The *Secure Channel* splits the object content into chunks of a fixed size of `65536` bytes. The last chunk may be smaller to avoid adding additional overhead and is treated specially to prevent truncation attacks. The nonce value is 96 bits long and generated randomly per object / multi-part part. The *Secure Channel* supports plaintexts up to `65536 * 2^32 = 256 TiB`.

#### Randomness

 * 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.
 *

- **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);
    }

 * 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.
 *

  ) : 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

   * - 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;

### 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

   * non-static, and is initialized/removed frequently - this causes churn in the Thread specific
   * hashmaps. Using a static ThreadLocal to avoid that overhead would mean that different
   * ExecutionSequencer objects interfere with each other, which would be undesirable, in addition
   * to increasing the memory footprint of every thread that interacted with it. In order to release