Trey Spiva wrote:
Thanks for your help.
On Dec 4, 2007, at 10:08 AM, Jasper Kamperman wrote:
According to a hadoop tutorial
(http://wiki.apache.org/nutch/NutchHadoopTutorial) on wiki,
"you don't want to search using DFS, you want to search using local
filesystems. Once the index has been created on the DFS you can
use the hadoop copyToLocal command to move it to the local file
system as such" ... "Understand that at this point we are not using
the DFS or MapReduce to do the searching, all of it is on a local
machine".
So my understanding is that hadoop is only good for batch index
building, and is not proper for incremental index building and
search. Is this true?
That is correct. DFS for batch processing and MapReduce jobs.
Local servers (disks) for serving indexes. Even better put local
indexes (not segments, just indexes) in RAM.
In the NutchHadoopTutorial it says "the directory which it points to
should contain not just the index directory but also the linkdb,
segments, etc. All of these different databases are used by the
search. This is why we copied over the crawled directory and not just
the index directory."
Yes, that is correct. Only indexes in memory, other databases on local
disk.
We have found that a 4G machine can handle roughly 2M pages in the index
with no swapping occurring. Also load on the machine drop to
practically nothing even for 20+ queries per second because there is
virtually zero IO.
Dennis Kubes
If I understand your comment correctly, you are saying to not copy
the linkdb, and segments data just the index directory. Is that
correct? I think this is the source of my confusion, because it
sounds like the entire crawl data needs to be copied to each search
machine.
In the trick below, /path/to is the directory that has all the crawl
data, /path/to/linkdb, /path/to/segments, etc. The trick moves the
/path/to/indexes to another directory, then mounts a RAM filesystem on
/path/to/indexes. So to your nutch everything just looks like a big
crawl dir, but whenever it is accessing an index it is actually
getting it from RAM.
The reason I am asking is that when I read the article ACM article
by Mike Cafarella and Doug Cutting, to me it sounded like the
concern was to make the index structures fit in the primary memory,
not the entire crawled database. Did I miss understand the ACM
article?
No, what they are saying is the more pages per index per machine on
hard disk the slower the search. Keeping the main indexes, but not
the segments which hold raw page content, in RAM can speed up search
significantly.
One way to do this if you are running on linux is to create a tempfs
(which is ram) and then mount the filesystem in the ram. Then your
index acts normally to the application but is essentially served
from Ram. This is how we server the Nutch lucene indexes on our web
search engine (www.visvo.com) which is ~100M pages. Below is how
you can achieve this, assuming your indexes are in /path/to/indexes:
mv /path/to/indexes /path/to/indexes.dist
mkdir /path/to/indexes
cd /path/to
mount -t tmpfs -o size=2684354560 none /path/to/indexes
rsync --progress -aptv indexes.dist/* indexes/
chown -R user:group indexes
This would of course be limited by the amount of RAM you have on the
machine. But with this approach most searches are sub-second.
Thanks for the information.
Dennis Kubes
