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From Toke Eskildsen ...@statsbiblioteket.dk>
Subject Re: Sharding Techniques
Date Tue, 10 May 2011 08:01:48 GMT
On Mon, 2011-05-09 at 13:56 +0200, Samarendra Pratap wrote:
>  We have an index directory of 30 GB which is divided into 3 subdirectories
> (idx1, idx2, idx3) which are again divided into 21 sub-subdirectories
> (idx1-1, idx1-2, ...., idx2-1, ...., idx3-1, ...., idx3-21).

So each part is about ½ GB in size? That gives you a serious logistic
overhead. You state later that you only update the index once a day, so
it would seem that you have no need for the fast update times that such
small indexes give you. My guess is that you will get faster search
times by using a single index.


Down to basics, Lucene searches work by locating terms and resolving
documents from them. For standard term queries, a term is located by a
process akin to binary search. That means that it uses log(n) seeks to
get the term. Let's say you have 10M terms in your corpus. If you stored
that in a single field in a single index with a single segment, it would
take log(10M) ~= 24 seeks to locate a term. This is of course very
simplified.

When you have 63 indexes, log(n) works against you. Even with the
unrealistic assumption that the 10M terms are evenly distributed and
without duplicates, the number of seeks for a search that hits all parts
will still be 63 * log(10M/63) ~= 63 * 18 = 1134. And we haven't even
begun to estimate the merging part.

Due to caching, a seek is not equal to the storage being hit, but the
probability for a storage hit rises with the number of seeks and the
inevitable term duplicates when splitting the index.

> We have almost 40 fields in each index (is it a bad to have so many
> fields?). most of them are id based fields.

Nah, our index is about 40GB with 100+ fields and 8M documents. We use a
single index, optimized to 5 segments. Response times for raw searches
are a few ms, while response times for the full package (heavy faceting)
is generally below 300ms. Our queries are mostly simple boolean queries
across 13 fields.

> Keeping parts of indexes on different servers search on all of them and then
> merging the results - what could be the best approach?

Locate your bottleneck. Some well-placed log statements or a quick peek
with visualvm (comes with the Oracle JVM) should help a lot.


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