable insights, relying on the elasticity and reliability of Azure for critical workloads.

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