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#!/usr/bin/env python
from __future__ import print_function
import hashlib
import itertools
import os
import random
import perf
from kafka.record.memory_records import MemoryRecords, MemoryRecordsBuilder
DEFAULT_BATCH_SIZE = 1600 * 1024
KEY_SIZE = 6
VALUE_SIZE = 60
TIMESTAMP_RANGE = [1505824130000, 1505824140000]
BATCH_SAMPLES = 5
MESSAGES_PER_BATCH = 100
def random_bytes(length):
buffer = bytearray(length)
for i in range(length):
buffer[i] = random.randint(0, 255)
return bytes(buffer)
def prepare(magic):
samples = []
for _ in range(BATCH_SAMPLES):
batch = MemoryRecordsBuilder(
magic, batch_size=DEFAULT_BATCH_SIZE, compression_type=0)
for _ in range(MESSAGES_PER_BATCH):
size = batch.append(
random.randint(*TIMESTAMP_RANGE),
random_bytes(KEY_SIZE),
random_bytes(VALUE_SIZE),
headers=[])
assert size
batch.close()
samples.append(bytes(batch.buffer()))
return iter(itertools.cycle(samples))
def finalize(results):
# Just some strange code to make sure PyPy does execute the code above
# properly
hash_val = hashlib.md5()
for buf in results:
hash_val.update(buf)
print(hash_val, file=open(os.devnull, "w"))
def func(loops, magic):
# Jit can optimize out the whole function if the result is the same each
# time, so we need some randomized input data )
precomputed_samples = prepare(magic)
results = []
# Main benchmark code.
batch_data = next(precomputed_samples)
t0 = perf.perf_counter()
for _ in range(loops):
records = MemoryRecords(batch_data)
while records.has_next():
batch = records.next_batch()
batch.validate_crc()
for record in batch:
results.append(record.value)
res = perf.perf_counter() - t0
finalize(results)
return res
runner = perf.Runner()
runner.bench_time_func('batch_read_v0', func, 0)
runner.bench_time_func('batch_read_v1', func, 1)
runner.bench_time_func('batch_read_v2', func, 2)
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