FAQ
We have worked on this problem on the server level as well. We have also
open sourced it at:

http://code.google.com/p/zoie/

wiki on the realtime aspect:

http://code.google.com/p/zoie/wiki/ZoieSystem

-John
On Fri, Dec 26, 2008 at 12:34 PM, Robert Engels wrote:

If you move to the "either embedded, or server model", the post reopen is
trivial, as the structures can be created as the segment is written.

It is the networked shared access model that causes a lot of these
optimizations to be far more complex than needed.

Would it maybe be simpler to move the "embedded or server" model, and add a
network shared file (e.g. nfs) access model as a layer? The latter is going
to perform far worse anyway.

I guess I don't understand why Lucene continues to try and support this
model. NO ONE does it any more. This is the way MS Access worked, and
everyone that wanted performance needed to move to SQL server for the server
model.

-----Original Message-----
From: Marvin Humphrey <marvin@rectangular.com>
Sent: Dec 26, 2008 12:53 PM
To: java-dev@lucene.apache.org
Subject: Re: Realtime Search
On Fri, Dec 26, 2008 at 06:22:23AM -0500, Michael McCandless wrote:
4) Allow 2 concurrent writers: one for small, fast updates, and one
for
big background merges.
Marvin can you describe more detail here?
The goal is to improve worst-case write performance.

Currently, writes are quick most of the time, but occassionally you'll trigger
a big merge and get stuck. To solve this problem, we can assign a merge
policy to our primary writer which tells it to merge no more than
mergeThreshold documents. The value of mergeTheshold will need tuning
depending on document size, change rate, and so on, but the idea is that we
want this writer to do as much merging as it can while still keeping
worst-case write performance down to an acceptable number.

Doing only small merges just puts off the day of reckoning, of course. By
avoiding big consolidations, we are slowly accumulating small-to-medium sized
segments and causing a gradual degradation of search-time performance.

What we'd like is a separate write process, operating (mostly) in the
background, dedicated solely to merging segments which contain at least
mergeThreshold docs.

If all we have to do is add documents to the index, adding that second write
process isn't a big deal. We have to worry about competion for segment,
snapshot, and temp file names, but that's about it.

Deletions make matters more complicated, but with a tombstone-based deletions
mechanism, the problems are solvable.

When the background merge writer starts up, it will see a particular view of
the index in time, including deletions. It will perform nearly all of its
operations based on this view of the index, mapping around documents which
were marked as deleted at init time.

In between the time when the background merge writer starts up and the time it
finishes consolidating segment data, we assume that the primary writer will
have modified the index.

* New docs have been added in new segments.
* Tombstones have been added which suppress documents in segments which
didn't even exist when the background merge writer started up.
* Tombstones have been added which suppress documents in segments which
existed when the background merge writer started up, but were not merged.
* Tombstones have been added which suppress documents in segments which have
just been merged.

Only the last category of deletions matters.

At this point, the background merge writer aquires an exclusive write lock on
the index. It examines recently added tombstones, translates the document
numbers and writes a tombstone file against itself. Then it writes the
snapshot file to commit its changes and releases the write lock.

Worst case update performance for the system is now the sum of the time it
takes the background merge writer consolidate tombstones and worst-case
performance of the primary writer.
It sounds like this is your solution for "decoupling" segments changes
due
to merges from changes from docs being indexed, from a reader's
standpoint?
It's true that we are decoupling the process of making logical changes to the
index from the process of internal consolidation. I probably wouldn't
describe that as being done from the reader's standpoint, though.

With mmap and data structures optimized for it, we basically solve the
read-time responsiveness cost problem. From the client perspective, the delay
between firing off a change order and seeing that change made live is now
dominated by the time it takes to actually update the index. The time between
the commit and having an IndexReader which can see that commit is
negligible
in comparision.
Since you are using mmap to achieve near zero brand-new IndexReader
creation, whereas in Lucene we are moving towards achieving real-time
by always reopening a current IndexReader (not a brand new one), it
seems like you should not actually have to worry about the case of
reopening a reader after a large merge has finished?
Even though we can rely on mmap rather than slurping, there are
potentially a
lot of files to open and a lot of JSON-encoded metadata to parse, so I'm not
certain that Lucy/KS will never have to worry about the time it takes to open
a new IndexReader. Fortunately, we can implement reopen() if we need to.
We need to deal with this case (background the warming) because
creating that new SegmentReader (on the newly merged segment) can take
a non-trivial amount of time.
Yes. Without mmap or some other solution, I think improvements to
worst-case
update performance in Lucene will continue to be constrained by
post-commit
IndexReader opening costs.

Marvin Humphrey


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