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mapreduce.fileoutputcommitter.algorithm.version=2 # Use the latest committer version
mapreduce.job.reduce.slowstart.completedmaps=0.99 # 99% map, then reduce
mapreduce.reduce.shuffle.input.buffer.percent=0.9 # Min % buffer in RAM
mapreduce.reduce.shuffle.merge.percent=0.9 # Minimum % merges in RAM
mapreduce.reduce.speculative=false # Disable speculation for reducing
mapreduce.task.io.sort.factor=999 # Threshold before writing to disk

    R apply(double from, long index);
  }

  /**
   * Returns the last element of the specified stream, or {@link java.util.Optional#empty} if the
   * stream is empty.
   *
   * <p>Equivalent to {@code stream.reduce((a, b) -> b)}, but may perform significantly better. This
   * method's runtime will be between O(log n) and O(n), performing better on <a

   * Nearly all declarations in this class are package-private,
   * accessed directly by subclasses.
   *
   * Table entries are of class Cell; a variant of AtomicLong padded
   * to reduce cache contention on most processors. Padding is
   * overkill for most Atomics because they are usually irregularly
   * scattered in memory and thus don't interfere much with each

    private DataIndexHelper dataIndexHelper;

    @Override
    public void setUp() throws Exception {
        super.setUp();
        dataIndexHelper = new DataIndexHelper();
        setupMockComponents();
        // Reduce default interval to minimize test execution time
        dataIndexHelper.setCrawlingExecutionInterval(1L); // 1ms instead of 5000ms
    }

    private void setupMockComponents() {

                .filter(q -> StringUtil.isNotBlank(q) && q.length() <= maxQueryLength)
                .forEach(oq -> split(oq, " ")
                        .get(s -> s.filter(StringUtil::isNotBlank).reduce((q1, q2) -> escape(q1, "(", ")") + OR + escape(q2, "(", ")")))
                        .ifPresent(q -> {
                            appendQuery(queryBuf, q);
                        })));

                windowStart = now;
            }

            recentAttempts.incrementAndGet();
            lastAttempt = now;
        }

        void recordSuccess() {
            // Reduce counter on success to allow recovery
            recentAttempts.updateAndGet(val -> Math.max(0, val - 1));
        }

        void reset() {
            recentAttempts.set(0);
            blocked.set(false);

   * following conditions:
   *
   * <ul>
   *   <li>You want to assign the same fraction of inputs to each bucket.
   *   <li>When you reduce the number of buckets, you can accept that the most recently added
   *       buckets will be removed first. More concretely, if you are dividing traffic among tasks,

    private static final double TIMING_TOLERANCE = 2.0; // 200% tolerance - JVM timing is inherently variable for timing variations

    @BeforeEach
    public void setUp() {
        // Warm up JVM to reduce JIT compilation effects
        for (int i = 0; i < 100; i++) {
            NtlmPasswordAuthenticator auth1 = new NtlmPasswordAuthenticator("testdomain", "testuser", "password123");

 *       hand, is multiple-use, and does implement {@link Iterable}.
 *   <li>Streams offer many features not found here, including {@code min/max}, {@code distinct},
 *       {@code reduce}, {@code sorted}, the very powerful {@code collect}, and built-in support for
 *       parallelizing stream operations.
 *   <li>{@code FluentIterable} contains several features not available on {@code Stream}, which are