Issue 395 - Filter hits statistics backend

sitescripts/filterhits/geometrical_mean.py

 # coding: utf-8
 
 # This file is part of the Adblock Plus web scripts,
-# Copyright (C) 2006-2014 Eyeo GmbH
+# Copyright (C) 2006-2015 Eyeo GmbH
 #
 # Adblock Plus is free software: you can redistribute it and/or modify
 # it under the terms of the GNU General Public License version 3 as
 # published by the Free Software Foundation.
 #
 # Adblock Plus is distributed in the hope that it will be useful,
 # but WITHOUT ANY WARRANTY; without even the implied warranty of
 # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 # GNU General Public License for more details.
 #
 # You should have received a copy of the GNU General Public License
 # along with Adblock Plus.  If not, see <http://www.gnu.org/licenses/>.
 
 import itertools
 import sitescripts.filterhits.db as db
 from functools import partial
 
-def update_sql(interval, filter, domain, hits, timestamp):
-  """
-    Returns the SQL required to insert / update the hits for a
-    given domain + filter combo and to insert the filter if required.
-  """
-  filter = db.escape(filter)
-
-  return (
-    ("INSERT IGNORE INTO `filters` " +
-     "(filter, md5) VALUES ('%s', UNHEX(MD5(filter)));" +
-     "INSERT INTO `geometrical_mean` " +
-     "(filter_md5, domain, hits, timestamp) " +
-     "VALUES (UNHEX(MD5('%s')), '%s', %d, FROM_UNIXTIME(%d)) " +
-     "ON DUPLICATE KEY UPDATE " +
-     "hits = (" +
-     " POW(hits, 1 - (UNIX_TIMESTAMP(VALUES(timestamp)) - " +
-     "                UNIX_TIMESTAMP(timestamp)) / %d) * " +
-     " POW(VALUES(hits), (UNIX_TIMESTAMP(VALUES(timestamp)) - " +
-     "                    UNIX_TIMESTAMP(timestamp)) / %d)), " +
-     "timestamp = VALUES(timestamp);") % (
-       filter,
-       filter, db.escape(domain), int(hits), int(timestamp),
-       int(interval), int(interval)))
-
 def filter_hits(data):
   """
-    Generator that provides all filter hits for the given data,
-    in tuples like (filter, domain, hits, latest).
+  Generator that provides all filter hits for the given data,
+  in tuples like (filter, domain, hits, latest).
   """
   for filter, filter_data in data['filters'].iteritems():
     domains = itertools.chain(filter_data.get("thirdParty", {}).iteritems(),
                               filter_data.get("firstParty", {}).iteritems())
     for domain, domain_data in domains:
       yield (filter, domain, domain_data["hits"], domain_data["latest"] / 1000)
 
+def update_query(interval, filter, domain, hits, latest):
+  """
+  Function that takes the fields for a filter hit and returns them arranged
+  as the update SQL requires along with the SQL itself.
+  """
+  return (("""INSERT IGNORE INTO `filters`
+              (filter, sha1) VALUES (%s, UNHEX(SHA1(filter)))""",
+           filter),
+          ("""INSERT INTO `geometrical_mean`
+              (filter_sha1, domain, hits, timestamp)
+              VALUES (UNHEX(SHA1(%s)), %s, %s, FROM_UNIXTIME(%s))
+              ON DUPLICATE KEY UPDATE
+              hits = (
+                POW(hits, 1 - (UNIX_TIMESTAMP(VALUES(timestamp)) -
+                               UNIX_TIMESTAMP(timestamp)) / %s) *
+                POW(VALUES(hits), (UNIX_TIMESTAMP(VALUES(timestamp)) -
+                                   UNIX_TIMESTAMP(timestamp)) / %s)),
+              timestamp = VALUES(timestamp)""",
+           filter, domain, hits, int(latest), interval, interval))
+
 def update(interval, data):
   """
-    Returns an iterator of all the SQL statements needed to
-    update the aggregations for the given data + interval.
+  Returns an iterator of all the SQL and parameters needed to
+  update the aggregations for the given data + interval in the database.
   """
-  return itertools.imap(lambda fields: apply(partial(update_sql, interval), fields),
-                        filter_hits(data))
+  flatten = itertools.chain.from_iterable
+  return flatten(itertools.imap(lambda fields: update_query(interval, *fields),

[Sebastian Noack, 2015/02/17 14:59:17]

I feel that you are kinda overusing itertools. A generator seems to be more
appropriate and easier to read here: 

for fields in filter_hits(data):
  for query in update_query(interval, *fields):
    yield query

[kzar, 2015/02/24 18:05:11]

Done.

+                                filter_hits(data)))