This algorithm is specifically optimized for machine generated content.
Write throughput is typically at least 500MB/s per CPU core,
and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

This means that in cases where raw IO is below these numbers
compression will not only reduce disk usage but also help increase system throughput.
Typically, enabling compression on spinning disk systems

  }

  /**
   * Constructs an empty {@code LinkedHashMultimap} with enough capacity to hold the specified
   * numbers of keys and values without rehashing.
   *
   * @param expectedKeys the expected number of distinct keys
   * @param expectedValuesPerKey the expected average number of values per key
   * @throws IllegalArgumentException if {@code expectedKeys} or {@code expectedValuesPerKey} is
   *     negative
   */

  }

  /**
   * Constructs an empty {@code LinkedHashMultimap} with enough capacity to hold the specified
   * numbers of keys and values without rehashing.
   *
   * @param expectedKeys the expected number of distinct keys
   * @param expectedValuesPerKey the expected average number of values per key
   * @throws IllegalArgumentException if {@code expectedKeys} or {@code expectedValuesPerKey} is
   *     negative
   */

	// FlagEOF the stream (either direction) is at EOF.
	FlagEOF

	// FlagStateless indicates the message is stateless.
	// This will retain clients across reconnections or
	// if sequence numbers are unexpected.
	FlagStateless

	// FlagPayloadIsErr can be used by individual ops to signify that
	// The payload is a string error converted to byte slice.
	FlagPayloadIsErr

            assertNotNull(e);
        }
    }

    @Test
    public void testConvertWithNumbersAndSymbols() throws IOException {
        // Test convert with numbers and symbols
        String text = "123 456.789 #hashtag @mention";
        String field = "content";

        List<String> results = converter.convert(text, field, "en");

        assertNotNull(results);
    }

    * Add `-prof async` to see hotspots.
* Have your benchmarks peer-reviewed.

### Don't

* Blindly believe the numbers that your microbenchmark produces but verify them by measuring e.g. with `-prof perfasm`.
* Run more threads than your number of CPU cores (in case you run multi-threaded microbenchmarks).

   *
   * <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,
   *       you can decrease the number of tasks from 15 and 10, killing off the final 5 tasks, and

        return hash;
    }

    /**
     * Appends line numbers to each line of content with a given prefix.
     * Useful for debugging and displaying content with line references.
     *
     * @param prefix the prefix to add before each line number
     * @param content the content to add line numbers to
     * @return the content with line numbers prepended, or empty string if content is blank
     */

        .snapshot();
  }

  /**
   * Returns a {@link Collector} which accumulates statistics from a {@link java.util.stream.Stream}
   * of any type of boxed {@link Number} into a {@link Stats}. Use by calling {@code
   * boxedNumericStream.collect(toStats())}. The numbers will be converted to {@code double} values
   * (which may cause loss of precision).
   *

    assertEquals(42, queue.maximumSize);
    checkNatural(queue);
  }

  private static final ImmutableList<Integer> NUMBERS = ImmutableList.of(4, 8, 15, 16, 23, 42);

  public void testCreation_withContents() {
    MinMaxPriorityQueue<Integer> queue = MinMaxPriorityQueue.create(NUMBERS);
    assertEquals(6, queue.size());
    assertEquals(11, queue.capacity());
    checkUnbounded(queue);
    checkNatural(queue);