# Building a Zite Replacement (Part 8)

Posted by Graham Wheeler on Saturday, October 31, 2015

Happy Halloween, all!

I’m sitting here handing out candy and glow necklaces to all comers so its a good time to write a new post.

It’s been a while since much happened as I’ve been really busy with the beta release of Google Cloud Datalab, which is my day job. But now that is out and it’s the weekend and lousy weather here in the Pacific Northwest it’s been a good day t get back to things.

Today I did something I’ve been meaning to do for a while, which is to change the code to populate a MongoDB database rather than writing files to the file system. Interestingly it seems to be quite a bit slower than the file system but hopefully it will scale better and make up for things when I have ad-hoc queries to do.

I hadn’t used MongoDB before so it was a learning opportunity. Mongo is a no-SQL database that stores collections of documents in its databases, rather than tables of records, so it is well suited to storing the JSON RSS feed objects. It’s very easy to use too. I modified my exitisting code to take two callbacks, one to create an ID from an article, and the other to save the article give the ID. For the old code, the ID just corresponds to the pathname of the file, and saving just saves the JSON as a string to that file. The ID creator has a secondary role of checking if we have already fetched that object before, in which case we don’t need to save it.

Each document has a special unique key ‘_id’, and can have secondary keys. The document itself is an ordered set of keys with associated values (which can themselves be documents), so this maps reasonably well to Javascript objects. Keys are any UTF-8 strings but should avoid ‘$’ and ‘.’ which have special usage, and NUL which is used as a key terminator. Keys must be unique, and are case sensitive. Collections (groups of documents) can store different types of documents; they do not have a fixed schema. That said, it is generally more sensible and efficient to keep similar documents in the same collection, and make use of multiple collections, rather than putting everything in one collection. Collection names are non-empty UTF-8 strings that don’t include NUL or ‘$’ and don’t start with ‘system.’ (the latter two constraints are for implementation-specific reasons).

Database names should be non-empty alphanumeric ASCII strings of 64 bytes or less (not actually quite that restrictive but that’s a good guideline). ‘admin’ is a special root database, and ‘config’ is a special database that stores sharding information. ’local’ is a database that can be used to store collections that should not be replicated.

I now have an articles collection, and my callbacks to use Mongo just look like this:

#!python
import pymongo

if articles.find_one({'guid': id}):
return None
return id



Very simple, no? But I’m getting a bit ahead of myself; it’s worth mentioning how I installed Mongo. You can download it from mongodb.org. I went for version 2.6 rather than 3 as there is a nice free GUI tool for inspecting and querying the database called RoboMongo and it doesn’t work yet with Mongo 3.

If you download the Mac version of Mongo you get compiled binaries, not an installer. These need to be put in your path somewhere. I use Homebrew for some software on the Mac so I already have a /usr/local/bin directory in my path, and just put the Mongo files there. I know, this is polluting the Homebrew install somewhat, but it seemed the most practical anyway. You then need to create a directory for the databases, which by default on the Mac should be /data/db. Make sure you have write permission, or at least the user account that is going to run the mongo server does. You can then start Mongo by running ‘mongod’ at the command line.

Mongo creates databases and collections when they are accessed which makes things very simple. The initial code in my fetcher looks like this:

#!python
import datetime
import sys
import pymongo

if __name__ == '__main__':
con = pymongo.MongoClient()
# Creates DB if needed
db = con.feed_database
# Creates collection if needed
articles = db.articles
feedlist = sys.argv[2] if len(sys.argv) > 2 else 'feeds.txt'
start = datetime.datetime.now() - datetime.timedelta(days=30)
# Do the fetch.


I mentioned before how I changed the fetcher to use parallel fetches. That uses the Python thread library, so the code continues:

#!python

# Spawn 20 fetchers
for i in range(20):
t = Fetcher(queue, start, articles, dictionary, stop_words)
t.setDaemon(True)
t.start()

# Queue up the feeds and wait until done.
feed_list = sys.argv[2] if len(sys.argv) > 2 else 'feeds.txt'
queue.put(feed)
queue.join()


Each thread is handled by an instance of the Fetcher class:

#!python

def __init__(self, queue, start, articles, dictionary, stop_words):
super(Fetcher, self).__init__()
self.queue = queue
self._start = start
self._articles = articles
self._dictionary = dictionary
self._stop_words = stop_words

def run(self):
while True:
feed = self.queue.get()

try:
feedlib.process_feed(feed, self._dictionary,
self._stop_words,
start_date=self._start,
idmaker=idmaker, handler=handler)
except Exception as e:
msg = "Failed to process %s: %s" % (feed, str(e))
print msg
logging.getlogger().error(msg)