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This commit is contained in:
Daniel Hládek 2023-04-05 17:09:42 +02:00
parent deced51d48
commit a26613ebb1
1 changed files with 140 additions and 148 deletions
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288
mongo/mongocwarler.py
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@ -18,11 +18,11 @@ import re
import time
import collections
import math
import json
import random
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
@ -102,39 +102,34 @@ 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:
@ -227,7 +222,7 @@ 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 +258,28 @@ 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},{"$set":{"status":status,"updated_at":datetime.utcnow()}})
def get_link_features(link):
a, urlpath = courlan.get_host_and_path(link)
features = re.split("[/?&]",urlpath)
#features = re.split("[/?-_=]",urlpath)
res = []
for feature in features:
for i,feature in enumerate(features):
if len(feature) < 1:
continue
if feature.isdigit():
feature = "<NUM>"
res.append(feature)
res.append(str(i)+ "-" + feature)
if len(res) < 2:
return None
res = res[:-1]
@ -295,11 +295,9 @@ class LinkClassifier:
self.bad_count = 0
self.alpha = 0.001
def train(self,db,hostname):
linkcol = db["links"]
res = linkcol.find({"host":hostname,"status": {"$not":{"$in":["frontlink","backlink"]}}})
def train(self,links):
testset = []
for i,item in enumerate(res):
for i,item in enumerate(links):
link = item["url"]
state = item["status"]
cl = 0
@ -336,9 +334,13 @@ class LinkClassifier:
print("MISS",l,cl,pcp)
print("Accuracy:")
print(len(testset))
print(gg / len(testset))
acc = gg / len(testset)
print(acc)
return acc
def classify(self,link):
if self.good_count + self.bad_count == 0:
return random.uniform(-0.1,0.1)
features = get_link_features(link)
res = 0
gp = math.log(self.good_count) - math.log(self.good_count + self.bad_count)
@ -363,51 +365,15 @@ class LinkClassifier:
return pa - pb
def get_links(db,hostname,status,batch_size):
linkcol = db["links"]
# count downloaded links
res = linkcol.aggregate([
{ "$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 +381,48 @@ 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":{"$count":{}},
}
},
])
badcount = 0
goodcount = 0
out = ["good","frontlink","backlink"]
info = {}
crawled_count = 0
for item in res:
if item["_id"] not in out:
badcount += item["count"]
if item["_id"] == "good":
goodcount = item["count"]
info[item["_id"]] = item["count"]
good_prob = goodcount / (goodcount + badcount)
count = item["count"]
st = item["_id"]
print(st,count)
if st == "good":
goodcount += count
if st != "frontlink" and st != "backlink":
crawled_count += count
info[st] = count
baclink_cout = 0
if "backlink" in info:
backlink_count = info["backlink"]
good_prob= 0
if crawled_count > 0:
good_prob = goodcount / crawled_count
info["good_prob"] = good_prob
info["bad_documents"] = badcount
print(">>>Domain Content")
contentcol = db["content"]
res = contentcol.aggregate([
@ -465,35 +437,53 @@ def link_summary(db,hostname):
for item in res:
text_size = item["text_size_sum"]
good_document_characters = 0
fetch_average_characters = 0
if goodcount > 0:
good_document_characters = text_size / goodcount
fetch_average_characters = text_size / (goodcount + badcount)
fetch_average_characters = text_size / crawled_count
info["total_good_characters"] = text_size
info["average_good_characters"] = good_document_characters
info["average_fetch_characters"] = fetch_average_characters
domaincol = db["domain"]
if goodcount + badcount > 100:
cl = LinkClassifier()
cl.train(db,hostname)
res = linkcol.aggregate([
{ "$match": { "status": "backlink","host":hostname } },
{ "$sample": { "size": BATCHSIZE * 100 } }
])
predicted_good = 0
predicted_bad = 0
for item in res:
cll = cl.classify(item["url"])
if cll > 0:
predicted_good += 1
else:
predicted_bad += 1
predicted_good_prob = 0
if predicted_good + predicted_bad > 0:
predicted_good_prob = predicted_good / (predicted_good + predicted_bad)
info["predicted_good_prob"] = predicted_good_prob
print(info)
domaincol.update_one({"host":hostname},{"$set":info},upsert=True)
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)
min_train_size = 200
prediction_accuracy = 0
if crawled_count > min_train_size:
# train on crawled links
prediction_accuracy = cl.train(crawled_links)
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"])
sample_links.append((item["url"],cll))
if cll > 0:
predicted_good += 1
# TODO frontlinks are not unique!
sample_links.sort(key=lambda x: x[1],reverse=True)
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})
links = [l[0] for l in sample_links[0:batch_size]]
return links
def domain_summary(db,hostname):
linkcol = db["links"]
#res = linkcol.distinct("hostname",{"hostname":hostname})
@ -572,23 +562,25 @@ 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)