def output(self, crawl, exact_count=True, min_surt_hll_size=50000):
counts = (self.pages, self.unique_urls())
host_domain_count = HostDomainCount()
surt_hll = None
if self.unique_urls() >= min_surt_hll_size:
surt_hll = HyperLogLog(HYPERLOGLOG_ERROR)
for url, count in self.url.items():
host_domain_count.add(url, count)
if exact_count:
yield (CST.url.value, self.surt_domain, url), (crawl, count)
if surt_hll is not None:
surt_hll.add(url)
if exact_count:
for digest, counts in self.digest.items():
yield (CST.digest.value, digest), (crawl, counts)
for mime, counts in self.mime.items():
yield (CST.mimetype.value, mime, crawl), counts
for mime, counts in self.mime_detected.items():
yield (CST.mimetype_detected.value, mime, crawl), counts
for key, val in host_domain_count.output(crawl):
yield key, val
yield ((CST.surt_domain.value, self.surt_domain, crawl),
(self.pages, self.unique_urls(), len(host_domain_count.hosts)))
if surt_hll is not None:
yield ((CST.size_estimate_for.value, CST.surt_domain.value,
self.surt_domain, CST.url.value, crawl),
(self.unique_urls(),
CrawlStatsJSONEncoder.json_encode_hyperloglog(surt_hll)))
for status, counts in self.http_status.items():
yield (CST.http_status.value, status, crawl), counts
for url, count in self.robotstxt_url.items():
yield (CST.size_robotstxt.value, CST.url.value, crawl), 1
yield (CST.size_robotstxt.value, CST.page.value, crawl), count
for status, counts in self.robotstxt_status.items():
yield (CST.robotstxt_status.value, status, crawl), counts
def json_decode_hyperloglog(dic):
hll = HyperLogLog(HYPERLOGLOG_ERROR)
hll.p = dic['p']
hll.m = dic['m']
hll.alpha = dic['alpha']
hll.M = dic['M']
return hll
def count_mapper_init(self):
"""Because cdx.gz files cannot be split and
mapreduce.input.fileinputformat.split.minsize is set to a value larger
than any cdx.gz file, the mapper is guaranteed to process the content
of a single cdx file. Input lines of a cdx file are sorted by SURT URL
which allows to aggregate URL counts for one SURT domain in memory.
It may happen that one SURT domain spans over multiple cdx files.
In this case (and without --exact-counts) the count of unique URLs
and the URL histograms may be slightly off in case the same URL occurs
also in a second cdx file. However, this problem is negligible because
there are only 300 cdx files."""
self.counters = Counter()
self.cdx_path = os.environ['mapreduce_map_input_file']
logging.info('Reading {0}'.format(self.cdx_path))
self.crawl_name = 'unknown'
self.crawl = None
crawl_name_match = self.crawlpattern.search(self.cdx_path)
if crawl_name_match is not None:
self.crawl_name = crawl_name_match.group(1)
self.crawl = MonthlyCrawl.get_by_name(self.crawl_name)
else:
raise InputError(
"Cannot determine ID of monthly crawl from input path {}".
format(self.cdx_path))
self.fetches_total = 0
self.pages_total = 0
self.urls_total = 0
self.urls_hll = HyperLogLog(HYPERLOGLOG_ERROR)
self.digest_hll = HyperLogLog(HYPERLOGLOG_ERROR)
self.url_histogram = Counter()
self.count = None
# first and last SURT may continue in previous/next cdx
self.min_surt_hll_size = 1
self.increment_counter('cdx-stats', 'cdx files processed', 1)
def createcounter(nodes):
counter = {}
for node in nodes:
h = HyperLogLog()
h.update(str(node))
counter[node] = h.reg
return counter
def test_json_hyperloglog():
hll1 = HyperLogLog(.01)
for i in range(0, 50):
hll1.add(i)
jsons = json.dumps(hll1, cls=CrawlStatsJSONEncoder)
hll2 = json.loads(jsons, cls=CrawlStatsJSONDecoder)
assert(hll1.card() == hll2.card())
# test jsonpickle serialization
jsonp = jsonpickle.encode(hll2)
hll3 = jsonpickle.decode(jsonp)
assert(hll1.card() == hll3.card())
def hyperloglog():
h = HyperLogLog()
data = []
for pair in stream:
for node in pair:
data.append(node)
for item in data:
h.update(str(item).encode('utf8'))
print "the number of node"
num = count(h)
print num
def update(self, group_key, bindings):
"""Update the aggregator with a new value for a group of bindings"""
if self._variable in bindings:
if group_key not in self._groups:
self._groups[group_key] = HyperLogLog(self._error_rate)
self._size += self._groups[group_key].size()
self._groups[group_key].add(bindings[self._variable])
def update_uids(new_uids, hll):
hll = hll or HyperLogLog(0.01)
for uid in new_uids:
hll.add(uid)
return hll
def add(self, key, identifier):
"""
Adds a key to the counter
:param key:
:param identifier: any object that can be represented
as a string
"""
self.counter.setdefault(key, HyperLogLog(0.005))
self.counter[key].add(str(identifier))
def output(self, crawl, exact_count=True, min_surt_hll_size=50000):
counts = (self.pages, self.unique_urls())
host_domain_count = HostDomainCount()
surt_hll = None
if self.unique_urls() >= min_surt_hll_size:
surt_hll = HyperLogLog(HYPERLOGLOG_ERROR)
for url, count in self.url.items():
host_domain_count.add(url, count)
if exact_count:
yield (CST.url.value, self.surt_domain, url), (crawl, count)
if surt_hll is not None:
surt_hll.add(url)
if exact_count:
for digest, counts in self.digest.items():
yield (CST.digest.value, digest), (crawl, counts)
for mime, counts in self.mime.items():
yield (CST.mimetype.value, mime, crawl), counts
for mime, counts in self.mime_detected.items():
yield (CST.mimetype_detected.value, mime, crawl), counts
for charset, counts in self.charset.items():
yield (CST.charset.value, charset, crawl), counts
for languages, counts in self.languages.items():
yield (CST.languages.value, languages, crawl), counts
# yield primary language
prim_l = languages.split(',')[0]
yield (CST.primary_language.value, prim_l, crawl), counts
for key, val in host_domain_count.output(crawl):
yield key, val
yield((CST.surt_domain.value, self.surt_domain, crawl),
(self.pages, self.unique_urls(), len(host_domain_count.hosts)))
if surt_hll is not None:
yield((CST.size_estimate_for.value, CST.surt_domain.value,
self.surt_domain, CST.url.value, crawl),
(self.unique_urls(),
CrawlStatsJSONEncoder.json_encode_hyperloglog(surt_hll)))
for status, counts in self.http_status.items():
yield (CST.http_status.value, status, crawl), counts
for url, count in self.robotstxt_url.items():
yield (CST.size_robotstxt.value, CST.url.value, crawl), 1
yield (CST.size_robotstxt.value, CST.page.value, crawl), count
for status, counts in self.robotstxt_status.items():
yield (CST.robotstxt_status.value, status, crawl), counts
def get_unique_states(self, states=None, limits=None):
if states is None:
states = copy.deepcopy(self.states)
for axis in range(len(states[0])):
if limits is None:
axmin, axmax = np.min(states[:, axis]), np.max(states[:, axis])
else:
axmin, axmax = limits[axis*2:axis*2+2]
states[:, axis] = np.digitize(states[:, axis],
np.linspace(axmin, axmax, num=100))
states.astype(int)
hll = HyperLogLog(0.01)
for state in tqdm(states,
desc=f"Search for Unique States in whole dataset ({self.environment} @ {self.buffer_type})",
total=len(states)):
hll.add(",".join([str(s) for s in state]))
return len(hll)
def json_decode_hyperloglog(dic):
hll = HyperLogLog(HYPERLOGLOG_ERROR)
hll.p = dic['p']
hll.m = dic['m']
hll.alpha = dic['alpha']
hll.M = dic['M']
return hll
def count_mapper_init(self):
"""Because cdx.gz files cannot be split and
mapreduce.input.fileinputformat.split.minsize is set to a value larger
than any cdx.gz file, the mapper is guaranteed to process the content
of a single cdx file. Input lines of a cdx file are sorted by SURT URL
which allows to aggregate URL counts for one SURT domain in memory.
It may happen that one SURT domain spans over multiple cdx files.
In this case (and without --exact-counts) the count of unique URLs
and the URL histograms may be slightly off in case the same URL occurs
also in a second cdx file. However, this problem is negligible because
there are only 300 cdx files."""
self.counters = Counter()
self.cdx_path = os.environ['mapreduce_map_input_file']
LOG.info('Reading {0}'.format(self.cdx_path))
self.crawl_name = None
self.crawl = None
if self.options.crawl is not None:
self.crawl_name = self.options.crawl
else:
crawl_name_match = self.crawlpattern.search(self.cdx_path)
if crawl_name_match is not None:
self.crawl_name = crawl_name_match.group(1)
else:
raise InputError(
"Cannot determine ID of monthly crawl from input path {}"
.format(self.cdx_path))
if self.crawl_name is None:
raise InputError("Name of crawl not given")
self.crawl = MonthlyCrawl.get_by_name(self.crawl_name)
self.fetches_total = 0
self.pages_total = 0
self.urls_total = 0
self.urls_hll = HyperLogLog(HYPERLOGLOG_ERROR)
self.digest_hll = HyperLogLog(HYPERLOGLOG_ERROR)
self.url_histogram = Counter()
self.count = None
# first and last SURT may continue in previous/next cdx
self.min_surt_hll_size = 1
self.increment_counter('cdx-stats', 'cdx files processed', 1)
def test_json_hyperloglog():
hll1 = HyperLogLog(.01)
for i in range(0, 50):
hll1.add(i)
jsons = json.dumps(hll1, cls=CrawlStatsJSONEncoder)
hll2 = json.loads(jsons, cls=CrawlStatsJSONDecoder)
assert (hll1.card() == hll2.card())
# test jsonpickle serialization
jsonp = jsonpickle.encode(hll2)
hll3 = jsonpickle.decode(jsonp)
assert (hll1.card() == hll3.card())
def cumulative_size(self):
total_pages = 0
for crawl in sorted(self.crawls):
total_pages += self.size['page'][self.crawls[crawl]]
self.add_by_type(crawl, 'page cumul.', total_pages)
for item_type in self.hll.keys():
item_type_cumul = ' '.join([item_type, 'cumul.'])
item_type_new = ' '.join([item_type, 'new'])
cumul_hll = HyperLogLog(HYPERLOGLOG_ERROR)
n = 0
hlls = []
for crawl in sorted(self.hll[item_type]):
n += 1
hll = self.hll[item_type][crawl]
last_cumul_hll_len = len(cumul_hll)
cumul_hll.update(hll)
# cumulative size
self.add_by_type(crawl, item_type_cumul, len(cumul_hll))
# new unseen items this crawl (since the first analyzed crawl)
unseen = (len(cumul_hll) - last_cumul_hll_len)
if unseen > len(hll):
# 1% error rate for cumulative HLLs is large in comparison
# to crawl size, adjust to size of items in this crawl
# (there can be no more new items than the size of the crawl)
unseen = len(hll)
self.add_by_type(crawl, item_type_new, unseen)
hlls.append(hll)
# cumulative size for last N crawls
for n_crawls in [2, 3, 4, 6, 9, 12]:
item_type_n_crawls = '{} cumul. last {} crawls'.format(
item_type, n_crawls)
if n_crawls <= len(hlls):
cum_hll = HyperLogLog(HYPERLOGLOG_ERROR)
for i in range(1, (n_crawls + 1)):
if i > len(hlls):
break
cum_hll.update(hlls[-i])
size_last_n = len(cum_hll)
else:
size_last_n = 'nan'
self.add_by_type(crawl, item_type_n_crawls, size_last_n)
def execute(self, set_size, m, p):
print "t", set_size, m, p
hll = HyperLogLog(m)
for i in range(set_size):
hll.offer(str(i))
estimate = hll.count()
error = abs(estimate / float(set_size) - 1)
strdata = hll.datastr()
print "e", estimate, error, 1 << m, len(strdata)
self.assertLess(len(hll.datastr()), 1 << m)
self.assertLess(error, p)
def cumulative_size(self):
total_pages = 0
for crawl in sorted(self.crawls):
total_pages += self.size['page'][self.crawls[crawl]]
self.add_by_type(crawl, 'page cumul.', total_pages)
for item_type in self.hll.keys():
item_type_cumul = ' '.join([item_type, 'cumul.'])
item_type_new = ' '.join([item_type, 'new'])
cumul_hll = HyperLogLog(HYPERLOGLOG_ERROR)
n = 0
hlls = []
for crawl in sorted(self.hll[item_type]):
n += 1
hll = self.hll[item_type][crawl]
last_cumul_hll_len = len(cumul_hll)
cumul_hll.update(hll)
# cumulative size
self.add_by_type(crawl, item_type_cumul, len(cumul_hll))
# new unseen items this crawl (since the first analyzed crawl)
unseen = (len(cumul_hll) - last_cumul_hll_len)
if unseen > len(hll):
# 1% error rate for cumulative HLLs is large in comparison
# to crawl size, adjust to size of items in this crawl
# (there can be no more new items than the size of the crawl)
unseen = len(hll)
self.add_by_type(crawl, item_type_new, unseen)
hlls.append(hll)
# cumulative size for last N crawls
for n_crawls in [2, 3, 6, 12]:
item_type_n_crawls = '{} cumul. last {} crawls'.format(
item_type, n_crawls)
if n_crawls <= len(hlls):
cum_hll = HyperLogLog(HYPERLOGLOG_ERROR)
for i in range(1, (n_crawls+1)):
if i > len(hlls):
break
cum_hll.update(hlls[-i])
size_last_n = len(cum_hll)
else:
size_last_n = 'nan'
self.add_by_type(crawl, item_type_n_crawls, size_last_n)
def count_reducer(self, key, values):
outputType = key[0]
if outputType in (CST.size.value, CST.size_robotstxt.value):
yield key, sum(values)
elif outputType == CST.histogram.value:
yield key, sum(values)
elif outputType in (CST.url.value, CST.digest.value):
# only with --exact-counts
crawls = MonthlyCrawlSet()
new_crawls = set()
page_count = MultiCount(2)
for val in values:
if type(val) is list:
if (outputType == CST.url.value):
(crawl, pages) = val
page_count.incr(crawl, pages, 1)
else: # digest
(crawl, (pages, urls)) = val
page_count.incr(crawl, pages, urls)
crawls.add(crawl)
new_crawls.add(crawl)
else:
# crawl set bit mask
crawls.update(val)
yield key, crawls.get_bits()
for new_crawl in new_crawls:
if crawls.is_new(new_crawl):
self.counters[(CST.new_items.value, outputType,
new_crawl)] += 1
# url/digest duplicate histograms
for crawl, counts in page_count.items():
items = (1 + counts[0] - counts[1])
self.counters[(CST.histogram.value, outputType, crawl,
CST.page.value, items)] += 1
# size in terms of unique URLs and unique content digests
for crawl, counts in page_count.items():
self.counters[(CST.size.value, outputType, crawl)] += 1
elif outputType in (CST.mimetype.value, CST.mimetype_detected.value,
CST.charset.value, CST.languages.value,
CST.primary_language.value, CST.scheme.value,
CST.tld.value, CST.domain.value,
CST.surt_domain.value, CST.host.value,
CST.http_status.value, CST.robotstxt_status.value):
yield key, MultiCount.sum_values(values)
elif outputType == CST.size_estimate.value:
hll = HyperLogLog(HYPERLOGLOG_ERROR)
for val in values:
hll.update(CrawlStatsJSONDecoder.json_decode_hyperloglog(val))
yield (key, CrawlStatsJSONEncoder.json_encode_hyperloglog(hll))
elif outputType == CST.size_estimate_for.value:
res = None
hll = None
cnt = 0
for val in values:
if res:
if hll is None:
cnt = res[0]
hll = CrawlStatsJSONDecoder.json_decode_hyperloglog(
res[1])
cnt += val[0]
hll.update(
CrawlStatsJSONDecoder.json_decode_hyperloglog(val[1]))
else:
res = val
if hll is not None and cnt >= MIN_SURT_HLL_SIZE:
yield (key,
(cnt,
CrawlStatsJSONEncoder.json_encode_hyperloglog(hll)))
elif res[0] >= MIN_SURT_HLL_SIZE:
yield (key, res)
else:
raise UnhandledTypeError(outputType)
class CCStatsJob(MRJob):
'''Job to get crawl statistics from Common Crawl index
--job=count
run count job (first step) to get counts
from Common Crawl index files (cdx-*.gz)
--job=stats
run statistics job (second step) on output
from count job'''
OUTPUT_PROTOCOL = JSONProtocol
JOBCONF = {
'mapreduce.task.timeout': '9600000',
'mapreduce.map.speculative': 'false',
'mapreduce.reduce.speculative': 'false',
'mapreduce.job.jvm.numtasks': '-1',
}
s3pattern = re.compile('^s3://([^/]+)/(.+)')
gzpattern = re.compile('\.gz$')
crawlpattern = re.compile('(CC-MAIN-2\d{3}-\d{2})')
def configure_args(self):
"""Custom command line options for common crawl index statistics"""
super(CCStatsJob, self).configure_args()
self.add_passthru_arg(
'--job',
dest='job_to_run',
default='',
choices=['count', 'stats', ''],
help='''Job(s) to run ("count", "stats", or empty to run both)''')
self.add_passthru_arg(
'--exact-counts',
dest='exact_counts',
action='store_true',
default=None,
help='''Exact counts for URLs and content digests,
this increases the output size significantly''')
self.add_passthru_arg(
'--no-exact-counts',
dest='exact_counts',
action='store_false',
default=None,
help='''No exact counts for URLs and content digests
to save storage space and computation time''')
self.add_passthru_arg(
'--max-top-hosts-domains',
dest='max_hosts',
type=int,
default=200,
help='''Max. number of most frequent hosts or domains shown
in final statistics (cf. --min-urls-top-host-domain)''')
self.add_passthru_arg(
'--min-urls-top-host-domain',
dest='min_domain_frequency',
type=int,
default=1,
help='''Min. number of URLs required per host or domain shown
in final statistics (cf. --max-top-hosts-domains).''')
self.add_passthru_arg(
'--min-lang-comb-freq',
dest='min_lang_comb_freq',
type=int,
default=1,
help='''Min. number of pages required for a combination of detected
languages to be shown in final statistics.''')
self.add_passthru_arg(
'--crawl',
dest='crawl',
default=None,
help='''ID/name of the crawl analyzed (if not given detected
from input path)''')
def input_protocol(self):
if self.options.job_to_run != 'stats':
LOG.debug('Reading text input from cdx files')
return RawValueProtocol()
LOG.debug('Reading JSON input from count job')
return JSONProtocol()
def hadoop_input_format(self):
input_format = self.HADOOP_INPUT_FORMAT
if self.options.job_to_run != 'stats':
input_format = 'org.apache.hadoop.mapred.TextInputFormat'
LOG.info("Setting input format for {} job: {}".format(
self.options.job_to_run, input_format))
return input_format
def count_mapper_init(self):
"""Because cdx.gz files cannot be split and
mapreduce.input.fileinputformat.split.minsize is set to a value larger
than any cdx.gz file, the mapper is guaranteed to process the content
of a single cdx file. Input lines of a cdx file are sorted by SURT URL
which allows to aggregate URL counts for one SURT domain in memory.
It may happen that one SURT domain spans over multiple cdx files.
In this case (and without --exact-counts) the count of unique URLs
and the URL histograms may be slightly off in case the same URL occurs
also in a second cdx file. However, this problem is negligible because
there are only 300 cdx files."""
self.counters = Counter()
self.cdx_path = os.environ['mapreduce_map_input_file']
LOG.info('Reading {0}'.format(self.cdx_path))
self.crawl_name = None
self.crawl = None
if self.options.crawl is not None:
self.crawl_name = self.options.crawl
else:
crawl_name_match = self.crawlpattern.search(self.cdx_path)
if crawl_name_match is not None:
self.crawl_name = crawl_name_match.group(1)
else:
raise InputError(
"Cannot determine ID of monthly crawl from input path {}".
format(self.cdx_path))
if self.crawl_name is None:
raise InputError("Name of crawl not given")
self.crawl = MonthlyCrawl.get_by_name(self.crawl_name)
self.fetches_total = 0
self.pages_total = 0
self.urls_total = 0
self.urls_hll = HyperLogLog(HYPERLOGLOG_ERROR)
self.digest_hll = HyperLogLog(HYPERLOGLOG_ERROR)
self.url_histogram = Counter()
self.count = None
# first and last SURT may continue in previous/next cdx
self.min_surt_hll_size = 1
self.increment_counter('cdx-stats', 'cdx files processed', 1)
def count_mapper(self, _, line):
self.fetches_total += 1
if (self.fetches_total % 1000) == 0:
self.increment_counter('cdx-stats', 'cdx lines read', 1000)
if (self.fetches_total % 100000) == 0:
LOG.info('Read {0} cdx lines'.format(self.fetches_total))
else:
LOG.debug('Read {0} cdx lines'.format(self.fetches_total))
parts = line.split(' ')
[surt_domain, path] = parts[0].split(')', 1)
if self.count is None:
self.count = SurtDomainCount(surt_domain)
if surt_domain != self.count.surt_domain:
# output accumulated statistics for one SURT domain
for pair in self.count.output(self.crawl,
self.options.exact_counts,
self.min_surt_hll_size):
yield pair
self.urls_total += self.count.unique_urls()
for url, cnt in self.count.url.items():
self.urls_hll.add(url)
self.url_histogram[cnt] += 1
for digest in self.count.digest:
self.digest_hll.add(digest)
self.pages_total += self.count.pages
self.count = SurtDomainCount(surt_domain)
self.min_surt_hll_size = MIN_SURT_HLL_SIZE
json_string = ' '.join(parts[2:])
try:
metadata = ujson.loads(json_string)
self.count.add(path, metadata)
except ValueError as e:
LOG.error('Failed to parse json: {0} - {1}'.format(e, json_string))
def count_mapper_final(self):
self.increment_counter('cdx-stats', 'cdx lines read',
self.fetches_total % 1000)
if self.count is None:
return
for pair in self.count.output(self.crawl, self.options.exact_counts,
1):
yield pair
self.urls_total += self.count.unique_urls()
for url, cnt in self.count.url.items():
self.urls_hll.add(url)
self.url_histogram[cnt] += 1
for digest in self.count.digest:
self.digest_hll.add(digest)
self.pages_total += self.count.pages
if not self.options.exact_counts:
for count, frequency in self.url_histogram.items():
yield ((CST.histogram.value, CST.url.value, self.crawl,
CST.page.value, count), frequency)
yield (CST.size.value, CST.page.value, self.crawl), self.pages_total
yield (CST.size.value, CST.fetch.value, self.crawl), self.fetches_total
if not self.options.exact_counts:
yield (CST.size.value, CST.url.value, self.crawl), self.urls_total
yield ((CST.size_estimate.value, CST.url.value, self.crawl),
CrawlStatsJSONEncoder.json_encode_hyperloglog(self.urls_hll))
yield ((CST.size_estimate.value, CST.digest.value, self.crawl),
CrawlStatsJSONEncoder.json_encode_hyperloglog(self.digest_hll))
self.increment_counter('cdx-stats', 'cdx files finished', 1)
def reducer_init(self):
self.counters = Counter()
self.mostfrequent = defaultdict(list)
def count_reducer(self, key, values):
outputType = key[0]
if outputType in (CST.size.value, CST.size_robotstxt.value):
yield key, sum(values)
elif outputType == CST.histogram.value:
yield key, sum(values)
elif outputType in (CST.url.value, CST.digest.value):
# only with --exact-counts
crawls = MonthlyCrawlSet()
new_crawls = set()
page_count = MultiCount(2)
for val in values:
if type(val) is list:
if (outputType == CST.url.value):
(crawl, pages) = val
page_count.incr(crawl, pages, 1)
else: # digest
(crawl, (pages, urls)) = val
page_count.incr(crawl, pages, urls)
crawls.add(crawl)
new_crawls.add(crawl)
else:
# crawl set bit mask
crawls.update(val)
yield key, crawls.get_bits()
for new_crawl in new_crawls:
if crawls.is_new(new_crawl):
self.counters[(CST.new_items.value, outputType,
new_crawl)] += 1
# url/digest duplicate histograms
for crawl, counts in page_count.items():
items = (1 + counts[0] - counts[1])
self.counters[(CST.histogram.value, outputType, crawl,
CST.page.value, items)] += 1
# size in terms of unique URLs and unique content digests
for crawl, counts in page_count.items():
self.counters[(CST.size.value, outputType, crawl)] += 1
elif outputType in (CST.mimetype.value, CST.mimetype_detected.value,
CST.charset.value, CST.languages.value,
CST.primary_language.value, CST.scheme.value,
CST.tld.value, CST.domain.value,
CST.surt_domain.value, CST.host.value,
CST.http_status.value, CST.robotstxt_status.value):
yield key, MultiCount.sum_values(values)
elif outputType == CST.size_estimate.value:
hll = HyperLogLog(HYPERLOGLOG_ERROR)
for val in values:
hll.update(CrawlStatsJSONDecoder.json_decode_hyperloglog(val))
yield (key, CrawlStatsJSONEncoder.json_encode_hyperloglog(hll))
elif outputType == CST.size_estimate_for.value:
res = None
hll = None
cnt = 0
for val in values:
if res:
if hll is None:
cnt = res[0]
hll = CrawlStatsJSONDecoder.json_decode_hyperloglog(
res[1])
cnt += val[0]
hll.update(
CrawlStatsJSONDecoder.json_decode_hyperloglog(val[1]))
else:
res = val
if hll is not None and cnt >= MIN_SURT_HLL_SIZE:
yield (key,
(cnt,
CrawlStatsJSONEncoder.json_encode_hyperloglog(hll)))
elif res[0] >= MIN_SURT_HLL_SIZE:
yield (key, res)
else:
raise UnhandledTypeError(outputType)
def stats_mapper_init(self):
self.counters = Counter()
def stats_mapper(self, key, value):
if key[0] in (CST.url.value, CST.digest.value,
CST.size_estimate_for.value):
return
if ((self.options.min_domain_frequency > 1)
and (key[0] in (CST.host.value, CST.domain.value,
CST.surt_domain.value))):
# quick skip of infrequent host and domains,
# significantly limits amount of tuples processed in reducer
page_count = MultiCount.get_count(0, value)
url_count = MultiCount.get_count(1, value)
self.counters[(CST.size.value, key[0], key[2])] += 1
self.counters[(CST.histogram.value, key[0], key[2], CST.page.value,
page_count)] += 1
self.counters[(CST.histogram.value, key[0], key[2], CST.url.value,
url_count)] += 1
if key[0] in (CST.domain.value, CST.surt_domain.value):
host_count = MultiCount.get_count(2, value)
self.counters[(CST.histogram.value, key[0], key[2],
CST.host.value, host_count)] += 1
if url_count < self.options.min_domain_frequency:
return
if key[0] == CST.languages.value:
# yield only frequent language combinations (if configured)
page_count = MultiCount.get_count(0, value)
if ((self.options.min_lang_comb_freq > 1)
and (page_count < self.options.min_lang_comb_freq)
and (',' in key[1])):
return
yield key, value
def stats_mapper_final(self):
for (counter, count) in self.counters.items():
yield counter, count
def stats_reducer(self, key, values):
outputType = CST(key[0])
item = key[1]
crawl = MonthlyCrawl.to_name(key[2])
if outputType in (CST.size, CST.new_items, CST.size_estimate,
CST.size_robotstxt):
verbose_key = (outputType.name, CST(item).name, crawl)
if outputType in (CST.size, CST.size_robotstxt):
val = sum(values)
elif outputType == CST.new_items:
val = MultiCount.sum_values(values)
elif outputType == CST.size_estimate:
# already "reduced" in count job
for val in values:
break
yield verbose_key, val
elif outputType == CST.histogram:
yield ((outputType.name, CST(item).name, crawl, CST(key[3]).name,
key[4]), sum(values))
elif outputType in (CST.mimetype, CST.mimetype_detected, CST.charset,
CST.languages, CST.primary_language, CST.scheme,
CST.surt_domain, CST.tld, CST.domain, CST.host,
CST.http_status, CST.robotstxt_status):
item = key[1]
for counts in values:
page_count = MultiCount.get_count(0, counts)
url_count = MultiCount.get_count(1, counts)
if outputType in (CST.domain, CST.surt_domain, CST.tld):
host_count = MultiCount.get_count(2, counts)
if (self.options.min_domain_frequency <= 1 or outputType
not in (CST.host, CST.domain, CST.surt_domain)):
self.counters[(CST.size.name, outputType.name, crawl)] += 1
self.counters[(CST.histogram.name, outputType.name, crawl,
CST.page.name, page_count)] += 1
self.counters[(CST.histogram.name, outputType.name, crawl,
CST.url.name, url_count)] += 1
if outputType in (CST.domain, CST.surt_domain, CST.tld):
self.counters[(CST.histogram.name, outputType.name,
crawl, CST.host.name, host_count)] += 1
if outputType == CST.tld:
domain_count = MultiCount.get_count(3, counts)
self.counters[(CST.histogram.name, outputType.name, crawl,
CST.domain.name, domain_count)] += 1
if outputType in (CST.domain, CST.host, CST.surt_domain):
outKey = (outputType.name, crawl)
outVal = (page_count, url_count, item)
if outputType in (CST.domain, CST.surt_domain):
outVal = (page_count, url_count, host_count, item)
# take most common
if len(self.mostfrequent[outKey]) < self.options.max_hosts:
heapq.heappush(self.mostfrequent[outKey], outVal)
else:
heapq.heappushpop(self.mostfrequent[outKey], outVal)
else:
yield ((outputType.name, item, crawl), counts)
else:
raise UnhandledTypeError(outputType)
def reducer_final(self):
for (counter, count) in self.counters.items():
yield counter, count
for key, mostfrequent in self.mostfrequent.items():
(outputType, crawl) = key
if outputType in (CST.domain.name, CST.surt_domain.name):
for (pages, urls, hosts, item) in mostfrequent:
yield ((outputType, item, crawl),
MultiCount.compress(3, [pages, urls, hosts]))
else:
for (pages, urls, item) in mostfrequent:
yield ((outputType, item, crawl),
MultiCount.compress(2, [pages, urls]))
def steps(self):
reduces = 10
cdxminsplitsize = 2**32 # do not split cdx map input files
if self.options.exact_counts:
# with exact counts need many reducers to aggregate the counts
# in reasonable time and to get not too large partitions
reduces = 200
count_job = \
MRStep(mapper_init=self.count_mapper_init,
mapper=self.count_mapper,
mapper_final=self.count_mapper_final,
reducer_init=self.reducer_init,
reducer=self.count_reducer,
reducer_final=self.reducer_final,
jobconf={'mapreduce.job.reduces': reduces,
'mapreduce.input.fileinputformat.split.minsize':
cdxminsplitsize,
'mapreduce.output.fileoutputformat.compress':
"true",
'mapreduce.output.fileoutputformat.compress.codec':
'org.apache.hadoop.io.compress.BZip2Codec'})
stats_job = \
MRStep(mapper_init=self.stats_mapper_init,
mapper=self.stats_mapper,
mapper_final=self.stats_mapper_final,
reducer_init=self.reducer_init,
reducer=self.stats_reducer,
reducer_final=self.reducer_final,
jobconf={'mapreduce.job.reduces': 1,
'mapreduce.output.fileoutputformat.compress':
"true",
'mapreduce.output.fileoutputformat.compress.codec':
'org.apache.hadoop.io.compress.GzipCodec'})
if self.options.job_to_run == 'count':
return [count_job]
if self.options.job_to_run == 'stats':
return [stats_job]
return [count_job, stats_job]
from datetime import datetime, timedelta from hll_custom import HyperLogLog as HyperLogLogCustom from hyperloglog import HyperLogLog from json import loads from sseclient import SSEClient from sys import getsizeof start_time = datetime.now() duration_counter = 1 max_duration = 24 duration = timedelta(hours=duration_counter) hll = HyperLogLog(0.01) hll_custom = HyperLogLogCustom(0.01) naive = set() write_counter = 0 hll_log = [] hll_size_log = [] hll_custom_log = [] hll_custom_size_log = [] naive_log = [] naive_size_log = [] url = 'https://stream.wikimedia.org/v2/stream/' url_stream = 'recentchange' stream = SSEClient(url + url_stream) for event in stream:
def profile(csvreader, colnames, samplesize=1000):
# set the chunksize to be read from the file equal to the
# number of records that are being profiled when
# the profile size is less than the chunk size
# this reduces unnecessary reads on the file
stats = {}
# build a dictionary with the initial set of statistics
for i, col in enumerate(colnames):
stats[i] = {
'Name': col,
'TypeList': [],
'MaxVal': None,
'MinVal': None,
'MaxLen': None,
'DecPlaces': None,
'Nulls': False,
'Cardinality': HyperLogLog(0.01)
}
rec_cnt = 0
for row in csvreader:
if rec_cnt == samplesize:
break
rec_cnt += 1
for i, col in enumerate(row):
if col:
t = find_type(col)
if t not in stats[i]['TypeList']:
stats[i]['TypeList'].append(t)
if col > stats[i]['MaxVal']:
stats[i]['MaxVal'] = col
if col < stats[i]['MinVal'] or stats[i]['MinVal'] == None:
stats[i]['MinVal'] = col
v_len = len(col)
if v_len > stats[i]['MaxLen']:
stats[i]['MaxLen'] = v_len
if t == 'float':
dec_places = len(str(col).split('.')[1])
#print str(col)
if stats[i]['DecPlaces'] < dec_places:
stats[i]['DecPlaces'] = dec_places
try:
stats[i]['Cardinality'].add(col)
except:
pass
else:
stats[i]['Nulls'] = True
filestats = []
for key, v in enumerate(stats):
val = stats[key]
# set default type to string when no type was found
if not val['TypeList']:
val['TypeList'].append('string')
types_found = [TYPES[t] for t in val['TypeList']]
recommend_type = max(types_found, key=lambda x: x['weight'])
filestats.append([
val['Name'], ','.join(val['TypeList']), val['MaxLen'],
val['MinVal'], val['MaxVal'], val['DecPlaces'], val['Nulls'],
int(val['Cardinality'].card()), recommend_type['ansi']
])
return rec_cnt, filestats
def update_group(self, group_key, aggregation):
hyperloglog = HyperLogLog(
0.01) # do not care about the error rate, it will be overwritten
hyperloglog.load(aggregation['__value__'])
self._groups[group_key].update(hyperloglog)
class CCStatsJob(MRJob):
'''Job to get crawl statistics from Common Crawl index
--job=count
run count job (first step) to get counts
from Common Crawl index files (cdx-*.gz)
--job=stats
run statistics job (second step) on output
from count job'''
OUTPUT_PROTOCOL = JSONProtocol
JOBCONF = {
'mapreduce.task.timeout': '9600000',
'mapreduce.map.speculative': 'false',
'mapreduce.reduce.speculative': 'false',
'mapreduce.job.jvm.numtasks': '-1',
}
s3pattern = re.compile('^s3://([^/]+)/(.+)')
gzpattern = re.compile('\.gz$')
crawlpattern = re.compile('(CC-MAIN-2\d{3}-\d{2})')
def configure_args(self):
"""Custom command line options for common crawl index statistics"""
super(CCStatsJob, self).configure_args()
self.add_passthru_arg(
'--job', dest='job_to_run',
default='', choices=['count', 'stats', ''],
help='''Job(s) to run ("count", "stats", or empty to run both)''')
self.add_passthru_arg(
'--exact-counts', dest='exact_counts',
action='store_true', default=None,
help='''Exact counts for URLs and content digests,
this increases the output size significantly''')
self.add_passthru_arg(
'--no-exact-counts', dest='exact_counts',
action='store_false', default=None,
help='''No exact counts for URLs and content digests
to save storage space and computation time''')
self.add_passthru_arg(
'--max-top-hosts-domains', dest='max_hosts',
type=int, default=200,
help='''Max. number of most frequent hosts or domains shown
in final statistics (cf. --min-urls-top-host-domain)''')
self.add_passthru_arg(
'--min-urls-top-host-domain', dest='min_domain_frequency',
type=int, default=1,
help='''Min. number of URLs required per host or domain shown
in final statistics (cf. --max-top-hosts-domains).''')
self.add_passthru_arg(
'--min-lang-comb-freq', dest='min_lang_comb_freq',
type=int, default=1,
help='''Min. number of pages required for a combination of detected
languages to be shown in final statistics.''')
self.add_passthru_arg(
'--crawl', dest='crawl', default=None,
help='''ID/name of the crawl analyzed (if not given detected
from input path)''')
def input_protocol(self):
if self.options.job_to_run != 'stats':
LOG.debug('Reading text input from cdx files')
return RawValueProtocol()
LOG.debug('Reading JSON input from count job')
return JSONProtocol()
def hadoop_input_format(self):
input_format = self.HADOOP_INPUT_FORMAT
if self.options.job_to_run != 'stats':
input_format = 'org.apache.hadoop.mapred.TextInputFormat'
LOG.info("Setting input format for {} job: {}".format(
self.options.job_to_run, input_format))
return input_format
def count_mapper_init(self):
"""Because cdx.gz files cannot be split and
mapreduce.input.fileinputformat.split.minsize is set to a value larger
than any cdx.gz file, the mapper is guaranteed to process the content
of a single cdx file. Input lines of a cdx file are sorted by SURT URL
which allows to aggregate URL counts for one SURT domain in memory.
It may happen that one SURT domain spans over multiple cdx files.
In this case (and without --exact-counts) the count of unique URLs
and the URL histograms may be slightly off in case the same URL occurs
also in a second cdx file. However, this problem is negligible because
there are only 300 cdx files."""
self.counters = Counter()
self.cdx_path = os.environ['mapreduce_map_input_file']
LOG.info('Reading {0}'.format(self.cdx_path))
self.crawl_name = None
self.crawl = None
if self.options.crawl is not None:
self.crawl_name = self.options.crawl
else:
crawl_name_match = self.crawlpattern.search(self.cdx_path)
if crawl_name_match is not None:
self.crawl_name = crawl_name_match.group(1)
else:
raise InputError(
"Cannot determine ID of monthly crawl from input path {}"
.format(self.cdx_path))
if self.crawl_name is None:
raise InputError("Name of crawl not given")
self.crawl = MonthlyCrawl.get_by_name(self.crawl_name)
self.fetches_total = 0
self.pages_total = 0
self.urls_total = 0
self.urls_hll = HyperLogLog(HYPERLOGLOG_ERROR)
self.digest_hll = HyperLogLog(HYPERLOGLOG_ERROR)
self.url_histogram = Counter()
self.count = None
# first and last SURT may continue in previous/next cdx
self.min_surt_hll_size = 1
self.increment_counter('cdx-stats', 'cdx files processed', 1)
def count_mapper(self, _, line):
self.fetches_total += 1
if (self.fetches_total % 1000) == 0:
self.increment_counter('cdx-stats', 'cdx lines read', 1000)
if (self.fetches_total % 100000) == 0:
LOG.info('Read {0} cdx lines'.format(self.fetches_total))
else:
LOG.debug('Read {0} cdx lines'.format(self.fetches_total))
parts = line.split(' ')
[surt_domain, path] = parts[0].split(')', 1)
if self.count is None:
self.count = SurtDomainCount(surt_domain)
if surt_domain != self.count.surt_domain:
# output accumulated statistics for one SURT domain
for pair in self.count.output(self.crawl,
self.options.exact_counts,
self.min_surt_hll_size):
yield pair
self.urls_total += self.count.unique_urls()
for url, cnt in self.count.url.items():
self.urls_hll.add(url)
self.url_histogram[cnt] += 1
for digest in self.count.digest:
self.digest_hll.add(digest)
self.pages_total += self.count.pages
self.count = SurtDomainCount(surt_domain)
self.min_surt_hll_size = MIN_SURT_HLL_SIZE
json_string = ' '.join(parts[2:])
try:
metadata = ujson.loads(json_string)
self.count.add(path, metadata)
except ValueError as e:
LOG.error('Failed to parse json: {0} - {1}'.format(
e, json_string))
def count_mapper_final(self):
self.increment_counter('cdx-stats',
'cdx lines read', self.fetches_total % 1000)
if self.count is None:
return
for pair in self.count.output(self.crawl, self.options.exact_counts, 1):
yield pair
self.urls_total += self.count.unique_urls()
for url, cnt in self.count.url.items():
self.urls_hll.add(url)
self.url_histogram[cnt] += 1
for digest in self.count.digest:
self.digest_hll.add(digest)
self.pages_total += self.count.pages
if not self.options.exact_counts:
for count, frequency in self.url_histogram.items():
yield((CST.histogram.value, CST.url.value, self.crawl,
CST.page.value, count), frequency)
yield (CST.size.value, CST.page.value, self.crawl), self.pages_total
yield (CST.size.value, CST.fetch.value, self.crawl), self.fetches_total
if not self.options.exact_counts:
yield (CST.size.value, CST.url.value, self.crawl), self.urls_total
yield((CST.size_estimate.value, CST.url.value, self.crawl),
CrawlStatsJSONEncoder.json_encode_hyperloglog(self.urls_hll))
yield((CST.size_estimate.value, CST.digest.value, self.crawl),
CrawlStatsJSONEncoder.json_encode_hyperloglog(self.digest_hll))
self.increment_counter('cdx-stats', 'cdx files finished', 1)
def reducer_init(self):
self.counters = Counter()
self.mostfrequent = defaultdict(list)
def count_reducer(self, key, values):
outputType = key[0]
if outputType in (CST.size.value, CST.size_robotstxt.value):
yield key, sum(values)
elif outputType == CST.histogram.value:
yield key, sum(values)
elif outputType in (CST.url.value, CST.digest.value):
# only with --exact-counts
crawls = MonthlyCrawlSet()
new_crawls = set()
page_count = MultiCount(2)
for val in values:
if type(val) is list:
if (outputType == CST.url.value):
(crawl, pages) = val
page_count.incr(crawl, pages, 1)
else: # digest
(crawl, (pages, urls)) = val
page_count.incr(crawl, pages, urls)
crawls.add(crawl)
new_crawls.add(crawl)
else:
# crawl set bit mask
crawls.update(val)
yield key, crawls.get_bits()
for new_crawl in new_crawls:
if crawls.is_new(new_crawl):
self.counters[(CST.new_items.value,
outputType, new_crawl)] += 1
# url/digest duplicate histograms
for crawl, counts in page_count.items():
items = (1+counts[0]-counts[1])
self.counters[(CST.histogram.value, outputType,
crawl, CST.page.value, items)] += 1
# size in terms of unique URLs and unique content digests
for crawl, counts in page_count.items():
self.counters[(CST.size.value, outputType, crawl)] += 1
elif outputType in (CST.mimetype.value,
CST.mimetype_detected.value,
CST.charset.value,
CST.languages.value,
CST.primary_language.value,
CST.scheme.value,
CST.tld.value,
CST.domain.value,
CST.surt_domain.value,
CST.host.value,
CST.http_status.value,
CST.robotstxt_status.value):
yield key, MultiCount.sum_values(values)
elif outputType == CST.size_estimate.value:
hll = HyperLogLog(HYPERLOGLOG_ERROR)
for val in values:
hll.update(
CrawlStatsJSONDecoder.json_decode_hyperloglog(val))
yield(key,
CrawlStatsJSONEncoder.json_encode_hyperloglog(hll))
elif outputType == CST.size_estimate_for.value:
res = None
hll = None
cnt = 0
for val in values:
if res:
if hll is None:
cnt = res[0]
hll = CrawlStatsJSONDecoder.json_decode_hyperloglog(res[1])
cnt += val[0]
hll.update(CrawlStatsJSONDecoder.json_decode_hyperloglog(val[1]))
else:
res = val
if hll is not None and cnt >= MIN_SURT_HLL_SIZE:
yield(key, (cnt, CrawlStatsJSONEncoder.json_encode_hyperloglog(hll)))
elif res[0] >= MIN_SURT_HLL_SIZE:
yield(key, res)
else:
raise UnhandledTypeError(outputType)
def stats_mapper_init(self):
self.counters = Counter()
def stats_mapper(self, key, value):
if key[0] in (CST.url.value, CST.digest.value,
CST.size_estimate_for.value):
return
if ((self.options.min_domain_frequency > 1) and
(key[0] in (CST.host.value, CST.domain.value,
CST.surt_domain.value))):
# quick skip of infrequent host and domains,
# significantly limits amount of tuples processed in reducer
page_count = MultiCount.get_count(0, value)
url_count = MultiCount.get_count(1, value)
self.counters[(CST.size.value, key[0], key[2])] += 1
self.counters[(CST.histogram.value, key[0],
key[2], CST.page.value, page_count)] += 1
self.counters[(CST.histogram.value, key[0],
key[2], CST.url.value, url_count)] += 1
if key[0] in (CST.domain.value, CST.surt_domain.value):
host_count = MultiCount.get_count(2, value)
self.counters[(CST.histogram.value, key[0],
key[2], CST.host.value, host_count)] += 1
if url_count < self.options.min_domain_frequency:
return
if key[0] == CST.languages.value:
# yield only frequent language combinations (if configured)
page_count = MultiCount.get_count(0, value)
if ((self.options.min_lang_comb_freq > 1) and
(page_count < self.options.min_lang_comb_freq) and
(',' in key[1])):
return
yield key, value
def stats_mapper_final(self):
for (counter, count) in self.counters.items():
yield counter, count
def stats_reducer(self, key, values):
outputType = CST(key[0])
item = key[1]
crawl = MonthlyCrawl.to_name(key[2])
if outputType in (CST.size, CST.new_items,
CST.size_estimate, CST.size_robotstxt):
verbose_key = (outputType.name, CST(item).name, crawl)
if outputType in (CST.size, CST.size_robotstxt):
val = sum(values)
elif outputType == CST.new_items:
val = MultiCount.sum_values(values)
elif outputType == CST.size_estimate:
# already "reduced" in count job
for val in values:
break
yield verbose_key, val
elif outputType == CST.histogram:
yield((outputType.name, CST(item).name, crawl,
CST(key[3]).name, key[4]), sum(values))
elif outputType in (CST.mimetype, CST.mimetype_detected, CST.charset,
CST.languages, CST.primary_language, CST.scheme,
CST.surt_domain, CST.tld, CST.domain, CST.host,
CST.http_status, CST.robotstxt_status):
item = key[1]
for counts in values:
page_count = MultiCount.get_count(0, counts)
url_count = MultiCount.get_count(1, counts)
if outputType in (CST.domain, CST.surt_domain, CST.tld):
host_count = MultiCount.get_count(2, counts)
if (self.options.min_domain_frequency <= 1 or
outputType not in (CST.host, CST.domain,
CST.surt_domain)):
self.counters[(CST.size.name, outputType.name, crawl)] += 1
self.counters[(CST.histogram.name, outputType.name,
crawl, CST.page.name, page_count)] += 1
self.counters[(CST.histogram.name, outputType.name,
crawl, CST.url.name, url_count)] += 1
if outputType in (CST.domain, CST.surt_domain, CST.tld):
self.counters[(CST.histogram.name, outputType.name,
crawl, CST.host.name, host_count)] += 1
if outputType == CST.tld:
domain_count = MultiCount.get_count(3, counts)
self.counters[(CST.histogram.name, outputType.name,
crawl, CST.domain.name, domain_count)] += 1
if outputType in (CST.domain, CST.host, CST.surt_domain):
outKey = (outputType.name, crawl)
outVal = (page_count, url_count, item)
if outputType in (CST.domain, CST.surt_domain):
outVal = (page_count, url_count, host_count, item)
# take most common
if len(self.mostfrequent[outKey]) < self.options.max_hosts:
heapq.heappush(self.mostfrequent[outKey], outVal)
else:
heapq.heappushpop(self.mostfrequent[outKey], outVal)
else:
yield((outputType.name, item, crawl), counts)
else:
raise UnhandledTypeError(outputType)
def reducer_final(self):
for (counter, count) in self.counters.items():
yield counter, count
for key, mostfrequent in self.mostfrequent.items():
(outputType, crawl) = key
if outputType in (CST.domain.name, CST.surt_domain.name):
for (pages, urls, hosts, item) in mostfrequent:
yield((outputType, item, crawl),
MultiCount.compress(3, [pages, urls, hosts]))
else:
for (pages, urls, item) in mostfrequent:
yield((outputType, item, crawl),
MultiCount.compress(2, [pages, urls]))
def steps(self):
reduces = 10
cdxminsplitsize = 2**32 # do not split cdx map input files
if self.options.exact_counts:
# with exact counts need many reducers to aggregate the counts
# in reasonable time and to get not too large partitions
reduces = 200
count_job = \
MRStep(mapper_init=self.count_mapper_init,
mapper=self.count_mapper,
mapper_final=self.count_mapper_final,
reducer_init=self.reducer_init,
reducer=self.count_reducer,
reducer_final=self.reducer_final,
jobconf={'mapreduce.job.reduces': reduces,
'mapreduce.input.fileinputformat.split.minsize':
cdxminsplitsize,
'mapreduce.output.fileoutputformat.compress':
"true",
'mapreduce.output.fileoutputformat.compress.codec':
'org.apache.hadoop.io.compress.BZip2Codec'})
stats_job = \
MRStep(mapper_init=self.stats_mapper_init,
mapper=self.stats_mapper,
mapper_final=self.stats_mapper_final,
reducer_init=self.reducer_init,
reducer=self.stats_reducer,
reducer_final=self.reducer_final,
jobconf={'mapreduce.job.reduces': 1,
'mapreduce.output.fileoutputformat.compress':
"true",
'mapreduce.output.fileoutputformat.compress.codec':
'org.apache.hadoop.io.compress.GzipCodec'})
if self.options.job_to_run == 'count':
return [count_job]
if self.options.job_to_run == 'stats':
return [stats_job]
return [count_job, stats_job]
},
{
"name": "Log Log Register",
"init": LLRegister,
},
#{
#"name" : "LogLog",
#"init" : lambda : LL(4),
#},
#{
#"name" : "SuperLogLog",
#"init" : lambda : SuperLL(4),
#},
{
"name": "HyperLogLog",
"init": lambda: HyperLogLog(4),
},
{
"name": "KMinValues",
"init": lambda: KMinValues(2 << 4),
},
{
"name": "ScalingBloom",
"init": lambda: ScalingBloomFilter(2048),
},
]
def run_experiment(exp_name, filename, key_generator, data, sample_freq=3000):
for item in data:
item["_tmp"] = item["init"]()
def count_reducer(self, key, values):
outputType = key[0]
if outputType in (CST.size.value, CST.size_robotstxt.value):
yield key, sum(values)
elif outputType == CST.histogram.value:
yield key, sum(values)
elif outputType in (CST.url.value, CST.digest.value):
# only with --exact-counts
crawls = MonthlyCrawlSet()
new_crawls = set()
page_count = MultiCount(2)
for val in values:
if type(val) is list:
if (outputType == CST.url.value):
(crawl, pages) = val
page_count.incr(crawl, pages, 1)
else: # digest
(crawl, (pages, urls)) = val
page_count.incr(crawl, pages, urls)
crawls.add(crawl)
new_crawls.add(crawl)
else:
# crawl set bit mask
crawls.update(val)
yield key, crawls.get_bits()
for new_crawl in new_crawls:
if crawls.is_new(new_crawl):
self.counters[(CST.new_items.value,
outputType, new_crawl)] += 1
# url/digest duplicate histograms
for crawl, counts in page_count.items():
items = (1+counts[0]-counts[1])
self.counters[(CST.histogram.value, outputType,
crawl, CST.page.value, items)] += 1
# size in terms of unique URLs and unique content digests
for crawl, counts in page_count.items():
self.counters[(CST.size.value, outputType, crawl)] += 1
elif outputType in (CST.mimetype.value,
CST.mimetype_detected.value,
CST.charset.value,
CST.languages.value,
CST.primary_language.value,
CST.scheme.value,
CST.tld.value,
CST.domain.value,
CST.surt_domain.value,
CST.host.value,
CST.http_status.value,
CST.robotstxt_status.value):
yield key, MultiCount.sum_values(values)
elif outputType == CST.size_estimate.value:
hll = HyperLogLog(HYPERLOGLOG_ERROR)
for val in values:
hll.update(
CrawlStatsJSONDecoder.json_decode_hyperloglog(val))
yield(key,
CrawlStatsJSONEncoder.json_encode_hyperloglog(hll))
elif outputType == CST.size_estimate_for.value:
res = None
hll = None
cnt = 0
for val in values:
if res:
if hll is None:
cnt = res[0]
hll = CrawlStatsJSONDecoder.json_decode_hyperloglog(res[1])
cnt += val[0]
hll.update(CrawlStatsJSONDecoder.json_decode_hyperloglog(val[1]))
else:
res = val
if hll is not None and cnt >= MIN_SURT_HLL_SIZE:
yield(key, (cnt, CrawlStatsJSONEncoder.json_encode_hyperloglog(hll)))
elif res[0] >= MIN_SURT_HLL_SIZE:
yield(key, res)
else:
raise UnhandledTypeError(outputType)
from hyperloglog import HyperLogLog
sheep_seen = set()
sheep_seen_hll = HyperLogLog(0.01)
for m in range(0, 100000):
sheep_id = str(m)
sheep_seen.add(sheep_id)
sheep_seen_hll.add(sheep_id)
print(f"There are {len(sheep_seen)} sheep (set).")
print(f"There are {len(sheep_seen_hll)} sheep (hyperloglog).")
