I have a search that's purely based on attributes rather than any text searching. I'd like to know if there's a way to interpret the scores returned from elasticsearch in such a way as to determine if a match is good or not (or how good it is on a scale of 0-100)..
The scores obviously change based on the query - if I ask for things that have 5 attributes using an OR search - those that have all 5 get a highscore, whilst those with 1 get a lower score (which is fine..) - I'd like to know if there's an easy way to ask ES: given this query, what's the max score anything could give me?
I could do things like say that this result is a 90% match to your query, this one is a 50% match. Rather than this one scored 1.746373..
I'd rather not be double checking each result against the search to work this out..
Related
Based on this article - link there are some serious performance implications with having track_total_hits property set to true.
We currently use it to get the number of documents matching after users search. Then user can use pagination to scroll through the results. The number of documents for such a search usually ranges from 10k - 5M.
Example of a user work flow:
User performs a search which matches 150.000 documents
We show him the first 200 results which he can scroll through but we also show him the total number of documents found in the search.
Since we always show the number of document searches and often those numbers can be quite high we need some kind of a way to get that count. I'm not sure but if we almost always perform paginated searches I would assume a lot of the things would be in memory ? Maybe then this actually effects us less then how it's shown in the provided article?
Some kind of an approximation and not an exact count would be ok for us if it would improve performance.
Is there such an option in Elasticsearch where we can get approximated count on search requests ?
There is no option to get an approximate count, but you may want to consider assigning track_total_hits a lower bound instead of true , which is a good compromise from a performance standpoint ( https://www.elastic.co/guide/en/elasticsearch/reference/master/search-your-data.html#track-total-hits)
That way, you can show users that there are at least k results - but there could be more.
Also, try using search_after (if you are not using it already) for pagination.
I've implemented a fuzzy search algorithm based on a N closest neighbors query for given search terms. Each query returns a pre-set number of raw results, in my case a max. of 200 hits / query, sorted descending by score, highest score first.
The raw search already produces good results, but in some rather rare cases not good enough so I've added another post-processing layer or better said another metric to the raw search results based on Levenshtein-Damerau algorithm that measures the word / phrase distance between query term(s) and raw results. The lower the resulting score the better, 0.0 would be an exact match.
Using the Levenshtein-Damerau post-processing algorithm I sort the results ascending, from the lowest to the highest.
The quality of matches is amazingly good and all relevant hits are ranked to the top. Still I have the bulk of 200 hits from the core search and I am looking for a smart way to limit the final result set down to a maximum of 10-20 hits. I could just add a static limit as it is basically done. But I wonder if there is a better way to do this based on the individual metrics I get with each search result set.
I have the following result metrics:
The result score of the fuzzy core search search, a value of type float/double. The higher the better
The Levenshtein-Damerau post processing weight, another value of type float/double. The lower the better
And finally each result set knows its minimum and maximum score limits. Using the Levenshtein-Damerau post processing algorithm on the raw results I take the min/max values from there.
The only ideas I have is to take a sub-range out of the result set, something like the top 20% results which is simple to achieve. More interesting would be to analyse the top result scores/metrics and find some indication where it gets too fuzzy. I could use the metrics I gather inside my Levenshtein-Damerau algorithm layer, respectively the word- and phrase-distance parameters - these values along with 2 other parameters make up the final distance score. For example if the word- and/or phrase distance exceed a certain threshold, then skip the result. This way is a bit more complicated but possible.
Well, I wonder if there are more opportunities I could use and just not obviously see. Once again, I would like to omit a static limit and make it more flexible on each individual result set.
Any hints or further ideas are greatly appreciated.
I'm trying to understand boosting and if boosting is the answer to my problem.
I have an index and that has different types of data.
EG: Index Animals. One of the fields is animaltype. This value can be Carnivorous, herbivorous etc.
Now when a we query in search, I want to show results of type carnivorous at top, and then the herbivorous type.
Also would it be possible to show only say top 3 results from a type and then remaining from other types?
Let assume for a herbivourous type we have a field named vegetables. This will have values only for a herbivourous animaltype.
Now, can it be possible to have boosting rules specified as follows:
Boost Levels:
animaltype:Carnivorous
then animaltype:Herbivorous and vegatablesfield: spinach
then animaltype:herbivoruous and vegetablesfield: carrot
etc. Basically boosting on various fields at various levels. Im new to this concept. It would really helpful to get some inputs/guidance.
Thanks,
Kasturi Chavan
Your example is closer to sorting than boosting, as you have a priority list for how important each document is - while boosting (in Solr) is usually applied a bit more fluent, meaning that there is no hard line between documents of type X and type Y.
However - boosting with appropriately large values will in effect give you the same result, putting the documents into different score "areas" which will then give you the sort order you're looking for. You can see the score contributed by each term by appending debugQuery=true to your query. Boosting says that 'a document with this value is z times more important than those with a different value', but if the document only contains low scoring tokens from the search (usually words that are very common), while other documents contain high scoring tokens (words that are infrequent), the latter document might still be considered more important.
Example: Searching for "city paris", where most documents contain the word 'city', but only a few contain the word 'paris' (but does not contain city). Even if you boost all documents assigned to country 'germany', the score contributed from city might still be lower - even with the boost factor than what 'paris' contributes alone. This might not occur in real life, but you should know what the boost actually changes.
Using the edismax handler, you can apply the boost in two different ways - one is to use boost=, which is multiplicative, or to use either bq= or bf=, which are additive. The difference is how the boost contributes to the end score.
For your example, the easiest way to get something similar to what you're asking, is to use bq (boost query):
bq=animaltype:Carnivorous^1000&
bq=animaltype:Herbivorous^10
These boosts will probably be large enough to move all documents matching these queries into their own buckets, without moving between groups. To create "different levels" as your example shows, you'll need to tweak these values (and remember, multiple boosts can be applied to the same document if something is both herbivorous and eats spinach).
A different approach would be to create a function query using query, if and similar functions to result in a single integer value that you can use as a sorting value. You can also calculate this value when indexing the document if it's static (which your example is), and then sort by that field instead. It will require you to reindex your documents if the sorting values change, but it might be an easy and effective solution.
To achieve the "Top 3 results from a type" you're probably going to want to look at Result grouping support - which makes it possible to get "x documents" for each value in a single field. There is, as far as I know, no way to say "I want three of these at the top, then the rest from other values", except for doing multiple queries (and excluding the three you've already retrieved from the second query). Usually issuing multiple queries works just as fine (or better) performance wise.
I'm looking for a setup that actually returns the top 10% of results of a certain query. After the result we also want to sort the subset.
Is there an easy way to do this?
Can anyone provide a simple example for this.
I was thinking scaling the results scores between 0 and 1.0 and basically sepcifiying min_score to 0.9.
I was trying to create function_score queries but those seem a bit complex for a simple requirement such as this one, plus I was not sure how sorting would effect the results, since I want the sort functions work always on the 10% most relevant articles of course.
Thanks,
Peter
As you want to slice response in % of overall docs count, you need to know that anyway. And using from / size params will cut off the required amount at query time.
Assuming this, seems that easiest way to achieve your goal is to make 2 queries:
Filtered query with all filters, no queries and search_type=count to get overall document count.
Perform your regular matching query, applying {"from": 0, "size": count/10} with count got from 1st response.
Talking about tweaking the scoring. For me, it seems as bad idea, as getting multiple documents with the same score is pretty generic situation. So, cutting dataset by min_score will probably result in skewed data.
I want to query elasticsearch documents within a date range. I have two options now, both work fine for me. Have tested both of them.
1. Range Query
2. Range Filter
Since I have a small data set for now, I am unable to test the performance for both of them. What is the difference between these two? and which one would result in faster retrieval of documents and faster response?
The main difference between queries and filters has to do with scoring. Queries return documents with a relative ranked score for each document. Filters do not. This difference allows a filter to be faster for two reasons. First, it does not incur the cost of calculating the score for each document. Second, it can cache the results as it does not have to deal with possible changes in the score from moment to moment - it's just a boolean really, does the document match or not?
From the documentation:
Filters are usually faster than queries because:
they don’t have to calculate the relevance _score for each document —
the answer is just a boolean “Yes, the document matches the filter” or
“No, the document does not match the filter”. the results from most
filters can be cached in memory, making subsequent executions faster.
As a practical matter, the question is do you use the relevance score in any way? If not, filters are the way to go. If you do, filters still may be of use but should be used where they make sense. For instance, if you had a language field (let's say language: "EN" as an example) in your documents and wanted to query by language along with a relevance score, you would combine a query for the text search along with a filter for language. The filter would cache the document ids for all documents in english and then the query could be applied to that subset.
I'm over simplifying a bit, but that's the basics. Good places to read up on this:
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/query-dsl-filtered-query.html
http://www.elasticsearch.org/guide/en/elasticsearch/reference/0.90/query-dsl-filtered-query.html
http://exploringelasticsearch.com/searching_data.html
http://elasticsearch-users.115913.n3.nabble.com/Filters-vs-Queries-td3219558.html
Filters are cached so they are faster!
http://www.elasticsearch.org/guide/en/elasticsearch/guide/current/filter-caching.html