def run_benchmark(benchmark, syncdb=True, setup=None, trials=None, handle_argv=True, meta={}):
"""
Run a benchmark a few times and report the results.
Arguments:
benchmark
The benchmark callable. ``run_benchmark`` will time
the executation of this function and report those times
back to the harness. However, if ``benchmark`` returns
a value, that result will reported instead of the
raw timing.
syncdb
If True, a syncdb will be performed before running
the benchmark.
setup
A function to be called before running the benchmark
function(s).
trials
The number of times to run the benchmark function. If not given
and if ``handle_argv`` is ``True`` this'll be automatically
determined from the ``--trials`` flag.
handle_argv
``True`` if the script should handle ``sys.argv`` and set
the number of trials accordingly.
meta
Key/value pairs to be returned as part of the benchmark results.
"""
if handle_argv:
args = benchmark_parser.parse_args()
trials = trials or args.trials
print_benchmark_header(benchmark, meta)
if syncdb:
from django.core.management import call_command
call_command("syncdb", verbosity=0)
if setup:
setup()
for x in xrange(trials):
start = time_f()
profile_file = os.environ.get('DJANGOBENCH_PROFILE_FILE', None)
if profile_file is not None:
loc = locals().copy()
profile.runctx('benchmark_result = benchmark()', globals(), loc, profile_file)
benchmark_result = loc['benchmark_result']
else:
benchmark_result = benchmark()
if benchmark_result is not None:
print benchmark_result
else:
print time_f() - start
def run_benchmark(benchmark, syncdb=True, setup=None, trials=None, handle_argv=True, meta={}):
"""
Run a benchmark a few times and report the results.
Arguments:
benchmark
The benchmark callable. ``run_benchmark`` will time
the executation of this function and report those times
back to the harness. However, if ``benchmark`` returns
a value, that result will reported instead of the
raw timing.
syncdb
If True, a syncdb will be performed before running
the benchmark.
setup
A function to be called before running the benchmark
function(s).
trials
The number of times to run the benchmark function. If not given
and if ``handle_argv`` is ``True`` this'll be automatically
determined from the ``--trials`` flag.
handle_argv
``True`` if the script should handle ``sys.argv`` and set
the number of trials accordingly.
meta
Key/value pairs to be returned as part of the benchmark results.
"""
if handle_argv:
args = benchmark_parser.parse_args()
trials = trials or args.trials
print_benchmark_header(benchmark, meta)
if syncdb:
from django.core.management import call_command
call_command("syncdb", verbosity=0, interactive=False)
if setup:
setup()
for x in xrange(trials):
start = time_f()
profile_file = os.environ.get('DJANGOBENCH_PROFILE_FILE', None)
if profile_file is not None:
loc = locals().copy()
profile.runctx('benchmark_result = benchmark()', globals(), loc, profile_file)
benchmark_result = loc['benchmark_result']
else:
benchmark_result = benchmark()
if benchmark_result is not None:
print benchmark_result
else:
print time_f() - start
def run_benchmark(benchmark, setup=None, max_time=None, handle_argv=True, meta={}):
"""
Run a benchmark a few times and report the results.
Arguments:
benchmark
The benchmark callable. ``run_benchmark`` will time
the executation of this function and report those times
back to the harness. However, if ``benchmark`` returns
a value, that result will reported instead of the
raw timing.
setup
A function to be called before running the benchmark
function(s).
max_time
The number of seconds to run the benchmark function. If not given
and if ``handle_argv`` is ``True`` this'll be automatically
determined from the ``--max_time`` flag.
handle_argv
``True`` if the script should handle ``sys.argv`` and configure
itself from command-line arguments
meta
Key/value pairs to be returned as part of the benchmark results.
"""
if handle_argv:
args = benchmark_parser.parse_args()
max_time = max_time or args.max_time
if setup:
setup()
cumulative_time = 0.0
times = []
while cumulative_time < max_time:
start = time_f()
if args.profile_file is not None:
loc = locals().copy()
profile.runctx('benchmark_result = benchmark()', globals(), loc, args.profile_file)
benchmark_result = loc['benchmark_result']
else:
benchmark_result = benchmark()
elapsed = benchmark_result or time_f() - start
cumulative_time += elapsed
times.append(elapsed)
return {"times": times, "meta": meta}
def run_comparison_benchmark(benchmark_a,
benchmark_b,
migrate=True,
setup=None,
trials=None,
handle_argv=True,
meta={}):
"""
Benchmark the difference between two functions.
Arguments are as for ``run_benchmark``, except that this takes 2
benchmark functions, an A and a B, and reports the difference between
them.
For example, you could use this to test the overhead of an ORM query
versus a raw SQL query -- pass the ORM query as ``benchmark_a`` and the
raw query as ``benchmark_b`` and this function will report the
difference in time between them.
For best results, the A function should be the more expensive one
(otherwise djangobench will report results like "-1.2x slower", which
is just confusing).
"""
if handle_argv:
args = benchmark_parser.parse_args()
trials = trials or args.trials
print_benchmark_header(benchmark_a, meta)
import django
if hasattr(django, 'setup'):
django.setup()
if migrate:
from django.core.management import call_command
call_command("migrate", verbosity=0)
if setup:
setup()
for x in range(trials):
start_a = time_f()
result_a = benchmark_a()
result_a = result_a or time_f() - start_a
start_b = time_f()
result_b = benchmark_b()
result_b = result_b or time_f() - start_b
print(result_a - result_b)
def run_comparison_benchmark(benchmark_a, benchmark_b, migrate=True, setup=None, trials=None, handle_argv=True, meta={}):
"""
Benchmark the difference between two functions.
Arguments are as for ``run_benchmark``, except that this takes 2
benchmark functions, an A and a B, and reports the difference between
them.
For example, you could use this to test the overhead of an ORM query
versus a raw SQL query -- pass the ORM query as ``benchmark_a`` and the
raw query as ``benchmark_b`` and this function will report the
difference in time between them.
For best results, the A function should be the more expensive one
(otherwise djangobench will report results like "-1.2x slower", which
is just confusing).
"""
if handle_argv:
args = benchmark_parser.parse_args()
trials = trials or args.trials
print_benchmark_header(benchmark_a, meta)
import django
if hasattr(django, 'setup'):
django.setup()
if migrate:
from django.core.management import call_command
call_command("migrate", verbosity=0)
if setup:
setup()
for x in range(trials):
start_a = time_f()
result_a = benchmark_a()
result_a = result_a or time_f() - start_a
start_b = time_f()
result_b = benchmark_b()
result_b = result_b or time_f() - start_b
print(result_a - result_b)
def run_comparison_benchmark(benchmark_a, benchmark_b, setup=None, max_time=None, handle_argv=True, meta={}):
"""
Benchmark the difference between two functions.
Arguments are as for ``run_benchmark``, except that this takes 2
benchmark functions, an A and a B, and reports the difference between
them.
For example, you could use this to test the overhead of an ORM query
versus a raw SQL query -- pass the ORM query as ``benchmark_a`` and the
raw query as ``benchmark_b`` and this function will report the
difference in time between them.
For best results, the A function should be the more expensive one
(otherwise djangobench will report results like "-1.2x slower", which
is just confusing).
"""
if handle_argv:
args = benchmark_parser.parse_args()
max_time = max_time or args.max_time
if setup:
setup()
elapsed = 0.0
times = []
while elapsed < max_time:
start_a = time_f()
result_a = benchmark_a()
result_a = result_a or time_f() - start_a
elapsed += result_a
start_b = time_f()
result_b = benchmark_b()
result_b = result_b or time_f() - start_b
times.append(result_a - result_b)
return {"times": times, "meta": meta}
def run_benchmark(benchmark, migrate=True, setup=None, trials=None, handle_argv=True, meta={}):
"""
Run a benchmark a few times and report the results.
Arguments:
benchmark
The benchmark callable. ``run_benchmark`` will time
the executation of this function and report those times
back to the harness. However, if ``benchmark`` returns
a value, that result will reported instead of the
raw timing.
migrate
If True, a migrate will be performed before running
the benchmark.
setup
A function to be called before running the benchmark
function(s).
trials
The number of times to run the benchmark function. If not given
and if ``handle_argv`` is ``True`` this'll be automatically
determined from the ``--trials`` flag.
handle_argv
``True`` if the script should handle ``sys.argv`` and set
the number of trials accordingly.
meta
Key/value pairs to be returned as part of the benchmark results.
"""
if handle_argv:
args = benchmark_parser.parse_args()
trials = trials or args.trials
print_benchmark_header(benchmark, meta)
import django
if hasattr(django, 'setup'):
django.setup()
if migrate:
from django.core.management import CommandError, call_command
if django.VERSION < (1, 7):
call_command("syncdb", run_syncdb=True, verbosity=0)
else:
call_command("migrate", run_syncdb=True, verbosity=0)
if django.VERSION >= (1, 8):
try:
call_command("loaddata", "initial_data", verbosity=0)
except CommandError as exc:
# Django 1.10+ raises if the file doesn't exist and not
# all benchmarks have files.
if 'No fixture named' not in str(exc):
raise
if setup:
setup()
for x in range(trials):
start = time_f()
profile_file = os.environ.get('DJANGOBENCH_PROFILE_FILE', None)
if profile_file is not None:
loc = locals().copy()
profile.runctx('benchmark_result = benchmark()', globals(), loc, profile_file)
benchmark_result = loc['benchmark_result']
else:
benchmark_result = benchmark()
if benchmark_result is not None:
print(benchmark_result)
else:
print(time_f() - start)