Created
February 10, 2017 18:07
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Parquet multithreaded benchmarks
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import gc | |
import os | |
import time | |
import numpy as np | |
import pandas as pd | |
from pyarrow.compat import guid | |
import pyarrow as pa | |
import pyarrow.parquet as pq | |
import snappy | |
def generate_floats(n, pct_null, repeats=1): | |
nunique = int(n / repeats) | |
unique_values = np.random.randn(nunique) | |
num_nulls = int(nunique * pct_null) | |
null_indices = np.random.choice(nunique, size=num_nulls, replace=False) | |
unique_values[null_indices] = np.nan | |
return unique_values.repeat(repeats) | |
DATA_GENERATORS = { | |
'float64': generate_floats | |
} | |
def generate_data(nrows, ncols, pct_null=0.1, repeats=1, dtype='float64'): | |
type_ = np.dtype('float64') | |
datagen_func = DATA_GENERATORS[dtype] | |
data = { | |
'c' + str(i): datagen_func(nrows, pct_null, repeats) | |
for i in range(ncols) | |
} | |
return pd.DataFrame(data) | |
def write_to_parquet(df, out_path, use_dictionary=True, | |
compression='SNAPPY'): | |
arrow_table = pa.Table.from_pandas(df) | |
if compression.lower() == 'uncompressed': | |
compression = None | |
pq.write_table(arrow_table, out_path, use_dictionary=use_dictionary, | |
compression=compression) | |
def read_pyarrow(path, nthreads=1): | |
return pq.read_table(path, nthreads=nthreads).to_pandas() | |
def get_timing(f, path, niter): | |
start = time.clock_gettime(time.CLOCK_MONOTONIC) | |
for i in range(niter): | |
f(path) | |
elapsed = time.clock_gettime(time.CLOCK_MONOTONIC) - start | |
return elapsed | |
MEGABYTE = 1 << 20 | |
DATA_SIZE = 1024 * MEGABYTE | |
NCOLS = 16 | |
NROWS = DATA_SIZE / NCOLS / np.dtype('float64').itemsize | |
cases = { | |
'low_entropy_dict': { | |
'pct_null': 0.1, | |
'repeats': 1000, | |
'use_dictionary': True | |
}, | |
'low_entropy': { | |
'pct_null': 0.1, | |
'repeats': 1000, | |
'use_dictionary': False | |
}, | |
'high_entropy_dict': { | |
'pct_null': 0.1, | |
'repeats': 1, | |
'use_dictionary': True | |
}, | |
'high_entropy': { | |
'pct_null': 0.1, | |
'repeats': 1, | |
'use_dictionary': False | |
} | |
} | |
NITER = 5 | |
results = [] | |
readers = [ | |
('pyarrow', lambda path: read_pyarrow(path)), | |
('pyarrow 2 threads', lambda path: read_pyarrow(path, nthreads=2)), | |
('pyarrow 4 threads', lambda path: read_pyarrow(path, nthreads=4)) | |
] | |
COMPRESSIONS = ['UNCOMPRESSED', 'SNAPPY'] # , 'GZIP'] | |
case_files = {} | |
for case, params in cases.items(): | |
for compression in COMPRESSIONS: | |
path = '{0}_{1}.parquet'.format(case, compression or 'UNCOMPRESSED') | |
df = generate_data(NROWS, NCOLS, repeats=params['repeats']) | |
write_to_parquet(df, path, compression=compression, | |
use_dictionary=params['use_dictionary']) | |
df = None | |
case_files[case, compression] = path | |
for case, params in cases.items(): | |
for compression in COMPRESSIONS: | |
path = case_files[case, compression] | |
compression = compression if compression != 'UNCOMPRESSED' else None | |
# prime the file cache | |
read_pyarrow(path) | |
read_pyarrow(path) | |
for reader_name, f in readers: | |
elapsed = get_timing(f, path, NITER) / NITER | |
result = case, compression, reader_name, elapsed | |
print(result) | |
results.append(result) |
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