# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file 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
#
# 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.
#
"""A microbenchmark for measuring performance of coders.
This runs a sequence of encode-decode operations on random inputs
to collect performance of various coders.
To evaluate coders performance we approximate the behavior
how the coders are used in PCollections: we encode and decode
a list of elements. An element can be a string, a list of integers,
a windowed value, or any other object we want a coder to process.
Run as:
python -m apache_beam.tools.coders_microbenchmark
"""
from __future__ import absolute_import
from __future__ import print_function
import random
import string
import sys
from past.builtins import unicode
from apache_beam.coders import coders
from apache_beam.tools import utils
from apache_beam.transforms import window
from apache_beam.utils import windowed_value
[docs]def coder_benchmark_factory(coder, generate_fn):
"""Creates a benchmark that encodes and decodes a list of elements.
Args:
coder: coder to use to encode an element.
generate_fn: a callable that generates an element.
"""
class CoderBenchmark(object):
def __init__(self, num_elements_per_benchmark):
self._coder = coders.IterableCoder(coder)
self._list = [generate_fn()
for _ in range(num_elements_per_benchmark)]
def __call__(self):
# Calling coder operations on a single element at a time may incur
# unrelevant overhead. To compensate, we use a list elements.
_ = self._coder.decode(self._coder.encode(self._list))
CoderBenchmark.__name__ = "%s, %s" % (
generate_fn.__name__, str(coder))
return CoderBenchmark
[docs]def small_int():
return random.randint(0, 127)
[docs]def large_int():
return random.randint(sys.maxsize >> 2, sys.maxsize)
[docs]def random_string(length):
return unicode(''.join(random.choice(
string.ascii_letters + string.digits) for _ in range(length)))
[docs]def small_string():
return random_string(4)
[docs]def large_string():
return random_string(100)
[docs]def list_int(size):
return [small_int() for _ in range(size)]
[docs]def dict_int_int(size):
return {i: i for i in list_int(size)}
[docs]def small_list():
return list_int(10)
[docs]def large_list():
return list_int(1000)
[docs]def small_tuple():
# Benchmark a common case of 2-element tuples.
return tuple(list_int(2))
[docs]def large_tuple():
return tuple(large_list())
[docs]def small_dict():
return {i: i for i in small_list()}
[docs]def large_dict():
return {i: i for i in large_list()}
[docs]def random_windowed_value(num_windows):
return windowed_value.WindowedValue(
value=small_int(),
timestamp=12345678,
windows=tuple(
window.IntervalWindow(i * 10, i * 10 + small_int())
for i in range(num_windows)
))
[docs]def wv_with_one_window():
return random_windowed_value(num_windows=1)
[docs]def wv_with_multiple_windows():
return random_windowed_value(num_windows=32)
[docs]def run_coder_benchmarks(num_runs, input_size, seed, verbose):
random.seed(seed)
# TODO(BEAM-4441): Pick coders using type hints, for example:
# tuple_coder = typecoders.registry.get_coder(typehints.Tuple[int, ...])
benchmarks = [
coder_benchmark_factory(
coders.FastPrimitivesCoder(), small_int),
coder_benchmark_factory(
coders.FastPrimitivesCoder(), large_int),
coder_benchmark_factory(
coders.FastPrimitivesCoder(), small_string),
coder_benchmark_factory(
coders.FastPrimitivesCoder(), large_string),
coder_benchmark_factory(
coders.FastPrimitivesCoder(),
small_list),
coder_benchmark_factory(
coders.IterableCoder(coders.FastPrimitivesCoder()),
small_list),
coder_benchmark_factory(
coders.FastPrimitivesCoder(),
large_list),
coder_benchmark_factory(
coders.IterableCoder(coders.FastPrimitivesCoder()),
large_list),
coder_benchmark_factory(
coders.FastPrimitivesCoder(),
small_tuple),
coder_benchmark_factory(
coders.FastPrimitivesCoder(),
large_tuple),
coder_benchmark_factory(
coders.FastPrimitivesCoder(),
small_dict),
coder_benchmark_factory(
coders.FastPrimitivesCoder(),
large_dict),
coder_benchmark_factory(
coders.WindowedValueCoder(coders.FastPrimitivesCoder()),
wv_with_one_window),
coder_benchmark_factory(
coders.WindowedValueCoder(coders.FastPrimitivesCoder()),
wv_with_multiple_windows),
]
suite = [utils.BenchmarkConfig(b, input_size, num_runs) for b in benchmarks]
utils.run_benchmarks(suite, verbose=verbose)
if __name__ == "__main__":
utils.check_compiled("apache_beam.coders.coder_impl")
num_runs = 20
num_elements_per_benchmark = 1000
seed = 42 # Fix the seed for better consistency
run_coder_benchmarks(num_runs, num_elements_per_benchmark, seed,
verbose=True)