What are Network Packets?​

How do we transmit data over a network? Establishing a connection with the recipient and sending the data all at once might seem like the most straightforward approach. However, this method becomes inefficient when handling multiple requests because a single connection can only maintain one data transfer at a time. If a connection is prolonged due to a large data transfer, other data will have to wait.

To efficiently handle the data transmission process, networks divide data into multiple pieces and require the receiving end to reassemble them. These fragmented data structures are called packets. Packets include additional information to allow the receiving end to reassemble the data in the correct order.

While transmitting data in multiple packets enables efficient processing of many requests through packet switching, it can also lead to various errors such as data loss or incorrect delivery order. How should we debug such issues? 🤔

Situation​

• As the codebase grows, the number of configuration values required for running a Spring application is increasing.
• While most situations are validated with test code, there are times when testing with bootRun locally is necessary.

Complication​

• Want to separate configuration values into environment variables for better management.
• .env files are typically ignored in Git, making version tracking difficult and prone to fragmentation.
  • Need a way to synchronize files across multiple machines.

Question​

• Is there a convenient method that minimizes friction among developers and is easy to apply?
  • Preferably a familiar method for easier maintenance.
• Can the version of .env files be managed?
• Is the learning curve low?
  • Want to avoid a situation where the solution is more complex than the problem.
• Can it be applied directly to the production environment?

Answer​

AWS S3​

• Updating .env files is convenient with AWS CLI.
• Version control of .env files can be done through snapshots.
• AWS S3 is a service familiar to most developers and has a low learning curve.
• In the AWS ECS production environment, system variables can be directly applied using S3 ARNs.

.

..

...

....

Is that all?​

If that's it, the article might seem a bit dull, right? Of course, there are still a few issues remaining.

Which Bucket is it in?​

When using S3, it's common to end up with many buckets due to file structure optimization or business-specific categorization.

aws s3 cp s3://something.service.com/enviroment/.env .env

If the .env file is missing, you'll need to download it using AWS CLI as shown above. Without someone sharing the bucket with you in advance, you might have to search through all buckets to find the environment variable file, which could be inconvenient. The intention was to avoid sharing, but having to receive something to share again might feel a bit cumbersome.

Too many buckets. Where's the env...?

Automating the process of exploring buckets in S3 to find and download the necessary .env file would make things much easier. This can be achieved by writing a script using tools like fzf or gum.

Spring Boot Requires System Environment Variables, Not .env...​

Some of you may have already noticed that Spring Boot reads system environment variables to fill in placeholders in YAML files. However, using just the .env file won't apply the system environment variables, thus not being picked up during Spring Boot's initialization process.

Let's briefly look at how it works.

# .env
HELLO=WORLD

# application.yml
something:
    hello: ${HELLO} # Retrieves the value from the HELLO environment variable on the OS.

@Slf4j
@Component
public class HelloWorld {

    @Value("${something.hello}")
    private String hello;

    @PostConstruct
    public void init() {
        log.info("Hello: {}", hello);
    }
}

SystemEnvironmentPropertySource.java

You can see that the placeholder in @Value is not resolved, causing the bean registration to fail and resulting in an error.

Just having a .env file doesn't register it as a system environment variable.

To apply the .env file, you can either run the export command or register the .env file in IntelliJ's run configurations. However, using the export command to register too many variables globally on your local machine can lead to unintended behavior like overwriting, so it's recommended to manage them individually through IntelliJ's GUI.

IntelliJ supports configuring .env files via GUI.

The placeholder is resolved and applied correctly.

Final Answer - The Real Final One​

Phew, the long process of problem identification and scoping has come to an end. Let's summarize the workflow once more and introduce a script.

1. Use an automation script to find and download the appropriate .env from S3.
2. Set the .env as system environment variables.

The shell script is written to be simple yet stylized using gum.

Full Code

#!/bin/bash

S3_BUCKET=$(aws s3 ls | awk '{print $3}' | gum filter --reverse --placeholder "Select...") # 1.

# Choose deployment environment
TARGET=$(gum choose --header "Select a environment" "Elastic Container Service" "EC2")
if [ "$TARGET" = "Elastic Container Service" ]; then
    TARGET="ecs"
else
    TARGET="ec2"
fi

S3_BUCKET_PATH=s3://$S3_BUCKET/$TARGET/

# Search for the env file
ENV_FILE=$(aws s3 ls "$S3_BUCKET_PATH" | grep env | awk '{print $4}' | gum filter --reverse --placeholder "Select...") # 2.

# Confirm
if (gum confirm "Are you sure you want to use $ENV_FILE?"); then
    echo "You selected $ENV_FILE"
else
    die "Aborted."
fi

ENV_FILE_NAME=$(gum input --prompt.foreground "#04B575" --prompt "Enter the name of the env file: " --value ".env" --placeholder ".env")
gum spin -s meter --title "Copying env file..." -- aws s3 cp "$S3_BUCKET_PATH$ENV_FILE" "$ENV_FILE_NAME" # 3.

echo "Done."

1. Use gum filter to select the desired S3 bucket.
2. Search for items containing the word env and assign it to a variable named ENV_FILE.
3. Finalize the object key of the .env file and proceed with the download.

I've created a demo video of the execution process.

Demo

After this, you just need to apply the .env file copied to the current directory to IntelliJ as mentioned earlier.

Conclusion​

• The script is easy to maintain due to its simplicity.
• Team response has been very positive.
• Developers appreciate aesthetics.
• For sensitive credentials, consider using AWS Secret Manager.

This article discusses the inefficient existing implementation and documents the methods attempted to improve it.

Existing Issues​

While it wasn't impossible to join tables scattered across multiple databases in a single query, it was challenging...

1. Is a specific coordinate within area "a"?
2. Writing join queries was difficult due to tables existing on physically different servers
   1. Why the need for a single query? Due to the large size of the data to be queried, I wanted to minimize the amount loaded into application memory as much as possible.
3. Since DB joins were not possible, application joins were necessary, resulting in around 24 billion loops (60000 * 40000)
   1. Although processing time was minimized through partitioning, CPU load remained high due to the loops.
4. Through the migration process of merging physically different databases into one, the opportunity for query optimization was achieved as joins became possible.

Approach​

Given that the primary reason for not being able to use database joins had been resolved, I actively considered utilizing index scans for geometry processing.

• Using PostGIS's GIST index allows for creating a spatial index similar to R-tree, enabling direct querying through index scans.
• To use spatial indexing, a column of type geometry is required.
• While latitude and longitude coordinates were available, there was no geometry type, so it was necessary to first create geometry POINT values using the coordinates.

To simulate this process, I prepared the exact same data as in the live DB and conducted experiments.

First, I created the index:

CREATE INDEX idx_port_geom ON port USING GIST (geom);

Then, I ran the PostGIS contains function:

SELECT *
FROM ais AS a
JOIN port AS p ON st_contains(p.geom, a.geom);

Awesome...

Results​

Before Applying Spatial Index​

1 minute 47 seconds to 2 minutes 30 seconds

After Applying Spatial Index​

0.23 milliseconds to 0.243 milliseconds

I didn't prepare a capture, but before applying the index, queries took over 1 minute and 30 seconds.

Let's start with the conclusion and then delve into why these results were achieved.

GiST (Generalized Search Tree)​

A highly useful index for querying complex geometric data, the internal structure is illustrated below.

The idea of an R-tree is to divide the plane into rectangles to encompass all indexed points. Index rows store rectangles and can be defined as follows:

"The point we are looking for is inside the given rectangle."

The root of the R-tree contains several of the largest rectangles (which may intersect). Child nodes contain smaller rectangles included in the parent node, collectively encompassing all base points.

In theory, leaf nodes should contain indexed points, but since all index rows must have the same data type, rectangles reduced to points are repeatedly stored.

To visualize this structure, let's look at images for three levels of an R-tree. The points represent airport coordinates.

Level one: two large intersecting rectangles are visible.

Two intersecting rectangles are displayed.

Level two: large rectangles are split into smaller areas.

Large rectangles are divided into smaller areas.

Level three: each rectangle contains as many points as to fit one index page.

Each rectangle contains points that fit one index page.

These areas are structured into a tree, which is scanned during queries. For more detailed information, it is recommended to refer to the following article.

Conclusion​

In this article, I briefly introduced the specific conditions, the problems encountered, the efforts made to solve them, and the basic concepts needed to address these issues. To summarize:

• Efficient joins using indexes could not be performed on physically separated databases.
• By enabling physical joins through migration, significant performance improvements were achieved.
• With the ability to utilize index scans, overall performance was greatly enhanced.
• There was no longer a need to unnecessarily load data into application memory.
• CPU load due to loops was alleviated.

Reference​

• Spatial Indexing
• https://dbknowledge.tistory.com/48
• https://medium.com/postgres-professional/indexes-in-postgresql-5-gist-86e19781b5db

"Write once, Test anywhere"

Fixture Monkey is a testing object creation library being developed as open source by Naver. The name seems to be inspired by Netflix's open source tool, Chaos Monkey. By generating test fixtures randomly, it allows you to experience chaos engineering in practice.

Since I first encountered it about 2 years ago, it has become one of my favorite open source libraries. I even ended up writing two articles about it.

• Fixture Monkey Object Creation Strategy
• Fixture Monkey 0.4.x

I haven't written any additional articles as there were too many changes with each version update, but now that version 1.x has been released, I am revisiting it with a fresh perspective.

While my previous articles were based on Java, I am now writing in Kotlin to align with current trends. The content of this article is based on the official documentation with some added insights from my actual usage.

Why Fixture Monkey is Needed​

Let's examine the following code to see what issues exist with the traditional approach.

data class Product (
    val id: Long,

    val productName: String,

    val price: Long,

    val options: List<String>,

    val createdAt: Instant,

    val productType: ProductType,

    val merchantInfo: Map<Int, String>
)

enum class ProductType {
    ELECTRONICS,
    CLOTHING,
    FOOD
}

@Test
fun basic() {
    val actual: Product = Product(
        id = 1L,
        price = 1000L,
        productName = "productName",
        productType = ProductType.FOOD,
        options = listOf(
            "option1",
            "option2"
        ),
        createdAt = Instant.now(),
        merchantInfo = mapOf(
            1 to "merchant1",
            2 to "merchant2"
        )
    )

    // The preparation process is lengthy compared to the test purpose
    actual shouldNotBe null
}

Challenges of Test Object Creation​

Looking at the test code, it feels like there is too much code to write just to create objects for assertion. Due to the nature of the implementation, if properties are not set, a compilation error occurs, so even meaningless properties must be written.

When the preparation required for assertion in test code becomes lengthy, the meaning of the test purpose in the code can become unclear. The person reading this code for the first time would have to examine even seemingly meaningless properties to see if there is any hidden significance. This process increases developers' fatigue.

Difficulty in Recognizing Edge Cases​

When directly setting properties to create objects, many edge cases that could occur in various scenarios are often overlooked because the properties are fixed.

val actual: Product = Product(
    id = 1L, // What if the id becomes negative?
    // ...omitted
)

To find edge cases, developers have to set properties one by one and verify them, but in reality, it is often only after runtime errors occur that developers become aware of edge cases. To easily discover edge cases before errors occur, object properties need to be set with a certain degree of randomness.

Issues with the Object Mother Pattern​

To reuse test objects, a pattern called the Object Mother pattern involves creating a factory class to generate objects and then executing test code using objects created from that class.

However, this method is not favored because it requires continuous management not only of the test code but also of the factory. Furthermore, it does not help in identifying edge cases.

Using Fixture Monkey​

Fixture Monkey elegantly addresses the issues of reusability and randomness as mentioned above. Let's see how it solves these problems.

First, add the dependency.

testImplementation("com.navercorp.fixturemonkey:fixture-monkey-starter-kotlin:1.0.13")

Apply KotlinPlugin() to ensure that Fixture Monkey works smoothly in a Kotlin environment.

@Test
fun test() {
    val fixtureMonkey = FixtureMonkey.builder()
        .plugin(KotlinPlugin())
        .build()
}

Let's write a test again using the Product class we used before.

data class Product (
    val id: Long,

    val productName: String,

    val price: Long,

    val options: List<String>,

    val createdAt: Instant,

    val productType: ProductType,

    val merchantInfo: Map<Int, String>
)

enum class ProductType {
    ELECTRONICS,
    CLOTHING,
    FOOD
}

@Test
fun test() {
    val fixtureMonkey = FixtureMonkey.builder()
        .plugin(KotlinPlugin())
        .build()

    val actual: Product = fixtureMonkey.giveMeOne()

    actual shouldNotBe null
}

You can create an instance of Product without the need for unnecessary property settings. All property values are filled randomly by default.

Fills in multiple properties nicely

Post Condition​

However, in most cases, specific property values are required. For example, in the example, the id was generated as a negative number, but in reality, id is often used as a positive number. There might be a validation logic like this:

init {
    require(id > 0) { "id should be positive" }
}

After running the test a few times, if the id is generated as a negative number, the test fails. The fact that all values are randomly generated makes it particularly useful for finding unexpected edge cases.

Let's maintain the randomness but restrict the range slightly to ensure the validation logic passes.

@RepeatedTest(10)
fun postCondition() {
    val fixtureMonkey = FixtureMonkey.builder()
        .plugin(KotlinPlugin())
        .build()

    val actual = fixtureMonkey.giveMeBuilder<Product>()
        .setPostCondition { it.id > 0 } // Specify property conditions for the generated object
        .sample()

    actual.id shouldBeGreaterThan 0
}

I used @RepeatedTest to run the test 10 times.

You can see that all tests pass.

Setting Various Properties​

When using postCondition, be cautious as setting conditions too narrowly can make object creation costly. This is because the creation is repeated internally until an object that meets the condition is generated. In such cases, it is much better to use setExp to fix specific values.

val actual = fixtureMonkey.giveMeBuilder<Product>()
    .setExp(Product::id, 1L) // Only the specified value is fixed, the rest are random
    .sample()

actual.id shouldBe 1L

If a property is a collection, you can use sizeExp to specify the size of the collection.

val actual = fixtureMonkey.giveMeBuilder<Product>()
    .sizeExp(Product::options, 3)
    .sample()

actual.options.size shouldBe 3

Using maxSize and minSize, you can easily set the maximum and minimum size constraints for a collection.

val actual = fixtureMonkey.giveMeBuilder<Product>()
    .maxSizeExp(Product::options, 10)
    .sample()

actual.options.size shouldBeLessThan 11

There are various other property setting methods available, so I recommend exploring them when needed.

Conclusion​

Fixture Monkey really resolves the inconveniences encountered while writing unit tests. Although not mentioned in this article, you can create conditions in the builder and reuse them, add randomness to properties, and help developers discover edge cases they may have missed. As a result, test code becomes very concise, and the need for additional code like Object Mother disappears, making maintenance easier.

Even before the release of Fixture Monkey 1.x, I found it very helpful in writing test code. Now that it has become a stable version, I hope you can introduce it without hesitation and enjoy writing test code.

Reference​

• Fixture Monkey

In the previous chapter, we compiled Java and examined the bytecode structure. In this chapter, we will explore how the JVM executes the 'Hello World' code block.

Chapter 3: Running Java on the JVM​

• Class Loader
• Java Virtual Machine
• Java Native Interface
• JVM Memory Loading Process
• Interaction of Hello World with Memory Areas

Class Loader​

To understand when, where, and how Java classes are loaded into memory and initialized, we need to first look at the * Class Loader* of the JVM.

The class loader dynamically loads compiled Java class files (.class) and places them in the Runtime Data Area, which is the memory area of the JVM.

The process of loading class files by the class loader consists of three stages:

1. Loading: Bringing the class file into JVM memory.
2. Linking: The process of verifying the class file for use.
3. Initialization: Initializing the class file with appropriate values.

It is important to note that class files are not loaded into memory all at once but are dynamically loaded into memory * when needed by the application*.

A common misconception is the timing of when classes or static members within classes are loaded into memory. Many mistakenly believe that all classes and static members are loaded into memory as soon as the source is executed. However, static members are only loaded into memory when the class is dynamically loaded into memory upon calling a member within the class.

By using the verbose option, you can observe the process of loading into memory.

java -verbose:class VerboseLanguage

You can see that the VerboseLanguage class is loaded before 'Hello World' is printed.

Runtime Data Area​

The Runtime Data Area is the space where data is stored during program execution. It is divided into Shared Data Areas and Per-thread Data Areas.

Shared Data Areas​

Within the JVM, there are several areas where data can be shared among multiple threads running within the JVM. This allows various threads to access one of these areas simultaneously.

Heap​

Where instances of the VerboseLanguage class exist

The Heap area is where all Java objects or arrays are allocated when created. It is created when the JVM starts and is destroyed when the JVM exits.

According to the Java specification, this space should be automatically managed. This role is performed by a tool known as the Garbage Collector (GC).

There are no constraints on the size of the Heap specified in the JVM specification. Memory management is also left to the JVM implementation. However, if the Garbage Collector fails to secure enough space to create new objects, the JVM will throw an OutOfMemory error.

Method Area​

The Method Area is a shared data area that stores class and interface definitions. Similar to the Heap, it is created when the JVM starts and is destroyed when the JVM exits.

Global variables and static variables of a class are stored in this area, making them accessible from anywhere in the program from start to finish. (= Run-Time Constant Pool)

Specifically, the class loader loads the bytecode (.class) of a class and passes it to the JVM, which then generates the internal representation of the class used for creating objects and invoking methods. This internal representation collects information about fields, methods, and constructors of the class and interfaces.

In fact, according to the JVM specification, the Method Area is an area with no clear definition of 'how it should be'. It is a logical area and depending on the implementation, it can exist as part of the Heap. In a simple implementation, it can be part of the Heap without undergoing GC or compression.

Run-Time Constant Pool​

The Run-Time Constant Pool is part of the Method Area and contains symbolic references to class and interface names, field names, and method names. The JVM uses the Run-Time Constant Pool to find the actual memory addresses for references.

As seen when analyzing bytecode, the constant pool was found inside the class file. During runtime, the constant pool, which was part of the class file structure, is read and loaded into memory by the class loader.

String Constant Pool​

Where the "Hello World" string is stored

As mentioned earlier, the Run-Time Constant Pool is part of the Method Area. However, there is also a Constant Pool in the Heap, known as the String Constant Pool.

When creating a string using new String("Hello World"), the string is treated as an object and is managed in the Heap. Let's look at an example:

String s1 = "Hello World";
String s2 = new String("Hello World");

The string literal used inside the constructor is retrieved from the String Pool, but the new keyword guarantees the creation of a new and unique string.

0: ldc           #7                  // String Hello World
2: astore_1
3: new           #9                  // class java/lang/String
6: dup
7: ldc           #7                  // String Hello World
9: invokespecial #11                 // Method java/lang/String."<init>":(Ljava/lang/String;)V
12: astore_2
13: return

If we examine the bytecode, we can see that the string is 'created' using the invokespecial instruction.

The invokespecial instruction means that the object initialization method is directly called.

Why does the String Constant Pool exist in the Heap, unlike the Run-Time Constant Pool in the Method Area? 🤔

• Strings belong to very large objects. Also, it is difficult to predict how many strings will be created, so a process is needed to efficiently use memory space by cleaning up unused strings. This means that it is necessary for the String Constant Pool to exist in the Heap.
  • Storing in the stack would make it difficult to find space, and declaring a string could fail.
  • The stack size is typically around 320kb1MB for 32-bit and 1MB2MB for 64-bit systems.
• Strings are managed as immutable. They cannot be modified and are always created anew. By reusing already created strings, memory space is saved (interning). However, unused (unreachable) strings may accumulate over the application's lifecycle. To efficiently utilize memory, there is a need to clean up unreferenced strings, which again leads to the need for GC.

In conclusion, the String Constant Pool needs to exist in the Heap to be under the influence of GC.

String comparison operations require N operations for perfect matching if the length is N. In contrast, using the pool, the equals comparison only requires checking the reference, incurring a cost of $O(1)$.

It is possible to move a string that is outside the String Constant Pool into the String Constant Pool by creating a string using new.

String greeting = new String("Hello World");
greeting.intern(); // using the constant pool

// Now, comparison with the string literal in the SCP is possible.
assertThat(greeting).isEqualTo("Hello World"); // true

While this was provided as a trick in the past to save memory, it is no longer necessary, so it is best to use strings as literals.

To summarize:

1. Numbers have a maximum value, whereas strings, due to their nature, have an unclear maximum size.
2. Strings can become very large and are likely to be used frequently after creation compared to other types.
3. Naturally, high memory efficiency is required. To achieve this while increasing usability, they should be globally referable.
4. If placed in the Per-Thread Data Area within the Stack, they cannot be reused by other threads, and if the size is large, finding allocation space becomes difficult.
5. It is rational to have them in the Shared Data Area + in the Heap, but since they need to be treated as immutable at the JVM level, a dedicated Constant Pool is created within the Heap to manage them separately.

Per-thread Data Areas​

In addition to the Shared Data Area, the JVM manages data for individual threads separately. The JVM actually supports the concurrent execution of quite a few threads.

PC Register​

Each JVM thread has a PC (program counter) register.

The PC register stores the current position of the execution of instructions to enable the CPU to continue executing instructions. It also holds the memory address of the next instruction to be executed, aiding in optimizing instruction execution.

The behavior of the PC depends on the nature of the method:

• For non-native methods, the PC register stores the address of the currently executing instruction.
• For native methods, the PC register holds an undefined value.

The lifecycle of the PC register is essentially the same as the thread's lifecycle.

JVM Stack​

Each JVM thread has its own independent stack. The JVM stack is a data structure that stores method invocation information. A new frame is created on the stack for each method invocation, containing the method's local variables and the address of the return value. If it is a primitive type, it is stored directly on the stack, while if it is a wrapper type, it holds a reference to an instance created in the Heap. This results in int and double types having a slight performance advantage over Integer and Double.

Thanks to the JVM stack, the JVM can trace program execution and record stack traces as needed.

• This is known as a stack trace. printStackTrace is an example of this.
• In scenarios like webflux's event loop where a single operation traverses multiple threads, the significance of a stack trace may be difficult to understand.

The memory size and allocation method of the stack can be determined by the JVM implementation. Typically, around 1MB of space is allocated when a thread starts.

JVM memory allocation errors can result in a stack overflow error. However, if a JVM implementation allows dynamic expansion of the JVM stack size and a memory error occurs during expansion, the JVM may throw an OutOfMemory error.

Native Method Stack​

Native methods are methods written in languages other than Java. These methods cannot be compiled into bytecode (as they are not Java, javac cannot be used), so they require a separate memory area.

• The Native Method Stack is very similar to the JVM Stack but is exclusively for native methods.
• The purpose of the Native Method Stack is to track the execution of native methods.

JVM implementations can determine how to manipulate the size and memory blocks of the Native Method Stack.

In the case of memory allocation errors originating from the Native Method Stack, a stack overflow error occurs. However, if an attempt to increase the size of the Native Method Stack fails, an OutOfMemory error occurs.

In conclusion, a JVM implementation can decide not to support Native Method calls, emphasizing that such an implementation does not require a Native Method Stack.

The usage of the Java Native Interface will be covered in a separate article.

Execution Engine​

Once the loading and storage stages are complete, the JVM executes the Class File. It consists of three elements:

• Interpreter
• JIT Compiler
• Garbage Collector

Interpreter​

When a program starts, the Interpreter reads the bytecode line by line, converting it into machine code that the machine can understand.

Interpreters are generally slower. Why is that?

Compiled languages can define resources and types needed for a program to run during the compilation process before execution. However, in interpreted languages, necessary resources and variable types cannot be known until execution, making optimization difficult.

JIT Compiler​

The Just In Time Compiler was introduced in Java 1.1 to overcome the shortcomings of the Interpreter.

The JIT compiler compiles bytecode into machine code at runtime, improving the execution speed of Java applications. It detects frequently executed parts (hot code) and compiles them.

You can use the following keywords to check JIT-related behaviors if needed:

• -XX:+PrintCompilation: Outputs JIT-related logs
• -Djava.compiler=NONE: Deactivates JIT. You can observe a performance drop.

Garbage Collector​

The Garbage Collector is a critical component that deserves a separate document, and there is already a document on it, so it will be skipped this time.

• Optimizing the GC is not common.
  • However, there are cases where a delay of over 500ms due to GC operations occurs, and in scenarios handling high traffic or tight TTLs in caches, a 500ms delay can be a significant issue.

Conclusion​

Java is undoubtedly a complex language.

In interviews, you often get asked questions like this:

How well do you think you know Java?

Now, you should be able to answer more confidently.

Um... 🤔 Just about Hello World.

Reference​

• inpa blog
• https://docs.oracle.com/javase/specs/jvms/se8/html/jvms-2.html#jvms-2.5
• https://www.baeldung.com/java-jvm-run-time-data-areas
• https://sgcomputer.tistory.com/64
• https://johngrib.github.io/wiki/java/run-time-constant-pool/
• https://johngrib.github.io/wiki/jvm-stack/
• https://code-run.tistory.com/8

Continuing from the previous post, let's explore how the code evolves to print "Hello World."

Chapter 2. Compilation and Disassembly​

Programming languages have levels.

The closer a programming language is to human language, the higher-level language it is, and the closer it is to the language a computer can understand (machine language), the lower-level language it is. Writing programs in a high-level language makes it easier for humans to understand and increases productivity, but it also creates a gap with machine language, requiring a process to bridge this gap.

The process of a high-level language descending to a lower level is called compilation.

Since Java is not a low-level language, there is a compilation process. Let's take a look at how this compilation process works in Java.

Compilation​

As mentioned earlier, Java code cannot be directly executed by the computer. To execute Java code, it needs to be transformed into a form that the computer can read and interpret. This transformation involves the following major steps:

The resulting .class file from compilation is in bytecode. However, it is still not machine code that the computer can execute. The Java Virtual Machine (JVM) reads this bytecode and further processes it into machine code. We will cover how the JVM handles this in the final chapter.

First, let's compile the .java file to create a .class file. You can compile it using the javac command.

// VerboseLanguage.java
public class VerboseLanguage {
    public static void main(String[] args) {
        System.out.println("Hello World");
    }
}

javac VerboseLanguage.java

You can see that the class file has been created. You can run the class file using the java command, and this is the basic flow of running a Java program.

java VerboseLanguage
// Hello World

Are you curious about the contents of the class file? Wondering how the computer reads and executes the language? What secrets lie within this file? It feels like opening Pandora's box.

Expecting something, you open it up, and...

No way!

Only a brief binary content is displayed.

Wait, wasn't the result of compilation supposed to be bytecode...?

Yes, it is bytecode. At the same time, it is also binary code. At this point, let's briefly touch on the differences between bytecode and binary code before moving on.

Binary Code : Code composed of 0s and 1s. While machine language is made up of binary code, not all binary code is machine language.

Bytecode : Code composed of 0s and 1s. However, bytecode is not intended for the machine but for the VM. It is converted into machine code by the VM through processes like the JIT compiler.

Still, as this article claims to be a deep dive, we reluctantly tried to read through the conversion.

Fortunately, our Pandora's box contains only 0s and 1s, with no other hardships or challenges.

While we succeeded in reading it, it is quite difficult to understand the content with just 0s and 1s 🤔

Now, let's decipher this code.

Disassembly​

During the compilation process, the code is transformed into bytecode composed of 0s and 1s. As seen earlier, interpreting bytecode directly is quite challenging. Fortunately, the JDK includes tools that help developers read compiled bytecode, making it useful for debugging purposes.

The process of converting bytecode into a more readable form for developers is called disassembly. Sometimes this process can be confused with decompilation, but decompilation results in a higher-level programming language, not assembly language. Also, since the javap documentation clearly uses the term disassemble, we will follow suit.

Virtual Machine Assembly Language​

Let's use javap to disassemble the bytecode. The output is much more readable than just 0s and 1s.

javap -c VerboseLanguage.class

Compiled from "VerboseLanguage.java"
public class VerboseLanguage {
  public VerboseLanguage();
    Code:
       0: aload_0
       1: invokespecial #1                  // Method java/lang/Object."<init>":()V
       4: return

  public static void main(java.lang.String[]);
    Code:
       0: getstatic     #7                  // Field java/lang/System.out:Ljava/io/PrintStream;
       3: ldc           #13                 // String Hello World
       5: invokevirtual #15                 // Method java/io/PrintStream.println:(Ljava/lang/String;)V
       8: return
}

What can we learn from this?

Firstly, this language is called virtual machine assembly language.

The Java Virtual Machine code is written in the informal “virtual machine assembly language” output by Oracle's javap utility, distributed with the JDK release. - JVM Spec

The format is as follows:

<index> <opcode> [ <operand1> [ <operand2>... ]] [<comment>]

index : Index of the JVM code byte array. It can be thought of as the method's starting offset.

opcode : Mnemonic symbol representing the set of instructions opcode. We remember the order of the rainbow colors as 'ROYGBIV' to distinguish the instruction set. If the rainbow colors represent the instruction set, each syllable of 'ROYGBIV' can be considered as a mnemonic symbol defined to differentiate them.

operandN : Operand of the instruction. The operand of a computer instruction is the address field. It points to where the data to be processed is stored in the constant pool.

Let's take a closer look at the main method part of the disassembled result.

Code:
   0: getstatic     #7                  // Field java/lang/System.out:Ljava/io/PrintStream;
   3: ldc           #13                 // String Hello World
   5: invokevirtual #15                 // Method java/io/PrintStream.println:(Ljava/lang/String;)V
   8: return

• invokevirtual: Call an instance method
• getstatic: Get a static field from a class
• ldc: Load data into the run-time constant pool.

The 3: ldc #13 on the third line means to put an item at index 13, and the item being put is kindly indicated in the comment.

Hello World

Note that bytecode instructions like getstatic and invokevirtual are represented by a single-byte opcode number. For example, getstatic=0xb2, invokevirtual=0xb6, and so on. It can be understood that Java bytecode instructions also have a maximum of 256 different opcodes.

JVM Instruction Set showing the bytecode for invokevirtual

If we look at the bytecode of the main method in hex, it would be as follows:

b2 00 07 12 0d b6

It might still be a bit hard to notice the pattern. As a hint, remember that earlier we mentioned the number before the opcode is the index in the JVM array. Let's slightly change the representation.

arr = [b2, 00, 07, 12, 0d, b6]

• arr[0] = b2 = getstatic
• arr[3] = 12 = ldc
• arr[5] = b6 = invokevirtual

It becomes somewhat clearer what the index meant. The reason for skipping some indices is quite simple: getstatic requires a 2-byte operand, and ldc requires a 1-byte operand. Therefore, the ldc instruction, which is the next instruction after getstatic, is recorded at index 3, skipping 1 and 2. Similarly, skipping 4, the invokevirtual instruction is recorded at index 5.

Lastly, notice the comment (Ljava/lang/String;)V on the 4th line. Through this comment, we can see that in Java bytecode, classes are represented as L;, and void is represented as V. Other types also have their unique representations, summarized as follows:

Using the -verbose option, you can see a more detailed disassembly result, including the constant pool. It would be interesting to examine the operands and constant pool together.

  Compiled from "VerboseLanguage.java"
public class VerboseLanguage
  minor version: 0
  major version: 65
  flags: (0x0021) ACC_PUBLIC, ACC_SUPER
  this_class: #21                         // VerboseLanguage
  super_class: #2                         // java/lang/Object
  interfaces: 0, fields: 0, methods: 2, attributes: 1
Constant pool:
   #1 = Methodref          #2.#3          // java/lang/Object."<init>":()V
   #2 = Class              #4             // java/lang/Object
   #3 = NameAndType        #5:#6          // "<init>":()V
   #4 = Utf8               java/lang/Object
   #5 = Utf8               <init>
   #6 = Utf8               ()V
   #7 = Fieldref           #8.#9          // java/lang/System.out:Ljava/io/PrintStream;
   #8 = Class              #10            // java/lang/System
   #9 = NameAndType        #11:#12        // out:Ljava/io/PrintStream;
  #10 = Utf8               java/lang/System
  #11 = Utf8               out
  #12 = Utf8               Ljava/io/PrintStream;
  #13 = String             #14            // Hello World
  #14 = Utf8               Hello World
  #15 = Methodref          #16.#17        // java/io/PrintStream.println:(Ljava/lang/String;)V
  #16 = Class              #18            // java/io/PrintStream
  #17 = NameAndType        #19:#20        // println:(Ljava/lang/String;)V
  #18 = Utf8               java/io/PrintStream
  #19 = Utf8               println
  #20 = Utf8               (Ljava/lang/String;)V
  #21 = Class              #22            // VerboseLanguage
  #22 = Utf8               VerboseLanguage
  #23 = Utf8               Code
  #24 = Utf8               LineNumberTable
  #25 = Utf8               main
  #26 = Utf8               ([Ljava/lang/String;)V
  #27 = Utf8               SourceFile
  #28 = Utf8               VerboseLanguage.java
{
  public VerboseLanguage();
    descriptor: ()V
    flags: (0x0001) ACC_PUBLIC
    Code:
      stack=1, locals=1, args_size=1
         0: aload_0
         1: invokespecial #1                  // Method java/lang/Object."<init>":()V
         4: return
      LineNumberTable:
        line 1: 0

  public static void main(java.lang.String[]);
    descriptor: ([Ljava/lang/String;)V
    flags: (0x0009) ACC_PUBLIC, ACC_STATIC
    Code:
      stack=2, locals=1, args_size=1
         0: getstatic     #7                  // Field java/lang/System.out:Ljava/io/PrintStream;
         3: ldc           #13                 // String Hello World
         5: invokevirtual #15                 // Method java/io/PrintStream.println:(Ljava/lang/String;)V
         8: return
      LineNumberTable:
        line 3: 0
        line 4: 8
}
SourceFile: "VerboseLanguage.java"

Conclusion​

In the previous chapter, we explored why a verbose process is required to print Hello World. In this chapter, we looked at the compilation and disassembly processes before printing Hello World. Next, we will finally examine the execution flow of the Hello World printing method with the JVM.

Reference​

• Opcode Codes
• Naver D2
• JVM specification

In the world of programming, it always starts with printing the sentence Hello World. It's like an unwritten rule.

# hello.py
print("Hello World")

python hello.py
// Hello World

Python? Excellent.

// hello.js
console.log("Hello World");

node hello.js
// Hello World

JavaScript? Not bad.

public class VerboseLanguage {
    public static void main(String[] args) {
        System.out.println("Hello World");
    }
}

javac VerboseLanguage.java
java VerboseLanguage
// Hello World

However, Java feels like it's from a different world. We haven't even mentioned yet that the class name must match the file name.

What is public, what is class, what is static, and going through void, main, String[], and System.out.println, we finally reach the string "Hello World". Now, let's go learn another language.¹

Even for simply printing "Hello World", Java demands quite a bit of background knowledge. Why does Java require such verbose processes?

This series is divided into 3 chapters. The goal is to delve into what happens behind the scenes to print the 2 words " Hello World" in detail. The specific contents of each chapter are as follows:

• In the first chapter, we introduce the reasons behind the Hello World as the starting point.
• In the second chapter, we examine the compiled class files and how the computer interprets and executes Java code.
• Finally, we explore how the JVM loads and executes public static void main and the operating principles behind it.

By combining the contents of the 3 chapters, we can finally grasp the concept of "Hello World". It's quite a long journey, so let's take a deep breath and embark on it.

Chapter 1. Why?​

Before printing Hello World in Java, there are several "why moments" that need to be considered.

Why must the class name match the file name?​

More precisely, it is the name of the public class that must match the file name. Why is that?

Java programs are not directly understandable by computers. A virtual machine called JVM assists the computer in executing the program. To make a Java program executable by the computer, it needs to go through several steps to convert it into machine code that the JVM can interpret. The first step is using a compiler to convert the program into bytecode that the JVM can interpret. The converted bytecode is then passed through an interpreter inside the JVM to be translated into machine code and executed.

Let's briefly look at the compilation process.

public class Outer {
    public static void main(String[] args) {
        System.out.println("This is Outer class");
    }

    private class Inner {
    }
}

javac Outer.java

Permissions Size User   Date Modified Name
.rw-r--r--   302 haril  30 Nov 16:09  Outer$Inner.class
.rw-r--r--   503 haril  30 Nov 16:09  Outer.class
.rw-r--r--   159 haril  30 Nov 16:09  Outer.java

Java generates a .class file for every class at compile time.

Now, the JVM needs to find the main method for program execution. How does it know where the main method is?

Why does it have to find main() specifically? Just wait a little longer.

If the Java file name does not match the public class name, the Java interpreter has to read all class files to find the main method. If the file name matches the name of the public class, the Java interpreter can better identify the file it needs to interpret.

Imagine a file named Java1000 with 1000 classes inside. To identify where main() is among the 1000 classes, the interpreter would have to examine all the class files.

However, if the file name matches the name of the public class, it can access main() more quickly (since main exists in the public class), and it can easily access other classes since all the logic starts from main().

Why must it be public?​

The JVM needs to find the main method inside the class. If the JVM, which accesses the class from outside, needs to find a method inside the class, that method must be public. In fact, changing the access modifier to private will result in an error message instructing you to declare main as public.

Error: Main method not found in class VerboseLanguage, please define the main method as:
   public static void main(String[] args)

Why must it be static?​

The JVM has found the public main() method. However, to invoke this method, an object must first be created. Does the JVM need this object? No, it just needs to be able to call main. By declaring it as static, the JVM does not need to create an unnecessary object, saving memory.

Why must it be void?​

The end of the main method signifies the end of Java's execution. The JVM cannot do anything with the return value of main, so the presence of a return value is meaningless. Therefore, it is natural to declare it as void.

Why must it be named main?​

The method name main is designed for the JVM to find the entry point for running the application.

Although the term "design" sounds grand, in reality, it is hard-coded to find the method named main. If the name to be found was not main but haril, it would have searched for a method named haril. Of course, the Java creators likely had reasons for choosing main, but that's about it.

mainClassName = GetMainClassName(env, jarfile);
mainClass = LoadClass(env, classname);

// Find the main method
mainID = (*env)->GetStaticMethodID(env, mainClass, "main", "([Ljava/lang/String;)V");

jbject obj = (*env)->ToReflectedMethod(env, mainClass, mainID, JNI_TRUE);

Why args?​

Until now, we omitted mentioning String[] args in main(). Why must this argument be specified, and why does an error occur if it is omitted?

As public static void main(String[] args) is the entry point of a Java application, this argument must come from outside the Java application.

All types of standard input are entered as strings.

This is why args is declared as a string array. If you think about it, it makes sense. Before the Java application even runs, can you create custom object types directly? 🤔

So why is args necessary?

By passing arguments in a simple way from outside to inside, you can change the behavior of a Java application, a mechanism widely used since the early days of C programming to control program behavior. Especially for simple applications, this method is very effective. Java simply adopted this widely used method.

The reason String[] args cannot be omitted is that Java only allows one public static void main(String[] args) as the entry point. The Java creators thought it would be less confusing to declare and not use args than to allow it to be omitted.

System.out.println​

Finally, we can start talking about the method related to output.

Just to mention it again, in Python it was print("Hello World"). ²

A Java program runs not directly on the operating system but on a virtual machine called JVM. This allows Java programs to be executed anywhere regardless of the operating system, but it also makes it difficult to use specific functions provided by the operating system. This is why coding at the system level, such as creating a CLI in Java or collecting OS metrics, is challenging.

However, there is a way to leverage limited OS functionality (JNI), and System provides this functionality. Some of the key functions include:

• Standard input
• Standard output
• Setting environment variables
• Terminating the running application and returning a status code

To print Hello World, we are using the standard output function of System.

In fact, as you follow the flow of System.out.println, you will encounter a writeBytes method with the native keyword attached, which delegates the operation to C code and transfers it to standard output.

// FileOutputStream.java
private native void writeBytes(byte b[], int off, int len, boolean append)
    throws IOException;

The invocation of a method with the native keyword works through the Java Native Interface (JNI). This will be covered in a later chapter.

String​

Strings in Java are somewhat special. No, they seem quite special. They are allocated separate memory space, indicating they are definitely treated as special. Why is that?

It is important to note the following properties of strings:

• They can become very large.
• They are relatively frequently reused.

Therefore, strings are designed with a focus on how to reuse them once created. To fully understand how large string data is managed in memory, you need an understanding of the topics to be covered later. For now, let's briefly touch on the principles of memory space saving.

First, let's look at how strings are declared in Java.

String greeting = "Hello World";

Internally, it works as follows:

Strings are created in the String Constant Pool and have immutable properties. Once a string is created, it does not change, and if the same string is found in the Constant Pool when creating a new string, it is reused.

We will cover JVM Stack, Frame, Heap in the next chapter.

Another way to declare strings is by instantiation.

String greeting = new String("Hello World");

This method is rarely used because there is a difference in internal behavior, as shown below.

When a string is used directly without the new keyword, it is created in the String Constant Pool and can be reused. However, if instantiated with the new keyword, it is not created in the Constant Pool. This means the same string can be created multiple times, potentially wasting memory space.

Summary​

In this chapter, we answered the following questions:

• Why must the .java file name match the class name?
• Why must it be public static void main(String[] args)?
• The flow of the output operation
• The characteristics of strings and the basic principles of their creation and use

In the next chapter, we will compile Java code ourselves and explore how bytecode is generated, its relationship with memory areas, and more.

Reference​

• OpenJDK java.c
• OpenJDK
• https://www.geeksforgeeks.org/java-main-method-public-static-void-main-string-args/
• https://www.geeksforgeeks.org/myth-file-name-class-name-java/
• https://www.includehelp.com/java/why-does-java-file-name-must-be-same-as-public-class-name.aspx
• https://www.devkuma.com/docs/java/system-class/
• Inpa blog
• https://docs.oracle.com/javase/specs/jvms/se8/html/jvms-2.html#jvms-2.5
• https://www.baeldung.com/java-jvm-run-time-data-areas
• https://sgcomputer.tistory.com/64
• https://johngrib.github.io/wiki/java/run-time-constant-pool/
• https://johngrib.github.io/wiki/jvm-stack/
• https://code-run.tistory.com/8
• https://www.baeldung.com/java-command-line-arguments

Overview​

How many concurrent users can a Spring MVC web application accommodate? 🤔

To estimate the approximate number of users a server needs to handle to provide stable service while accommodating many users, this article explores changes in network traffic focusing on Spring MVC's Tomcat configuration.

For the sake of convenience, the following text will be written in a conversational tone 🙏

Overview​

Shortening URLs started to prevent URLs from being fragmented in email or SMS transmissions. However, nowadays, it is more actively used for sharing specific links on social media platforms like Twitter or Instagram. It improves readability by not looking verbose and can also provide additional features such as collecting user statistics before redirecting to the URL.

In this article, we will implement a URL shortener from scratch and explore how it works.

What is a URL Shortener?​

Let's first take a look at the result.

You can run the URL shortener we will implement in this article directly with the following command:

docker run -d -p 8080:8080 songkg7/url-shortener

Here is how to use it. Simply input the long URL you want to shorten as the value of longUrl.

curl -X POST --location "http://localhost:8080/api/v1/shorten" \
    -H "Content-Type: application/json" \
    -d "{
            \"longUrl\": \"https://www.google.com/search?q=url+shortener&sourceid=chrome&ie=UTF-8\"
        }"
# You will receive a random value like tN47tML.

Now, if you access http://localhost:8080/tN47tML in your web browser,

You will see that it correctly redirects to the original URL.

Before Shortening

• https://www.google.com/search?q=url+shortener&sourceid=chrome&ie=UTF-8

After Shortening

• http://localhost:8080/tN47tML

Now, let's see how we can shorten URLs.

Rough Design​

Shortening URLs​

1. Generate an ID before storing the longUrl.
2. Encode the ID to base62 to create the shortUrl.
3. Store the ID, shortUrl, and longUrl in the database.

Memory is finite and relatively expensive. RDB can be quickly queried through indexes and is relatively cheaper compared to memory, so we will use RDB to manage URLs.

To manage URLs, we first need to secure an ID generation strategy. There are various methods for ID generation, but it may be too lengthy to cover here, so we will skip it. I will simply use the current timestamp for ID generation.

Base62 Conversion​

By using ULID, you can generate a unique ID that includes a timestamp.

val id: Long = Ulid.fast().time // e.g., 3145144998701, used as a primary key

Converting this number to base62, we get the following string.

tN47tML

This string is stored in the database as the shortUrl.

The retrieval process will proceed as follows:

1. A GET request is made to localhost:8080/tN47tML.
2. Decode tN47tML from base62.
3. Obtain the primary key 3145144998701 and query the database.
4. Redirect the request to the longUrl.

Now that we have briefly looked at it, let's implement it and delve into more details.

Implementation​

Just like the previous article on Consistent Hashing, we will implement it ourselves. Fortunately, implementing a URL shortener is not that difficult.

Model​

First, we implement the model to receive requests from users. We simplified the structure to only receive the URL to be shortened.

data class ShortenRequest(
    val longUrl: String
)

We implement a Controller to handle POST requests.

@PostMapping("/api/v1/shorten")
fun shorten(@RequestBody request: ShortenRequest): ResponseEntity<ShortenResponse> {
    val url = urlShortenService.shorten(request.longUrl)
    return ResponseEntity.ok(ShortenResponse(url))
}

Base62 Conversion​

Finally, the most crucial part. After generating an ID, we encode it to base62 to shorten it. This shortened string becomes the shortUrl. Conversely, we decode the shortUrl to find the ID and use it to query the database to retrieve the longUrl.

private const val BASE62 = "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ"

class Base62Conversion : Conversion {
    override fun encode(input: Long): String {
        val sb = StringBuilder()
        var num = BigInteger.valueOf(input)
        while (num > BigInteger.ZERO) {
            val remainder = num % BigInteger.valueOf(62)
            sb.append(BASE62[remainder.toInt()])
            num /= BigInteger.valueOf(62)
        }
        return sb.reverse().toString()
    }

    override fun decode(input: String): Long {
        var num = BigInteger.ZERO
        for (c in input) {
            num *= BigInteger.valueOf(62)
            num += BigInteger.valueOf(BASE62.indexOf(c).toLong())
        }
        return num.toLong()

    }
}

The length of the shortened URL is inversely proportional to the size of the ID number. The smaller the generated ID number, the shorter the URL can be made.

If you want the length of the shortened URL to not exceed 8 characters, you should ensure that the size of the ID does not exceed 62^8. Therefore, how you generate the ID is also crucial. As mentioned earlier, to simplify the content in this article, we handled this part using a timestamp value.

Test​

Let's send a POST request with curl to shorten a random URL.

curl -X POST --location "http://localhost:8080/api/v1/shorten" \
    -H "Content-Type: application/json" \
    -d "{
            \"longUrl\": \"https://www.google.com/search?q=url+shortener&sourceid=chrome&ie=UTF-8\"
        }"

You can confirm that it correctly redirects by accessing http://localhost:8080/{shortUrl}.

Conclusion​

Here are some areas for improvement:

• By controlling the ID generation strategy more precisely, you can further shorten the shortUrl.
  • If there is heavy traffic, you must consider issues related to concurrency.
  • Snowflake
• Using DNS for the host part can further shorten the URL.
• Applying cache to the Persistence Layer can achieve faster responses.

Let's take a brief look at the Docker Compose Support introduced in Spring Boot 3.1.

Overview​

When developing with the Spring framework, it seems that using Docker for setting up DB environments is more common than installing them directly on the local machine. Typically, the workflow involves:

1. Using docker run before bootRun to prepare the DB in a running state
2. Performing development and validation tasks using bootRun
3. Stopping bootRun and using docker stop to stop the container DB

The process of running and stopping Docker before and after development tasks used to be quite cumbersome. However, starting from Spring Boot 3.1, you can use a docker-compose.yaml file to synchronize the lifecycle of Spring and Docker containers.

Contents​

First, add the dependency:

dependencies {
    // ...
    developmentOnly 'org.springframework.boot:spring-boot-docker-compose'
    // ...
}

Next, create a compose file as follows:

services:
  elasticsearch:
    image: 'docker.elastic.co/elasticsearch/elasticsearch:7.17.10'
    environment:
      - 'ELASTIC_PASSWORD=secret'
      - 'discovery.type=single-node'
      - 'xpack.security.enabled=false'
    ports:
      - '9200' # random port mapping
      - '9300'

During bootRun, the compose file is automatically recognized, and the docker compose up operation is executed first.

However, if you are mapping the container port to a random host port, you may need to update the application.yml every time docker compose down is triggered. Fortunately, starting from Spring Boot 3.1, once you write the compose file, Spring Boot takes care of the rest. It's incredibly convenient!

If you need to change the path to the compose file, simply modify the file property:

spring:
  docker:
    compose:
      file: infrastructure/compose.yaml

There are also properties related to lifecycle management, allowing you to appropriately adjust the container lifecycle. If you don't want the container to stop every time you shut down Boot, you can use the start_only option:

spring:
  docker:
    compose:
      lifecycle-management: start_and_stop # none, start_only

There are various other options available, so exploring them should help you choose what you need.

Conclusion​

No matter how much test code you write, verifying the interaction with the actual DB was essential during the development process. Setting up that environment felt like a tedious chore. While container technology made configuration much simpler, remembering to run docker commands before and after starting Spring Boot was definitely a hassle.

Now, starting from Spring Boot 3.1, developers can avoid situations where they forget to start or stop containers, preventing memory consumption. It allows developers to focus more on development. The seamless integration of Docker with Spring is both fascinating and convenient. Give it a try!

Reference​

• Docker Compose Support in Spring Boot 3.1