- **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

        byte[] decompressed = searchHelper.gzipDecompress(compressed);

        assertEquals(testData, new String(decompressed));
        // For small strings, gzip overhead might make compressed data larger
        // So we just verify that compression/decompression works correctly
        assertNotNull(compressed);
        assertTrue(compressed.length > 0);
    }

 * form}), the memory requirement is 8*N bytes for the copy of the dataset plus an overhead which is
 * independent of N (but depends on the quantiles being computed). When calling {@link
 * ScaleAndIndex#computeInPlace computeInPlace} (in {@linkplain ScaleAndIndexes#computeInPlace
 * either form}), only the overhead is required. The number of object allocations is independent of
 * N in both cases.
 *

   * - quartiles with index and with indexes taking int-varargs, and with compute taking a
   *   double-collection and with computeInPlace;
   * - scale with index and with indexes taking int-varargs, and with all overloads of compute
   *   taking a double-collection and with computeInPlace;
   * - scale with indexes taking integer-collection with compute taking a double-collection and with
   *   computeInPlace;

   * - quartiles with index and with indexes taking int-varargs, and with compute taking a
   *   double-collection and with computeInPlace;
   * - scale with index and with indexes taking int-varargs, and with all overloads of compute
   *   taking a double-collection and with computeInPlace;
   * - scale with indexes taking integer-collection with compute taking a double-collection and with
   *   computeInPlace;

  ) : Sink {
    /**
     * Buffer of outgoing data. This batches writes of small writes into this sink as larges frames
     * written to the outgoing connection. Batching saves the (small) framing overhead.
     */
    private val sendBuffer = Buffer()

    /** Trailers to send at the end of the stream. */
    var trailers: Headers? = null

    var closed: Boolean = false

### Major Themes

  * <strong>Significant scale improvements</strong>. Increased cluster scale by 400% to 1000 nodes with 30,000 pods per cluster.
Kubelet supports 100 pods per node with 4x reduced system overhead.
  * <strong>Simplified application deployment and management. </strong>
     * Dynamic Configuration (ConfigMap API in the core API group) enables application

 * if/throw idiom instead.
 *
 * <p>Depending on your message arguments, memory may be allocated for boxing and varargs array
 * creation. However, the methods of this class have a large number of overloads that prevent such
 * allocations in many common cases.
 *
 * <p>The message string is not formatted unless the exception will be thrown, so the cost of the
 * string formatting itself should not be a concern.
 *

    /**
     * The smallest message that will be compressed. We use 1024 because smaller messages already
     * fit comfortably within a single ethernet packet (1500 bytes) even with framing overhead.
     *
     * For tests this must be big enough to realize real compression on test messages like
     * 'aaaaaaaaaa...'. Our tests check if compression was applied just by looking at the size if
     * the inbound buffer.

 * is satisfied. The limit is not strict: the cache may temporarily exceed it while waiting for
 * files to be deleted. The limit does not include filesystem overhead or the cache journal so
 * space-sensitive applications should set a conservative limit.
 *
 * Clients call [edit] to create or update the values of an entry. An entry may have only one editor