My problem is to search data of thousands of users, e.g. mailboxes. Almost all the time search is filtered by user id. How this locality of searches could be taken into consideration? I'm trying to achieve performance comparable to a case where each user has dedicated index.
Sharding is not an option because it will be used (total number of users ~ 1M), and I'm looking for a solution to use inside a shard of ~4k users.
Well it can be done in Sphinx with Attributes. Most of the time can make the search more efficient by adding the user-id as a fake keyword too*. Then the documents can be filtered during the full-text stage. (still keep the attribute too, so as avoid possibility of manipulating results by constructing a careful query to return results from other users)
eg, add _user1234 as a full-text field, then add to query WHERE MATCH('example _user1234') AND user = 1234 then finds documents just from that user.
One possible solution is to group documents of the same user in inverted index block. Given that inverted index block is sorted by document id, such grouping can be done only by assigning ids to documents appropriately. Same user's documents should have monotonic ids. There could be minor violations of this rule - it would not harm performance significantly.
Implementations.
index sorting having just become a first-class citizen in Lucene 6.21
Could be achieved in elasticsearch 2.3 (see here). And I think it's achievable in Solr in the same way.
As for sphinx, I suppose the same technique of assigning monotonic document ids should work.
For more technical reasoning see previous link.
Related
I am using Elasticsearch 6.2, and I have some queries that analyze a massive amount of documents. I am sorting to one field inside the index. Elasticsearch examines 10.000 documents (default configuration value) and then returns them paginated.
I tried to read the documentation, but I cannot find any information if the database applies the sorting before or after the analysis process of the documents from the index.
In other words, the sort is applied directly during the index analysis or the documents are sorted once analyzed? If the last option is correct, which kind of sort applies Elasticsearch during the scan?
Thanks a lot.
Sorting, aggregations, and access to field values in scripts requires
a different data access pattern. Instead of looking up the term and
finding documents, we need to be able to look up the document and find
the terms that it has in a field.
This quote from the Elasticsearch reference documentation implies to me, that sorting is happening on the non-analyzed level, but I've also decided to double check and do some tests on it.
In the Elasticsearch we have capabilities to do sorting on non-analyzed fields - e.g. keyword. Those fields are using doc-values to do sorting and after the test I could say that it's using pre-analyzed values to do sorting according to the codes representing characters (numbers, uppercase letters, lowercase letters)
It's also possible to do a sorting on text fields with some caveat and tuning (e.g. need to enable fielddata, since text fields do not support doc_values)
In this case the documents are sorted according to analyzed values. Of course a lot depends on analyzing pipeline, since it could do various stuff to the text. Also, just as a reminder:
Fielddata can consume a lot of heap space, especially when loading
high cardinality text fields. Once fielddata has been loaded into the
heap, it remains there for the lifetime of the segment. Also, loading
fielddata is an expensive process which can cause users to experience
latency hits. This is why fielddata is disabled by default.
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.
Elasticsearch takes the length of a document into account when ranking (they call this field normalization). The default behavior is to rank shorter matching documents higher than longer matching documents.
Is there anyway to turn off or modify field normalization at query time? I am aware of the index time omit_norms option, but I would prefer to not reindex everything to try this out.
Also, instead of simply turning off field normalization, I wanted to try out a few things. I would like to take field length into account, but not as heavily as elasticsearch currently does. With the default behavior, a document will rank 2 times higher than a document which is two times longer. I wanted to try a non-linear relationship between ranking and length.
Is it possible to implement reliable paging of elasticsearch search results if multiple documents have equal scores?
I'm experimenting with custom scoring in elasticsearch. Many of the scoring expressions I try yield result sets where many documents have equal scores. They seem to come in the same order each time I try, but can it be guaranteed?
AFAIU it can't, especially not if there is more than one shard in a cluster. Documents with equal score wrt. a given elasticsearch query are returned in random, non-deterministic order that can change between invocations of the same query, even if the underlying database does not change (and therefore paging is unreliable) unless one of the following holds:
I use function_score to guarantee that the score is unique for each document (e.g. by using a unique number field).
I use sort and guarantee that the sorting defines a total order (e.g. by using a unique field as fallback if everything else is equal).
Can anyone confirm (and maybe point at some reference)?
Does this change if I know that there is only one primary shard without any replicas (see other, similar querstion: Inconsistent ordering of results across primary /replica for documents with equivalent score) ? E.g. if I guarantee that there is one shard AND there is no change in the database between two invocations of the same query then that query will return results in the same order?
What are other alternatives (if any)?
I ended up using additional sort in cases where equal scores are likely to happen - for example searching by product category. This additional sort could be id, creation date or similar. The setup is 2 servers, 3 shards and 1 replica.
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