def test_procs(loop):
with LocalCluster(2,
scheduler_port=0,
nanny=False,
threads_per_worker=3,
diagnostic_port=None,
silence_logs=False) as c:
assert len(c.workers) == 2
assert all(isinstance(w, Worker) for w in c.workers)
with Executor((c.scheduler.ip, c.scheduler.port), loop=loop) as e:
assert all(w.ncores == 3 for w in c.workers)
repr(c)
with LocalCluster(2,
scheduler_port=0,
nanny=True,
threads_per_worker=3,
diagnostic_port=None,
silence_logs=False) as c:
assert len(c.workers) == 2
assert all(isinstance(w, Nanny) for w in c.workers)
with Executor((c.scheduler.ip, c.scheduler.port), loop=loop) as e:
assert all(v == 3 for v in e.ncores().values())
c.start_worker(nanny=False)
assert isinstance(c.workers[-1], Worker)
repr(c)
def dont_test_dataframes(s, a): # slow
pytest.importorskip('pandas')
n = 3000000
fn = '/tmp/test/file.csv'
with make_hdfs() as hdfs:
data = (b'name,amount,id\r\n' + b'Alice,100,1\r\nBob,200,2\r\n' * n)
with hdfs.open(fn, 'w') as f:
f.write(data)
e = Executor((s.ip, s.port), start=False)
yield e._start()
futures = read_binary(fn, hdfs=hdfs, delimiter=b'\r\n')
assert len(futures) > 1
def load(b, **kwargs):
assert b
from io import BytesIO
import pandas as pd
bio = BytesIO(b)
return pd.read_csv(bio, **kwargs)
dfs = e.map(load, futures, names=['name', 'amount', 'id'], skiprows=1)
dfs2 = yield e._gather(dfs)
assert sum(map(len, dfs2)) == n * 2 - 1
def test_bokeh():
pytest.importorskip('bokeh')
try:
proc = Popen(['dscheduler'], stdout=PIPE, stderr=PIPE)
e = Executor('127.0.0.1:%d' % Scheduler.default_port)
while True:
line = proc.stderr.readline()
if b'Start Bokeh UI' in line:
break
start = time()
while True:
try:
for name in [
socket.gethostname(), 'localhost', '127.0.0.1',
get_ip()
]:
response = requests.get('http://%s:8787/status/' % name)
assert response.ok
break
except:
sleep(0.1)
assert time() < start + 5
finally:
with ignoring(Exception):
e.shutdown()
with ignoring(Exception):
os.kill(proc.pid, signal.SIGINT)
def dsubmit(*a, args=(), kwargs=None, rtn='', **kw):
"""Returns a distributed submission context manager, DSubmitter(),
with a new executor instance.
Parameters
----------
args : Sequence of str, optional
A tuple of argument names for DSubmitter.
kwargs : Mapping of str to values or list of item tuples, optional
Keyword argument names and values for DSubmitter.
rtn : str, optional
Name of object to return for DSubmitter.
a, kw : Sequence and Mapping
All other arguments and keyword arguments are used to construct
the executor instance.
Returns
-------
dsub : DSubmitter
An instance of the DSubmitter context manager.
"""
from distributed import Executor
e = Executor(*a, **kw)
dsub = DSubmitter(e, args=args, kwargs=kwargs, rtn=rtn)
return dsub
def test_lazy_values(s, a, b):
with make_hdfs() as hdfs:
data = b'a'
for i in range(3):
hdfs.mkdir('/tmp/test/data-%d' % i)
for j in range(2):
fn = '/tmp/test/data-%d/file-%d.csv' % (i, j)
with hdfs.open(fn, 'w', repl=1) as f:
f.write(data)
e = Executor((s.ip, s.port), start=False)
yield e._start()
values = read_binary('/tmp/test/', hdfs=hdfs, lazy=True)
assert all(isinstance(v, Value) for v in values)
while not s.restrictions:
yield gen.sleep(0.01)
assert not s.dask
results = e.compute(*values, sync=False)
results = yield e._gather(results)
assert len(results) == 6
assert all(x == b'a' for x in results)
def test_with_data(s, a, b):
ss = HTTPScheduler(s)
ss.listen(0)
e = Executor((s.ip, s.port), start=False)
yield e._start()
L = e.map(inc, [1, 2, 3])
L2 = yield e._scatter(['Hello', 'world!'])
yield _wait(L)
client = AsyncHTTPClient()
response = yield client.fetch('http://localhost:%s/memory-load.json' %
ss.port)
out = json.loads(response.body.decode())
assert all(isinstance(v, int) for v in out.values())
assert set(out) == {a.address_string, b.address_string}
assert sum(out.values()) == sum(
map(sys.getsizeof, [1, 2, 3, 'Hello', 'world!']))
response = yield client.fetch(
'http://localhost:%s/memory-load-by-key.json' % ss.port)
out = json.loads(response.body.decode())
assert set(out) == {a.address_string, b.address_string}
assert all(isinstance(v, dict) for v in out.values())
assert all(k in {'inc', 'data'} for d in out.values() for k in d)
assert all(isinstance(v, int) for d in out.values() for v in d.values())
assert sum(v for d in out.values() for v in d.values()) == \
sum(map(sys.getsizeof, [1, 2, 3, 'Hello', 'world!']))
ss.stop()
yield e._shutdown()
def test_bokeh_non_standard_ports():
pytest.importorskip('bokeh')
try:
proc = Popen(['dscheduler',
'--port', '3448',
'--http-port', '4824',
'--bokeh-port', '4832'], stdout=PIPE, stderr=PIPE)
e = Executor('127.0.0.1:3448')
while True:
line = proc.stderr.readline()
if b'Bokeh UI' in line:
break
start = time()
while True:
try:
response = requests.get('http://localhost:4832/status/')
assert response.ok
break
except:
sleep(0.1)
assert time() < start + 5
finally:
with ignoring(Exception):
e.shutdown()
with ignoring(Exception):
os.kill(proc.pid, signal.SIGINT)
def test_read_csv_sync(loop):
import dask.dataframe as dd
import pandas as pd
with cluster(nworkers=3) as (s, [a, b, c]):
with make_hdfs() as hdfs:
with hdfs.open('/tmp/test/1.csv', 'wb') as f:
f.write(b'name,amount,id\nAlice,100,1\nBob,200,2')
with hdfs.open('/tmp/test/2.csv', 'wb') as f:
f.write(b'name,amount,id\nCharlie,300,3\nDennis,400,4')
with Executor(('127.0.0.1', s['port']), loop=loop) as e:
futures = read_csv('/tmp/test/*.csv',
lineterminator='\n',
collection=False,
lazy=False,
header=0)
assert all(isinstance(f, Future) for f in futures)
L = e.gather(futures)
assert isinstance(L[0], pd.DataFrame)
assert list(L[0].columns) == ['name', 'amount', 'id']
df = read_csv('/tmp/test/*.csv',
lineterminator='\n',
collection=True,
lazy=False,
header=0)
assert isinstance(df, dd.DataFrame)
assert list(df.head().iloc[0]) == ['Alice', 100, 1]
def test_dataframe_groupby_tasks(loop):
df = pd.util.testing.makeTimeDataFrame()
df['A'] = df.A // 0.1
df['B'] = df.B // 0.1
ddf = dd.from_pandas(df, npartitions=10)
with cluster() as (c, [a, b]):
with Executor(('127.0.0.1', c['port']), loop=loop) as e:
with dask.set_options(get=e.get):
for ind in [lambda x: 'A', lambda x: x.A]:
a = df.groupby(ind(df)).apply(len)
b = ddf.groupby(ind(ddf)).apply(len)
assert_equal(a, b.compute(get=dask.get).sort_index())
assert not any('partd' in k[0] for k in b.dask)
a = df.groupby(ind(df)).B.apply(len)
b = ddf.groupby(ind(ddf)).B.apply(len)
assert_equal(a, b.compute(get=dask.get).sort_index())
assert not any('partd' in k[0] for k in b.dask)
with pytest.raises(NotImplementedError):
ddf.groupby(ddf[['A', 'B']]).apply(len)
a = df.groupby(['A', 'B']).apply(len)
b = ddf.groupby(['A', 'B']).apply(len)
assert_equal(a, b.compute(get=dask.get).sort_index())
def test__dask_array_collections(s, a, b):
import dask.array as da
e = Executor((s.ip, s.port), start=False)
yield e._start()
x_dsk = {('x', i, j): np.random.random((3, 3))
for i in range(3) for j in range(2)}
y_dsk = {('y', i, j): np.random.random((3, 3))
for i in range(2) for j in range(3)}
x_futures = yield e._scatter(x_dsk)
y_futures = yield e._scatter(y_dsk)
dt = np.random.random(0).dtype
x_local = da.Array(x_dsk, 'x', ((3, 3, 3), (3, 3)), dt)
y_local = da.Array(y_dsk, 'y', ((3, 3), (3, 3, 3)), dt)
x_remote = da.Array(x_futures, 'x', ((3, 3, 3), (3, 3)), dt)
y_remote = da.Array(y_futures, 'y', ((3, 3), (3, 3, 3)), dt)
exprs = [
lambda x, y: x.T + y, lambda x, y: x.mean() + y.mean(),
lambda x, y: x.dot(y).std(axis=0),
lambda x, y: x - x.mean(axis=1)[:, None]
]
for expr in exprs:
local = expr(x_local, y_local).compute(get=dask.get)
remote, = e.compute(expr(x_remote, y_remote))
remote = yield remote._result()
assert np.all(local == remote)
yield e._shutdown()
def test__futures_to_collection(s, a, b):
e = Executor((s.ip, s.port), start=False)
yield e._start()
remote_dfs = e.map(identity, dfs)
ddf = yield _futures_to_collection(remote_dfs, divisions=True)
ddf2 = yield _futures_to_dask_dataframe(remote_dfs, divisions=True)
assert isinstance(ddf, dd.DataFrame)
assert ddf.dask == ddf2.dask
remote_arrays = e.map(np.arange, range(3, 5))
x = yield _futures_to_collection(remote_arrays)
y = yield _futures_to_dask_array(remote_arrays)
assert type(x) == type(y)
assert x.dask == y.dask
remote_lists = yield e._scatter([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
b = yield _futures_to_collection(remote_lists)
c = yield _futures_to_dask_bag(remote_lists)
assert type(b) == type(c)
assert b.dask == b.dask
yield e._shutdown()
def test__futures_to_dask_bag(s, a, b):
import dask.bag as db
e = Executor((s.ip, s.port), start=False)
yield e._start()
L = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
futures = yield e._scatter(L)
rb = yield _futures_to_dask_bag(futures)
assert isinstance(rb, db.Bag)
assert rb.npartitions == len(L)
lb = db.from_sequence([1, 2, 3, 4, 5, 6, 7, 8, 9], npartitions=3)
exprs = [
lambda x: x.map(lambda x: x + 1).sum(),
lambda x: x.filter(lambda x: x % 2)
]
for expr in exprs:
local = expr(lb).compute(get=dask.get)
remote = e.compute(expr(rb))
remote = yield remote._result()
assert local == remote
yield e._shutdown()
def test_nanny_worker_ports(loop):
try:
worker = Popen([
'dworker', '127.0.0.1:8989', '--host', '127.0.0.1',
'--worker-port', '8788', '--nanny-port', '8789'
],
stdout=PIPE,
stderr=PIPE)
sched = Popen(['dscheduler', '--port', '8989'],
stdout=PIPE,
stderr=PIPE)
with Executor('127.0.0.1:8989', loop=loop) as e:
start = time()
while True:
d = sync(e.loop, e.scheduler.identity)
if d['workers']:
break
else:
assert time() - start < 5
sleep(0.1)
assert d['workers']['127.0.0.1:8788']['services']['nanny'] == 8789
finally:
with ignoring(Exception):
w = rpc('127.0.0.1:8789')
sync(loop, w.terminate)
with ignoring(Exception):
os.kill(sched.pid, signal.SIGINT)
with ignoring(Exception):
worker.kill()
def test_avro(s, a, b):
e = Executor((s.ip, s.port), start=False)
yield e._start()
avro_files = {
'/tmp/test/1.avro': avro_bytes,
'/tmp/test/2.avro': avro_bytes
}
with make_hdfs() as hdfs:
for k, v in avro_files.items():
with hdfs.open(k, 'wb') as f:
f.write(v)
assert hdfs.info(k)['size'] > 0
L = yield _read_avro('/tmp/test/*.avro', lazy=False)
assert isinstance(L, list)
assert all(isinstance(x, Future) for x in L)
results = yield e._gather(L)
assert all(isinstance(r, list) for r in results)
assert results[0][:5] == data[:5]
assert results[-1][-5:] == data[-5:]
L = yield _read_avro('/tmp/test/*.avro', lazy=True)
assert isinstance(L, list)
assert all(isinstance(x, Value) for x in L)
yield e._shutdown()
def client_context(dask_client=None, dask_scheduler=None):
'''client_context creates a dask distributed or threadpool client or None
Parameters:
dask_client: str from choices ("DISTRIBUTED", 'THREAD_POOL', 'SERIAL')
or None to take DASK_CLIENT from environment
dask_scheduler: Distributed scheduler url or None to take
DASK_SCHEDULER from environment
'''
env = parse_env_vars()
dask_client = dask_client or env.get('DASK_CLIENT', 'SERIAL')
dask_scheduler = dask_scheduler or env.get('DASK_SCHEDULER')
if dask_client == 'DISTRIBUTED':
if Executor is None:
raise ValueError('distributed is not installed - "conda install distributed"')
client = Executor(dask_scheduler)
elif dask_client == 'THREAD_POOL':
client = ThreadPool(env.get('DASK_THREADS'))
elif dask_client == 'SERIAL':
client = None
else:
raise ValueError('Did not expect DASK_CLIENT to be {}'.format(dask_client))
get_func = _find_get_func_for_client(client)
with da.set_options(pool=dask_client):
yield client
def test_stress_gc(loop, func, n):
with cluster() as (s, [a, b]):
with Executor(('127.0.0.1', s['port']), loop=loop) as e:
x = e.submit(func, 1)
for i in range(n):
x = e.submit(func, x)
assert x.result() == n + 2
def test_read_text_bucket_key_inputs(loop):
with cluster() as (s, [a, b]):
with Executor(('127.0.0.1', s['port']), loop=loop) as e:
a = read_text(test_bucket_name, '/text/accounts', lazy=True)
b = read_text(test_bucket_name, 'text/accounts', lazy=True)
c = read_text(test_bucket_name + '/text/accounts', lazy=True)
assert a._keys() == b._keys() == c._keys()
def test_submit_after_failed_worker(loop):
with cluster() as (s, [a, b]):
with Executor(('127.0.0.1', s['port']), loop=loop) as e:
L = e.map(inc, range(10))
wait(L)
a['proc'].terminate()
total = e.submit(sum, L)
assert total.result() == sum(map(inc, range(10)))
def test_no_divisions(s, a, b):
e = Executor((s.ip, s.port), start=False)
yield e._start()
dfs = e.map(tm.makeTimeDataFrame, range(5, 10))
df = yield _futures_to_dask_dataframe(dfs)
assert not df.known_divisions
assert list(df.columns) == list(tm.makeTimeDataFrame(5).columns)
def test_progress_function(loop, capsys):
with cluster() as (s, [a, b]):
with Executor(('127.0.0.1', s['port']), loop=loop) as e:
f = e.submit(lambda: 1)
g = e.submit(lambda: 2)
progress([[f], [[g]]], notebook=False)
check_bar_completed(capsys)
def test_gather_after_failed_worker(loop):
with cluster() as (s, [a, b]):
with Executor(('127.0.0.1', s['port']), loop=loop) as e:
L = e.map(inc, range(10))
wait(L)
a['proc'].terminate()
result = e.gather(L)
assert result == list(map(inc, range(10)))
def test_Executor_with_local(loop):
with LocalCluster(1,
scheduler_port=0,
silence_logs=False,
diagnostic_port=None,
loop=loop) as c:
with Executor(c, loop=loop) as e:
assert len(e.ncores()) == len(c.workers)
assert c.scheduler_address in repr(e)
def test_futures_to_dask_dataframe(loop):
with cluster() as (c, [a, b]):
with Executor(('127.0.0.1', c['port']), loop=loop) as e:
remote_dfs = e.map(lambda x: x, dfs)
ddf = futures_to_dask_dataframe(remote_dfs, divisions=True)
assert isinstance(ddf, dd.DataFrame)
assert ddf.x.sum().compute(get=e.get) == sum(
[df.x.sum() for df in dfs])
def test_read_text_sync(loop):
with make_hdfs() as hdfs:
with hdfs.open('/tmp/test/data.txt', 'wb') as f:
f.write(b'hello\nworld')
with cluster(nworkers=3) as (s, [a, b, c]):
with Executor(('127.0.0.1', s['port']), loop=loop) as e:
b = read_text('/tmp/test/*.txt', lazy=False)
assert list(b.str.upper()) == ['HELLO', 'WORLD']
def test_restart_sync_no_center(loop):
with cluster(nanny=True) as (s, [a, b]):
with Executor(('127.0.0.1', s['port']), loop=loop) as e:
x = e.submit(inc, 1)
e.restart()
assert x.cancelled()
y = e.submit(inc, 2)
assert y.result() == 3
assert len(e.ncores()) == 2
def test_futures_to_dask_arrays(loop):
with cluster() as (c, [a, b]):
with Executor(('127.0.0.1', c['port']), loop=loop) as e:
futures = e.map(np.ones, [(5, i) for i in range(1, 6)])
x = future_to_dask_array(futures[0])
assert x.shape == (5, 1)
assert (x.compute(get=e.get) == 1).all()
xs = futures_to_dask_arrays(futures)
assert [x.shape for x in xs] == [(5, i) for i in range(1, 6)]
def test_read_text_sync(loop):
import dask.bag as db
with cluster() as (s, [a, b]):
with Executor(('127.0.0.1', s['port']), loop=loop) as e:
b = read_text(test_bucket_name+'/test/accounts*', lazy=True,
collection=True)
assert isinstance(b, db.Bag)
c = b.filter(None).map(json.loads).pluck('amount').sum()
result = c.compute(get=e.get)
assert result == (1 + 2 + 3 + 4 + 5 + 6 + 7 + 8) * 100
def test_defaults():
try:
proc = Popen(['dscheduler', '--no-bokeh'], stdout=PIPE, stderr=PIPE)
e = Executor('127.0.0.1:%d' % Scheduler.default_port)
response = requests.get('http://127.0.0.1:9786/info.json')
assert response.ok
assert response.json()['status'] == 'running'
finally:
e.shutdown()
os.kill(proc.pid, signal.SIGINT)
def test_futures_to_dask_bag(loop):
import dask.bag as db
with cluster() as (c, [a, b]):
with Executor(('127.0.0.1', c['port']), loop=loop) as e:
data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
futures = e.scatter(data)
b = futures_to_dask_bag(futures)
assert isinstance(b, db.Bag)
assert b.map(lambda x: x + 1).sum().compute(get=e.get) == sum(
range(2, 11))
def test_multiple_executors_restart(s, a, b):
e1 = Executor((s.ip, s.port), start=False)
yield e1._start()
e2 = Executor((s.ip, s.port), start=False)
yield e2._start()
x = e1.submit(inc, 1)
y = e2.submit(inc, 2)
xx = yield x._result()
yy = yield y._result()
assert xx == 2
assert yy == 3
yield e1._restart()
assert x.cancelled()
assert y.cancelled()
yield e1._shutdown(fast=True)
yield e2._shutdown(fast=True)
