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# Licensed to the Apache Software Foundation (ASF) under one
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# to you 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
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# software distributed under the License is distributed on an
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# specific language governing permissions and limitations
# under the License.
'''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
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.
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()
bytes_per_row = len(table_data_generator.output_file.getvalue()) / float(row_count)
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)