private static final long serialVersionUID = 1L;

            @Override
            public int getCrawlingThreadCount() {
                return 1; // Reduced from 2 to minimize thread overhead
            }

            @Override
            public String getIndexFieldConfigId() {
                return "config_id";
            }

            @Override

   * intended to be used as a metric: smaller headers are more efficient to encode and transmit.
   */
  fun byteCount(): Long {
    // Each header name has 2 bytes of overhead for ': ' and every header value has 2 bytes of
    // overhead for '\r\n'.
    var result = (namesAndValues.size * 2).toLong()

    for (i in 0 until namesAndValues.size) {
      result += namesAndValues[i].length.toLong()
    }

- **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.
 *

 *       allocating garbage).
 * </ul>
 *
 * <p>Disadvantages compared to {@code long[]}:
 *
 * <ul>
 *   <li>Memory footprint has a fixed overhead (about 24 bytes per instance).
 *   <li><i>Some</i> construction use cases force the data to be copied (though several construction
 *       APIs are offered that don't).

 *       allocating garbage).
 * </ul>
 *
 * <p>Disadvantages compared to {@code int[]}:
 *
 * <ul>
 *   <li>Memory footprint has a fixed overhead (about 24 bytes per instance).
 *   <li><i>Some</i> construction use cases force the data to be copied (though several construction
 *       APIs are offered that don't).

  // There are a few design constraints to consider
  // * We want to be responsive to small timeouts, unpark() has non trivial latency overheads (I
  //   have observed 12 micros on 64-bit linux systems to wake up a parked thread). So if the
  //   timeout is small we shouldn't park(). This needs to be traded off with the cpu overhead of
  //   spinning, so we use SPIN_THRESHOLD_NANOS which is what AbstractQueuedSynchronizer uses for
  //   similar purposes.

  // There are a few design constraints to consider
  // * We want to be responsive to small timeouts, unpark() has non trivial latency overheads (I
  //   have observed 12 micros on 64-bit linux systems to wake up a parked thread). So if the
  //   timeout is small we shouldn't park(). This needs to be traded off with the cpu overhead of
  //   spinning, so we use SPIN_THRESHOLD_NANOS which is what AbstractQueuedSynchronizer uses for
  //   similar purposes.

  ) : 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