Reading text files using the Stream API in Java 8

We discussed the Java 8 Stream API thoroughly on this blog starting here. We mostly looked at how the API is applied to MapReduce operations to analyse data in a stream.

The same API can be applied to File I/O. Java 8 adds a new method called “lines” to the BufferedReader object which opens a Stream of String. From then on it’s just standard Stream API usage to filter the lines in the file – and perform other operations on them in parallel such as filtering out the lines that you don’t need.

Here’s an example how you can read all lines in a file:

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Waiting for background tasks to finish using the CompletableFuture class in Java

Introduction

In this post we saw how to wait for a number background tasks to finish using the CountDownLatch class. The starting point for the discussion was the following situation:

Imagine that you execute a number of long running methods. Also, let’s say that the very last time consuming process depends on the previous processes, let’s call them prerequisites. The dependence is “sequential” meaning that the final stage should only run if the prerequisites have all completed and returned. The first implementation may very well be sequential where the long running methods are called one after the other and each of them blocks the main thread.

However, in case the prerequisites can be executed independently then there’s a much better solution: we can execute them in parallel instead. Independence in this case means that prerequisite A doesn’t need any return value from prerequisite B in which case parallel execution of A and B is not an option.

In this post we’ll look at an alternative solution using the CompletableFuture class. It is way more versatile than CountDownLatch which is really only sort of like a simple lock object. CompletableFuture offers a wide range of possibilities to organise your threads with a fluent API. Here we’ll start off easy with a simple application of this class.

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Exploring a directory with the Java 8 Stream API

We saw an example of using the Java 8 Stream API in File I/O in this post. We saw how the Files object was enhanced with the lines() method to open a line reader stream to a text file.

There are other enhancements related to streams that make is simple to explore a directory on your hard drive. The following code example will collect all folders and files within the c:\gitrepos folder and add them to an ArrayList:

Path gitReposFolderPath = Paths.get("c:\\gitrepos");
gitReposFolderPath.toFile().getName();
try (Stream<Path> foldersWithinGitReposStream = Files.list(gitReposFolderPath))            
{
    List<String> elements = new ArrayList<>();
    foldersWithinGitReposStream.forEach(p -> elements.add(p.toFile().getName()));            
    System.out.println(elements);
}
catch (IOException ioe)
{

}

I got the following output:

[cryptographydotnet, dotnetformsbasedmvc5, entityframeworksixdemo, owinkatanademo, signalrdemo, singletondemoforcristian, text.txt, webapi2demo, windowsservicedemo]

The code returns both files and folders one level below the top directory, i.e. the “list” method does not dive into the subfolders. I put a text file into the folder – text.txt – just to test whether in fact all elements are returned.

Say you only need files – you can use the filter method:

foldersWithinGitReposStream.filter(p -> p.toFile().isFile()).forEach(p -> elements.add(p.toFile().getName())); 

This will only collect text.txt.

Let’s try something slightly more complex. We’ll organise the elements within the directory into a Map of Boolean and List of Paths. The key indicates whether the group of files are directories or not. We can use the collect method that we saw in this post:

try (Stream<Path> foldersWithinGitReposStream = Files.list(gitReposFolderPath))            
{
    Map<Boolean, List<Path>> collect = foldersWithinGitReposStream.collect(Collectors.groupingBy(p -> p.toFile().isDirectory()));
    System.out.println(collect);
}

This prints the following:

{false=[c:\gitrepos\text.txt], true=[c:\gitrepos\cryptographydotnet, c:\gitrepos\dotnetformsbasedmvc5, c:\gitrepos\entityframeworksixdemo, c:\gitrepos\owinkatanademo, c:\gitrepos\signalrdemo, c:\gitrepos\singletondemoforcristian, c:\gitrepos\webapi2demo, c:\gitrepos\windowsservicedemo]}

So we successfully grouped the paths.

As mentioned above the “list” method goes only one level deep. The “walk” method in turn digs deeper and extracts sub-directories as well:

try (Stream<Path> foldersWithinGitReposStream = Files.walk(gitReposFolderPath))
{
    List<String> elements = new ArrayList<>();
    foldersWithinGitReposStream.filter(p -> p.toFile().isFile()).forEach(p -> elements.add(p.toFile().getAbsolutePath()));
    System.out.println(elements);
}

We can also instruct the walk method to go n levels down with an extra integer argument:

try (Stream<Path> foldersWithinGitReposStream = Files.walk(gitReposFolderPath, 3))

View all posts related to Java here.

Waiting for background tasks to finish using the CountDownLatch class in Java

Imagine the situation where you execute a number of long running methods. Also, let’s say that the very last time consuming process depends on the previous processes, let’s call them prerequisites. The dependence is “sequential” meaning that the final stage should only run if the prerequisites have completed and returned. The first implementation may very well be sequential where the long running methods are called one after the other and each of them blocks the main thread.

However, in case the prerequisites can be executed independently then there’s a much better solution: we can execute them in parallel instead. Independence in this case means that prerequisite A doesn’t need any return value from prerequisite B in which case parallel execution of A and B is not an option.

In this post we’ll examine this situation and see how to implement it in Java using the CountDownLatch class.

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Explicit interface implementation in .NET

Introduction

The generic and well-known Dictionary object and its generic and thread-safe counterpart, i.e. the ConcurrentDictionary object both implement the generic IDictionary interface. The IDictionary interface has an Add method where you can insert a new key-value pair into a dictionary:

Dictionary<string, string> singleThreadedDictionary = new Dictionary<string, string>();
singleThreadedDictionary.Add("Key", "Value");

I can even rewrite the above code as follows:

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Sharing numeric values across threads using Java 8 LongAdder

In this post we saw how to share primitive values across threads using the various atomic objects in the java.util.concurrent.atomic package. The example code demonstrated the AtomicInteger object which is the thread-safe variant of a “normal” integer. Mathematical operations like adding a value to an integer are carried out atomically for that object. This means that the low-level instructions involved in adding two integers are carried out as one unit without the risk of another interfering thread. The same package includes atomic versions of other primitive values such as AtomicBoolean or AtomicLong.

In this post we’ll take a quick look at an addition in Java 8 relevant to sharing integers, longs and doubles.

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Sharing primitives across threads in Java using atomic objects

Threading and parallel execution are popular choices when making applications more responsive and resource-efficient. Various tasks are carried out on separate threads where they either produce some result relevant to the main thread or just run in the background “unnoticed”. Often these tasks work autonomously meaning they have their own set of dependencies and variables. That is they do not interfere with a resource that is common to 2 or more threads.

However, that’s not always the case. Imagine that multiple threads are trying to update the same primitive like an integer counter. They perform some action and then update this counter. In this post we’ll see what can go wrong.

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