I am using a Lucene.Net query like this
(PropertyID:1 OR PropertyID:25 OR PropertyID:5 OR PropertyID:10 OR PropertyID:15)
I want result from Lucene.Net in order of PropertyId. I passed for example first record should be for PropertyId 1 second for 25 and third for 5. But currently Lucene.Net arranging result set in different way.
The order of fields in the query has no effect on sorting.
There are 2 ways to achieve the sorting you're looking for:
Use boosts in your query. You can boost PropertyID:1 higher than the rest so that these matches are scored higher and thus appear first in the results, then score PropertyID:2 second highest, etc. For example:
(PropertyID:1^5 OR PropertyID:25^4 OR PropertyID:5^3 OR PropertyID:10^2 OR PropertyID:15) This is simple to implement but may not work right if you're including other criteria in your query because that other criteria will affecting the scoring.
Implement custom sorting via your own Comparator class. This may take quite a bit of work especially given the lack of resources on the web for doing this, however it will give you the greatest control over your sorting. Here is an example of a custom Comparator used to sort by a string value alphabetically that may be a good place for you to start.
Related
I'm scraping few sites and relisting their products, each site has their own index in Elasticsearch. Some sites have affiliate programs, I'd like to list those first in my search results.
Is there a way for me to "boost" results from a certain index?
Should I write a field hasAffiliate: true into ES when I'm scraping and then boosting the query clauses that have that has that value? Or is there a better way?
Using boost could be difficult to guarantee that they appear first in the search. According to the official guide:
Practically, there is no simple formula for deciding on the “correct”
boost value for a particular query clause. It’s a matter of
try-it-and-see. Remember that boost is just one of the factors
involved in the relevance score
https://www.elastic.co/guide/en/elasticsearch/guide/current/query-time-boosting.html
It depends on the type of queries you are doing, but here you have other couple of options:
A score function with weights: could be a more predictable option.
Simply using a sort by hasAffiliate (the easiest one).
Note: Not sure if sorting by boolean field is possible, in that case you could set hasAffiliate mapping as integer byte (smallest one), setting it as 1 when true.
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.
My use case is for searching UK addresses where there is a well defined postal code system however my users may still make mistakes in the postcode. I want to use a filter as in most cases the user will get the postcode right and I do not want to make Elasticsearch work harder than it needs to however I want to avoid roundtrips from my application to ES.
I am using an edge n-gram analyzer as described in the docs, so, taking the postcode ME4 4NR as an example I have ME4 4NR, ME4 4N, ME4 4 and ME4 indexed. I want to first filter by ME4 4NR and only widen to ME4 4N if this yields no matches.
Can I achieve this in my ES query or do I need to implement this in my application logic? Any advice would be much appreciated. I could use a boolean filter with a must on the ME4 and shoulds on the others but I wondered if there is a better way?
I think you are a bit over-complicating the matter here. This if-this-then-that-else-somethingelse can be achieved with ES, but the cases when this is possible are limited. For example - this question - the "else" part was a must in which the statement was a bool filter that first checked another must with a missing "condition". So, something must still be true in order for the other part of a "if-then-else" statement to be applied. Is not a strict matter of doing this only if "a certain condition" is true or false like in programming. You need to approach this Elasticsearch way, not programming way.
Your solution - use a must on ME4 and shoulds on the others - is not necessary imo. If you have analyzer set to an edge n-gram, then the same analyzer is used at indexing time but also at search time. Which means that, depending on the query/filter used, your input text will be analyzed before the search is performed.
For example, if you use at search time match query, then the input text you provide is analyzed. What this means is that if you input ME4 4N as search text, first ES will edge n-gram the input text and use the resulting tokens to search the inverted index. So, no need of doing this in your own code or come up with multiple shoulds in your ES query.
My suggestion here is to have a well-defined set of requirements set up properly first. Meaning, know what you want your search to do: think about the tokens that should be put in the inverted index and think about what users input. Decide if you need analysis at index time, but also at search time. Depending on this, think about the ways to use filters/queries at search time, meaning which analyze the input text and which don't (term doesn't for example while match does). Then, test you approaches and see the performance. Don't assume something is putting more work on ES than it should because you might be wrong. Test and compare the results, then start improving and coming up with other ideas.
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