// 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.

This setting will be removed in Go 1.27.

Go 1.22 changed how the runtime interacts with transparent huge pages on Linux.
In particular, a common default Linux kernel configuration can result in
significant memory overheads, and Go 1.22 no longer works around this default.
To work around this issue without adjusting kernel settings, transparent huge
pages can be disabled for Go memory with the

        issue found, _BigtableXYZDataset (of type DatasetV2) does not implement
        the _as_variant_tensor() of DatasetV1, when moving contrib.bigtable to
        tensorflow_io. Converting into DatasetV2 removes the overheads to
        maintain V1 while we are moving into TF 2.0.
    *   Add dataset ops to the graph (or create kernels in Eager execution)
        during the python Dataset object creation instead doing it during

    * Wenn Sie FastAPI nicht verwenden und stattdessen Starlette direkt (oder ein anderes Tool wie Sanic, Flask, Responder, usw.) verwenden würden, müssten Sie die gesamte Datenvalidierung und Serialisierung selbst implementieren. Ihre finale Anwendung hätte also immer noch den gleichen Overhead, als ob sie mit FastAPI erstellt worden wäre. Und in vielen Fällen...

Blocking APIs may be inefficient because you hold a thread idle while waiting on the network. Threads are expensive because they have both a memory overhead and a context-switching overhead.

#### Framed protocols

`jstream` is a streaming JSON parser and value extraction library for Go.

Unlike most JSON parsers, `jstream` is document position- and depth-aware -- this enables the extraction of values at a specified depth, eliminating the overhead of allocating encompassing arrays or objects; e.g:

Using the below example document:
<img width="85%" src="https://bradley.codes/static/img/jstream-levels.gif" alt="jstream"/>

 * along with information about the read operation.
 *
 * @author mbechler
 */
public class Smb2ReadResponse extends ServerMessageBlock2Response {

    /**
     * Protocol overhead size for SMB2 read response
     */
    public static final int OVERHEAD = Smb2Constants.SMB2_HEADER_LENGTH + 16;

    private int dataRemaining;
    private int dataLength;
    private final byte[] outputBuffer;
    private final int outputBufferOffset;

 *
 * @author mbechler
 *
 */
public class Smb2QueryInfoResponse extends ServerMessageBlock2Response {

    /**
     * Protocol overhead size for SMB2 query info response
     */
    public static final int OVERHEAD = Smb2Constants.SMB2_HEADER_LENGTH + 8;

    private final byte expectInfoType;
    private final byte expectInfoClass;
    private Decodable info;

    /**

    * If you didn't use FastAPI and used Starlette directly (or another tool, like Sanic, Flask, Responder, etc) you would have to implement all the data validation and serialization yourself. So, your final application would still have the same overhead as if it was built using FastAPI. And in many cases, this data validation and serialization...

 *
 * @author mbechler
 */
public class Smb2WriteRequest extends ServerMessageBlock2Request<Smb2WriteResponse> implements RequestWithFileId {

    /**
     * The overhead size in bytes for an SMB2 write request packet.
     */
    public static final int OVERHEAD = Smb2Constants.SMB2_HEADER_LENGTH + 48;

    private byte[] data;
    private int dataOffset;
    private int dataLength;

    private byte[] fileId;