void cacheCapacityAndRetrievalOrder() {
        BufferCacheImpl impl = new BufferCacheImpl(2, 3);
        byte[] a = new byte[1];
        byte[] c = new byte[2];
        byte[] d = new byte[4];

        impl.releaseBuffer(a); // goes to slot 0
        impl.releaseBuffer(c); // goes to slot 1
        impl.releaseBuffer(d); // dropped (cache full)

// 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

  static final class Whitespace extends NamedFastMatcher {

    // TABLE is a precomputed hashset of whitespace characters. MULTIPLIER serves as a hash function
    // whose key property is that it maps 25 characters into the 32-slot table without collision.
    // Basically this is an opportunistic fast implementation as opposed to "good code". For most
    // other use-cases, the reduction in readability isn't worth it.
    static final String TABLE =

			Key:   "trace",
			Value: slog.GroupValue(fields...),
		})

	case l.SlowThreshold != 0 && elapsed > l.SlowThreshold:
		l.Logger.WarnContext(ctx, "SQL executed", slog.Attr{
			Key:   "trace",
			Value: slog.GroupValue(fields...),
		})

	case l.LogLevel >= Info:
		l.Logger.InfoContext(ctx, "SQL executed", slog.Attr{
			Key:   "trace",
			Value: slog.GroupValue(fields...),
		})
	}

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.

      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
         * InterruptibleChannel and JavaLangAccess.blockedOn(Thread, Interruptible), it isn't

Let's imagine that loading the model can **take quite some time**, because it has to read a lot of **data from disk**. So you don't want to do it for every request.

                "aaa");
        assertQueryBuilder(MatchPhraseQueryBuilder.class,
                "{\"match_phrase\":{\"title\":{\"query\":\"aaa\",\"slop\":0,\"zero_terms_query\":\"NONE\",\"boost\":1.0}}}", //