- **Memory efficient** - Specialized collections like `LruHashMap` and `ArrayMap` for memory-conscious applications
- **Java 21 features** - Switch expressions and pattern matching for reduced overhead

## 🧪 Building and Testing

### Development Setup
```bash
# Clone the repository
git clone https://github.com/codelibs/corelib.git
cd corelib

# Compile the project
mvn clean compile

# Run all tests

 *   <li>Adding tags, roles, fields, kinds, and languages to the request.</li>
 *   <li>Configuring suggestion details, reading converter, normalizer, prefix match weight, match word first, and skip duplicate words options.</li>
 *   <li>Building the query and filter query for the suggestion request.</li>
 *   <li>Processing the request and handling the response or failure.</li>
 *   <li>Creating the response from the search results.</li>
 * </ul>
 *

	if err != nil {
		r.err = err
		return r.err == io.EOF
	}
	return false
}

// forwardTo will forward to the first entry that is >= s.
// Will return io.EOF if end of stream is reached without finding any.
func (r *metacacheReader) forwardTo(s string) error {
	r.checkInit()
	if r.err != nil {
		return r.err
	}

	if s == "" {
		return nil
	}
	if r.current.name != "" {

 *
 * @author Dimitris Andreou
 * @author Kurt Alfred Kluever
 */
enum BloomFilterStrategies implements BloomFilter.Strategy {
  /**
   * See "Less Hashing, Same Performance: Building a Better Bloom Filter" by Adam Kirsch and Michael
   * Mitzenmacher. The paper argues that this trick doesn't significantly deteriorate the
   * performance of a Bloom filter (yet only needs two 32bit hash functions).
   */

- **Extractors**: Content extraction from various formats
- **Transformers**: Data transformation and enrichment
- **Filters**: URL filtering with regex patterns
- **Rules**: Content processing rules and validation

## Building and Testing

### Build Commands

```bash
# Build all modules
mvn clean install

# Build without tests
mvn clean install -DskipTests

# Build specific module

  private static
  java.util.regex.Pattern
      fpPattern() {
    /*
     * We use # instead of * for possessive quantifiers. This lets us strip them out when building
     * the regex for RE2 (which doesn't support them) but leave them in when building it for
     * java.util.regex (where we want them in order to avoid catastrophic backtracking).
     */
    String decimal = "(?:\\d+#(?:\\.\\d*#)?|\\.\\d+#)";

  /**
   * Returns a new source that returns the same bytes as upstream. Returns null if this relay has
   * been closed and no further sources are possible. In that case callers should retry after
   * building a new relay with [.read].
   */
  fun newSource(): Source? {
    synchronized(this@Relay) {
      if (file == null) return null
      sourceCount++
    }

    return RelaySource()
  }

    * Bởi vì các cấu trúc dữ liệu của Pydantic chỉ là các instances của class bạn định nghĩa; auto-completion, linting, mypy và trực giác của bạn nên làm việc riêng biệt với những dữ liệu mà bạn đã validate.
* Validate **các cấu trúc phức tạp**:
    * Sử dụng các models Pydantic phân tầng, `List` và `Dict` của Python `typing`,...

 * href="https://github.com/ReactiveX/RxJava/wiki">RxJava</a> (supplemented with its <a
 * href="https://github.com/ReactiveX/RxAndroid">RxAndroid</a> extension if you are building for
 * Android) or <a href="https://projectreactor.io/">Project Reactor</a>. (For the basics of
 * translating code from using an event bus to using a reactive-streams framework, see these two

    * 如果你知道 Python 型別，你就知道如何使用 Pydantic。
* 和你的 **<abbr title="Integrated Development Environment，和程式碼編輯器類似">IDE</abbr>/<abbr title="一個檢查程式碼錯誤的工具">linter</abbr>/brain** 都能好好配合：
    * 因為 Pydantic 的資料結構其實就是你自己定義的類別實例，所以自動補齊、linting、mypy 以及你的直覺都能很好地在經過驗證的資料上發揮作用。
* 驗證**複雜結構**：
    * 使用 Pydantic 模型時，你可以把資料結構分層設計，並且用 Python 的 `List` 和 `Dict` 等型別來定義。
    * 驗證器讓我們可以輕鬆地定義和檢查複雜的資料結構，並把它們轉換成 JSON Schema 進行記錄。
    * 你可以擁有深層**巢狀的 JSON** 物件，並對它們進行驗證和註釋。