brozzler/hq-notes.txt

possible architecture of brozzler-hq
====================================

keeps queues in rdbms
because easy to update, index on priority, index on canonicalized url
also easy to inspect
initially sqlite

-- sqlite3 syntax
create table brozzler_sites (
	id integer primary key,
	-- claimed boolean,
	site_json text,
	-- data_limit integer,  -- bytes
	-- time_limit integer,  -- seconds
	-- page_limit integer,
);

create table brozzler_urls (
	id integer primary key,
	site_id integer,
	priority integer,
	in_progress boolean,
	canon_url varchar(4000),
	crawl_url_json text,
	index(priority), 
	index(canon_url),
	index(site_id)
);

feeds rabbitmq:
 - json payloads
 - queue per site brozzler.{site_id}.crawl_urls
 - queue of unclaimed sites brozzler.sites.unclaimed

reads from rabbitmq
 - queue of new sites brozzler.sites.new
 - queue per site brozzler.{site_id}.completed_urls
   * json blob fed to this queue includes urls extracted to schedule

??? brozzler-hq considers site unclaimed if brozzler.{site_id}.crawl_urls has
not been read in some amount of time ??? or do workers need to explicitly
disclaim ???
 
brozzler-worker 
 - decides if it can run a new browser
 - if so reads site from brozzler.sites.unclaimed
 - site includes scope definition, crawl job info, ...
 - starts browser
 - reads urls from brozzler.{site-id}.crawl_urls
 - after each(?) (every n?) urls, feeds brozzler.{site_id}.completed_urls

=== considering distributed database ===
preferred database requirements:
- secondary index (so we can look up by url or priority)
- good performance on updates since we will be doing many updates
- good performance of secondary index on updates that change the value of secondarily indexed field
- ideally strong consistency to support multiple instances of brozzler-hq (but we can probably tolerate eventual consistency)
- redundancy, fault tolerance

alternative to distrubuted database: each brozzler-hq instance has its own local db (sqlite?) and distribution is handled at application level
but implementing redundancy, fault tolerance, etc sounds daunting

cassandra:
 - pluses
   - easy to set up cluster, add nodes, administer (all nodes are basically the same)
   - sharding, replication, fault tolerance are native, default features
   - seems more reliable than others?
 - minuses
   - not so good for looking up pages by both url and priority because 
     - secondary indexes are bad for columns with high cardinality (url), and also bad for columns that get updated frequently (priority)
     - other approach with second table by "priority_key" also not great because you can't update the value of a primary key, have to delete it and add a new row, and deletion in cassandra seems kind of heavy ("tombstones")
     - cqlsh:brozzler> select * from priorities order by priority_key desc limit 1;
     - InvalidRequest: code=2200 [Invalid query] message="ORDER BY is only supported when the partition key is restricted by an EQ or an IN."
     - cqlsh:brozzler> select * from priorities where priority_key >= 999900000000;
     - InvalidRequest: code=2200 [Invalid query] message="Only EQ and IN relation are supported on the partition key (unless you use the token() function)"
   - possible solution: finite set of possible priorities, e.g. 0-1000, then secondary-indexable etc
redis:
 - pluses
   - fast, reliable, already known at ia
   - perhaps can use the data structures
 - minuses
   - no experience with cluster at ia nor ilya
   - all data being in memory limits amount of data
   - Sam says sync to disk is slow
   - no real namespaces
hbase:
 - pluses
   - already deployed, known, dedup data is already in there
 - minuses
   - no secondary indexes
   - has not been very reliable for us, lots of moving parts
mongodb:
 - pluses
   - very popular according to http://db-engines.com/en/ranking
   - secondary indexes
   - some institutional knowledge (kenji)
 - minuses
   - according to kenji (https://webarchive.jira.com/wiki/display/~nlevitt/2015/08/10/Kenji%27s+thoughts+on+MongoDB)
     - cluster is very cumbersome to setup & manage
     - cluster member names are hard-wired
     - each shard must be configured with master-slave pair if you want high availability.
     - you cannot easily replace one shard with different VM
     - mongodb is known to be slow on writes
couchdb:
 - pluses
   - mature, more reliable?
 - minuses
   - doesn't support sharding natively
   - sharded implementations seem stale (bigcouch, lounge, ...)
multi-master rdbms (postgres-xl, mysql-cluster):
 - pluses
   - yes secondary indexes
 - minuses:
   - more difficult to deploy, administer?
   - seem to be less uses than other distributed dbs, smaller community, less knowledge and experience available
   - fault tolerance not so great? see http://www.slideshare.net/mason_s/postgres-xl-scaling slide 9