old_contents: #!/usr/bin/env python
  # Copyright 2009 Google Inc. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import math
  import random
  
  # When running a weighted distribution, never repeat a domain more than this:
  MAX_REPEAT = 3
  
  TYPES = {
    'weighted': 'Chooses based on a weighted distribution, preferring entries in the top of the list',
    'random': 'Random selection, including repeats.',
    'chunk': 'Chooses a random contiguous segment of entries'
  }
  
  def MaxRepeatCount(elements, count):
    # Avoid stalling out looking for the one unique choice
    if count >= len(elements):
      return 2**32
    else:
      return MAX_REPEAT
  
  
  def GetTypes(self):
    """Return a tuple of type names with a description."""
    return sorted(TYPES.items())
    
  def WeightedDistribution(elements, count):
    """Given a set of elements, return a weighted distribution back.
  
    Args:
      elements: A list of elements to choose from
      count: how many elements to return
  
    Returns:
      A random but fairly distributed list of elements of count count.
  
    The distribution is designed to mimic real-world DNS usage. The observed
    formula for request popularity was:
  
    522.520776 * math.pow(x, -0.998506)-2
    """
  
    def FindY(x, total):
      return total * math.pow(x, -0.408506)
  
    total = len(elements)
    picks = []
    picked = {}
    offset = FindY(total, total)
    max_repeat = MaxRepeatCount(elements, count)
    while len(picks) < count:
      x = random.random() * total
      y = FindY(x, total) - offset
      index = abs(int(y))
      if index < total:
        if picked.get(index, 0) < max_repeat:
          picks.append(elements[index])
          picked[index] = picked.get(index, 0) + 1
  #        print '%s: %s' % (index, elements[index])
    return picks
  
  
  def ChunkSelect(elements, count):
    """Return a random count-sized contiguous chunk of elements."""
    if len(elements) <= count:
      return elements
    start = random.randint(0, len(elements) - count)
    return elements[start:start + count]
  
  def RandomSelect(elements, count):
    picks = []
    picked = {}
  
    max_repeat = MaxRepeatCount(elements, count)
    while len(picks) < count:
      choice = random.choice(elements)
      if picked.get(choice, 0) < max_repeat:
        picks.append(choice)
        picked[choice] = picked.get(choice, 0) + 1
    return picks
  
new_contents: #!/usr/bin/env python
  # Copyright 2009 Google Inc. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import math
  import random
  
  # When running a weighted distribution, never repeat a domain more than this:
  MAX_REPEAT = 3
  
  TYPES = {
    'weighted': 'Chooses based on a weighted distribution, preferring entries in the top of the list',
    'random': 'Random selection, including repeats.',
    'chunk': 'Chooses a random contiguous segment of entries'
  }
  
  def MaxRepeatCount(elements, count):
    # Avoid stalling out looking for the one unique choice
    if count >= len(elements) * 0.5:
      return 2**32
    else:
      return MAX_REPEAT
  
  
  def GetTypes(self):
    """Return a tuple of type names with a description."""
    return sorted(TYPES.items())
    
  def WeightedDistribution(elements, count):
    """Given a set of elements, return a weighted distribution back.
  
    Args:
      elements: A list of elements to choose from
      count: how many elements to return
  
    Returns:
      A random but fairly distributed list of elements of count count.
  
    The distribution is designed to mimic real-world DNS usage. The observed
    formula for request popularity was:
  
    522.520776 * math.pow(x, -0.998506)-2
    """
  
    def FindY(x, total):
      return total * math.pow(x, -0.408506)
  
    total = len(elements)
    picks = []
    picked = {}
    offset = FindY(total, total)
    max_repeat = MaxRepeatCount(elements, count)
    while len(picks) < count:
      x = random.random() * total
      y = FindY(x, total) - offset
      index = abs(int(y))
      if index < total:
        if picked.get(index, 0) < max_repeat:
          picks.append(elements[index])
          picked[index] = picked.get(index, 0) + 1
  #        print '%s: %s' % (index, elements[index])
    return picks
  
  
  def ChunkSelect(elements, count):
    """Return a random count-sized contiguous chunk of elements."""
    if len(elements) <= count:
      return elements
    start = random.randint(0, len(elements) - count)
    return elements[start:start + count]
  
  def RandomSelect(elements, count):
    picks = []
    picked = {}
  
    max_repeat = MaxRepeatCount(elements, count)
    while len(picks) < count:
      choice = random.choice(elements)
      if picked.get(choice, 0) < max_repeat:
        picks.append(choice)
        picked[choice] = picked.get(choice, 0) + 1
    return picks
  
subject: Change MaxRepeatCount() to require twice as many records as the requested count