riaksearch performace when numFound is high
rzezeski at basho.com
Fri Jul 15 14:49:00 EDT 2011
I'm curious to know how you are querying search (i.e. which client/API) and
what your setup looks like (i.e. # of nodes, disk, network between them,
etc). How many of these #2 query types are you seeing and what's the
average # of results being returned? Is your search instance opened up to
Ignoring any other inefficiencies in the Search code I'd say that there are
two main points working against you in query #2
1.) Search uses a _global index_  which means that, as Rusty would say,
it's partitioned by term (as opposed to a _local index_ which would
partition by document). It has been found, in cases where the query
contains less terms than the processor count, that a global index scales out
better as it has better concurrency characteristics . However, a local
index can balance load better and in the case of #2 could possibly drop
latency times. When you perform that "text:guitar AND date:07/14/11" each
term is queried by one, and only one, vnode. These results are then
combined by a coordinator (called a broker in ). How much using a local
index would change your observed latency is unknown to me, but it would have
other effects that may offset any latency drops.
2.) Search is not limiting the results returned. Instead of sorting the
results based on relevance and only taking the top N from each query term
it's getting all data from each query, passing it to the coordinator and
saying "hey, you figure this out." When you run that #2 query, as you said,
you are bounded by your largest result set because that's the only way for
search to know it's correctly answered the query. Said another way, you may
only have 10 entries for the 14th but you still need to search all 100K
guitar entries to determine which ones fall on that date. Performing some
sort of relevance calculation beforehand and limiting the result
would certainly help but it also means you won't get the full result set. I
don't think there's any way to have your cake and eat it too unless you can
afford to put everything in memory.
At this point in time I think inline fields will be your best bet. Inline
fields help dramatically here because you are essentially bringing part of
your document into each term entry in the index. This means you can query
on high cardinality terms and filter on lower ones, i.e. low latency (high
and low being relative here). If you're worried about disk space then you
can make use of the `only` value for the inline attribute. This tells
Search to store this field's value inline but _don't_ index it. If you're
only using the `text` field to filter results then this is exactly what you
should do. In fact, I would recommend you do that because any search
against the `text` field for a corpus of tweets is probably going to have a
large result set.
: C.S. Badue. Distributed query processing using partitioned inverted
files. Master's thesis, Federal University of Minas Gerais, Belo Horizonte,
Minas Gerias, Brazil, March 2001.
On Thu, Jul 14, 2011 at 6:20 PM, Greg Pascale <greg at clipboard.com> wrote:
> Hi Ryan,
> Yes we are using 14.2.
> I think I get what you are saying about inline fields. It looks like it
> will fix some of our problems, but not all of them. If you'll indulge me in
> a little contrived example, I think I can explain what I mean.
> Let's say I'm implementing a simple twitter clone. In my system, tweets
> have a user, text, date and can be either public or private - so one might
> look like
> username: greg,
> public: true,
> text: "I bought a new guitar!",
> date: 07/14/11
> Let's say that my service is popular - I have 100k users, each of whom has
> published exactly 100 tweets, and exactly half of the tweets are public, the
> rest are private. So I have 10 million tweets total and 5 million of them
> are public.
> Query 1: "username:greg AND public:false" - this finds all tweets by greg
> that are private. In this case "username:greg" matches 100 results and
> "public:false" matches 5 million. My tests have shown that the performance
> of this compound query will be roughly equivalent to the worse of the two
> ("public:false") - so this query will be way too slow.
> However, since "public" values are nice and small, I can inline it for a
> big win.
> Query 2: "text:guitar AND date:07/14/11" - this finds all tweets from today
> that contain "guitar". Suppose there are 100k tweets that contain "guitar",
> but it's early in the day so there have only been 1k tweets in total on
> 07/14/11. My result set is therefore no bigger than 1k (probably much
> smaller unless all my users bought new guitars today), but this query is
> still bounded by the "text:guitar" piece which matches 100k results. In this
> case, inlining date wouldn't help because it's not the slow part, and
> indexing on the text field isn't practical - it's too big.
> If you could follow that, am I understanding this correctly? In our system,
> we have some queries like #1 above, but since we support full text search,
> many like #2 as well. Do you have any suggestions for what we could do to
> make queries like #2 performant?
> On Wed, Jul 13, 2011 at 7:19 PM, Ryan Zezeski <rzezeski at basho.com> wrote:
>> I'm assuming you are using 14.2.
>> 1) There is a bug in 14.2 that will cause a (potentially very fast
>> growing) memory leak when using AND. This is unfortunate, sorry. The good
>> news I have since patched it .
>> 2) This is your best course of action, and you were so close but you've
>> actually crossed your fields. That is, the inline field should be the one
>> that contains the more common term (i.e. the 'text' field). So you should
>> perform a range query on your date with a filter on the text inline field.
>> Obviously, the more terms in this field the more the index will inflate
>> (space-wise), but if you can live with that then it should reduce your
>> latency substantially (famous last words). Please try this and get back to
>> 3) That is a very well written article, props to the author. However, I
>> would leave this as a last resort. Try what I mentioned in #2, and if
>> that's not enough to get you by then let's brainstorm.
>> On Wed, Jul 6, 2011 at 2:43 PM, Greg Pascale <greg at clipboard.com> wrote:
>>> I'm looking at ways to improve riaksearch queries that produce a lot of
>>> In my use case, I only ever want the top 20 results for any query, and
>>> results should be ordered by date (which is encoded in the key). For
>>> searches with few matches (numFound < ~1000), performance is great. For
>>> searches with more matches (numFound > ~10000), performance starts to lag
>>> even though I only ever want the top 20. I assume this is because the system
>>> needs to fetch and sort all of the results to know what the top 20 are, but
>>> I'm hoping I can exploit the constraints of my use case in some way to
>>> increase performance. I've looked at the following approaches.
>>> 1) AND the "text:" term with a small date range (e.g. text:<common word>
>>> AND date:[<yesterday to today>]). This reduces the result set, but
>>> performance does not improve. At best, the performance is as good as simply
>>> doing the "text:<common word>" search without the date range, and in some
>>> cases worse.
>>> 2) Same as above, but make the date an inline field. From what I could
>>> find on the topic, it sounded like this is exactly what inline fields or
>>> for, but I was disappointed to discover it performed far worse than even the
>>> compound query above.
>>> 3) In this article <http://blog.inagist.com/searching-with-riaksearch>,
>>> which I was linked to from somewhere on the basho site, the author describes
>>> a technique in which he calls search_fold directly and stops after he's
>>> received enough results. He claims this is possible in his case because
>>> results are returned in key order, and he's chosen his keys to match the
>>> desired ordering of his results. My keys have the same property, as I'm
>>> already using the presort=key option. Is this behavior of search_fold a
>>> lucky side-effect, or is this actually guaranteed to work?
>>> Am I simply expecting too much of riaksearch here, or is there a way to
>>> make this work? If all else fails, I suppose I could divide my data into
>>> more buckets, but I'm hoping to avoid that as it would make querying much
>>> more complex.
>>> riak-users mailing list
>>> riak-users at lists.basho.com
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