def fit_predict(training_data, fitting_data, tau=1, samples_per_job=0, save_results=True, show=False):
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job, result_iterator
from disco.core import Disco
"""
training_data - training samples
fitting_data - dataset to be fitted to training data.
tau - controls how quickly the weight of a training sample falls off with distance of its x(i) from the query point x.
samples_per_job - define a number of samples that will be processed in single mapreduce job. If 0, algorithm will calculate number of samples per job.
"""
try:
tau = float(tau)
if tau <= 0:
raise Exception("Parameter tau should be >= 0.")
except ValueError:
raise Exception("Parameter tau should be numerical.")
if fitting_data.params["id_index"] == -1:
raise Exception("Predict data should have id_index set.")
job = Job(worker=Worker(save_results=save_results))
job.pipeline = [
("split", Stage("map", input_chain=fitting_data.params["input_chain"], init=simple_init, process=map_predict))
]
job.params = fitting_data.params
job.run(name="lwlr_read_data", input=fitting_data.params["data_tag"])
samples = {}
results = []
tau = float(2 * tau ** 2) # calculate tau once
counter = 0
for test_id, x in result_iterator(job.wait(show=show)):
if samples_per_job == 0:
# calculate number of samples per job
if len(x) <= 100: # if there is less than 100 attributes
samples_per_job = 100 # 100 samples is max per on job
else:
# there is more than 100 attributes
samples_per_job = len(x) * -25 / 900.0 + 53 # linear function
samples[test_id] = x
if counter == samples_per_job:
results.append(_fit_predict(training_data, samples, tau, save_results, show))
counter = 0
samples = {}
counter += 1
if len(samples) > 0: # if there is some samples left in the the dictionary
results.append(_fit_predict(training_data, samples, tau, save_results, show))
# merge results of every iteration into a single tag
ddfs = Disco().ddfs
ddfs.tag(job.name, [[list(ddfs.blobs(tag))[0][0]] for tag in results])
return ["tag://" + job.name]
def submit(master, jobpack):
from disco.settings import DiscoSettings
from disco.core import Disco
settings = DiscoSettings()
dmaster = Disco(master)
print "Submitting job to ", master
status, response = json.loads(dmaster.request('/disco/job/new', jobpack))
if status != 'ok':
errmsg('Failed to start job. Server replied: %s' % response)
print response
def __init__(self, *args, **kwargs):
# load the defaults
super(Settings, self).update(defaults)
# override with the settings file
path = kwargs.get('settings_file') or self['settings_file']
if path and os.path.exists(path):
try:
import yaml
self.update(yaml.load(open(path)))
except:
pass # if ya can't ya can't
# final overrides
super(Settings, self).update(overrides)
super(Settings, self).__init__(*args, **kwargs)
# set up ddfs and disco
if not self['server'].startswith('disco://'):
self['server'] = 'disco://' + self['server']
if 'ddfs' not in self:
self['ddfs'] = DDFS(self['server'])
self['server'] = Disco(self['server'])
# set up worker
if 'worker' not in self:
worker_mod, _, worker_class = self['worker_class'].rpartition('.')
mod = __import__(worker_mod, {}, {}, worker_mod)
self['worker'] = getattr(mod, worker_class)()
def get_disco_handle(server):
from disco.core import Disco
from disco.ddfs import DDFS
if server and not server.startswith('disco://'):
server = 'disco://' + server
return Disco(server), DDFS(server)
def __init__(self, spec, discodex, disco_addr="disco://localhost", profile=False):
# TODO(sqs): refactoring potential with PagerankJob
self.spec = spec
self.discodex = discodex
self.docset = Docset(spec.docset_name)
self.disco = Disco(DiscoSettings()["DISCO_MASTER"])
self.nr_partitions = 8
self.profile = profile
class IndexJob(object):
def __init__(self, spec, discodex, disco_addr="disco://localhost", profile=False):
# TODO(sqs): refactoring potential with PagerankJob
self.spec = spec
self.discodex = discodex
self.docset = Docset(spec.docset_name)
self.disco = Disco(DiscoSettings()["DISCO_MASTER"])
self.nr_partitions = 8
self.profile = profile
def start(self):
results = self.__run_job(self.__index_job())
self.__run_discodex_index(results)
def __run_job(self, job):
results = job.wait()
if self.profile:
self.__profile_job(job)
return results
def __index_job(self):
return self.disco.new_job(
name="index_tfidf",
input=["tag://" + self.docset.ddfs_tag],
map_reader=docparse,
map=TfIdf.map,
reduce=TfIdf.reduce,
sort=True,
partitions=self.nr_partitions,
partition=TfIdf.partition,
merge_partitions=False,
profile=self.profile,
params=dict(doc_count=self.docset.doc_count),
)
def __run_discodex_index(self, results):
opts = {
"parser": "disco.func.chain_reader",
"demuxer": "freequery.index.tf_idf.TfIdf_demux",
"nr_ichunks": 1, # TODO(sqs): after disco#181 fixed, increase this
}
ds = DataSet(input=results, options=opts)
origname = self.discodex.index(ds)
self.disco.wait(origname) # origname is also the disco job name
self.discodex.clone(origname, self.spec.invindex_name)
def __init__(self, spec, disco_addr="disco://localhost", alpha=0.15, niter=2, profile=False):
self.spec = spec
self.docset = Docset(spec.docset_name)
self.disco = Disco("disco://localhost")
self.alpha = alpha
self.niter = niter
self.nr_partitions = 16
self.merge_partitions = False
self.profile = profile
class LinkParseJob(object):
def __init__(self, spec, verbose=False, **kwargs):
self.spec = spec
self.docset = Docset(self.spec.docset_name)
self.disco = Disco("disco://localhost")
self.verbose = verbose
def start(self):
from disco import func
job = self.disco.new_job(
name="linkparse",
input=self.docset.dump_uris(),
map_reader=docparse,
map=linkparse_map,
map_output_stream=(func.map_output_stream,
func.disco_output_stream,
LinkFileOutputStream.disco_output_stream),
partitions=0,
save=True,
)
results = job.wait()
self.__tag_results(results)
if self.verbose:
self.__print_results(results)
def __tag_results(self, results):
from disco.ddfs import DDFS
ddfs = DDFS()
results_tag = results[0]
ddfs.put(self.docset.ddfs_link_file_tag, list(ddfs.blobs(results_tag)))
# remove old, temporary tag
ddfs.delete(results_tag)
def __print_results(self, results):
for doc in result_iterator(results, tempdir=False, reader=doclinksparse):
print "%s\n\t%s" % (doc.uri, "\n\t".join(doc.link_uris))
Results,
Query)
from disco.core import Disco
from disco.ddfs import DDFS
from disco.error import DiscoError
from disco.util import ddfs_name, flatten, parse_dir
from discodb import Q
discodex_settings = settings.DiscodexSettings()
disco_master_url = discodex_settings['DISCODEX_DISCO_MASTER']
disco_prefix = discodex_settings['DISCODEX_DISCO_PREFIX']
index_prefix = discodex_settings['DISCODEX_INDEX_PREFIX']
purge_file = discodex_settings['DISCODEX_PURGE_FILE']
disco_master = Disco(disco_master_url)
ddfs = DDFS(disco_master_url)
NOT_FOUND, OK, ACTIVE, DEAD = 'unknown job', 'ready', 'active', 'dead'
class IndexCollection(Collection):
allowed_methods = ('GET', 'POST')
def delegate(self, request, *args, **kwargs):
name = str(kwargs.pop('name'))
return IndexResource(name)(request, *args, **kwargs)
@property
def names(self):
return ddfs.list(index_prefix)
input = inputs or [
maybe_list(line.split()) for line in fileinput.input(inputs)
]
job = reify(jobclass)(program.disco, name)
try:
params = job.params
except AttributeError:
params = Params()
params.__dict__.update(**dict(program.options.params))
job.run(input=input, **program.option_parser.jobdict)
print job.name
@Disco.command
def wait(program, jobname):
"""Usage: jobname
Wait for the named job to complete.
"""
program.disco.wait(jobname)
if __name__ == '__main__':
Disco(option_parser=DiscoOptionParser()).main()
# Workaround for "disco test" in Python2.5 which doesn't shutdown the
# test_server thread properly.
sys.exit(0) # XXX still needed?
def data_gen(path):
return "\n".join(ani)
def fun_map(e, params):
if type(e) == tuple:
return [(e[0] + params['suffix'], int(e[1]) + 1)]
else:
return [(e + params['suffix'], 0)]
def fun_reduce(iter, out, params):
for k, v in iter:
out.add(k + "-", v)
tserver.run_server(data_gen)
disco = Disco(sys.argv[1])
results = disco.new_job(name = "test_chain_0", input = tserver.makeurl([""] * 100),
map = fun_map, reduce = fun_reduce, nr_reduces = 4,
sort = False, params = {'suffix': '0'}).wait()
i = 1
while i < 10:
nresults = disco.new_job(name = "test_chain_%d" % i, input = results,
map = fun_map, reduce = fun_reduce, nr_reduces = 4,
map_reader = chain_reader, sort = False,
params = {'suffix': str(i)}).wait()
disco.purge(jobname(results[0]))
results = nresults
i += 1
def fun_reduce(iter, out, params):
for k, v in iter:
out.add("red:" + k, v)
def data_gen(path):
return path[1:]
tserver.run_server(data_gen)
inputs = ["apple", "orange", "pear"]
job = Disco(sys.argv[1]).new_job(
name="test_streams",
input=tserver.makeurl(inputs),
map=fun_map,
reduce=fun_reduce,
nr_reduces=1,
map_reader = map_reader,
map_input_stream =
[map_input_stream, map_input1, map_input2, map_input3],
reduce_output_stream = [reduce_output1, reduce_output2])
for k, v in result_iterator(job.wait(),
input_stream = [resultiter_input1, map_input_stream]):
if not k.startswith("red:cba"):
raise Exception("Invalid prefix in key. Got '%s' "\
"expected prefix 'red:cba'" % k)
if k[7:] not in inputs:
raise Exception("Invalid result '%s'" % k)
inputs.remove(k[7:])
return [(w, 1) for w in re.sub("\W", " ", e).lower().split()]
def fun_reduce(iter, out, params):
s = {}
for k, v in iter:
if k in s:
s[k] += int(v)
else:
s[k] = int(v)
for k, v in s.iteritems():
out.add(k, v)
tserver.run_server(data_gen)
job = Disco(sys.argv[1]).new_job(name="test_50k",
input=tserver.makeurl([""] * int(5e4)),
map=fun_map,
reduce=fun_reduce,
nr_reduces=300,
sort=False)
ANS = {"gutta": int(5e6), "cavat": int(1e7), "capidem": int(5e6)}
i = 0
for key, value in result_iterator(job.wait()):
i += 1
if ANS[key] == int(value):
print "Correct: %s %s" % (key, value)
else:
raise "Results don't match"
if i != 3:
raise "Wrong number of results: Got %d expected 3" % i
job.purge()
if v != results[k]:
raise "%s: Invalid result for key %s, got %s, "\
"expected %s" % (job.name, k, v, results[k])
tserver.run_server(data_gen)
N = 10
results = {}
inputs = []
for i in range(N):
a = [i] * 10
b = range(i, i + 10)
inputs += ["%d:%d" % x for x in zip(a, b)]
results[str(i)] = sum(b)
disco = Disco(sys.argv[1])
# map results in individual files, one per input file (default mode)
job1 = disco.new_job(\
name = "test_partfile1",
input = tserver.makeurl(inputs),
map = fun_map)
# map results in one big partition file per host
job2 = disco.new_job(\
name = "test_partfile2",
input = tserver.makeurl(inputs),
map = fun_map,
nr_reduces = 1)
check_results(job1)
import sys
from disco.core import Disco, result_iterator
def fun_map(e, params):
for i in range(3):
msg("--special_test_string_%d--" % i)
return [(e, "")]
inputs = ["raw://discoapi"]
job = Disco(sys.argv[1]).new_job(name = "test_discoapi",
input = inputs,
map = fun_map)
r = list(result_iterator(job.wait()))
if [("discoapi", "")] != r:
raise Exception("Invalid result: <%s> " % r)
n = job.jobspec()["name"]
if not n.startswith("test_discoapi"):
raise Exception("Invalid jobspec: Expected name prefix test_discoapi, "\
"got %s" % n)
events = [ev[2] for offs, ev in job.events()]
for i in range(3):
m = "--special_test_string_%d--" % i
if not [x for x in events if m in x]:
raise Exception("Message '%s' not found in events" % m)
job.purge()
def __init__(self, spec, verbose=False, **kwargs):
self.spec = spec
self.docset = Docset(self.spec.docset_name)
self.disco = Disco("disco://localhost")
self.verbose = verbose
import tserver, sys, time
from disco.core import Disco
def data_gen(path):
return "1 2 3\n"
def fun_map(e, params):
import time
time.sleep(100)
return []
disco = Disco(sys.argv[1])
num = sum(x['max_workers'] for x in disco.nodeinfo()['available'])
print >> sys.stderr, num, "slots available"
tserver.run_server(data_gen)
job = disco.new_job(name = "test_kill",
input = tserver.makeurl([""] * num * 2), map = fun_map)
time.sleep(10)
print >> sys.stderr, "Killing", job.name
job.kill()
time.sleep(5)
if job.jobinfo()['active'] == "dead":
print "ok"
job.purge()
else:
raise Exception("Killing failed")
fail = ["1", "2", "3"]
def data_gen(path):
lock.acquire()
e = path[1:]
if e in fail:
fail.remove(e)
lock.release()
raise tserver.FailedReply()
else:
lock.release()
return str(int(e) * 10) + "\n"
def fun_map(e, params):
return [(int(e) * 10, "")]
tserver.run_server(data_gen)
job = Disco(sys.argv[1]).new_job(
name = "test_tempfail",
input = tserver.makeurl(map(str, range(10))),
map = fun_map)
res = sum(int(x) for x, y in result_iterator(job.wait()))
if res != 4500:
raise Exception("Invalid result: Got %d, expected 4500" % res)
job.purge()
print "ok"
2. Online ODAT; 3. Offline dim')
parser.add_option('--post-fix',
default=1,
help='Does post-fixing for ODAT? (default=1): 1. Yes; 2. No')
parser.add_option('--go-live',
default=1,
help='Load offline dim data to DW DBMS? (default=1): 1. yes; 2. No')
parser.add_option('--profile',
default=False,
help='Profile (default=False)')
parser.add_option('--config',
default='conf/config.py',
help='The path to config.py (default=conf/config.py)')
(options, input_paths) = parser.parse_args()
master = Disco("disco://"+options.disco_master)
load_method = odotetlmr
seq_process = None
post_fixing = -1
load_step = int(options.load_step)
if options.load_method=='2':
load_method = odatetlmr
if load_step==1:
post_fixing = int(options.post_fix)
seq_process = multiprocessing.Process(target=seq_server)
seq_process.start()
elif options.load_method=='3':
load_method = offdimetlmr
input_file_urls = []
import sys
import json
from disco.core import Disco, result_iterator
from disco.settings import DiscoSettings
from disco import func
import time
from mapper import map
from reducer import reduce
name = "gap-%s" % int(time.time())
disco = Disco(DiscoSettings()['DISCO_MASTER'])
print "Starting Disco job (%s).." % name
print "Go to %s to see status of the job." % disco.master
results = disco.new_job(name=name,
input=["tag://gap:1million"],
map_input_stream=(
func.map_input_stream,
func.chain_reader,
),
map=map,
reduce=reduce,
save=True).wait()
print "Job done. Results:"
f = open('data.js', 'w')
for time_of_day, scores in result_iterator(results):
except ValueError:
print msg(line)
# bad hack :-(
time = timestamp.replace("'", "")
date_obj = datetime.fromtimestamp(float(time[:-3])) # timestamp has milliseconds, shave em off
nearest_minute = date_obj - timedelta(minutes=date_obj.minute % 1, seconds=date_obj.second, microseconds=date_obj.microsecond)
yield (nearest_minute, {'unique_id': uid, 'query': query, 'frequency': frequency})
def reduce(iter, params):
# This doesn't work at all, its from an old example.
for unique_id, counts in kvgroup(sorted(iter)):
yield unique_id, sum(counts)
disco = Disco(DiscoSettings()['DISCO_MASTER'])
print "Starting Disco job.."
print "Go to %s to see status of the job." % disco.master
"""
:clicks (ad id,people who clicked the ads)
"""
results = disco.new_job(name="bartekc",
input=["tag://hackreduce:search:history"],
map_input_stream=(
func.map_input_stream,
func.chain_reader,
),
map=map,
reduce=reduce,
save=True).wait()
import sys
from disco.core import Disco, result_iterator
def fun_map(e, params):
return [("", e + ":map")]
inputs = ["raw://eeny", "raw://meeny", "raw://miny", "raw://moe"]
job = Disco(sys.argv[1]).new_job(name = "test_raw",
input = inputs,
map = fun_map)
res = dict((x[6:] + ":map", True) for x in inputs)
for x in result_iterator(job.wait()):
if x[1] not in res:
raise "Invalid result: <%s> " % x[1]
del res[x[1]]
if res:
raise "Invalid number of results %d" %\
(len(inputs) - len(res))
job.purge()
print "ok"
import sys
from disco.core import Disco
OK_STATUS = ['job_ready', 'job_died']
disco = Disco(sys.argv[1])
def op_show(n, s):
print n
def op_kill(n, s):
if s == "job_active":
print "Killing", n
disco.kill(n)
def op_clean(n, s):
print "Cleaning", n
disco.clean(n)
def op_purge(n, s):
print "Purging", n
disco.purge(n)
for t, s, name in disco.joblist():
if sys.argv[3] in name:
globals()["op_" + sys.argv[2]](name, s)
checkl("testfun4-norecurse", modutil.find_modules([testfun4],\
recurse = False), [("mod1", abspath("extra/mod1.py"))])
print "local tests ok"
local_tests()
def data_gen(path):
return path[1:] + "\n"
def fun_map(e, params):
x, y = map(float, e.split("|"))
return [(mod1.plusceil(x, y) + math.ceil(1.5), "")]
tserver.run_server(data_gen)
disco = Disco(sys.argv[1])
inputs = ["0.5|1.2"]
print "disco tests.."
# default
job = disco.new_job(
name = "test_modutil1",
input = tserver.makeurl(inputs),
map = fun_map)
checkl("test_modutil1", result_iterator(job.wait()), [("4.0", "")])
job.purge()
print "test_modutil1 ok"
job = disco.new_job(
import sys
from disco.core import Disco, result_iterator
from disco.settings import DiscoSettings
def map(entry, params):
for word in entry.split():
yield word, 1
def reduce(iter, out, params):
s = {}
for word, freq in iter:
s[word] = s.get(word, 0) + int(freq)
for word, freq in s.iteritems():
out.add(word, freq)
disco = Disco(DiscoSettings()['DISCO_MASTER'])
print "Starting Disco job.."
print "Go to %s to see status of the job." % disco.master
results = disco.new_job(name="wordcount",
input=["http://discoproject.org/chekhov.txt"],
map=map,
reduce=reduce).wait()
print "Job done. Results:"
for word, freq in result_iterator(results):
print word, freq
s = 1
for k, v in iter:
if k != "=" + v:
raise Exception("Corrupted key")
s *= int(v)
out.add("result", s)
tserver.run_server(data_gen)
inputs = [3, 5, 7, 11, 13, 17, 19, 23, 29, 31]
job = Disco(sys.argv[1]).new_job(
name = "test_writers",
input = tserver.makeurl(inputs),
map = fun_map,
map_writer = fun_map_writer,
reduce = fun_reduce,
reduce_reader = fun_reduce_reader,
reduce_writer = fun_reduce_writer,
nr_reduces = 1,
sort = False)
res = list(result_iterator(job.wait(), reader = result_reader))
if res != [ANS]:
raise Exception("Invalid answer: %s" % res)
job.purge()
print "ok"
def __init__(self, name=None, master=None, worker=None, settings=None):
from disco.core import Disco
self.name = name or type(self).__name__
self.disco = master if isinstance(master, Disco) else Disco(master)
self.worker = worker or self.Worker()
self.settings = settings or DiscoSettings()
def fun_map(e, params):
return [({"PI": math.pi}, time.strptime(e, "%d/%m/%Y"))]
def fun_reduce(iter, out, params):
for k, v in iter:
out.add({"PI2": k["PI"]}, datetime.datetime(*v[0:6]))
tserver.run_server(data_gen)
inputs = ["01/11/1965", "14/03/1983", "12/12/2002"]
job = Disco(sys.argv[1]).new_job(name = "test_objectrw",
input = tserver.makeurl(inputs),
map = fun_map,
map_writer = func.object_writer,
reduce = fun_reduce,
reduce_reader = func.object_reader,
reduce_writer = func.object_writer,
required_modules = ["math", "datetime", "time"],
nr_reduces = 1,
sort = False)
i = 0
for k, v in result_iterator(job.wait(), reader = func.object_reader):
if k["PI2"] != math.pi:
raise "Invalid key: %s" % k
if v.strftime("%d/%m/%Y") not in inputs:
raise "Invalid value: %s" % v
i += 1
if i != 30:
raise "Wrong number of results, got %d, expected 30" % i
import sys
from disco.core import Disco, result_iterator
from disco.settings import DiscoSettings
def map(line, params):
for word in line.split():
yield word, 1
def reduce(iter, params):
from disco.util import kvgroup
for word, counts in kvgroup(sorted(iter)):
yield word, sum(counts)
disco = Disco(DiscoSettings()['DISCO_MASTER'])
print "Starting Disco job.."
print "Go to %s to see status of the job." % disco.master
results = disco.new_job(
name="wordcount",
input=["http://discoproject.org/media/text/chekhov.txt"],
map=map,
reduce=reduce,
save=True).wait()
print "Job done. Results:"
for word, count in result_iterator(results):
print word, count
from discodex import settings
from discodex.mapreduce import (Indexer, DiscoDBIterator)
from discodex.objects import (DataSet, IChunks, Indices, Index, Results, Dict)
from disco.core import Disco
from disco.ddfs import DDFS
from disco.error import DiscoError
from disco.util import flatten, parse_dir
discodex_settings = settings.DiscodexSettings()
disco_master_url = discodex_settings['DISCODEX_DISCO_MASTER']
disco_prefix = discodex_settings['DISCODEX_DISCO_PREFIX']
index_prefix = discodex_settings['DISCODEX_INDEX_PREFIX']
purge_file = discodex_settings['DISCODEX_PURGE_FILE']
disco_master = Disco(disco_master_url)
ddfs = DDFS(disco_master_url)
NOT_FOUND, OK, ACTIVE, DEAD = 'unknown job', 'ready', 'active', 'dead'
class IndexCollection(Collection):
allowed_methods = ('GET', 'POST')
def delegate(self, request, *args, **kwargs):
name = str(kwargs.pop('name'))
return IndexResource(name)(request, *args, **kwargs)
@property
def names(self):
return ddfs.list(index_prefix)
def disco(self):
from disco.core import Disco
return Disco(settings=self.settings)
def disco(self):
return Disco(self.disco_master_url)
time.sleep(2)
return []
def fun_map2(e, params):
time.sleep(3)
return []
def fun_map3(e, params):
fail
def fun_map4(e, params):
time.sleep(4)
return []
tserver.run_server(data_gen)
disco = Disco(sys.argv[1])
jobs = []
for i, m in enumerate([fun_map1, fun_map2, fun_map3, fun_map4]):
jobs.append(disco.new_job(
name = "test_waitmany_%d" % (i + 1),
input = tserver.makeurl([""] * 5),
map = m))
res = []
while jobs:
cont = False
ready, jobs = disco.results(jobs, timeout = 2000)
res += ready
for n, r in res:
def fun_reduce(iter, out, params):
s = {}
for k, v in iter:
if k in s:
s[k] += int(v)
else:
s[k] = int(v)
for k, v in s.iteritems():
out.add(k, v)
tserver.run_server(data_gen)
job = Disco(sys.argv[1]).new_job(\
name = "test_profile",\
input = tserver.makeurl([""] * int(100)),\
map = really_unique_function_name,\
reduce = fun_reduce,\
nr_reduces = 30,\
sort = False,\
profile = True)
ANS = {"gutta": int(1e4), "cavat": int(2e4), "capidem": int(1e4)}
i = 0
for key, value in result_iterator(job.wait()):
i += 1
if ANS[key] == int(value):
print "Correct: %s %s" % (key, value)
else:
raise "Results don't match (%s): Got %d expected %d" %\
(key, int(value), ANS[key])
if i != 3:
raise "Too few results"
def disco(self):
from disco.core import Disco
return Disco(self.settings['DISCO_MASTER'])
def data_gen(path):
return "\n".join([path[1:]] * 10)
def fun_map(e, params):
return [("=" + e, e)]
def fun_reduce(iter, out, params):
s = 1
for k, v in iter:
if k != "=" + v:
raise Exception("Corrupted key")
s *= int(v)
out.add("result", s)
tserver.run_server(data_gen)
inputs = [3, 5, 7, 11, 13, 17, 19, 23, 29, 31]
job = Disco(sys.argv[1]).new_job(
name="test_simple", input=tserver.makeurl(inputs), map=fun_map, reduce=fun_reduce, nr_reduces=1, sort=False
)
if list(result_iterator(job.wait())) != [("result", ANS)]:
raise Exception("Invalid answer")
job.purge()
print "ok"
def data():
return Disco(self.master).jobpack(self.jobname)
distances = list(distances)
newdistances = {}
def minFrom(d, a):
for k, v in a.items():
d[k] = mymin(d.get(k, -1), v)
for d in distances:
if d.get("nodes"):
nodes = d["nodes"]
minFrom(newdistances, d["distances"])
yield node, json.dumps([node, newdistances, nodes])
disco = Disco(DiscoSettings()['DISCO_MASTER'])
print "Starting Disco job.."
print "Go to %s to see status of the job." % disco.master
results = disco.new_job(name="shortestpath",
input=["file:///home/marko/tmp/disco/out.txt"],
map=map,
reduce=reduce,
save=True).wait()
print "Job done"
out = file("out.txt", "w")
for node, data in result_iterator(results):
print >> out, data
out.close()
def disco(self):
return Disco(settings=self.settings)
def data_gen(path):
return "\n".join([path[1:]] * 10)
def fun_map(e, params):
import time, random
time.sleep(random.randint(1, 3))
return [(e, 0)]
def fun_reduce(iter, out, params):
for k, v in iter:
out.add("[%s]" % k, v)
tserver.run_server(data_gen)
disco = Disco(sys.argv[1])
num = sum(x['max_workers'] for x in disco.nodeinfo()['available'])
print >> sys.stderr, num, "slots available"
inputs = tserver.makeurl(range(num * 10))
random.shuffle(inputs)
jobs = []
for i in range(5):
jobs.append(disco.new_job(name = "test_async_%d" % i,
input = inputs[i * (num * 2):(i + 1) * (num * 2)],
map = fun_map, reduce = fun_reduce, nr_reduces = 11,
sort = False))
time.sleep(1)
all = dict(("[%s]" % i, 0) for i in range(num * 10))
while jobs:
import csv
recordReader = csv.reader(output, delimiter=',', quotechar='"')
for line in recordReader:
title = line[-4]
year = line[-1]
yield year, title
def reduce(iter, params):
from disco.util import kvgroup
for year, titles in kvgroup(sorted(iter)):
romantic_titles = [title for title in titles if "love" in title.lower()]
yield year, len(romantic_titles)
disco = Disco(DiscoSettings()['DISCO_MASTER'])
print "Starting Disco job.."
print "Go to %s to see status of the job." % disco.master
results = disco.new_job(name="song-titles",
input=["tag://hackreduce:millionsongs:subset"],
map=map,
reduce=reduce,
save=True).wait()
print "Job done. Results:"
chart_url = "http://chart.apis.google.com/chart?chxr=0,0,15&chxt=y&chbh=a,4,10&chs=738x220&cht=bvs&chco=4D89F9&chds=0,15&chd=t:"
res_list = []
# Print result to user
for year, titles in result_iterator(results):
res_list.append(str(titles))
def fun_map(e, params):
msg(e)
return []
def fun_map2(e, params):
return []
def fun_map3(e, params):
for i in range(100000):
print "foobar"
return []
tserver.run_server(data_gen)
inputs = tserver.makeurl([1])
job = Disco(sys.argv[1]).new_job(name = "test_ratelimit",
input = inputs, map = fun_map)
time.sleep(5)
check_dead(job)
job = Disco(sys.argv[1]).new_job(name = "test_ratelimit2",
input = inputs, map = fun_map2, status_interval = 1)
time.sleep(5)
check_dead(job)
job = Disco(sys.argv[1]).new_job(name = "test_ratelimit3",
input = inputs, map = fun_map3, status_interval = 1)
time.sleep(5)
check_dead(job)
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
from disco.core import Disco, result_iterator
from disco.util import external
ext_map_exec = "java_map.sh"
ext_reduce_exec = "java_reduce.sh"
map_class = "rmaus.disco.external.sample.WordCountMap"
reduce_class = "rmaus.disco.external.sample.WordCountReduce"
job = Disco("http://discomaster-dr-01:8989").new_job(
name = "java_wordcount",
input = ["raw://foo", "raw://bar", "raw://foo"],
ext_params = { "mapFunction" : map_class, "reduceFunction" : reduce_class, "testKey" : "testValue" },
map = external([ext_map_exec]),
reduce = external([ext_reduce_exec]))
results = job.wait(show=True)
for result in sorted(result_iterator(results), key=lambda x:x[1]):
print result
from disco.core import Disco, result_iterator
from disco.util import kvgroup
from disco.settings import DiscoSettings
def fun_map((key, value), params):
bucket_range = (params.upper - params.lower) // params.num_buckets
bucket = value // bucket_range
if bucket >= params.num_buckets:
yield params.num_buckets - 1, value
yield bucket, value
def fun_reduce(iter, params):
for k, v in kvgroup(sorted(iter))
yield k, sorted(v)
disco = Disco(DiscoSettings()['DISCO_MASTER'])
print "Starting Disco job..
"
results = disco.new_job(name = "Sorting job",
input = [(1, 1), (2, 2), (5, 5), (4, 4), (-1, -1)]
map = fun_map,
reduce = fun_reduce,
params = disco.core.Params(lower = 0,
upper = 10,
num_buckets = 3)).wait()
print "Job done. Results:"
for k, v in result_iterator(results):
print k, v
"""
Predict the closest clusters for the datapoints in input.
"""
job = master.new_job(name='kcluster_predict',
input=input,
map_reader=map_reader,
map=predict_map,
params=Params(centers=centers, **center),
nr_reduces=0)
return job.wait()
if __name__ == '__main__':
parser = OptionParser(usage='%prog [options] inputs')
parser.add_option('--disco-master',
default=getenv('DISCO_MASTER'),
help='Disco master')
parser.add_option('--iterations', default=10, help='Numbers of iteration')
parser.add_option('--clusters', default=10, help='Numbers of clusters')
(options, input) = parser.parse_args()
master = Disco(options.disco_master)
centers = estimate(master, input, mean_point_center, int(options.clusters),
int(options.iterations))
res = predict(master, input, mean_point_center, centers)
print '\n'.join(res)
def fit_predict(training_data,
fitting_data,
tau=1,
samples_per_job=0,
save_results=True,
show=False):
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job, result_iterator
from disco.core import Disco
"""
training_data - training samples
fitting_data - dataset to be fitted to training data.
tau - controls how quickly the weight of a training sample falls off with distance of its x(i) from the query point x.
samples_per_job - define a number of samples that will be processed in single mapreduce job. If 0, algorithm will calculate number of samples per job.
"""
try:
tau = float(tau)
if tau <= 0:
raise Exception("Parameter tau should be >= 0.")
except ValueError:
raise Exception("Parameter tau should be numerical.")
if fitting_data.params["id_index"] == -1:
raise Exception("Predict data should have id_index set.")
job = Job(worker=Worker(save_results=save_results))
job.pipeline = [("split",
Stage("map",
input_chain=fitting_data.params["input_chain"],
init=simple_init,
process=map_predict))]
job.params = fitting_data.params
job.run(name="lwlr_read_data", input=fitting_data.params["data_tag"])
samples = {}
results = []
tau = float(2 * tau**2) # calculate tau once
counter = 0
for test_id, x in result_iterator(job.wait(show=show)):
if samples_per_job == 0:
# calculate number of samples per job
if len(x) <= 100: # if there is less than 100 attributes
samples_per_job = 100 # 100 samples is max per on job
else:
# there is more than 100 attributes
samples_per_job = len(x) * -25 / 900. + 53 # linear function
samples[test_id] = x
if counter == samples_per_job:
results.append(
_fit_predict(training_data, samples, tau, save_results, show))
counter = 0
samples = {}
counter += 1
if len(samples) > 0: # if there is some samples left in the the dictionary
results.append(
_fit_predict(training_data, samples, tau, save_results, show))
# merge results of every iteration into a single tag
ddfs = Disco().ddfs
ddfs.tag(job.name, [[list(ddfs.blobs(tag))[0][0]] for tag in results])
return ["tag://" + job.name]
