*/
    private double calculateSuccessRate() {
        long total = totalRequests.get();
        if (total == 0) {
            return 100.0;
        }
        return (totalSuccesses.get() * 100.0) / total;
    }

    /**
     * Calculate availability
     *
     * @return availability as percentage (0-100)
     */
    private double calculateAvailability() {

     */
    public double getAverageReadLatencyMicros() {
        long reads = rdmaReads.get();
        if (reads == 0) {
            return 0.0;
        }
        return totalReadTime.get() / (double) reads / 1000.0; // Convert to microseconds
    }

    /**
     * Calculate average write latency in microseconds
     *
     * @return average write latency
     */
    public double getAverageWriteLatencyMicros() {

            long timeEnd = measureEqualsTime(baseAuth, endAuth, TIMING_ITERATIONS);

            // Calculate relative timing differences
            double maxTime = Math.max(Math.max(timeStart, timeMiddle), timeEnd);
            double minTime = Math.min(Math.min(timeStart, timeMiddle), timeEnd);
            double timingRatio = (maxTime - minTime) / maxTime;

            // Timing differences should be minimal (within tolerance)

        executor.shutdown();

        long overallEnd = System.nanoTime();
        double overallTimeMs = (overallEnd - overallStart) / 1_000_000.0;
        double avgAllocTimeNs = totalAllocTime.get() / (double) allocations.get();
        double avgReleaseTimeNs = totalReleaseTime.get() / (double) releases.get();

   * #create(Funnel, int, double)}, or smaller. If it is significantly higher, it is usually the
   * case that too many elements (more than expected) have been put in the {@code BloomFilter},
   * degenerating it.
   *
   * @since 14.0 (since 11.0 as expectedFalsePositiveProbability())
   */
  public double expectedFpp() {
    return Math.pow((double) bits.bitCount() / bitSize(), numHashFunctions);
  }

        monitorTarget.append(buf, "shortKey", supplier);
        assertEquals("\"shortKey\":42", buf.toString());
    }

    // Test append method with double array
    public void test_append_doubleArray() {
        StringBuilder buf = new StringBuilder();
        double[] values = { 1.5, 2.3, 3.7 };
        Supplier<Object> supplier = () -> values;

        monitorTarget.append(buf, "arrayKey", supplier);

    }
    /*
     * Let k be the true value of floor(sqrt(x)), so that
     *
     *            k * k <= x          <  (k + 1) * (k + 1)
     * (double) (k * k) <= (double) x <= (double) ((k + 1) * (k + 1))
     *          since casting to double is nondecreasing.
     *          Note that the right-hand inequality is no longer strict.
     * Math.sqrt(k * k) <= Math.sqrt(x) <= Math.sqrt((k + 1) * (k + 1))

    }

    /**
     * Get error rate (errors / total requests)
     *
     * @return error rate between 0.0 and 1.0
     */
    public double getErrorRate() {
        long total = requestsSent.get();
        if (total == 0)
            return 0.0;
        return (double) errors.get() / total;
    }

    /**
     * Get throughput in bytes per second
     *
     * @return throughput in bps
     */

            return proactiveHealthCheckEnabled;
        }

        public double getHealthCheckSuccessRate() {
            return totalHealthChecks > 0 ? 1.0 - ((double) failedHealthChecks / totalHealthChecks) : 1.0;
        }

        public double getPoolUtilization() {
            return maxPoolSize > 0 ? (double) activeConnections / maxPoolSize : 0.0;
        }

        @Override

        // Simple heuristic based on data entropy
        // In practice, this would be more sophisticated
        int uniqueBytes = countUniqueBytes(data);
        double entropy = (double) uniqueBytes / 256.0;

        // Estimate compression ratio based on entropy
        // Lower entropy = better compression
        return 0.3 + (entropy * 0.6); // Range from 30% to 90%
    }

    @Override