Code Snapshot: Connection Borrowing Logic

Here’s a key piece of HikariCP internals when a thread tries to borrow a connection from the pool:

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// ②
// Get a connection from the pool, with a timeout
final PoolEntry poolEntry = connectionBag.borrow(timeout, MILLISECONDS);

// The borrow method returns null only if it times out
if (poolEntry == null) {
  break; // We timed out... break and throw exception
}

This code attempts to borrow a connection from the internal connectionBag. If it doesn’t succeed within the specified timeout, it returns null, and the calling code exits the loop and throws an exception.

Behind the Scenes: What’s connectionBag?

The connectionBag is a custom concurrent structure used by HikariCP to manage connections. Internally, it uses a CopyOnWriteArrayList to store available PoolEntry objects.

Why Use CopyOnWriteArrayList?

CopyOnWriteArrayList is a thread-safe variant of ArrayList where all mutative operations (like add, remove) are implemented by making a fresh copy of the underlying array. It shines in situations where:

• Reads are far more frequent than writes.
• Thread safety is critical, but locking overhead must be minimized.

This fits HikariCP’s use case perfectly—connections are borrowed and returned frequently under high concurrency, and most operations are reads (checking for available connections).

What Happens During borrow()?

The borrow() method performs the following steps:

1. Iterates over the CopyOnWriteArrayList of available connections.
2. Tries to atomically claim one via compareAndSet.
3. If no connection is immediately available, it waits until:
   • A connection is returned.
   • The timeout expires.

Thanks to CopyOnWriteArrayList, multiple threads can safely iterate and borrow connections without the risk of ConcurrentModificationException or complex locking strategies.

Timeout Behavior

If no connection is available within the timeout window:

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if (poolEntry == null) {
  break; // We timed out... break and throw exception
}

The system recognizes that it’s better to fail fast than to block indefinitely. This ensures predictability and avoids resource starvation under load.

Trade-offs of CopyOnWriteArrayList

While CopyOnWriteArrayList is great for safe, lock-free reads, it does have drawbacks:

• Writes (adds/removes) are costly since the array is copied.
• It’s not ideal if the list is modified very frequently.

In HikariCP’s case, connection availability doesn’t change every millisecond—so this trade-off is acceptable and even advantageous.

Takeaways

• CopyOnWriteArrayList plays a crucial role in enabling fast, concurrent access to connection entries in HikariCP.
• It ensures safety and performance without heavyweight synchronization.
• The timeout logic provides a safety net to prevent system hangs under high load.

Final Thoughts

This case study shows how a seemingly simple collection choice—like CopyOnWriteArrayList—can dramatically influence the performance and reliability of a high-throughput system like HikariCP. It’s a perfect example of using the right tool for the job in a multithreaded environment.

HikariCP Case Study: Understanding FAUX_LOCK

HikariCP, a high-performance JDBC connection pool, is renowned for its minimalist design and efficient concurrency handling. One of its clever optimizations is the FAUX_LOCK, a no-op (no operation) implementation of the SuspendResumeLock class. In this short case study, we’ll explore the purpose of FAUX_LOCK, its implementation, and how it leverages JIT (Just-In-Time) compilation to boost performance.

What is FAUX_LOCK?

The SuspendResumeLock class in HikariCP manages the suspension and resumption of connection acquisition, typically during pool maintenance or shutdown. The FAUX_LOCK is a static instance of SuspendResumeLock that overrides its methods—acquire, release, suspend, and resume—to do nothing:

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public static final SuspendResumeLock FAUX_LOCK = new SuspendResumeLock(false) {
    @Override
    public void acquire() {}
    @Override
    public void release() {}
    @Override
    public void suspend() {}
    @Override
    public void resume() {}
};

This “fake” lock acts as a placeholder when actual locking is unnecessary, minimizing overhead in high-performance scenarios.

Why Use FAUX_LOCK?

HikariCP is designed for speed, and every cycle matters in high-throughput applications. The FAUX_LOCK is used when the pool is configured to operate without suspension or locking, specifically when allowPoolSuspension is false (the default). Its key purposes are:

1. Single-Threaded or Non-Suspended Pools: When pool suspension is disabled, there’s no need for lock operations. FAUX_LOCK eliminates synchronization overhead.
2. Simplified Code Path: Using FAUX_LOCK avoids conditional logic to check whether locking is needed, maintaining a consistent SuspendResumeLock interface.
3. Performance Optimization: By providing empty method implementations, FAUX_LOCK reduces the cost of lock operations to zero.

JIT Optimization: The Hidden Benefit

So, what’s the real advantage of this approach? When pool suspension is disabled, FAUX_LOCK provides an empty implementation, with the expectation that the JVM’s Just-In-Time (JIT) compiler will optimize it away. Each call to acquire, release, suspend, or resume is an empty method that does nothing. After the code runs multiple times, the JIT compiler may recognize these methods as no-ops and inline or eliminate them entirely.

This means that, over time, the overhead of calling these methods disappears. When acquiring a connection, the application skips the token acquisition step entirely, as the JIT-optimized code bypasses the empty method calls. This results in significant performance savings, especially in high-concurrency scenarios where connection acquisition is frequent.

When is FAUX_LOCK Used?

FAUX_LOCK is employed when allowPoolSuspension is false. In this mode, HikariCP does not support suspending the pool for tasks like shrinking or reaping idle connections. By using FAUX_LOCK, calls to lock-related methods become no-ops, allowing HikariCP to focus solely on connection management. For example, in a web application with a fixed pool size and no need for suspension, FAUX_LOCK ensures minimal overhead.

Benefits of FAUX_LOCK

• Zero Overhead: Empty methods eliminate lock-related costs, and JIT optimization may remove them entirely.
• Code Simplicity: A consistent SuspendResumeLock interface avoids complex branching logic.
• Flexibility: Supports both high-performance (with FAUX_LOCK) and maintenance-friendly modes (with a real lock).
• Performance Boost: JIT-eliminated method calls reduce connection acquisition time.

Considerations

FAUX_LOCK is ideal for performance-critical applications but unsuitable when pool suspension is needed (e.g., for dynamic resizing). Enabling allowPoolSuspension requires a real SuspendResumeLock, and misconfiguration could disrupt pool maintenance.

Conclusion

The FAUX_LOCK in HikariCP is a brilliant optimization that showcases how small design choices can yield big performance gains. By providing a no-op lock and leveraging JIT compilation to eliminate method call overhead, FAUX_LOCK ensures HikariCP remains blazingly fast in non-suspended pools. For developers, this underscores the importance of aligning HikariCP’s configuration with application requirements to unlock its full potential.

When configuring your HikariCP pool, check if allowPoolSuspension is necessary. If not, FAUX_LOCK and JIT optimization will work behind the scenes to make your application faster and more efficient.

HikariCP Case Study: Understanding the getConnection Semaphore

One of its key mechanisms for managing connections efficiently is the use of a Semaphore in the getConnection method. In this case study, we’ll dive into how HikariCP leverages Semaphore to manage database connections, ensuring thread safety and optimal resource utilization.

Background on HikariCP

HikariCP is a JDBC connection pool designed for speed and simplicity. Unlike traditional connection pools that may rely on heavy synchronization or complex locking mechanisms, HikariCP uses modern concurrency utilities from Java’s java.util.concurrent package, such as ConcurrentBag and Semaphore, to achieve low-latency connection management.

The getConnection method is the primary entry point for applications to acquire a database connection from the pool. This method must balance speed, thread safety, and resource constraints, especially under high concurrency. The use of a Semaphore in this context is critical to controlling access to the finite number of connections.

The Role of Semaphore in getConnection

In HikariCP, a Semaphore is used to limit the number of threads that can simultaneously attempt to acquire a connection from the pool. A Semaphore is a concurrency primitive that maintains a set of permits. Threads must acquire a permit to proceed, and if no permits are available, they block until one is released.

Here’s how HikariCP employs a Semaphore in the getConnection process:

1. Connection Acquisition Limit: The Semaphore is initialized with a number of permits corresponding to the maximum pool size (maximumPoolSize). This ensures that no more than the configured number of connections are ever allocated.

2. Thread Safety: When a thread calls getConnection, it must first acquire a permit from the Semaphore. This prevents excessive threads from overwhelming the pool or attempting to create new connections beyond the pool’s capacity.

3. Timeout Handling: HikariCP’s getConnection method supports a timeout parameter (connectionTimeout). If a thread cannot acquire a permit within this timeout, the Semaphore’s tryAcquire method fails, and HikariCP throws a SQLException, informing the application that no connection is available.

4. Efficient Resource Management: Once a connection is acquired or created, the thread proceeds to use it. After the connection is returned to the pool (via close), the permit is released back to the Semaphore, allowing another thread to acquire a connection.

This approach ensures that HikariCP remains both thread-safe and efficient, avoiding the overhead of traditional locking mechanisms like synchronized blocks.

Case Study: High-Concurrency Scenario

Let’s consider a real-world scenario where a web application handles thousands of concurrent requests, each requiring a database connection. Without proper concurrency control, the application could exhaust the database’s connection limit, leading to errors or crashes. Here’s how HikariCP’s Semaphore-based getConnection handles this:

Setup

• HikariCP Configuration:
  • maximumPoolSize: 20
  • connectionTimeout: 30000ms (30 seconds)
  • minimumIdle: 5
• Application: A Java-based REST API using Spring Boot, handling 1000 concurrent requests.
• Database: PostgreSQL with a maximum of 100 connections.

Observations

1. Initial State: The pool starts with 5 idle connections (as per minimumIdle). The Semaphore has 20 permits available, corresponding to maximumPoolSize.

2. Spike in Requests: When 1000 requests hit the API simultaneously, each thread calls getConnection. The Semaphore ensures that only 20 threads can proceed at a time. Other threads wait for permits to become available.

3. Connection Reuse: As threads complete their database operations and return connections to the pool, permits are released. Waiting threads acquire these permits and reuse existing connections, preventing the need to create new ones unnecessarily.

4. Timeout Behavior: If the pool is fully utilized and no connections are available within 30 seconds, threads that cannot acquire a permit receive a SQLException. This allows the application to gracefully handle overload scenarios, perhaps by retrying or returning an error to the client.

Results

• Stability: The Semaphore prevented the pool from exceeding 20 connections, avoiding overwhelming the PostgreSQL server.
• Performance: Connection reuse and efficient concurrency control minimized latency, with most requests served within milliseconds.
• Error Handling: Threads that timed out received clear exceptions, allowing the application to implement fallback logic.

Code Example

Below is a simplified view of how HikariCP’s getConnection logic might look, focusing on the Semaphore usage:

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import java.sql.Connection;
import java.sql.SQLException;
import java.util.concurrent.Semaphore;
import java.util.concurrent.TimeUnit;

public class HikariPool {
    private final Semaphore connectionSemaphore;
    private final int maxPoolSize;
    private final long connectionTimeout;

    public HikariPool(int maxPoolSize, long connectionTimeoutMs) {
        this.maxPoolSize = maxPoolSize;
        this.connectionTimeout = connectionTimeoutMs;
        this.connectionSemaphore = new Semaphore(maxPoolSize, true);
    }

    public Connection getConnection() throws SQLException {
        try {
            // Attempt to acquire a permit within the timeout
            if (!connectionSemaphore.tryAcquire(connectionTimeout, TimeUnit.MILLISECONDS)) {
                throw new SQLException("Connection timeout after " + connectionTimeout + "ms");
            }
            // Logic to acquire or create a connection from the pool
            return acquireConnection();
        } catch (InterruptedException e) {
            Thread.currentThread().interrupt();
            throw new SQLException("Interrupted while waiting for connection", e);
        } finally {
            // Release the permit back to the semaphore after returning the connection
            connectionSemaphore.release();
        }
    }

    private Connection acquireConnection() {
        // Placeholder for actual connection acquisition logic
        return null;
    }
}

This example illustrates the Semaphore’s role in controlling access to the connection pool. In the actual HikariCP implementation, additional optimizations like the ConcurrentBag for connection storage and housekeeping threads for pool maintenance further enhance performance.

Advantages of Using Semaphore

• Lightweight Concurrency: Compared to traditional locks, Semaphore provides a more flexible and lightweight mechanism for controlling access.
• Fairness: HikariCP’s Semaphore is configured to be fair, ensuring that threads are served in the order they request permits, reducing starvation.
• Timeout Support: The ability to specify a timeout for permit acquisition aligns with HikariCP’s focus on predictable behavior under load.
• Scalability: The Semaphore scales well under high concurrency, allowing HikariCP to handle thousands of requests efficiently.

Challenges and Considerations

While the Semaphore-based approach is highly effective, there are some considerations:

1. Configuration Tuning: The maximumPoolSize and connectionTimeout must be carefully tuned based on the application’s workload and the database’s capacity. Setting maximumPoolSize too high can overwhelm the database, while setting it too low can lead to timeouts.

2. Timeout Handling: Applications must be prepared to handle SQLExceptions caused by timeouts, possibly with retry logic or user-friendly error messages.

3. Monitoring: Under high load, monitoring the pool’s metrics (e.g., active connections, wait time) is crucial to detect bottlenecks or misconfigurations.

Conclusion

HikariCP’s use of a Semaphore in the getConnection method is a brilliant example of leveraging Java’s concurrency utilities to build a high-performance connection pool. By limiting concurrent access to connections, enforcing timeouts, and ensuring thread safety, the Semaphore enables HikariCP to deliver reliable and efficient database access in demanding environments.

For developers and architects, understanding this mechanism provides valuable insights into designing scalable systems. Properly configuring HikariCP and monitoring its behavior can make the difference between a sluggish application and one that performs flawlessly under pressure.

If you’re using HikariCP in your projects, take the time to review your pool configuration and consider how the Semaphore-based concurrency control impacts your application’s performance. With the right setup, HikariCP can be a game-changer for your database-driven applications.

HikariCP Source Code Analysis: HikariPool Initialization

HikariCP is a high-performance JDBC connection pool framework, and one of its core components is the HikariPool class. This article dives into the initialization process of HikariPool, focusing on the following line of code:

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pool = fastPathPool = new HikariPool(this);

This line appears in the initialization flow of HikariDataSource or related configuration logic, serving as a critical step in creating the HikariCP connection pool. Below, we’ll analyze its meaning, context, and implementation details from the source code perspective.

1. Context: Background of HikariPool Creation

In HikariCP, HikariPool is the core class responsible for managing database connections, including their creation, recycling, borrowing, and destruction. When an application starts and configures a HikariDataSource, HikariCP initializes a HikariPool instance based on the provided configuration.

The line of code in question typically appears in the initialization logic of HikariDataSource, such as:

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private void initializePool() {
    if (pool == null) {
        pool = fastPathPool = new HikariPool(this);
    }
}

Here, pool and fastPathPool are member variables of HikariDataSource, both pointing to the same HikariPool instance. Let’s break down what this code does.

2. Code Analysis: pool = fastPathPool = new HikariPool(this)

2.1 Key Components

• pool: A member variable in HikariDataSource that stores the HikariPool instance. It serves as the primary entry point for interacting with the connection pool.
• fastPathPool: Another member variable pointing to the same HikariPool instance. The name fastPathPool suggests a potential performance optimization (more on this- new HikariPool(this): Creates a new HikariPool instance, passing the current HikariDataSource (or its configuration object) as a parameter to the HikariPool constructor.
• this: Refers to the HikariDataSource or its related configuration object (e.g., HikariConfig), used to pass configuration details to the pool.

2.2 Why Two Variables?

Assigning the same HikariPool instance to both pool and fastPathPool may seem redundant, but it reflects a design choice for flexibility:

• pool: Acts as the primary reference to the connection pool, used in most scenarios.
• fastPathPool: Indicates a potential performance-optimized path (fast path). While fastPathPool currently points to the same object as pool, this design allows HikariCP to potentially switch to a more optimized pool implementation in specific scenarios without altering the external interface.

This approach provides HikariCP with the flexibility to evolve its internal implementation while maintaining compatibility.

3. HikariPool Constructor Analysis

To understand what new HikariPool(this) does, let’s examine the HikariPool constructor (simplified version):

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public HikariPool(final HikariConfig config) {
    super(config);
    this.connectionTimeout = config.getConnectionTimeout();
    this.validationTimeout = config.getValidationTimeout();
    this.maxLifetime = config.getMaxLifetime();
    this.idleTimeout = config.getIdleTimeout();
    this.leakDetectionThreshold = config.getLeakDetectionThreshold();
    this.poolName = config.getPoolName();
    // Initialize other properties...
    initializeConnections();
}

3.1 Main Tasks of the Constructor

1. Inheritance and Configuration Setup:

   • HikariPool extends PoolBase, which handles foundational operations like creating and closing connections.
   • The constructor takes a HikariConfig object, extracts configuration parameters (e.g., maximum pool size, minimum idle connections, connection timeout), and assigns them to HikariPool member variables.

2. Connection Pool Initialization:

   • Calls initializeConnections() to create the initial set of database connections and populate the pool.
   • Starts background threads (e.g., HouseKeeper) to periodically check connection health, recycle idle connections, and perform other maintenance tasks.

3. Performance Optimization:

   • Uses efficient data structures like ConcurrentBag to manage connections, ensuring high concurrency and low-latency operations for borrowing and returning connections.

3.2 Role of the this Parameter

The this parameter (typically HikariDataSource or HikariConfig) provides the configuration details, such as:

• Database URL, username, and password
• Maximum pool size (maximumPoolSize)
• Minimum idle connections (minimumIdle)
• Connection timeout (connectionTimeout)
• Advanced settings (e.g., connection validation query, leak detection)

HikariPool uses these settings to determine how to initialize and manage connections.

4. Potential Role of fastPathPool

Although fastPathPool currently points to the same object as pool, its naming and design suggest performance optimization possibilities. Here are some speculations and insights:

• Fast Path Optimization: HikariCP might intend to use a specialized pool implementation in certain scenarios, potentially skipping checks (e.g., connection validation) for better performance.
• Dynamic Switching: The existence of fastPathPool allows HikariCP to dynamically switch to a more efficient pool implementation based on runtime conditions or configuration.
• Backward Compatibility: By maintaining both pool and fastPathPool, HikariCP can introduce new pool implementations without breaking existing code.

While fastPathPool’s full potential is not yet utilized, its design leaves room for future enhancements.

5. Conclusion

The line pool = fastPathPool = new HikariPool(this); is a pivotal part of HikariCP’s connection pool initialization. It creates a HikariPool instance and assigns it to both pool and fastPathPool, setting up the core component for managing database connections. The HikariPool constructor handles configuration parsing, pool initialization, and background maintenance tasks.

This code reflects HikariCP’s key strengths:

• High Performance: Efficient data structures and optimized logic ensure low latency and high throughput.
• Flexibility: The fastPathPool design allows for future performance enhancements.
• Simplicity: The initialization logic is clear and maintainable.

By analyzing this code, we gain insight into HikariCP’s connection pool creation process and appreciate its forward-thinking design. For those interested in diving deeper, exploring components like ConcurrentBag or HouseKeeper in the HikariCP source code can reveal even more about its robust implementation.

HikariCP case study 1 Thread Safety

HikariDataSource is a high-performance JDBC connection pooling library widely used in Java applications to manage database connections efficiently. This case study explores a critical aspect of HikariDataSource’s implementation: thread safety, focusing on how it ensures consistent behavior in high-concurrency environments.

Thread Safety in HikariDataSource

A key piece of code in HikariDataSource prevents the use of the connection pool after it has been closed:

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if (isClosed()) {
    throw new SQLException("HikariDataSource " + this + " has been closed.");
}

This code checks whether the connection pool is closed. If isClosed() returns true, it throws an exception to prevent further operations. While this appears to be a simple check, it reveals important design considerations for thread safety.

The isClosed() Method

The isClosed() method is implemented as:

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return isShutdown.get();

Here, isShutdown is a field defined as:

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private final AtomicBoolean isShutdown = new AtomicBoolean();

The use of AtomicBoolean ensures that the isShutdown state is thread-safe, meaning its value remains consistent across multiple threads, even in high-concurrency scenarios. Java’s Atomic classes, such as AtomicBoolean, AtomicInteger, and AtomicLong, provide atomic operations that guarantee thread safety without explicit synchronization.

This design ensures that when the connection pool is closed, all threads can reliably detect this state, preventing race conditions or inconsistent behavior.

Why Thread Safety Matters

To understand why AtomicBoolean is necessary, we need to explore the root cause of thread safety issues.

Modern CPUs have multiple levels of caching: L1, L2, and L3 caches, which are exclusive to each CPU core, and main memory, which is shared across all cores. When a CPU core performs a computation, it loads data from main memory into its L1 cache for faster access. However, this caching mechanism can lead to inconsistencies across cores.

For example, if one thread updates the isShutdown value on one CPU core, that update may remain in the core’s L1 cache and not immediately propagate to other cores. As a result, other threads running on different cores might read an outdated value of isShutdown, leading to thread-unsafe behavior.

How AtomicBoolean Ensures Thread Safety

AtomicBoolean addresses this issue through the use of a volatile field:

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private volatile int value;

The value field stores the boolean state (0 for false, 1 for true). The volatile keyword plays a crucial role in ensuring thread safety by enforcing the following:

1. Write Synchronization: When a thread modifies the value, the change is immediately written to main memory, bypassing the CPU cache.
2. Read Synchronization: When a thread reads the value, it always fetches the latest value from main memory, not from the CPU cache.

This ensures that all threads see a consistent value for isShutdown, regardless of which CPU core they are running on.

The Trade-Off of volatile

While volatile guarantees thread safety, it comes with a performance cost. Reading from and writing to main memory is significantly slower than accessing CPU caches. Therefore, using volatile introduces latency, which can impact performance in high-throughput systems.

This trade-off highlights an important lesson: volatile should only be used when thread safety is critical. In cases where a state variable is rarely updated or does not require real-time consistency, a non-volatile field might suffice to avoid the performance overhead.

Lessons from HikariCP’s Source Code

HikariCP’s use of AtomicBoolean demonstrates a careful consideration of thread safety in a high-performance system. However, this is just one example of the library’s low-level optimizations. Other aspects of HikariCP’s design include:

• Bytecode Size Control: HikariCP minimizes bytecode size to improve JVM optimization and reduce overhead.
• Concurrency Patterns: HikariCP employs advanced concurrency techniques, similar to those found in frameworks like Disruptor, which is known for its CPU cache-aware design and exceptional performance.

These optimizations show how understanding low-level details, such as CPU caching and memory synchronization, can lead to more efficient code. For developers, studying frameworks like HikariCP and Disruptor offers valuable insights into writing high-performance applications.

Takeaways

Reading HikariCP’s source code can feel like a deep dive into computer science fundamentals, from CPU caches to JVM optimizations. It serves as a reminder that the abstractions we use in high-level programming are built on intricate low-level mechanisms. As developers, investing time in understanding these details can help us write better, more efficient code.

Reflecting on this, I can’t help but think: All those naps I took in university lectures on operating systems and computer architecture? It’s time to pay them back by diving into the source code!

By learning from frameworks like HikariCP, we can bridge the gap between high-level programming and low-level optimizations, ultimately becoming better engineers.

Updating Hexo and Icarus Theme to Latest Version

Recently, I decided to upgrade my blog system from Hexo 6.3.0 to the latest 7.3.0 version, along with updating the Icarus theme. In this article, I’ll share the entire update process, including the challenges encountered and their solutions.

Pre-Update Versions

• Hexo Core Version: 6.3.0
• Hexo CLI Version: 4.3.0
• Icarus Theme Version: 5.1.0

Update Steps

1. Update Hexo CLI

First, we need to update Hexo CLI to the latest version:

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npm install -g hexo-cli@latest

2. Update Hexo Core

Next, update the local Hexo core:

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npm install hexo@latest --save

3. Fix Security Vulnerabilities

During the update process, several security vulnerabilities were detected and needed to be fixed:

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npm audit fix
npm audit fix --force

4. Update Other Dependencies

Update other related dependencies:

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npm update --save

5. Handle Styling Issues

We encountered a styling rendering issue related to bulma-stylus. Here’s how we resolved it:

1. Remove bulma-stylus:

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npm uninstall bulma-stylus

2. Install bulma:

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npm install bulma --save

6. Update Icarus Theme

Finally, update the Icarus theme to the latest version:

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npm uninstall hexo-theme-icarus
npm install hexo-theme-icarus@latest --save

7. Regenerate the Site

After completing all updates, clean and regenerate the site:

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hexo clean
hexo generate

Post-Update Versions

• Hexo Core Version: 7.3.0
• Hexo CLI Version: 4.3.2
• Icarus Theme Version: Latest

Issues Encountered and Solutions

Issue 1: Style Rendering Error

During the update process, we encountered the following error:

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ERROR Asset render failed: css/default.css
Error: Unexpected type inside the list.

This error was caused by version incompatibility between bulma-stylus and the new version of Hexo. We resolved it by removing bulma-stylus and installing bulma instead.

Issue 2: Security Vulnerabilities

During the update process, several security vulnerabilities were detected:

• 2 Low-risk vulnerabilities
• 8 Medium-risk vulnerabilities
• 6 High-risk vulnerabilities
• 2 Critical vulnerabilities

These were fixed by running npm audit fix and npm audit fix --force.

Conclusion

The update process went relatively smoothly overall. While we encountered some minor issues, they were all properly resolved. The updated blog system is now running more stably and has addressed known security vulnerabilities.

If you’re planning to update your Hexo blog, I recommend following these steps and ensuring you have backups of important data during the update process.

References

• Hexo Official Documentation
• Icarus Theme Documentation
• Bulma Official Documentation

JavaScript is a single-threaded language, which means that it can only do one thing at a time. However, it is still able to handle multiple tasks at once through the use of the event loop.

The event loop is a mechanism that allows JavaScript to run asynchronous code while still processing other code. It works by constantly checking the call stack for any pending function calls, and then executing them one by one. If a function call takes too long to complete, it gets deferred to the back of the queue and is processed later.

Browser Important Concepts

The entire runtime environment of a browser is not composed solely of the JavaScript engine. Because the language features of JS belong to a single thread, but in order to allow web pages to have functions similar to “listening for events”, “timing”, and “pulling third-party APIs”, the browser provides other parts to achieve these functions, which are:

• Event Queue
• Web API
• Event Table
• Event Loop

For the browser’s runtime environment, there are other important components in addition to the JavaScript engine, and these components work together to enable the browser to provide rich functionality and handle multiple tasks.

Among them, Event Queue, Web API, Event Table, and Event Loop are important components in the browser that can work together to handle asynchronous operations and event handlers.

Event Queue
The Event Queue is a FIFO data structure that stores events waiting to be processed. When an event occurs, it is added to the event queue and waits for processing. The event queue can store various events, such as user operation responses, timer events, network requests, and more.

Web API
Web API is a set of APIs provided by the browser for handling asynchronous operations, such as network requests, timers, local storage, and more. Web APIs are usually implemented in native code provided by the browser and are separate from the JavaScript engine. This means that when we call a Web API, the JavaScript engine delegates the task to the Web API and returns immediately without waiting for the task to complete.

Event Table
The Event Table is a data structure that stores event handlers. When an event occurs, the browser looks up the event table to determine which event handlers should be executed. The event table is usually implemented in native code provided by the browser.

Event Loop
The event loop is an infinite loop that listens to the event queue and calls the corresponding event handler. When there are events in the event queue, the event loop retrieves them and calls the corresponding event handler. The main function of the event loop is to ensure that the JavaScript engine can keep running when handling asynchronous operations, without blocking other operations in the browser.

The Call Stack

The call stack is a data structure that keeps track of the functions that are currently being executed. Whenever a function is called, it is added to the top of the call stack. When the function completes, it is removed from the stack, and the next function in line is executed.

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function multiply(a, b) {
  return a * b;
}

function add(a, b) {
  let result = a + b;
  result = multiply(result, result);
  return result;
}

console.log(add(2, 3)); // output: 25

In the code above, the add function calls the multiply function, which in turn returns a value that is used in the add function. The call stack keeps track of the order of execution and ensures that the code runs in the correct order.

Asynchronous Code

Asynchronous code is code that runs outside of the normal call stack. This can include things like user input, network requests, and timers. When asynchronous code is executed, it is added to a separate queue known as the event queue.

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console.log('Start');

setTimeout(() => {
  console.log('Timeout');
}, 0);

console.log('End');

In the code above, the setTimeout function is used to create a timer that will run after 0 milliseconds. Despite the short delay, the function is not executed immediately. Instead, it is added to the event queue and will be executed once the call stack is empty.

The Event Loop

The event loop is responsible for monitoring both the call stack and the event queue. When the call stack is empty, the event loop takes the first function in the event queue and adds it to the call stack. This function is then executed, and any resulting functions are added to the back of the event queue.

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console.log('Start');

setTimeout(() => {
  console.log('Timeout');
}, 0);

Promise.resolve().then(() => {
  console.log('Promise');
});

console.log('End');

In the code above, a Promise is used to create another asynchronous task. Despite being created after the setTimeout function, the Promise is executed first because it is added to the microtask queue, which has a higher priority than the event queue.

Conclusion

The JavaScript event loop is a powerful mechanism that allows asynchronous code to be executed without blocking the main thread. By understanding how the call stack, event queue, and event loop work together, you can write more efficient and responsive code. Remember to use asynchronous code whenever possible, and always be mindful of how your code will affect the event loop.

In Java, “async” and “sync” refer to different ways of executing code and handling concurrency.

Synchronous code is executed in a single thread, with each statement being executed in sequence. When a statement is executed, the program waits for it to finish before moving on to the next statement. This can be useful when you need to ensure that certain code is executed in a specific order, but it can be inefficient if the code is doing something that takes a long time to complete, as the program will be blocked until the code finishes.

Asynchronous code, on the other hand, allows multiple tasks to be executed at the same time. Instead of waiting for a task to finish before moving on to the next one, asynchronous code can start a task and then move on to the next one, while the first task is still running in the background. This can be much more efficient, as the program can continue doing other things while waiting for long-running tasks to complete.

In Java, you can write asynchronous code using the CompletableFuture class, which provides a way to execute tasks in the background and then handle the results when they are ready. CompletableFuture allows you to chain together multiple tasks and specify how they should be executed, such as in sequence or in parallel.

To summarize, synchronous code executes one statement at a time in sequence, while asynchronous code allows multiple tasks to be executed in parallel, improving performance and efficiency.

CompletableFuture is a class introduced in Java 8 that provides a way to write asynchronous, non-blocking code. It is a powerful tool for handling complex asynchronous operations in a clear and concise manner.

CompletableFuture is a type of Future that represents a computation that may or may not have completed yet. It can be used to execute a task in the background and then handle the result when it becomes available, or to execute multiple tasks concurrently and then combine the results when they are all ready.

Here are some of the key features of CompletableFuture:

1. Chaining: CompletableFuture allows you to chain together multiple asynchronous operations, so that one operation starts when the previous one finishes. This can be done using methods like thenApply(), thenCompose(), and thenCombine().

2. Combining: CompletableFuture also allows you to combine multiple asynchronous operations into a single operation, using methods like allOf() and anyOf().

3. Error handling: CompletableFuture provides methods for handling errors that may occur during the execution of an asynchronous operation, including exceptionally() and handle().

4. Timeout handling: CompletableFuture allows you to set a timeout for an asynchronous operation, using methods like completeOnTimeout() and orTimeout().

5. Asynchronous execution: CompletableFuture can execute tasks asynchronously on a separate thread, allowing the calling thread to continue with other tasks while the background task is executing.

6. Completion stages: CompletableFuture provides a way to break down complex asynchronous operations into smaller, more manageable stages, using methods like thenApplyAsync(), thenComposeAsync(), and thenAcceptAsync().

Overall, CompletableFuture provides a flexible and powerful way to write non-blocking, asynchronous code in Java, making it easier to handle complex operations and improve performance.

To talk about the history of Spring, we need to talk about J2EE. The widespread implementation of J2EE applications started in 1999 and 2000, bringing standardization of core middle-tier concepts such as transaction management, but it was not an absolute success in practice, as development efficiency, development difficulty and actual performance were disappointing.

Anyone who has ever used EJB to develop a JAVA EE application like me must know that learning and applying EJB is very hard at the beginning and many things are not easy to understand at once. The configuration is also complex and monotonous, and the code for object lookup using JNDI is also monotonous and boring. The high cost of learning EJB, and very low development efficiency, very high resource consumption, have caused the use of EJB difficulties. And Spring emerged initially to solve these problems like these.

One of the biggest purposes of Spring is to make Java EE development easier. Much more easier.

Core Container

Spring’s core container is the foundation on which other modules are built, consisting of

• Beans module,
• Core module
• Context context module
• The SpEL expression language module

Without these core containers, it is also impossible to have AOP, Web, and other upper layer features.

AOP module

It provides a aspect-oriented programming implementation, which provides functions such as logging, permission control, performance statistics, and other general functions that separated from business logic , and can dynamically add these functions to the required code, so that each has its own role to reduce the coupling of business logic and general functions

很多开发者入门都是从Spring Boot开始的，他对Spring整体框架底层，以及发展历史不是很了解； 特别是对于一些老旧项目维护和底层bug分析没有全局观。
Many developers start with Spring Boot, he does not have a good understanding of the overall framework underlying Spring, and the development history; especially for some older projects maintenance and underlying bug analysis does not have a global view.
Spring represents a framework design philosophy that requires a global understanding of how Spring Framework components work together and the need to understand the original intent of its design and future trends.

Spring框架管理这些Bean的创建工作，即由用户管理Bean转变为框架管理Bean，这个就叫控制反转 - Inversion of Control (IoC)Spring
框架托管创建的Bean放在哪里呢？ 这便是IoC Container;Spring 框架为了更好让用户配置Bean，必然会引入不同方式来配置Bean？ 这便是xml配置，Java配置，注解配置等支持Spring
框架既然接管了Bean的生成，必然需要管理整个Bean的生命周期等；
应用程序代码从Ioc Container中获取依赖的Bean，注入到应用程序中，这个过程叫 依赖注入(Dependency Injection，DI) ； 所以说控制反转是通过依赖注入实现的，其实它们是同一个概念的不同角度描述。通俗来说就是IoC是设计思想，DI是实现方式在依赖注入时，有哪些方式呢？这就是构造器方式，@Autowired, @Resource, @Qualifier… 同时Bean之间存在依赖（可能存在先后顺序问题，以及循环依赖问题等）

Updating Hexo Install

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npm install -g hexo-cli
hexo version

npm install -g npm-check
npm-check

npm install -g npm-upgrade
npm-upgrade

npm update -g
npm install -g npm

hexo clean
hexo g -s
hexo d

Updating hexo theme

1. Remove node_modules folder
2. remove "hexo-theme-icarus": "^5.1.0" in package.json

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npm install
npm install [email protected]
npm install hexo-renderer-inferno
hexo config theme icarus
hexo server

use the original icarus config