## Compiler {#compiler}

The build time overhead to building with [Profile Guided Optimization](/doc/pgo) has been reduced significantly.
Previously, large builds could see 100%+ build time increase from enabling PGO.
In Go 1.23, overhead should be in the single digit percentages.

## Assembler {#assembler}

    * 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...

The main limitation is that to update or delete content of a file inside a ZIP file the entire ZIP file must be replaced.

## How to enable S3 ZIP behavior ?

   * been shutdown).
   *
   * <p>Although all tasks are immediately executed in the thread that submitted the task, this
   * {@code ExecutorService} imposes a small locking overhead on each task submission in order to
   * implement shutdown and termination behavior.
   *
   * <p>The implementation deviates from the {@code ExecutorService} specification with regards to

But if you are certain that the content that you are returning is **serializable with JSON**, you can pass it directly to the response class and avoid the extra overhead that FastAPI would have by passing your return content through the `jsonable_encoder` before passing it to the response class.

```Python hl_lines="2  7"
{!../../../docs_src/custom_response/tutorial001b.py!}
```

  ) : 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

	if r.ContentLength <= 0 {
		writeErrorResponse(ctx, w, errorCodes.ToAPIErr(ErrMissingContentLength), r.URL)
		return
	}

	// The max. XML contains 100000 object names (each at most 1024 bytes long) + XML overhead
	const maxBodySize = 2 * 100000 * 1024

	if r.ContentLength > maxBodySize {
		writeErrorResponse(ctx, w, errorCodes.ToAPIErr(ErrEntityTooLarge), r.URL)
		return
	}

	case size == 0:
		buffer = make([]byte, 1) // Allocate at least a byte to reach EOF
	case size == -1:
		if size := data.ActualSize(); size > 0 && size < fi.Erasure.BlockSize {
			// Account for padding and forced compression overhead and encryption.
			buffer = make([]byte, data.ActualSize()+256+32+32, data.ActualSize()*2+512)
		} else {
			buffer = globalBytePoolCap.Load().Get()
			defer globalBytePoolCap.Load().Put(buffer)
		}

  // 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.

            [eager context](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/eager/context.py).
        *   Eager performance should be similar with this feature enabled.
            *   A roughly 5us per-op overhead may be observed when running many
                small functions.
            *   Note a
                [known issue](https://github.com/tensorflow/tensorflow/issues/55414)
                with GPU performance.