tests/comparison/data_generator_mapred_common.py

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'''This module provides random data generation and database population.

   When this module is run directly for purposes of database population, the default is
   to use a fixed seed for randomization. The result should be that the generated random
   data is the same regardless of when or where the execution is done.

'''

import base64
import pickle
import StringIO

from tests.comparison.db_types import Decimal
from tests.comparison.random_val_generator import RandomValGenerator

def serialize(value):
  '''Returns a serialized representation of 'value' suitable for use as a key in an MR
     streaming job.
  '''
  return base64.b64encode(pickle.dumps(value))


def deserialize(value):
  return pickle.loads(base64.b64decode(value))


class TextTableDataGenerator(object):

  def __init__(self):
    self.table = None
    self.randomization_seed = None
    self.row_count = None
    self.output_file = None

  def populate_output_file(self):
    cols = self.table.cols
    col_val_generators = [self._create_val_generator(c.exact_type) for c in cols]
    val_buffer_size = 1024
    col_val_buffers = [[None] * val_buffer_size for c in cols]
    for row_idx in xrange(self.row_count):
      val_buffer_idx = row_idx % val_buffer_size
      if val_buffer_idx == 0:
        for col_idx, col in enumerate(cols):
          val_buffer = col_val_buffers[col_idx]
          val_generator = col_val_generators[col_idx]
          for idx in xrange(val_buffer_size):
            val = next(val_generator)
            val_buffer[idx] = "\N" if val is None else val
      for col_idx, col in enumerate(cols):
        if col_idx > 0:
          # Postgres doesn't seem to have an option to specify that the last column value
          # has a terminator. Impala and Hive accept this format with the option
          # 'ROW FORMAT DELIMITED'.
          self.output_file.write(b"\x01")
        self.output_file.write(str(col_val_buffers[col_idx][val_buffer_idx]))
      self.output_file.write("\n")

  def _create_val_generator(self, val_type):
    val_generator = RandomValGenerator().create_val_generator(val_type)
    if isinstance(val_type, Decimal):
      fmt = '%%0.%sf' % val_type.MAX_FRACTIONAL_DIGITS
      def val():
        while True:
          val = next(val_generator)
          yield None if val is None else fmt % val
      return val
    return val_generator


# MR jobs are hard-coded to try to have each reducer generate this much data.
MB_PER_REDUCER = 120


def estimate_bytes_per_row(table_data_generator, row_count):
  original_row_count = table_data_generator.row_count
  original_output_file = table_data_generator.output_file
  table_data_generator.row_count = row_count
  table_data_generator.output_file = StringIO.StringIO()
  table_data_generator.populate_output_file()
  table_data_generator.output_file.flush()
  bytes_per_row = len(table_data_generator.output_file.getvalue()) / float(row_count)
  table_data_generator.output_file.close()
  table_data_generator.output_file = original_output_file
  table_data_generator.row_count = original_row_count
  return max(int(bytes_per_row), 1)


def estimate_rows_per_reducer(table_data_generator, mb_per_reducer):
  bytes_per_reducer = mb_per_reducer * 1024 ** 2
  bytes_per_row = estimate_bytes_per_row(table_data_generator, 1)
  if bytes_per_row >= bytes_per_reducer:
    return 1
  rows_per_reducer = bytes_per_reducer / bytes_per_row
  bytes_per_row = estimate_bytes_per_row(table_data_generator,
      max(int(rows_per_reducer * 0.001), 1))
  return max(bytes_per_reducer / bytes_per_row, 1)