merge
This commit is contained in:
commit
e06ef64c8f
7
mongo/Dockerfile
Normal file
7
mongo/Dockerfile
Normal file
@ -0,0 +1,7 @@
|
||||
FROM python:3.9
|
||||
RUN mkdir /app
|
||||
COPY requirements.txt /app
|
||||
RUN pip install -r /app/requirements.txt
|
||||
COPY *.py /app
|
||||
WORKDIR /app
|
||||
ENTRYPOINT ["python", "./mongocrawler.py"]
|
||||
@ -18,11 +18,12 @@ import re
|
||||
import time
|
||||
import collections
|
||||
import math
|
||||
import json
|
||||
import random
|
||||
import hashlib
|
||||
|
||||
LANGUAGE= os.getenv("SUCKER_LANGUAGE","sk")
|
||||
DOMAIN = os.getenv("SUCKER_DOMAIN","sk")
|
||||
BATCHSIZE=os.getenv("SUCKER_BATCHSIZE",10)
|
||||
BATCHSIZE=int(os.getenv("SUCKER_BATCHSIZE","10"))
|
||||
CONNECTION=os.getenv("SUCKER_CONNECTION","mongodb://root:example@localhost:27017/")
|
||||
DBNAME=os.getenv("SUCKER_DBNAME","crawler")
|
||||
MINFILESIZE=300
|
||||
@ -32,17 +33,15 @@ CHECK_PARAGRAPH_SIZE=150
|
||||
TEXT_TRASH_SIZE=200
|
||||
TEXT_TRASH_RATIO=0.6
|
||||
|
||||
def put_queue(db,channel,message):
|
||||
queuecol = db["queue"]
|
||||
queuecol.insert_one({"channel":channel,"message":message,"created_at":datetime.utcnow(),"started_at":None})
|
||||
|
||||
def reserve_queue(db,channel,message):
|
||||
queuecol = db["queue"]
|
||||
r = queuecol.find_one_and_delete({"channel":channel},sort={"created_at":-1})
|
||||
|
||||
def delete_queue(db,channel):
|
||||
queuecol = db["queue"]
|
||||
pass
|
||||
def split_train(res):
|
||||
trainset = []
|
||||
testset = []
|
||||
for i,item in enumerate(res):
|
||||
if i % 10 == 0:
|
||||
testset.append(item)
|
||||
else:
|
||||
trainset.append(item)
|
||||
return trainset,testset
|
||||
|
||||
def calculate_checksums(text):
|
||||
"""
|
||||
@ -102,39 +101,34 @@ def get_link_doc(link,status="frontlink"):
|
||||
return {"url":link,"host":host,"domain":domain,"status":status,"created_at":datetime.utcnow()}
|
||||
|
||||
|
||||
def fetch_pages(link_batch):
|
||||
htmls = []
|
||||
#print(link_batch)
|
||||
#print("zzzzzzzzzz")
|
||||
for link in link_batch:
|
||||
print("fetching:::::")
|
||||
print(link)
|
||||
final_link = link
|
||||
response = trafilatura.fetch_url(link,decode=False)
|
||||
time.sleep(2)
|
||||
html = None
|
||||
if response is not None :
|
||||
good = True
|
||||
if response.status != 200:
|
||||
good = False
|
||||
LOGGER.error('not a 200 response: %s for URL %s', response.status, url)
|
||||
elif response.data is None or len(response.data) < MINFILESIZE:
|
||||
LOGGER.error('too small/incorrect for URL %s', link)
|
||||
good = False
|
||||
# raise error instead?
|
||||
elif len(response.data) > MAXFILESIZE:
|
||||
good = False
|
||||
LOGGER.error('too large: length %s for URL %s', len(response.data), link)
|
||||
if good:
|
||||
html = trafilatura.utils.decode_response(response)
|
||||
final_link = response.url
|
||||
if html is not None:
|
||||
html, final_link = trafilatura.spider.refresh_detection(html, final_link)
|
||||
# is there a meta-refresh on the page?
|
||||
if final_link is None: # malformed or malicious content
|
||||
html = None
|
||||
htmls.append((final_link,html))
|
||||
return htmls
|
||||
def fetch_page(link):
|
||||
print("fetching:::::")
|
||||
print(link)
|
||||
final_link = link
|
||||
response = trafilatura.fetch_url(link,decode=False)
|
||||
time.sleep(2)
|
||||
html = None
|
||||
if response is not None :
|
||||
good = True
|
||||
if response.status != 200:
|
||||
good = False
|
||||
LOGGER.error('not a 200 response: %s for URL %s', response.status, url)
|
||||
elif response.data is None or len(response.data) < MINFILESIZE:
|
||||
LOGGER.error('too small/incorrect for URL %s', link)
|
||||
good = False
|
||||
# raise error instead?
|
||||
elif len(response.data) > MAXFILESIZE:
|
||||
good = False
|
||||
LOGGER.error('too large: length %s for URL %s', len(response.data), link)
|
||||
if good:
|
||||
html = trafilatura.utils.decode_response(response)
|
||||
final_link = response.url
|
||||
if html is not None:
|
||||
html, final_link = trafilatura.spider.refresh_detection(html, final_link)
|
||||
# is there a meta-refresh on the page?
|
||||
if final_link is None: # malformed or malicious content
|
||||
html = None
|
||||
return final_link,html
|
||||
|
||||
def fetch_robot(base_url):
|
||||
try:
|
||||
@ -186,6 +180,7 @@ def index_pages(db,hostname,extracted_pages):
|
||||
text = doc["text"]
|
||||
checksums,sizes = calculate_checksums(text)
|
||||
doc["text_size"] = len(text)
|
||||
doc["text_md5"] = hashlib.md5(text.encode("utf8")).hexdigest()
|
||||
doc["paragraph_checksums"] = checksums
|
||||
doc["paragraph_sizes"] = sizes
|
||||
goodsz = sum(sizes)
|
||||
@ -214,6 +209,7 @@ def index_pages(db,hostname,extracted_pages):
|
||||
htdoc = get_link_doc(link,state)
|
||||
htdoc["html"] = html
|
||||
htdoc["html_size"] = len(html)
|
||||
htdoc["html_md5"]= hashlib.md5(html.encode("utf8")).hexdigest()
|
||||
# can be revisited - upsert
|
||||
del htdoc["url"]
|
||||
htmlcol.update_one({"url":link},{"$set":htdoc},upsert=True)
|
||||
@ -227,7 +223,7 @@ def index_pages(db,hostname,extracted_pages):
|
||||
checkcol.insert_one({"_id":chs})
|
||||
except pymongo.errors.DuplicateKeyError as err:
|
||||
pass
|
||||
linkcol.update_one({"url":original_link},{"$set":{"status":state}})
|
||||
linkcol.update_one({"url":link},{"$set":{"status":state}})
|
||||
|
||||
|
||||
def extract_links(link_batch,responses,hostname,rules,default_status="frontlink"):
|
||||
@ -263,23 +259,27 @@ def index_links(db,extracted_links):
|
||||
for link,status in extracted_links:
|
||||
if not is_link_good(link):
|
||||
continue
|
||||
doc = get_link_doc(link,status)
|
||||
try:
|
||||
linkcol.insert_one(doc)
|
||||
except pymongo.errors.DuplicateKeyError as ex:
|
||||
pass
|
||||
if status == "frontlink" or status == "backlink":
|
||||
doc = get_link_doc(link,status)
|
||||
try:
|
||||
linkcol.insert_one(doc)
|
||||
# dont overwrite
|
||||
except pymongo.errors.DuplicateKeyError as ex:
|
||||
pass
|
||||
else:
|
||||
print("updating " + link,status)
|
||||
linkcol.update_one({"url":link},{"$set":{"status":status,"updated_at":datetime.utcnow()}})
|
||||
|
||||
def get_link_features(link):
|
||||
a, urlpath = courlan.get_host_and_path(link)
|
||||
features = re.split("[/?&]",urlpath)
|
||||
#features = re.split("[/?-_=]",urlpath)
|
||||
res = []
|
||||
for feature in features:
|
||||
for i,feature in enumerate(features):
|
||||
if len(feature) < 1:
|
||||
continue
|
||||
if feature.isdigit():
|
||||
feature = "<NUM>"
|
||||
res.append(feature)
|
||||
feature = re.sub("[0-9]","*",feature)
|
||||
res.append(str(i)+ "-" + feature)
|
||||
if len(res) < 2:
|
||||
return None
|
||||
res = res[:-1]
|
||||
@ -295,20 +295,14 @@ class LinkClassifier:
|
||||
self.bad_count = 0
|
||||
self.alpha = 0.001
|
||||
|
||||
def train(self,db,hostname):
|
||||
linkcol = db["links"]
|
||||
res = linkcol.find({"host":hostname,"status": {"$not":{"$in":["frontlink","backlink"]}}})
|
||||
testset = []
|
||||
for i,item in enumerate(res):
|
||||
def train(self,links):
|
||||
for i,item in enumerate(links):
|
||||
link = item["url"]
|
||||
state = item["status"]
|
||||
cl = 0
|
||||
if state == "good":
|
||||
cl = 1
|
||||
print(cl,state,link)
|
||||
if i % 10 == 1:
|
||||
testset.append((link,cl))
|
||||
continue
|
||||
features = get_link_features(link)
|
||||
if features is None:
|
||||
continue
|
||||
@ -323,22 +317,42 @@ class LinkClassifier:
|
||||
self.badcounter[feature] += 1
|
||||
self.bdictsize = len(self.badcounter)
|
||||
self.gdictsize = len(self.goodcounter)
|
||||
|
||||
def test(self,testset):
|
||||
# eval
|
||||
gg = 0
|
||||
for l,cl in testset:
|
||||
true_positive = 0
|
||||
positive = 0
|
||||
false_negative = 0
|
||||
for item in testset:
|
||||
l = item["url"]
|
||||
cl = 0
|
||||
if item["status"] == "good":
|
||||
cl = 1
|
||||
pcp = self.classify(l)
|
||||
r = 0
|
||||
if pcp > 0:
|
||||
r = 1
|
||||
if cl == 1:
|
||||
if r == 1:
|
||||
true_positive += 1
|
||||
positive += 1
|
||||
if r == 1 and cl == 0:
|
||||
false_negative += 1
|
||||
if r == cl:
|
||||
gg += 1
|
||||
else:
|
||||
print("MISS",l,cl,pcp)
|
||||
print("Accuracy:")
|
||||
print(len(testset))
|
||||
print(gg / len(testset))
|
||||
print("Precision: {}, Recall: {}".format(true_positive/positive,true_positive/(true_positive+false_negative)))
|
||||
print("Accuracy:")
|
||||
acc = gg / len(testset)
|
||||
print(acc)
|
||||
return acc
|
||||
|
||||
def classify(self,link):
|
||||
if self.good_count == 0 or self.bad_count == 0:
|
||||
return random.uniform(-0.1,0.1)
|
||||
features = get_link_features(link)
|
||||
res = 0
|
||||
gp = math.log(self.good_count) - math.log(self.good_count + self.bad_count)
|
||||
@ -350,64 +364,25 @@ class LinkClassifier:
|
||||
goodprob = 0
|
||||
badprob = 0
|
||||
for feature in features:
|
||||
g = math.log((self.goodcounter[feature] + self.alpha)) - gcc
|
||||
g = math.log((self.goodcounter[feature] + self.alpha)) - gcc
|
||||
goodprob += g
|
||||
b = math.log(self.badcounter[feature] + self.alpha) - bcc
|
||||
badprob += b
|
||||
print(feature,g,b)
|
||||
if (goodprob + gp) > (badprob + bp):
|
||||
#if goodprob > badprob:
|
||||
res = 1
|
||||
pa = math.exp(goodprob + gp)
|
||||
pb = math.exp(badprob + bp)
|
||||
return pa - pb
|
||||
|
||||
return pa - pb #+ random.uniform(-0.001,0.001)
|
||||
|
||||
|
||||
def get_links(db,hostname,status,batch_size):
|
||||
linkcol = db["links"]
|
||||
# count downloaded links
|
||||
res = linkcol.aggregate([
|
||||
{ "$match": { "status": {"$not":{"$in":["frontlink","backlink"]}},"host":hostname } },
|
||||
{"$group":{"_id":None,
|
||||
"count":{"$count":{}},
|
||||
}
|
||||
},
|
||||
])
|
||||
links = set()
|
||||
out = list(res)
|
||||
if len(out) == 0:
|
||||
return list()
|
||||
if out[0]["count"] < 200:
|
||||
#res = linkcol.find({"status":status,"host":hostname},{"url":1},limit=batch_size)
|
||||
# get random links
|
||||
res = linkcol.aggregate([
|
||||
{ "$match": { "status": status,"host":hostname } },
|
||||
{ "$sample": { "size": batch_size } }
|
||||
])
|
||||
for i,doc in enumerate(res):
|
||||
#print(">>>>>" + status)
|
||||
#print(doc);
|
||||
links.add(doc["url"])
|
||||
if i >= batch_size:
|
||||
break
|
||||
else:
|
||||
cl = LinkClassifier()
|
||||
cl.train(db,hostname)
|
||||
res = linkcol.aggregate([
|
||||
{ "$match": { "status": status,"host":hostname } },
|
||||
{ "$sample": { "size": batch_size * 100 } }
|
||||
])
|
||||
outlinks = []
|
||||
for i,doc in enumerate(res):
|
||||
#print(">>>>>" + status)
|
||||
#print(doc);
|
||||
link = doc["url"]
|
||||
outlinks.append((doc["url"],cl.classify(link)))
|
||||
outlinks = sorted(outlinks, key=lambda x: x[1],reverse=True)
|
||||
links = [l[0] for l in outlinks[0:batch_size]]
|
||||
# todo remove very bad links from database
|
||||
return list(links)
|
||||
res = linkcol.find({"host":hostname,"status":status},limit=batch_size)
|
||||
links = []
|
||||
for item in res:
|
||||
links.append(item["url"])
|
||||
print("Got {} {}".format(len(links),status))
|
||||
return links
|
||||
|
||||
|
||||
|
||||
def fetch_sitemap_links(start_link):
|
||||
@ -415,42 +390,53 @@ def fetch_sitemap_links(start_link):
|
||||
navigation_links = trafilatura.sitemaps.sitemap_search(start_link,target_lang=LANGUAGE)
|
||||
for link in navigation_links:
|
||||
out.append((link,"frontlink"))
|
||||
print("Fetched {} sitemap links".format(len(out)))
|
||||
return out
|
||||
|
||||
def process_links(db,hostname,status,links=[],rules=None,batch_size=BATCHSIZE):
|
||||
#print(links)
|
||||
responses = fetch_pages(links)
|
||||
#print(responses)
|
||||
extracted_pages = extract_pages(links,responses)
|
||||
#print(extracted_pages)
|
||||
extracted_links = extract_links(links,responses,hostname,rules,status)
|
||||
#print(extracted_links)
|
||||
index_links(db,extracted_links)
|
||||
index_pages(db,hostname,extracted_pages)
|
||||
def fetch_front_links(start_link,rules):
|
||||
start_link,hostname = courlan.check_url(start_link)
|
||||
response = fetch_page(start_link)
|
||||
extracted_links = extract_links([start_link],[response],hostname,rules,"frontlink")
|
||||
print("Fetched {} frontlinks".format(len(extracted_links)))
|
||||
return extracted_links
|
||||
|
||||
|
||||
|
||||
def link_summary(db,hostname):
|
||||
linkcol = db["links"]
|
||||
#res = linkcol.distinct("hostname",{"hostname":hostname})
|
||||
|
||||
# count links
|
||||
res = linkcol.aggregate([
|
||||
{"$match":{"host":hostname}},
|
||||
{"$group":{"_id":"$status","count":{"$sum":1}}},
|
||||
{"$group":{"_id":"$status",
|
||||
"count":{"$count":{}},
|
||||
}
|
||||
},
|
||||
])
|
||||
badcount = 0
|
||||
goodcount = 0
|
||||
out = ["good","frontlink","backlink"]
|
||||
info = {}
|
||||
crawled_count = 0
|
||||
bad_crawl_count = 0
|
||||
for item in res:
|
||||
if item["_id"] not in out:
|
||||
badcount += item["count"]
|
||||
if item["_id"] == "good":
|
||||
goodcount = item["count"]
|
||||
info[item["_id"]] = item["count"]
|
||||
good_prob = goodcount / (goodcount + badcount)
|
||||
count = item["count"]
|
||||
st = item["_id"]
|
||||
print(st,count)
|
||||
if st == "good":
|
||||
goodcount += count
|
||||
if st != "frontlink" and st != "backlink":
|
||||
crawled_count += count
|
||||
if st != "good":
|
||||
bad_crawl_count += count
|
||||
info[st] = count
|
||||
info["crawled_count"] = crawled_count
|
||||
info["bad_crawl_count"] = bad_crawl_count
|
||||
baclink_cout = 0
|
||||
if "backlink" in info:
|
||||
backlink_count = info["backlink"]
|
||||
good_prob= 0
|
||||
if crawled_count > 0:
|
||||
good_prob = goodcount / crawled_count
|
||||
info["good_prob"] = good_prob
|
||||
info["bad_documents"] = badcount
|
||||
print(">>>Domain Content")
|
||||
contentcol = db["content"]
|
||||
res = contentcol.aggregate([
|
||||
@ -465,34 +451,57 @@ def link_summary(db,hostname):
|
||||
for item in res:
|
||||
text_size = item["text_size_sum"]
|
||||
good_document_characters = 0
|
||||
fetch_average_characters = 0
|
||||
if goodcount > 0:
|
||||
good_document_characters = text_size / goodcount
|
||||
fetch_average_characters = text_size / (goodcount + badcount)
|
||||
fetch_average_characters = text_size / crawled_count
|
||||
info["total_good_characters"] = text_size
|
||||
info["average_good_characters"] = good_document_characters
|
||||
info["average_fetch_characters"] = fetch_average_characters
|
||||
domaincol = db["domain"]
|
||||
if goodcount + badcount > 100:
|
||||
cl = LinkClassifier()
|
||||
cl.train(db,hostname)
|
||||
res = linkcol.aggregate([
|
||||
{ "$match": { "status": "backlink","host":hostname } },
|
||||
{ "$sample": { "size": BATCHSIZE * 100 } }
|
||||
])
|
||||
predicted_good = 0
|
||||
predicted_bad = 0
|
||||
domaincol.update_one({"host":hostname},{"$set":info},upsert=True)
|
||||
res = domaincol.find_one({"host":hostname})
|
||||
print(res)
|
||||
|
||||
def sample_links(db,hostname,status,batch_size):
|
||||
print("Getting backlinks")
|
||||
linkcol = db["links"]
|
||||
res = linkcol.find({"host":hostname,"status": {"$not":{"$in":["frontlink","backlink"]}}})
|
||||
cl = LinkClassifier()
|
||||
crawled_links = list(res)
|
||||
crawled_count = len(crawled_links)
|
||||
prediction_accuracy = 0
|
||||
if crawled_count > 200:
|
||||
# train on crawled links
|
||||
trainset,testset = split_train(crawled_links)
|
||||
cl.train(trainset)
|
||||
prediction_accuracy = cl.test(testset)
|
||||
sample_set_size = 10000
|
||||
res = linkcol.find({"host":hostname,"status": status},limit = sample_set_size)
|
||||
sample_links = []
|
||||
predicted_good = 0
|
||||
for item in res:
|
||||
for item in res:
|
||||
cll = cl.classify(item["url"])
|
||||
#cll += random.uniform(-0.1,0.1)
|
||||
sample_links.append((item["url"],cll))
|
||||
if cll > 0:
|
||||
predicted_good += 1
|
||||
else:
|
||||
predicted_bad += 1
|
||||
predicted_good_prob = 0
|
||||
if predicted_good + predicted_bad > 0:
|
||||
predicted_good_prob = predicted_good / (predicted_good + predicted_bad)
|
||||
info["predicted_good_prob"] = predicted_good_prob
|
||||
# TODO frontlinks are not unique!
|
||||
sample_links.sort(key=lambda x: x[1],reverse=True)
|
||||
predicted_good_prob = 0
|
||||
if len(sample_links) > 0:
|
||||
predicted_good_prob = predicted_good / len(sample_links)
|
||||
domaincol = db["domain"]
|
||||
info = {
|
||||
"predicted_good_prob":predicted_good_prob,
|
||||
"prediction_accuracy": prediction_accuracy,
|
||||
"crawled_count": crawled_count,
|
||||
}
|
||||
print(info)
|
||||
domaincol.update_one({"host":hostname},{"$set":info},upsert=True)
|
||||
domaincol.update_one({"host":hostname},{"$set":info})
|
||||
links = [l[0] for l in sample_links[0:batch_size]]
|
||||
return links
|
||||
|
||||
def domain_summary(db,hostname):
|
||||
linkcol = db["links"]
|
||||
@ -517,13 +526,16 @@ def createdb():
|
||||
linkcol.create_index("url",unique=True)
|
||||
linkcol.create_index("host")
|
||||
contentcol = db["content"]
|
||||
contentcol.create_index("url",unique=True)
|
||||
contentcol.create_index("url")
|
||||
contentcol.create_index("text_md5",unique=True)
|
||||
#contentcol.create_index({"paragraph_checksums":1})
|
||||
contentcol.create_index("host")
|
||||
htmlcol = db["html"]
|
||||
htmlcol.create_index("url",unique=True)
|
||||
htmlcol.create_index("url")
|
||||
htmlcol.create_index("html_md5",unique=True)
|
||||
domaincol = db["domains"]
|
||||
domaincol.create_index("host",unique=True)
|
||||
domaincol.create_index("average_fetch_characters",unique=True)
|
||||
|
||||
@cli.command()
|
||||
@click.argument("link")
|
||||
@ -563,7 +575,12 @@ def classify(start_link):
|
||||
db=myclient[DBNAME]
|
||||
start_link,hostname = courlan.check_url(start_link)
|
||||
cl = LinkClassifier()
|
||||
cl.train(db,hostname)
|
||||
linkcol = db["links"]
|
||||
res = linkcol.find({"host":hostname,"status": {"$not":{"$in":["frontlink","backlink"]}}})
|
||||
trainset, testset = split_train(res)
|
||||
|
||||
cl.train(trainset)
|
||||
cl.test(testset)
|
||||
|
||||
@cli.command()
|
||||
@click.argument("start_link")
|
||||
@ -572,23 +589,25 @@ def visit(start_link):
|
||||
db=myclient[DBNAME]
|
||||
start_link,hostname = courlan.check_url(start_link)
|
||||
batch_size = BATCHSIZE
|
||||
|
||||
print("Getting frontlinks")
|
||||
links = get_links(db,hostname,"frontlink",batch_size)
|
||||
print(f"Got {len(links)} frontlinks")
|
||||
if len(links) < batch_size:
|
||||
print("Fetching sitemap links")
|
||||
sitemap_links = fetch_sitemap_links(start_link)
|
||||
index_links(db,sitemap_links)
|
||||
links = get_links(db,hostname,"frontlink",batch_size)
|
||||
links.insert(0,start_link)
|
||||
if len(links) < batch_size:
|
||||
back_links = get_links(db,hostname,"backlink",batch_size - len(links))
|
||||
links += back_links
|
||||
|
||||
print("Processing links")
|
||||
rules = fetch_robot(hostname)
|
||||
responses = fetch_pages(links)
|
||||
# renew front links
|
||||
sitemap_links = fetch_sitemap_links(start_link)
|
||||
index_links(db,sitemap_links)
|
||||
front_links = fetch_front_links(start_link,rules)
|
||||
index_links(db,front_links)
|
||||
# start crawling
|
||||
# frontlinks first
|
||||
links = sample_links(db,hostname,"frontlink",batch_size)
|
||||
links.insert(0,start_link)
|
||||
# then backlinks
|
||||
if len(links) < batch_size:
|
||||
back_links = sample_links(db,hostname,"backlink",batch_size - len(links))
|
||||
links += back_links
|
||||
# index results
|
||||
print("Processing links")
|
||||
responses = []
|
||||
for link in links:
|
||||
responses.append(fetch_page(link))
|
||||
extracted_pages = extract_pages(links,responses)
|
||||
extracted_links = extract_links(links,responses,hostname,rules,"backlink")
|
||||
index_links(db,extracted_links)
|
||||
@ -8,3 +8,18 @@ mycol = mydb["customers"]
|
||||
mydict = {"text":"ahoj svet"}
|
||||
|
||||
x = mycol.insert_one(mydict)
|
||||
|
||||
def createdb():
|
||||
myclient = pymongo.MongoClient(CONNECTION)
|
||||
db=myclient[DBNAME]
|
||||
linkcol = db["links"]
|
||||
linkcol.create_index("url",unique=True)
|
||||
linkcol.create_index("host")
|
||||
contentcol = db["content"]
|
||||
contentcol.create_index("url",unique=True)
|
||||
#contentcol.create_index({"paragraph_checksums":1})
|
||||
contentcol.create_index("host")
|
||||
htmlcol = db["html"]
|
||||
htmlcol.create_index("url")
|
||||
domaincol = db["domains"]
|
||||
domaincol.create_index("host",unique=True)
|
||||
|
||||
@ -1,2 +1,7 @@
|
||||
trafilatura
|
||||
py3langid
|
||||
courlan
|
||||
pymongo
|
||||
click
|
||||
lxml
|
||||
rq
|
||||
|
||||
Loading…
Reference in New Issue
Block a user