A Handful of JAVA Key Features of Each Version

Posted on 2026-03-16 by Elmar Dott

The object-oriented programming language Java was designed by James Gosling. The first version was released in 1995 by Sun Microsystems. After Oracle acquired Sun Microsystems in 2010, Java became part of Oracle’s product portfolio.

last changed:

The Java Series

Java 21 LTS
Java 20
Java 19
Java 18
Java 17 LTS
Java 16
Java 15
Java 14
Java 13
Java 12
Java 11 LTS
Java 10
Java 9
Java 8
Java 7
Java 6
Java 5 / J5SE
Java 1.4
Java 1.3
Java 1.2 / Java2

To run Java programs on your computer, you need a virtual machine (JVM). The download for this JVM is called the JRE (Java Runtime Environment). If you want to develop in the Java programming language yourself, you need the corresponding compiler, which is known by the general term SDK (Software Development Kit) or, more specifically, JDK (Java Development Kit). The JDK naturally includes the corresponding runtime environment (the JVM).

With the release of Java 9 in September 2017, Oracle announced a six-month release cycle for future Java versions. This means a new version of the popular programming language is released every year in March and September. But don’t worry, it’s not necessary to follow Oracle’s licensing terms and update your own Java version on this cycle. To provide companies with sufficient stability for their IT infrastructure, every sixth Java release is a so-called LTS (Long Term Support) and has a lifespan of three years from its release date. I have already published a more detailed article on this topic.

The new Java Release Cycle

With the frequent release cycle, each new Java version naturally brings several new key features to the core language. It’s easy to lose track of everything. Therefore, I’ve compiled a brief overview. For those who would also like an overview of the individual Java Enterprise versions, I recommend my corresponding article on Java EE.

Java Enterprise in briefly detail

Version overview

To avoid getting bogged down in details, I’ll start with version 1.2, also known as Java 2, which was released in 1998. The most important features of Java 2 were the graphical Swing API and the introduction of the Just-In-Time (JIT) compiler.

In 2000, version 1.3 was released, featuring JNDI (Java Naming and Directory Interface) and JPDA (Java Platform Debugger Architecture).

Just two years later, in 2002, version 1.4 was released, expanding the available standard library with features such as regular expressions, a logging API, an integrated XML & XSLT processor, an Image I/O API, and New I/O.

With the release of version 1.5 in 2004, the version numbering system changed. Java is now numbered as a major version, meaning that from then on, it’s referred to as Java5. Just like version 1.2, the Java 5 Standard Edition (SE), or J5SE for short, brings a host of changes for everyday development. These include: autoboxing/unboxing, annotations, enums, and generics.

Released in 2006, Java6 SE extended XML functionality with the StAX parser, introduced JAX-WS for web services, and brought scripting language support, enabling scripting languages like JavaScript to run on the JVM. The most important examples are the two JVM languages Kotlin (2011, JetBrains) and Scala (2004).

Five years later, and for the first time under Oracle’s direction, Java7 SE was released in 2011. This Java release introduced the diamond operator <>. Further measures to simplify the language and increase expressiveness included simplifying the declaration of varargs methods and enabling the use of strings in switch statements. The concerns that exceptions are slow and should be used sparingly were also addressed, and exception handling was improved.

// Diamond Operator
List<String> myList = new ArrayList<>();

// varargs (variable-length arguments)
public void myMethod(String... args);

// Diamond Operator
List<String> myList = new ArrayList<>();

// varargs (variable-length arguments)
public void myMethod(String... args);

Java8 SE took another significant step forward with its release in 2014. In this release, Oracle provided the Java community with long-awaited features such as lambda functions and the Stream API, to name just a few of the most important new features. Furthermore, the Date & Time API was completely redesigned, with changes inspired by the then widely used JODA-Time library.

// ForEach Lambda
myList.forEach(element -> System.out.println(element));

//Stream API
myList.stream()
    .filter(element -> element.startsWith("A"))
    .forEach(System.out.println);

// ForEach Lambda
myList.forEach(element -> System.out.println(element));

//Stream API
myList.stream()
    .filter(element -> element.startsWith("A"))
    .forEach(System.out.println);

The 2017 release of Java9 SE marked a turning point. Due to the introduction of the module system, the entire standard library was reworked so that the individual APIs were no longer bundled into a single, gigantic JAR file. The resulting modules now required less memory. These massive changes demanded enormous effort from many long-standing projects to migrate to the new Java version. Furthermore, Java 9 introduced the Java Shell, or JShell for short, a command-line tool that allows Java functions to be executed as scripts.

// module-info.java
module com.example.myapp {
  requires java.base;
  exports com.example.myapp;
}

// module-info.java
module com.example.myapp {
  requires java.base;
  exports com.example.myapp;
}

Further significant changes were brought with the release of Java10 SE, which was released just a few months after Java 9 in 2018 and necessitated additional migration efforts. This release introduced the Local Variable Type interface with its associated keyword var. Furthermore, Release 10 also marked the start of Time-Based Versioning for the Java language. A new major version is released every six months, typically in March and September. An LTS (Long Term Support) version is released every three years.

// the var KeyWord
public static void main(String[] args) {
  var message = "Hello World, Java10!";
  System.out.println(message);
}

// the var KeyWord
public static void main(String[] args) {
  var message = "Hello World, Java10!";
  System.out.println(message);
}

The first Long Term Support version for Java was released in the fall of 2018. Release Java11 carries the LTS designation and receives updates for three years. Due to the short release cycles, this version contains only a few API changes. The String class, in particular, received many new methods designed to simplify working with strings.

// String FUnctions
String text = new String(" Helle Java 11 LTS world! ");
text.strip();
text.isBlank();
text.lines().count();

// String FUnctions
String text = new String(" Helle Java 11 LTS world! ");
text.strip();
text.isBlank();
text.lines().count();

The most important change in the Java12 SE release, released in spring 2019, besides several API extensions, is the ability to use expressions in switch statements.

// switch expressions
Var day = 1;
String weekday = switch(day) {
    default -> "";
    case 1 -> "Monday";
    case 2 -> "Tuesday";
    ...  
}

// switch expressions
Var day = 1;
String weekday = switch(day) {
    default -> "";
    case 1 -> "Monday";
    case 2 -> "Tuesday";
    ...  
}

Java13 SE was released as planned in autumn 2019 and improved expressions in switch statements. It also introduced text blocks, which eliminate the need for resource-intensive string concatenations.

// textblock
String text = """
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do
eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim 
ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut 
aliquip ex ea commodo consequat. Duis aute irure dolor in 
reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla 
pariatur. Excepteur sint occaecat cupidatat non proident, sunt in 
culpa qui officia deserunt mollit anim id est laborum.
""";

// textblock
String text = """
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do
eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim 
ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut 
aliquip ex ea commodo consequat. Duis aute irure dolor in 
reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla 
pariatur. Excepteur sint occaecat cupidatat non proident, sunt in 
culpa qui officia deserunt mollit anim id est laborum.
""";

With the release of Java14 SE in March 2020, records were introduced. These data structures are intended to make code more compact and readable, as getters and setters no longer need to be defined.

// records
public record Person (String name, String address) {}

// records
public record Person (String name, String address) {}

The release of Java15 SE, released in autumn 2020, standardized text blocks and introduced sealed classes. Sealed means that a class cannot be inherited.

// inheritance protection
public sealed class Shape permits Circle {
    // Class body
}

public final class Circle extends Shape {
    // Class body
}

// inheritance protection
public sealed class Shape permits Circle {
    // Class body
}

public final class Circle extends Shape {
    // Class body
}

In 2021, the release of Java16 SE standardized various features, including sealed classes, pattern matching for instanceof, and records.

// Pattern Matching instanceof with auto cast
if (obj instanceof String s) {
    System.out.println(s);
}

// Pattern Matching instanceof with auto cast
if (obj instanceof String s) {
    System.out.println(s);
}

Java17 SE LTS was released as planned in autumn 2021, replacing Java 11 SE LTS after three years. In this version, the Java Applet API and the Security Manager were marked as deprecated.

Java18 SE established UTF-8 as the default code page for the JVM. The Vector API was also introduced with an experimental status.

// Java Vector API
import jdk.incubator.vector.*;
import java.util.Random;

public class VectorExample {
    // Use preferred vector species for optimal CPU performance
    static final VectorSpecies<Float> SPECIES = FloatVector.SPECIES_PREFERRED;

    // Vectorized implementation using the Vector API
    static void sqrtsumVector(float[] a, float[] b, float[] c) {
        int i = 0;
        int upperBound = SPECIES.loopBound(a.length); // Efficient loop bound

        // Process data in chunks matching the vector length
        for (; i < upperBound; i += SPECIES.length()) {
            var va = FloatVector.fromArray(SPECIES, a, i);
            var vb = FloatVector.fromArray(SPECIES, b, i);
            var vc = va.mul(va)           // a[i]²
                      .add(vb.mul(vb))    // + b[i]²
                      .neg();             // - (a[i]² + b[i]²)
            vc.intoArray(c, i);           // Store result
        }

        // Handle remaining elements (tail case) with scalar loop
        for (; i < a.length; ++i) {
            c[i] = -(a[i] * a[i] + b[i] * b[i]);
        }
    }
}

// Java Vector API
import jdk.incubator.vector.*;
import java.util.Random;

public class VectorExample {
    // Use preferred vector species for optimal CPU performance
    static final VectorSpecies<Float> SPECIES = FloatVector.SPECIES_PREFERRED;

    // Vectorized implementation using the Vector API
    static void sqrtsumVector(float[] a, float[] b, float[] c) {
        int i = 0;
        int upperBound = SPECIES.loopBound(a.length); // Efficient loop bound

        // Process data in chunks matching the vector length
        for (; i < upperBound; i += SPECIES.length()) {
            var va = FloatVector.fromArray(SPECIES, a, i);
            var vb = FloatVector.fromArray(SPECIES, b, i);
            var vc = va.mul(va)           // a[i]²
                      .add(vb.mul(vb))    // + b[i]²
                      .neg();             // - (a[i]² + b[i]²)
            vc.intoArray(c, i);           // Store result
        }

        // Handle remaining elements (tail case) with scalar loop
        for (; i < a.length; ++i) {
            c[i] = -(a[i] * a[i] + b[i] * b[i]);
        }
    }
}

Released at the end of 2022, Java19 SE included several optimizations and integrated the Loom project to enable virtual threads. The Foreign Function & Memory API was also incorporated into the JVM.

// Natice C Memory allocation & Slicing in Java 
Arena arena = Arena.ofAuto();
MemorySegment memorySegment = arena.allocate(12);

MemorySegment segment1 = memorySegment.asSlice(0, 4);
MemorySegment segment2 = memorySegment.asSlice(4, 4);
MemorySegment segment3 = memorySegment.asSlice(8, 4);

VarHandle intHandle = ValueLayout.JAVA_INT.varHandle();

intHandle.set(segment1, 0, Integer.MIN_VALUE);
intHandle.set(segment2, 0, 0);
intHandle.set(segment3, 0, Integer.MAX_VALUE);

// Natice C Memory allocation & Slicing in Java 
Arena arena = Arena.ofAuto();
MemorySegment memorySegment = arena.allocate(12);

MemorySegment segment1 = memorySegment.asSlice(0, 4);
MemorySegment segment2 = memorySegment.asSlice(4, 4);
MemorySegment segment3 = memorySegment.asSlice(8, 4);

VarHandle intHandle = ValueLayout.JAVA_INT.varHandle();

intHandle.set(segment1, 0, Integer.MIN_VALUE);
intHandle.set(segment2, 0, 0);
intHandle.set(segment3, 0, Integer.MAX_VALUE);

Java20 SE, released as usual in the spring of 2023, didn’t introduce any new features but focused on stabilizing existing ones.

In the fall of 2023, Java21 SE LTS, a Long Term Support version, was released. This release finalized Virtual Threads.

public class VirtualThreadsExample {
  public static void main(String[] args) throws
InterruptedException {
    var executor =
      Executors.newVirtualThreadPerTaskExecutor();
    executor.submit( () ->
      System.out.println("Running in virtual thread"));
    executor.submit( () ->
      System.out.println("Running another virtual thread"));
    Thread.sleep(1000);
    complete
  }
}

public class VirtualThreadsExample {
  public static void main(String[] args) throws
InterruptedException {
    var executor =
      Executors.newVirtualThreadPerTaskExecutor();
    executor.submit( () ->
      System.out.println("Running in virtual thread"));
    executor.submit( () ->
      System.out.println("Running another virtual thread"));
    Thread.sleep(1000);
    complete
  }
}

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High-performance hardware under Linux for local AI applications

Posted on 2026-03-09 by Elmar Dott

Anyone wanting to experiment a bit with local LLM will quickly discover its limitations. Not everyone has a massively upgraded desktop PC with 2 TB of RAM and a CPU that could fry an egg under full load. A laptop with 32 GB of RAM, or in my case, a Lenovo P14s with 64 GB of RAM, is more typical. Despite this generous configuration, it often fails to load a more demanding AI model, as 128 GB of RAM is fairly standard for many of these models. And you can’t upgrade the RAM in current laptops because the chips are soldered directly onto the motherboard. We have the same problem with the graphics card, of course. That’s why I’ve made it a habit when buying a laptop to configure it with almost all the available options, hoping to be set for 5-8 years. The quality of the Lenovo ThinkPad series, in particular, hasn’t disappointed me in this regard. My current system is about two years old and is still running reliably.

I’ve been using Linux as my operating system for years, and I’m currently running Debian 13. Compared to Windows, Linux and Unix distributions are significantly more resource-efficient and don’t use their resources for graphical animations and complex gradients, but rather provide a powerful environment for the applications they’re used in. Therefore, my urgent advice to anyone wanting to try local LLMs is to get a powerful computer and run Linux on it. But let’s take it one step at a time. First, let’s look at the individual hardware components in more detail.

Let’s start with the CPU. LLMs, CAD applications, and even computer games all perform calculations that can be processed very effectively in parallel. For parallel calculations, the number of available CPU cores is a crucial factor. The more cores, the more parallel calculations can be performed.

Of course, the processors need to be able to quickly request the data for the calculations. This is where RAM comes into play. The more RAM is available, the more efficiently the data can be provided for the calculations. Affordable laptops with 32 GB of RAM are already available. Of course, the purchase price increases exponentially with more RAM. While there are certainly some high-end gaming devices in the consumer market, I wouldn’t recommend them due to their typically short lifespan and comparatively high price.

The next logical step in the hardware chain is the hard drive. Simple SSDs significantly accelerate data transfer to RAM, but there are still improvements. NVMe cards with 2 GB of storage capacity or more can reach speeds of up to 7000 MB/s in the 4th generation.

We have some issues with graphics cards in laptops. Due to their size and the required performance, the graphics cards built into laptops are more of a compromise than a true highlight. A good graphics card would be ideal for parallel calculations, such as those performed in LLMs (Large Linear Machines). As a solution, we can connect the laptop to an external graphics card. Thanks to Bitcoin miners in the crypto community, considerable experience has already been gained in this area. However, to connect an external graphics card to the laptop, you need a port that can handle that amount of data. USB 3 is far too slow for our purposes and would severely limit the advantages of the external graphics card due to its low data rate.

The solution to our problem is Thunderbolt. Thunderbolt ports look like USB-C, but are significantly faster. You can identify Thunderbolt by the small lightning bolt symbol (see Figure 1) on the cables or connectors. These are not the power supply connections. To check if your computer has Thunderbolt, you can use a simple Linux shell command.

ed@local: $ lspci | grep -i thunderbolt
00:07.0 PCI bridge: Intel Corporation Raptor Lake-P Thunderbolt 4 PCI Express Root Port #0
00:07.2 PCI bridge: Intel Corporation Raptor Lake-P Thunderbolt 4 PCI Express Root Port #2
00:0d.0 USB controller: Intel Corporation Raptor Lake-P Thunderbolt 4 USB Controller
00:0d.2 USB controller: Intel Corporation Raptor Lake-P Thunderbolt 4 NHI #0
00:0d.3 USB controller: Intel Corporation Raptor Lake-P Thunderbolt 4 NHI #1

ed@local: $ lspci | grep -i thunderbolt
00:07.0 PCI bridge: Intel Corporation Raptor Lake-P Thunderbolt 4 PCI Express Root Port #0
00:07.2 PCI bridge: Intel Corporation Raptor Lake-P Thunderbolt 4 PCI Express Root Port #2
00:0d.0 USB controller: Intel Corporation Raptor Lake-P Thunderbolt 4 USB Controller
00:0d.2 USB controller: Intel Corporation Raptor Lake-P Thunderbolt 4 NHI #0
00:0d.3 USB controller: Intel Corporation Raptor Lake-P Thunderbolt 4 NHI #1

In my case, my computer’s output shows that two Thunderbolt 4 ports are available.

To connect an external graphics card, we need a mounting system onto which a PCI card can be inserted. ANQUORA offers a good solution here with the ANQ-L33 eGPU Enclosure. The board can accommodate a graphics card with up to three slots. It costs between €130 and €200. A standard ATX power supply is also required. The required power supply wattage depends on the graphics card’s power consumption. It’s advisable not to buy the cheapest power supply, as the noise level might bother some users. The open design of the board provides ample flexibility in choosing a graphics card.

Selecting a graphics card is a whole other topic. Since I use Linux as my operating system, I need a graphics card that is supported by Linux. For accelerating LLMs, a graphics card with as many GPU cores as possible and a correspondingly large amount of internal memory is necessary. To make the purchase worthwhile and actually notice a performance boost, the card should be equipped with at least 8 GB of RAM. More is always better, of course, but the price of the card will then increase exorbitantly. It’s definitely worth checking the used market.

If you add up all the costs, the investment for an external GPU amounts to at least 500 euros. Naturally, this only includes an inexpensive graphics card. High-end graphics cards can easily exceed the 500-euro price point on their own. Anyone who would like to contribute their expertise in the field of graphics cards is welcome to contribute an article.

To avoid starting your shopping spree blindly and then being disappointed with the result, it’s highly advisable to consider beforehand what you want to do with the local LLM. Supporting programming requires less processing power than generating graphics and audio. Those who use LLMs professionally can save considerably by purchasing a high-end graphics card with self-hosted models compared to the costs of, for example, cloud code. The specification of LLMs depends on the available parameters. The more parameters, the more accurate the response and the more computing power is required. Accuracy is further differentiated by:

FP32 (Single-Precision Floating Point): Standard precision, requires the most memory. (e.g., 32 bits per parameter)
FP16 (Half-Precision Floating Point): Half the precision, halves the memory requirement compared to FP32, but can slightly reduce precision. (e.g., 16 bits per parameter / 4 bytes)
BF16 (Brain Floating Point): Another option for half-precision calculations, often preferred in deep learning due to its better performance in certain operations. (e.g., 16 bits per parameter / 2 bytes)
INT8/INT4 (Integer Quantization): Even lower precision, drastically reduces memory requirements and speeds up inference, but can lead to a greater loss of precision. (e.g., 8 bits per parameter / 1 byte)

Other factors influencing the hardware requirements for LLM include:

Batch Size: The number of input requests processed simultaneously.
Context Length: The maximum length of text that the model can consider in a query. Longer context lengths require more memory because the entire context must be held in memory.
Model Architecture: Different architectures have different memory requirements.

To estimate the memory consumption of a model, you can use the following calculation: Parameters * Accuracy = Memory consumption for the model.

7,000,000,000 parameters * 2 bytes/parameter (BF16) = 14,000,000,000 bytes = 14 GB

When considering hardware recommendations, you should refer to the model’s documentation. This usually only specifies the minimum or average requirements. However, there are general guidelines you can use.

Small models (up to 7 billion parameters): A GPU with at least 8 GB of VRAM should be sufficient, especially if you are using quantization.
Medium-sized models (7-30 billion parameters): A GPU with 16 GB to 24 GB of VRAM is recommended.
Large models (over 30 billion parameters): Multiple GPUs, each with at least 24 GB of VRAM, or a single GPU with a very large amount of VRAM (e.g., 48 GB, 80 GB) are required.
CPU-only: For small models and simple experiments, the CPU may suffice, but inference will be significantly slower than on a GPU. Here, a large amount of RAM is crucial (several GB / 32+).

We can see that using locally running LLMs can be quite realistic if you have the necessary hardware available. It doesn’t always have to be a supercomputer; however, most solutions from typical electronics retailers are off-the-shelf and not really suitable. Therefore, with this article, I have laid the groundwork for your own experiments.

Risk Cloud & Serverless

Posted on 2026-03-02 by Elmar Dott

The cloud is one of the most innovative developments since the turn of the millennium and enables us to make widespread use of neural networks, which we popularly refer to as Large Language Models (LLM). This technological leap can only be surpassed by quantum computing. But enough of the buzzwords for SEO optimization, instead let’s take a look behind the scenes. Let’s start with what the cloud actually is and put all the marketing terms aside.

The best way to imagine the cloud is as a gigantic supercomputer made up of many small computers like building blocks. This theoretically allows you to combine any amount of CPU power, RAM and hard drive space. On this supercomputer, which runs in a data center, virtual machines can now be provided that simulate a real computer with freely definable hardware. In this way, the physical hardware resources can be optimally distributed among the provided virtual machines.

When it comes to cloud, we roughly distinguish between three different operating levels: Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service. The image below gives an idea of how these levels are divided.

To put it simply, you can say that with IaaS the provider only provides the hardware specification. So CPU, RAM, hard drive and internet connection. Via the administration software e.g. B. Kubernetes, you can now create your own virtual machines/containers and install the corresponding operating systems and services yourself. The entire responsibility for security and network routing lies with the customer. PaaS, on the other hand, already provides a rudimentary virtual machine including the selected operating system. What you ultimately install on this system above the operating system level is up to you. But here too, the issue of security is largely in the hands of the customer. For most hosting providers, typical PaaS products are so-called virtual servers. Users have the least freedom with SaaS. Here you usually only have permission to use software through a user account. Very typical SaaS products are email accounts, but also so-called managed servers. Managed servers are mostly used to provide your own websites. Here the version of the programming language and the database is specified by the server operator.

Managed servers in particular have a long tradition. They emerged at the turn of the millennium to provide an immediately usable environment for dynamic PHP websites with a MySQL database connection. The situation is similar with the serverless products that have recently become fashionable. Depending on your level of experience, you can now buy corresponding products from the major providers AWS, Google and Microsoft Azure.

The idea is to no longer operate your own servers for the services and thus outsource the entire hardware, operation and security effort to the cloud operators. In principle, this isn’t a bad idea, especially when it comes to small companies or startups that don’t have a lot of financial resources at their disposal or simply lack the administrative know-how for networks, Linux and server security.

Of course, serverless offerings that are completely managed externally quickly reach their limits. Especially if you want to provide your own developed individual serverless software in the cloud with as little effort as possible, you will come across many a stumbling block. A problem is often the flexible expandability when requirements change. You can certainly buy products from the various providers’ portfolios and combine them as you like like a building block set, but the costs incurred can quickly add up.

Basically, there is nothing wrong with a pay per use model (i.e. pay for what you use). At first glance, this is not a bad solution for people and organizations with small budgets. But here too, it’s the little details that can quickly grow into serious problems.

If you choose any cloud provider, you are well advised to avoid its proprietary management and automation products and instead use established general products if possible. If you commit yourself to one provider with all the consequences, it will only be possible to switch to another provider with great effort. Changes to the terms and conditions or continuously increasing costs are possible reasons for a forced change. Therefore, test whoever binds himself forever.

But also careless use of resources in cloud systems, e.g. B. due to incorrect configurations or unfavorable deployment strategies, can lead to an explosion in costs. Here you are well advised if there is the option to set limits and activate them. So that once you reach a certain amount, you will be informed that only a ‘certain’ quota is available. Especially with highly available services that suddenly receive an enormous number of new users, such limits can quickly lead to them being disconnected from the network. It is therefore always a good idea to use two cloud solutions, one for development and a separate one for the productive system, in order to minimize the offline risk.

Similar to stock market trading, you can also define limits for cloud services like AWS. Stop-loss orders on the stock market prevent you from selling a stock too cheaply or buying it too expensively. With the pay-per-use model, it’s not much different in the cloud. Here, you need to set appropriate limits with your provider to prevent bills from exceeding your available budget. These limits are also dynamic in the cloud. This means that the framework conditions are constantly changing, requiring the necessary limits to be regularly adjusted to meet current needs. To identify bottlenecks early, a robust monitoring system should be in place. The minimum requirement for an AWS node is determined by its requests. The upper limit of available resources is defined by the limit. Tools like IBM’s Kubecost can largely automate cost monitoring in Kubernetes clusters.

For cloud development environments, you should also keep a close eye on your own development and DevOps team. If an NPM Docker container of over 2 GB is created on the fly every time for a simple JavaScript Angular app, this strategy should definitely be questioned. Even if the cloud can allocate seemingly infinite resources dynamically, that doesn’t mean that this happens for free.

Of course, the issue of security is also an important factor. Of course, you can trust the cloud operator when he says that everything is encrypted and access to customer data and business secrets is not possible. One can certainly assume that the information that is to be accessed in most ventures rarely has any exciting or even exciting content that could be of interest to large cloud operators. If you still want to be on the safe side, you should write off the idea of serverless completely and consider running your own cloud. Thanks to modern and free software, this is now easier than expected.

I have learned from personal experience that, given the complexity of modern web applications, efficient monitoring with Grafana and Prometheus or other solutions such as the ELK Stack or Slunk is essential. But some DevOps teams have difficulties with data collection and proper evaluation. IT decision-makers in particular are asked to get a technical overview so as not to fall for the well-sounding marketing traps of cloud and serverless.

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The Future of Build Management

Posted on 2026-02-16 by Elmar Dott

It’s not just high-level languages, which need to convert source code into machine code to make it executable, that require build tools. These tools are now also available for modern scripting languages like Python, Ruby, and PHP, as their scope of responsibility continues to expand. Looking back at the beginnings of this tool category, one inevitably encounters make, the first official representative of what we now call a build tool. Make’s main task was to generate machine code and package the files into a library or executable. Therefore, build tools can be considered automation tools. It’s logical that they also take over many other recurring tasks that arise in a developer’s daily work. For example, one of the most important innovations responsible for Maven’s success was the management of dependencies on other program libraries.

Another class of automation tools that has almost disappeared is the installer. Products like Inno Setup and Wise Installer were used to automate the installation process for desktop applications. These installation routines are a special form of deployment. The deployment process, in turn, depends on various factors. First and foremost, the operating system used is, of course, a crucial criterion. But the type of application also has a significant influence. Is it, for example, a web application that requires a defined runtime environment (server)? We can already see here that many of the questions being asked now fall under the umbrella of DevOps.

As a developer, it’s no longer enough to simply know how to write program code and implement functions. Anyone wanting to build a web application must first get the corresponding server running on which the application will execute. Fortunately, there are now many solutions that significantly simplify the provisioning of a working runtime. But especially for beginners, it’s not always easy to grasp the whole topic. I still remember questions in relevant forums about downloading Java Enterprise, but only finding that the application server was included.

Java Enterprise in briefly detail

Where automation solutions were lacking in the early 2000s, the challenge today is choosing the right tool. There’s an analogy here from the Java universe. When the Gradle build tool appeared on the market, many projects migrated from Maven to Gradle. The argument was that it offered greater flexibility. Often, the ability to define orchestrated builds was needed—that is, the sequence in which subprojects are created. Instead of acknowledging that this requirement represented an architectural shortcoming and addressing it, complex and difficult-to-manage build logic was built in Gradle. This, in turn, made customizations difficult to implement, and many projects were migrated back to Maven.

From DevOps automations, so-called pipelines have become established. Pipelines can also be understood as processes, and these processes can, in turn, be standardized. The best example of a standardized process is the build lifecycle defined in Maven, also known as the default lifecycle. This process defines 23 sequential steps, which, broadly speaking, perform the following tasks:

Resolving and deploying dependencies
Compiling the source code
Compiling and running unit tests
Packaging the files into a library or application
Deploying the artifact locally for use in other local development projects
Running integration tests
Deploying the artifacts to a remote repository server.

This process has proven highly effective in countless Java projects over the years. However, if you run this process as a pipeline on a CI server like Jenkins, you won’t see much. The individual steps of the build lifecycle are interdependent and cannot be triggered individually. It’s only possible to exit the lifecycle prematurely. For example, after packaging, you can skip the subsequent steps of local deployment and running the integration tests.

A weakness of the build process described here becomes apparent when creating web applications. Web frontends usually contain CSS and JavaScript code, which is also automatically optimized. To convert variables defined in SCSS into correct CSS, a SASS preprocessor must be used. Furthermore, it is very useful to compress CSS and JavaScript files as much as possible. This obfuscation process optimizes the loading times of web applications. However, there are already countless libraries for CSS and JavaScript that can be managed with the NPM tool. NPM, in turn, provides so-called development libraries like Grunt, which enable CSS processing and optimization.

We can see how complex the build process of modern applications can become. Compilation is only a small part of it. An important feature of modern build tools is the optimization of the build process. An established solution for this is creating incremental builds. This is a form of caching where only changed files are compiled or processed.

But what needs to be done during a release? This process is only needed once an implementation phase is complete, to prepare the artifact for distribution. While it’s possible to include all the steps involved in a release in the build process, this would lead to longer build times. Longer local build times disrupt the developer’s workflow, making it more efficient to define a separate process for this.

An important condition for a release is that all used libraries must also be in their final release versions. If this isn’t the case, it cannot be guaranteed that subsequent releases of this version are identical. Furthermore, all test cases must run correctly, and a failure will abort the process. Additionally, a corresponding revision tag should be set in the source control repository. The finished artifacts must be signed, and API documentation must be created. Of course, the rules described here are just a small selection, and some of the tasks can even be parallelized. By using sophisticated caching, creating a release can be accomplished quickly, even for large monoliths.

Furthermore, by utilizing sophisticated caching, creating a release can be accomplished quickly, even for large monoliths. For Maven, for example, no complete release process, similar to the build process, has been defined. Instead, the community has developed a special plugin that allows for the semi-automation of simple tasks that arise during a release.

If we take a closer look at the topic of documentation and reporting, we find ample opportunities to describe a complete process. Creating API documentation would be just one minor aspect. Far more compelling about standardized reporting are the various code inspections, some of which can even be performed in parallel.

Of course, deployment is also essential. Due to the diversity of potential target environments, a different strategy is appropriate here. One possible approach would be broad support for configuration tools like Ansible, Chef, and Puppet. Virtualization technologies such as Docker and LXC containers are also standard in the age of cloud computing. The main task of deployment would then be provisioning the target environment and deploying the artifacts from a repository server. A wealth of different deployment templates would significantly simplify this process.

If we consistently extrapolate from these assumptions, we conclude that there can be different types of projects. These would be classic development projects, from which artifacts for libraries and applications are created; test projects, which in turn contain the created artifacts as dependencies; and, of course, deployment projects for providing the infrastructure. The area of automated deployment is also reflected in the concepts of Infrastructure as Code and GitOps, which can be taken up and further developed here.

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Clean Desk – More Than Just Security

Posted on 2026-02-09 by Elmar Dott

As a child, I liked to reply to my mother that only a genius could master chaos when she told me to tidy my room. A very welcome excuse to shirk my responsibilities. When I started an apprenticeship in a trade after finishing school, the first thing my master craftsman emphasized was: keeping things tidy. Tools had to be put back in their bags after use, opened boxes of the same materials had to be refilled, and of course, there was also the need to sweep up several times a day. I can say right away that I never perceived these things as harassment, even if they seemed annoying at first. Because we quickly learned the benefits of the motto “keep things clean.”

Tools that are always put back in their place give us a quick overview of whether anything is missing. So we can then go looking for it, and the likelihood of things being stolen is drastically reduced. With work materials, too, you maintain a good overview of what’s been used up and what needs to be replaced. Five empty boxes containing only one or two items not only take up space but also lead to miscalculations of available resources. Finally, it’s also true that one feels less comfortable in a dirty environment, and cleanliness demonstrates to the client that one works in a focused and planned manner.

Due to this early experience, when the concept of Clean Desk was introduced as a security measure in companies a few years ago, I didn’t immediately understand what was expected of me. After all, the Clean Desk principle had been second nature to me long before I completed my computer science degree. But let’s start at the beginning. First, let’s look at what Clean Desk actually is and how to implement it.

Anyone who delves deeply into the topic of security learns one of the first things they learn: most successful attacks aren’t carried out using complicated technical maneuvers. They’re much more mundane and usually originate from within, not from the outside. True to the adage, opportunity makes the thief. When you combine this fact with the insights of social engineering, a field primarily shaped by the hacker Kevin Mitnick, a new picture emerges. It’s not always necessary to immediately place your own employees under suspicion. In a building, there are external cleaning staff, security personnel, or tradespeople who usually have easy access to sensitive areas. Therefore, the motto should always be: trust is good, but control is better, which is why a Clean Desk Policy is implemented.

The first rule is: anyone leaving their workstation for an extended period must switch off their devices. This applies especially at the end of the workday. Otherwise, at least the desktop should be locked. The concept behind this is quite simple: Security vulnerabilities cannot be exploited from switched-off devices to hack into the company network from the outside. Furthermore, it reduces power consumption and prevents fires caused by short circuits. To prevent the devices from being physically stolen, they are secured to the desk with special locks. I’ve personally experienced devices being stolen during lunch breaks.

Since I myself have stayed in hotels a lot, my computer’s hard drive is encrypted as a matter of course. This also applies to all external storage devices such as USB sticks or external SSDs. If the device is stolen, at least no one can access the data stored on it.

It goes without saying that secure encryption is only possible with a strong password. Many companies have specific rules that employee passwords must meet. It’s also common practice to assign a new password every 30 to 90 days, and this new password must be different from the last three used.

Passwords, but secure?

It’s often pointed out that passwords shouldn’t be written on a sticky note stuck to the monitor. I’ve never personally experienced this. It’s much more typical for passwords to be written under the keyboard or mousepad.

Another aspect to consider is notes left on desks, wall calendars, and whiteboards. Even seemingly insignificant information can be quite valuable. Since it’s rather difficult to decide what truly needs protecting and what doesn’t, the general rule is: all notes should be stored securely at the end of the workday, inaccessible to outsiders. Of course, this only works if lockable storage space is available. In sensitive sectors like banking and insurance, the policy even goes so far as to prohibit colleagues from entering their vacation dates on wall calendars.

Of course, these considerations also include your own wastebasket. It’s essential to ensure that confidential documents are disposed of in specially secured containers. Otherwise, the entire effort to maintain confidentiality becomes pointless if you can simply pull them out of the trash after work.

But the virtual desktop is also part of the Clean Desk Policy. Especially in times of virtual video conferences and remote work, strangers can easily catch a glimpse of your workspace. This reminds me of my lecture days when a professor had several shortcuts to the trash on his desktop. We always joked that he was recycling. Separate trash folders for Word, Excel, etc. files.

The Clean Desk Policy has other effects as well. It’s much more than just a security concept. Employees who consistently implement this policy also bring more order to their thoughts and can thus work through tasks one by one with greater focus, leading to improved performance. Personal daily planning is usually structured so that all started tasks can be completed by the end of the workday. This is similar to the trades. Tradespeople also try to complete their jobs by the end of the workday to avoid having to return for a short time the next day. A considerable amount of time is spent on preparation.

Implementing a Clean Desk Policy follows the three Ps (Plan, Protect & Pick). At the beginning of the day, employees decide which tasks need to be completed (Plan), and select the corresponding documents and necessary materials for easy access. At the end of the day, everything is securely stored. During working hours, it must also be ensured that no unauthorized persons have access to information, for example, during breaks. This daily, easy-to-implement routine of preparation and follow-up quickly becomes a habit, and the time required can be reduced to just a few minutes, so that hardly any work time is wasted.

With a Clean Desk Policy, the overwhelming piles of paper disappear from your desk, and by considering which tasks need to be completed each day, you can focus better on them, which significantly improves productivity. At the end of the day, you can also mentally cross some items off your to-do list, leading to greater satisfaction.

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Spring Cleaning for Docker

Posted on 2026-02-02 by Elmar Dott

Anyone interested in this somewhat specialized article doesn’t need an explanation of what Docker is and what this virtualization tool is used for. Therefore, this article is primarily aimed at system administrators, DevOps engineers, and cloud developers. For those who aren’t yet completely familiar with the technology, I recommend our Docker course: From Zero to Hero.

In a scenario where we regularly create new Docker images and instantiate various containers, our hard drive is put under considerable strain. Depending on their complexity, images can easily reach several hundred megabytes to gigabytes in size. To prevent creating new images from feeling like downloading a three-minute MP3 with a 56k modem, Docker uses a build cache. However, if there’s an error in the Dockerfile, this build cache can become quite bothersome. Therefore, it’s a good idea to clear the build cache regularly. Old container instances that are no longer in use can also lead to strange errors. So, how do you keep your Docker environment clean?

While docker rm <container-nane> and docker rmi <image-id> will certainly get you quite far, in build environments like Jenkins or server clusters, this strategy can become a time-consuming and tedious task. But first, let’s get an overview of the overall situation. The command docker system df will help us with this.

root:/home# docker system df
TYPE            TOTAL     ACTIVE    SIZE      RECLAIMABLE
Images          15        9         5.07GB    2.626GB (51%)
Containers      9         7         11.05MB   5.683MB (51%)
Local Volumes   226       7         6.258GB   6.129GB (97%)
Build Cache     0         0         0B        0B

root:/home# docker system df
TYPE            TOTAL     ACTIVE    SIZE      RECLAIMABLE
Images          15        9         5.07GB    2.626GB (51%)
Containers      9         7         11.05MB   5.683MB (51%)
Local Volumes   226       7         6.258GB   6.129GB (97%)
Build Cache     0         0         0B        0B

Before I delve into the details, one important note: The commands presented are very efficient and will irrevocably delete the corresponding areas. Therefore, only use these commands in a test environment before using them on production systems. Furthermore, I’ve found it helpful to also version control the commands for instantiating containers in your text file.

Disk-Jock-Ey

The most obvious step in a Docker system cleanup is deleting unused containers. Specifically, this means that the delete command permanently removes all instances of Docker containers that are not running (i.e., not active). If you want to perform a clean slate on a Jenkins build node before deployment, you can first terminate all containers running on the machine with a single command.

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The -f parameter suppresses the confirmation prompt, making it ideal for automated scripts. Deleting containers frees up relatively little disk space. The main resource drain comes from downloaded images, which can also be removed with a single command. However, before images can be deleted, it must first be ensured that they are not in use by any containers (even inactive ones). Removing unused containers offers another practical advantage: it releases ports blocked by containers. A port in a host environment can only be bound to a container once, which can quickly lead to error messages. Therefore, we extend our script to include the option to delete all Docker images not currently used by containers.

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Another consequence of our efforts concerns Docker layers. For performance reasons, especially in CI environments, you should avoid using them. Docker volumes, on the other hand, are less problematic. When you remove the volumes, only the references in Docker are deleted. The folders and files linked to the containers remain unaffected. The -a parameter deletes all Docker volumes.

docker volume prune -a -f

Another area affected by our cleanup efforts is the build cache. Especially if you’re experimenting with creating new Dockerfiles, it can be very useful to manually clear the cache from time to time. This prevents incorrectly created layers from persisting in the builds and causing unusual errors later in the instantiated container. The corresponding command is:

docker buildx prune -f

The most radical option is to release all unused resources. There is also an explicit shell command for this.

docker volume prune -a -f

We can, of course, also use the commands just presented for CI build environments like Jenkins or GitLab CI. However, this might not necessarily lead to the desired result. A proven approach for Continuous Integration/Continuous Deployment is to set up your own Docker registry where you can deploy your self-built images. This approach provides a good backup and caching system for the Docker images used. Once correctly created, images can be conveniently deployed to different server instances via the local network without having to constantly rebuild them locally. This leads to a proven approach of using a build node specifically optimized for Docker images/containers to optimally test the created images before use. Even on cloud instances like Azure and AWS, you should prioritize good performance and resource efficiency. Costs can quickly escalate and seriously disrupt a stable project.

In this article, we have seen that in-depth knowledge of the tools used offers several opportunities for cost savings. The motto “We do it because we can” is particularly unhelpful in a commercial environment and can quickly degenerate into an expensive waste of resources.

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03. Release Management & Process Automation

Posted on 2026-02-01 by Elmar Dott

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Cookbook: Maven Source Code Samples

Posted on 2026-02-01 by Elmar Dott

Our Git repository contains an extensive collection of various code examples for Apache Maven projects. Everything is clearly organized by topic.

Back to table of contents: Apache Maven Master Class

Apache Maven Master Class

Posted on 2026-02-01 by Elmar Dott

Apache Maven (Maven for short) was first released on March 30, 2002, as an Apache Top-Level Project under the free Apache 2.0 License. This license also allows free use by companies in a commercial environment without paying license fees.

The word Maven comes from Yiddish and means something like “collector of knowledge.”

Maven is a pure command-line program developed in the Java programming language. It belongs to the category of build tools and is primarily used in Java software development projects. In the official documentation, Maven describes itself as a project management tool, as its functions extend far beyond creating (compiling) binary executable artifacts from source code. Maven can be used to generate quality analyses of program code and API documentation, to name just a few of its diverse applications.

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This online course is suitable for both beginners with no prior knowledge and experienced experts. Each lesson is self-contained and can be individually selected. Extensive supplementary material explains concepts and is supported by numerous references. This allows you to use the Apache Maven Master Class course as a reference. New content is continually being added to the course. If you choose to become an Apache Maven Master Class member, you will also have full access to exclusive content.