* the capacity, it is doubled in size unless some other thread
   * holds the lock. If a hashed slot is empty, and lock is
   * available, a new Cell is created. Otherwise, if the slot
   * exists, a CAS is tried.  Retries proceed by "double hashing",
   * using a secondary hash (Marsaglia XorShift) to try to find a
   * free slot.
   *
   * The table size is capped because, when there are more threads

// operation succeeded.
func (h *healSequence) pushHealResultItem(r madmin.HealResultItem) error {
	// start a timer to keep an upper time limit to find an empty
	// slot to add the given heal result - if no slot is found it
	// means that the server is holding the maximum amount of
	// heal-results in memory and the client has not consumed it
	// for too long.

        // We only ever register ArrayReferences with the queue so this is always safe.
        ArrayReference<? extends L> arrayRef = (ArrayReference<? extends L>) ref;
        // Try to clear out the array slot, n.b. if we fail that is fine, in either case the
        // arrayRef will be out of the array after this step.
        locks.compareAndSet(arrayRef.index, arrayRef, null);
      }
    }

    @Override

    //     fgha[c]|[b]de -> fgha[b]|[c]de. Because these two blocks are the same size, we are done.
    // The Dolphin algorithm is attractive because it does the fewest array reads and writes: each
    // array slot is read and written exactly once. However, it can have very poor memory locality:
    // benchmarking shows it can take 7 times longer than the other two in some cases. The other two

=====================

OkHttp 4.x upgrades our implementation language from Java to Kotlin and keeps everything else the
same. We’ve chosen Kotlin because it gives us powerful new capabilities while integrating closely
with Java.

We spent a lot of time and energy on retaining strict compatibility with OkHttp 3.x. We’re even
keeping the package name the same: `okhttp3`!

There are three kinds of compatibility we’re tracking:

In many cases you might already know the answer for those questions. 🤓

If you are helping a lot of people with their questions, you will become an official [FastAPI Expert](fastapi-people.md#fastapi-experts){.internal-link target=_blank}. 🎉

So, to balance that out, imagine the following short story:

> You have to clean a big, dirty house.

*Yep, that's the whole story*.

---

There's no waiting 🕙 anywhere, just a lot of work to be done, on multiple places of the house.

An interesting detail is that the percentage of the **CPU used** by each process can **vary** a lot over time, but the **memory (RAM)** normally stays more or less **stable**.

If you have an API that does a comparable amount of computations every time and you have a lot of clients, then the **CPU utilization** will probably *also be stable* (instead of constantly going up and down quickly).

      if (state instanceof Blocker) {
        blocker = (Blocker) state;
      }
      spinCount++;
      if (spinCount > MAX_BUSY_WAIT_SPINS) {
        /*
         * If we have spun a lot, just park ourselves. This will save CPU while we wait for a slow
         * interrupting thread. In theory, interruptTask() should be very fast, but due to

In Python 3.10, as an alternative to using the generics `Union` and `Optional`, you can use the <abbr title='also called "bitwise or operator", but that meaning is not relevant here'>vertical bar (`|`)</abbr> to declare unions of types, that's a lot better and simpler.

////

//// tab | Python 3.9+

You can use the same builtin types as generics (with square brackets and types inside):

* `list`
* `tuple`
* `set`
* `dict`