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From Li Li <fancye...@gmail.com>
Subject Re: is this rowkey schema feasible?
Date Mon, 13 Jan 2014 07:25:51 GMT
Here is the detailed architecture of my distributed vertical crawler.
http://www.flickr.com/photos/114261973@N07/

hope you can give me some advices.

1. goal
    I want to implement a distributed vertical(topical) crawler. it
will only store webpages of a certan topic. I will have a classifier
to do this.
    I estimated the amount of  webpages that need be store is about
tens of millions(maybe hundreds of millions as time goes).
    for vertical crawler, it should crawl the pages most likely
related to my target topics. So I need a frontier that can dispatch
task by priorities.
    for now, the priority is simple but we hope it can deal with
complicated priority algorithms.
    1.1 host priority
          we should crawl many hosts rather than only one single host
at the same time. initally, each hosts should be equally crawled. but
after time, we can calculate the priority of host dynamically
          e.g. we can control the speed of a certain host by it's
crawl history(some site will ban our crawler if we use too many
concurrent thread to it). or we can adjust the priority of a host by
whether it
          is relevant to our topic(we can calculate the relevance of
crawled page).
    1.2 enqueue time
          first enqueued webpages should get higher priority
    1.3 depth
          webpages with small depth will get higher priority(something
like BFS traverse)
    1.4 other page priorities
          e.g. page rank, list page/detail page ...

2. archeitecture
   see picture:   http://www.flickr.com/photos/114261973@N07/
    2.1 Seed Discover
          use google or other website to find some seed urls
    2.2 Url DB
          a distributed DB to store all metadata about urls(that's the
most hbase related)
    2.3 Task Scheduler
         as described before, the task scheduler select top N priority
webpages and dispatch them to fetcher clusters
    2.4 Message Queues
         we use ActiveMQ to decouple different modules and also load balance
    2.5 Fetchers
          Download webpages
    2.6 WebPageDB
          store webpages crawled and extracted metadata(such as
title,content, pub_time, author, etc ....) of this webpage. we
consider using hbase too.
    2.7 Extractors
          Using classifier to judge whether this page is related to
our topics and extracting metadata from it and store them to WebPageDB


3. main challenges
    3.1 Url DB
       as described before, this store(maybe hbase) should support
sophisticated pirority algorithms. and also we use it to avoid
crawling a webpage more than once.
    3.2 task scheduler
       how to achieve our goal

4. current solution
    4.1 use hbase(maybe together with phoenix) to store urls(we now
have not done the schema design, hoping get some advice here)
    4.2 scheduler algorithm
          int batchSize=10000;
          //dispatch batchSize tasks to different hosts by host priorities;
          Map<String,Integer> hostCount=...
          //select top priority urls from each host
          List<String> toBeCrawledUrls=new ArrayList<String>(batchSize);
          for(Entry<String,Integer> entry:hostCount.entrySet()){
               //select top priority N urls from a given host
               List<String>
urls=selectTopNUrlsFromHost(entry.getKey(), entry.getValue());
               toBeCrawledUrls.addAll(urls);
          }
          //dispatch this urls to message queue
          //monitor the message queue status
          //if the queue is all(or 3/4) consumed, goto top  and
dispatch another batch urls

5. using map-reduce or hbase?
     we discussed the possible usage of map-reduce or only hbase
     if the scheduling algorithm is very complicated and should
consider many things, maybe we should use map-reduce
     But for now, our algorithm is simple and using hbase
coprocesser(or phoenix) can be thought of a simple online map-reduce
     we can use coprocesser to implement simple aggregating function
or using phoenix sql like select count where group by having....




On Fri, Jan 3, 2014 at 8:19 PM, Jean-Marc Spaggiari
<jean-marc@spaggiari.org> wrote:
> Interesting. This is exactly what I'm doing ;)
>
> I'm using 3 tables to achieve this.
>
> One table with the URL already crawled (80 millions), one URL with the URL
> to crawle (2 billions) and one URL with the URLs been processed. I'm not
> running any SQL requests against my dataset but I have MR jobs doing many
> different things. I have many other tables to help with the work on the
> URLs.
>
> I'm "salting" the keys using the URL hash so I can find them back very
> quickly. There can be some collisions so I store also the URL itself on the
> key. So very small scans returning 1 or something 2 rows allow me to
> quickly find a row knowing the URL.
>
> I also have secondary index tables to store the CRCs of the pages to
> identify duplicate pages based on this value.
>
> And so on ;) Working on that for 2 years now. I might have been able to use
> Nuthc and others, but my goal was to learn and do that with a distributed
> client on a single dataset...
>
> Enjoy.
>
> JM
>
>
> 2014/1/3 James Taylor <jtaylor@salesforce.com>
>
>> Sure, no problem. One addition: depending on the cardinality of your
>> priority column, you may want to salt your table to prevent hotspotting,
>> since you'll have a monotonically increasing date in the key. To do that,
>> just add " SALT_BUCKETS=<n>" on to your query, where <n> is the number
of
>> machines in your cluster. You can read more about salting here:
>> http://phoenix.incubator.apache.org/salted.html
>>
>>
>> On Thu, Jan 2, 2014 at 11:36 PM, Li Li <fancyerii@gmail.com> wrote:
>>
>> > thank you. it's great.
>> >
>> > On Fri, Jan 3, 2014 at 3:15 PM, James Taylor <jtaylor@salesforce.com>
>> > wrote:
>> > > Hi LiLi,
>> > > Have a look at Phoenix (http://phoenix.incubator.apache.org/). It's a
>> > SQL
>> > > skin on top of HBase. You can model your schema and issue your queries
>> > just
>> > > like you would with MySQL. Something like this:
>> > >
>> > > // Create table that optimizes for your most common query
>> > > // (i.e. the PRIMARY KEY constraint should be ordered as you'd want
>> your
>> > > rows ordered)
>> > > CREATE TABLE url_db (
>> > >     status TINYINT,
>> > >     priority INTEGER NOT NULL,
>> > >     added_time DATE,
>> > >     url VARCHAR NOT NULL
>> > >     CONSTRAINT pk PRIMARY KEY (status, priority, added_time, url));
>> > >
>> > > int lastStatus = 0;
>> > > int lastPriority = 0;
>> > > Date lastAddedTime = new Date(0);
>> > > String lastUrl = "";
>> > >
>> > > while (true) {
>> > >     // Use row value constructor to page through results in batches of
>> > 1000
>> > >     String query = "
>> > >         SELECT * FROM url_db
>> > >         WHERE status=0 AND (status, priority, added_time, url) > (?,
?,
>> > ?,
>> > > ?)
>> > >         ORDER BY status, priority, added_time, url
>> > >         LIMIT 1000"
>> > >     PreparedStatement stmt = connection.prepareStatement(query);
>> > >
>> > >     // Bind parameters
>> > >     stmt.setInt(1, lastStatus);
>> > >     stmt.setInt(2, lastPriority);
>> > >     stmt.setDate(3, lastAddedTime);
>> > >     stmt.setString(4, lastUrl);
>> > >     ResultSet resultSet = stmt.executeQuery();
>> > >
>> > >     while (resultSet.next()) {
>> > >         // Remember last row processed so that you can start after that
>> > for
>> > > next batch
>> > >         lastStatus = resultSet.getInt(1);
>> > >         lastPriority = resultSet.getInt(2);
>> > >         lastAddedTime = resultSet.getDate(3);
>> > >         lastUrl = resultSet.getString(4);
>> > >
>> > >         doSomethingWithUrls();
>> > >
>> > >         UPSERT INTO url_db(status, priority, added_time, url)
>> > >         VALUES (1, ?, CURRENT_DATE(), ?);
>> > >
>> > >     }
>> > > }
>> > >
>> > > If you need to efficiently query on url, add a secondary index like
>> this:
>> > >
>> > > CREATE INDEX url_index ON url_db (url);
>> > >
>> > > Please let me know if you have questions.
>> > >
>> > > Thanks,
>> > > James
>> > >
>> > >
>> > >
>> > >
>> > > On Thu, Jan 2, 2014 at 10:22 PM, Li Li <fancyerii@gmail.com> wrote:
>> > >
>> > >> thank you. But I can't use nutch. could you tell me how hbase is used
>> > >> in nutch? or hbase is only used to store webpage.
>> > >>
>> > >> On Fri, Jan 3, 2014 at 2:17 PM, Otis Gospodnetic
>> > >> <otis.gospodnetic@gmail.com> wrote:
>> > >> > Hi,
>> > >> >
>> > >> > Have a look at http://nutch.apache.org .  Version 2.x uses HBase
>> > under
>> > >> the
>> > >> > hood.
>> > >> >
>> > >> > Otis
>> > >> > --
>> > >> > Performance Monitoring * Log Analytics * Search Analytics
>> > >> > Solr & Elasticsearch Support * http://sematext.com/
>> > >> >
>> > >> >
>> > >> > On Fri, Jan 3, 2014 at 1:12 AM, Li Li <fancyerii@gmail.com>
wrote:
>> > >> >
>> > >> >> hi all,
>> > >> >>      I want to use hbase to store all urls(crawled or not
crawled).
>> > >> >> And each url will has a column named priority which represent
the
>> > >> >> priority of the url. I want to get the top N urls order by
>> > priority(if
>> > >> >> priority is the same then url whose timestamp is ealier is
>> prefered).
>> > >> >>      in using something like mysql, my client application
may like:
>> > >> >>      while true:
>> > >> >>          select  url from url_db order by priority,addedTime
limit
>> > >> >> 1000 where status='not_crawled';
>> > >> >>          do something with this urls;
>> > >> >>          extract more urls and insert them into url_db;
>> > >> >>      How should I design hbase schema for this application?
Is
>> hbase
>> > >> >> suitable for me?
>> > >> >>      I found in this article
>> > >> >>
>> > >>
>> >
>> http://blog.semantics3.com/how-we-built-our-almost-distributed-web-crawler/
>> > >> >> ,
>> > >> >> they use redis to store urls. I think hbase is originated
from
>> > >> >> bigtable and google use bigtable to store webpage, so for
huge
>> number
>> > >> >> of urls, I prefer distributed system like hbase.
>> > >> >>
>> > >>
>> >
>>


On Sat, Jan 11, 2014 at 1:38 AM, Stack <stack@duboce.net> wrote:
> On Thu, Jan 9, 2014 at 6:08 PM, Li Li <fancyerii@gmail.com> wrote:
>
>> thanks.
>> 1. this is just a url frontier for url duplication and scheduler usage.
>>
>
>
> I have made a few attempts at trying to reply to this note but I keep
> running into the fact that I know little about how you are thinking of
> architecting the crawler, what scale you are hoping to achieve, how the
> crawlers will be fed and where they will put the crawled content when done,
> what is doing the page parse for new URLs to crawl, is it inline w/ the
> crawl or done offline, whether the crawl is continuous or stepped (what is
> wrong w/ nutch), how revisit priority is done, page fingerpriinting and how
> you will resolve when you find the same page via two different URLs,
> etc....... and so my response keeps running off into speculation.
>
> If you have a one pager you'd like to share off-list, I could give some
> feedback (no problem).  I have an interest in this topic.
>
> High level I'd consider HBase as a candidate repository for all URLs ever
> seen keeping all state related to an URL for a distributed crawler.  Ditto
> for domains.
>
> Regards already-seen/dup URLs lookup and for the queues the crawlers pull
> from allowing inserts of higher priority items, etc., while you could do
> this HBase -- and it might be ok to start here -- I think you will soon
> find that you will want to do more purposed implementations.  For example,
> if the already-seen check is being done inline w/ the page crawl, a lookup
> into hbase would be sub-millisecond if out of cache but milliseconds if
> not.  You do not want your crawler to stall on a millisecond lookup per URL
> discovered, etc.
>
> On 6., try and make it so you can scan rather than point get for each URL.
>  Less i/o.
>
> St.Ack

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