zz

mongo/mongocrawler.py

@@ -169,7 +169,7 @@ def extract_pages(link_batch:list,responses:list)->list:
         out.append((original_link,final_link,html,doc))
     return out
 
-def set_content_checksum(doc):
+def set_content_checksums(doc):
     text = doc["text"]
     checksums,sizes = calculate_checksums(text)
     doc["text_size"] = len(text)
@@ -191,7 +191,14 @@ def index_pages(db,hostname,extracted_pages):
     contentcol = db["content"]
     checkcol = db["check"]
     links = []
+    # stats of the batch
+    good_document_count = 0
+    document_count = 0
+    text_size = 0
+    good_text_size = 0
+    original_text_size = 0
     for original_link,final_link,html,doc in extracted_pages:
+        document_count += 1
         state = "good"
         link = original_link
         if original_link != final_link:
@@ -205,9 +212,10 @@ def index_pages(db,hostname,extracted_pages):
         if doc is not None:
             set_content_checksums(doc)
             tsz = doc["text_size"]
+            text_size += tsz
             psz = doc["paragraph_sizes_sum"]
             if tsz < TEXT_TRASH_SIZE or psz/tsz < TEXT_TRASH_RATIO:
-                state = "trash"
+                state = "small"
         # check copy
         if state == "good":
             origsz = 0
@@ -219,8 +227,11 @@ def index_pages(db,hostname,extracted_pages):
 
             if (1 - (origsz / tsz)) > TEXT_TRASH_RATIO:
                 state = "copy"
-            print(copysz)
+            original_text_size += origsz
+            print(origsz)
         if state == "good":
+            good_document_count += 1
+            good_text_size += doc["text_size"]
             htdoc = get_link_doc(link,state)
             htdoc["html"] = html
             htdoc["html_size"] = len(html)
@@ -242,6 +253,20 @@ def index_pages(db,hostname,extracted_pages):
         linkdoc = get_link_doc(link,state)
         del linkdoc["url"]
         linkcol.update_one({"url":link},{"$set":linkdoc})
+    batchdoc = {
+      "host": linkdoc["host"],
+      "domain": linkdoc["domain"],
+      "created_at":  datetime.utcnow(),
+      "good_document_count":good_document_count,
+      "document_count":document_count,
+      "text_size":text_size,
+      "good_text_size":good_text_size,
+      "original_text_size":original_text_size,
+      "batch_size": BATCHSIZE,
+      "average_fetch_characters": text_size / BATCHSIZE,
+    }
+    db["batches"].insert_one(batchdoc)
+    print(batchdoc)
 
 from bs4 import BeautifulSoup
 import urllib.parse
@@ -267,12 +292,11 @@ def get_bs_links(link,html):
             netloc = parsed.netloc
             path = os.path.normpath(parsed.path)
             scheme = parsed.scheme
+            # internal link
             if parsed.netloc == "":
                 scheme = base.scheme
-                if parsed.path == "/":
-                    netloc = base.netloc
-                else:
                 netloc = base.netloc
+                if not parsed.path.startswith("/"):
                     path = os.path.normpath(base.path +"/" + path)
             if not scheme.startswith("http"):
                 continue
@@ -304,8 +328,6 @@ def extract_links(link_batch:list,responses:list,hostname:str,rules,default_stat
             continue
         internal_links, external_links = get_bs_links(final_link,html)
         #external_links = courlan.extract_links(html,final_link,external_bool=True,language=LANGUAGE)
-        for link in external_links:
-            links[link] = "frontlink"
         #internal_links = courlan.extract_links(html,final_link,external_bool=False,language=LANGUAGE)
         #print(extracted_links)
         for link in internal_links:
@@ -547,9 +569,9 @@ def sample_links(db,hostname,status,batch_size):
         trainset,testset = split_train(crawled_links)
         cl.train(trainset)
         prediction_accuracy = cl.test(testset)
+
     sample_set_size = SAMPLE_SET_SIZE
     res = linkcol.find({"host":hostname,"status": status})
-    sample_links = []
     predicted_good = 0
     visitcounter = collections.Counter()
     good_links = []
@@ -567,7 +589,7 @@ def sample_links(db,hostname,status,batch_size):
             visitcounter[feature] += 1
     mls = int(min(batch_size*(1- DISCOVER_LINK_RATIO),len(good_links)))
     random.shuffle(good_links)
-    links = good_links[0:mls]
+    links = list(good_links[0:mls])
     numdiscover = len(discover_links)
     eval_discover_links = []
     for link in discover_links:
@@ -582,7 +604,7 @@ def sample_links(db,hostname,status,batch_size):
     #print(eval_discover_links)
     mls = int(min(batch_size * DISCOVER_LINK_RATIO,len(eval_discover_links)))
     links += [l[0] for l in eval_discover_links[0:mls]]
-    return links
+    return list(set(links))
 
 def domain_summary(db,hostname):
     linkcol = db["links"]
@@ -613,6 +635,9 @@ def createdb():
     domaincol = db["domains"]
     domaincol.create_index("host",unique=True)
     domaincol.create_index("average_fetch_characters",unique=True)
+    batchcol = db["batches"]
+    batchcol.create_index("host")
+    batchcol.create_index("created_at")
 
 def parseurl(link):
     link,hostname = courlan.check_url(link)
@@ -628,6 +653,11 @@ def parseurl(link):
     doc = trafilatura.bare_extraction(html)
     import pprint
     pprint.pprint(doc)
+    internal_links, external_links = get_bs_links(link,html)
+    print(internal_links)
+    print(external_links)
+
+
 
 def externaldomains(link):
     html = trafilatura.fetch_url(link,decode=True)
@@ -666,7 +696,10 @@ def visit(start_link):
     # start crawling
     # frontlinks first
     links = sample_links(db,hostname,"frontlink",batch_size)
+    if start_link not in links:
         links.insert(0,start_link)
+    print("sampled")
+    print(links)
     # index results
     print("Processing links")
     responses = []

mongo/requirements.txt

@@ -4,4 +4,5 @@ courlan
 pymongo
 click
 lxml
+bs4
 rq