I want to know how it's possible to get distinct value of a field in elastic search. I read an article here shows how to do that with facets, but I read facets are deprecated:
http://elasticsearch-users.115913.n3.nabble.com/Getting-Distinct-Values-td3830953.html
Is there any other way to do that? if not is it possible to tell me how to do that? it's abit hard to understand solutions like this: Elastic Search - display all distinct values of an array
Use aggregations:
GET /my_index/my_type/_search?search_type=count
{
"aggs": {
"my_fields": {
"terms": {
"field": "name",
"size": 1000
}
}
}
}
You can use the Cardinality metric
Although the counts returned aren't guaranteed to be 100% accurate, they almost always are for low cardinality terms and the precision is configurable via the precision_threshold param.
http://www.elastic.co/guide/en/elasticsearch/guide/current/cardinality.html
Related
I am trying to use ElasticSearch to compute a ranking. I'm not sure if this is possible and am trying to find out what my options might be. I need to run a query on all documents, sort them descending and then just return what number position in the list a specific record is located.
For example, I want to find out Julie's class ranking. I have records of each student in Julie's grade that contains their names and GPA's and I want to perform 1 query that will tell me what her rank in within her grade.
I am hoping there is an ES guru out there that can help because otherwise I am going to need to run a regular query, get back max 10,000 records and figure it out from there.
This cannot be found in a single query.
First you need to get GPA of "Julia" and then find count of docs which have score higher than Julia.
{
"query": {
"range": {
"gpa": {
"gt": 8 --> GPA of julia
}
}
},
"aggs": {
"count": {
"value_count": {
"field": "name.keyword" --> count where gpa is greater than 8
}
}
}
}
Better option is to store rank in document itself while indexing
I am trying to paginate over a specific field using the terms aggregation with partitions.
The problem is that the number of returned terms for each partition is not equal to the size parameter that I set.
These are the steps that I am doing:
Retrieve the number of different unique values for the field with "cardinality" aggregation.
In my data, the result is 21.
From the web page, the user wants to display a table with 10 items per page.
if unique_values % page_size != 0:
partitions_number = (unique_values // page_size) + 1
else:
partitions_number = (unique_values // page_size)
Than I am making this simple query:
POST my_index/_search?pretty
{
"size": 0,
"query": {
"bool": {
"filter": [
{
"match": {
"field_to_paginate": "foo"
}
}
]
}
},
"aggs": {
"by_pchostname": {
"terms": {
"size": 10,
"field": "field_to_paginate",
"include": {
"partition": 0,
"num_partitions": 3
}
}
}
}
}
I am expecting to retrieve 10 results. But if I run the query I have only 7 results.
What am I missing here? Do I need to use a different solution here?
As a side note, I can't use composite aggregation because I need to sort results by doc_count over the whole dataset.
Partitons in terms aggregation divide the values in equal chunks.
In your case no of partition num_partitions is 3 so 21/3 == 7.
Partitons are meant for getting large values in the order of 1000 s.
You may be able to leverage shard_size parameter. My suggestion is to read this part of manual and work with the shard_size param
Terms aggregation does not allow pagination. Use composite aggregation instead (requires ES >= 6.1.0). Below is the quote from reference docs:
If you want to retrieve all terms or all combinations of terms in a
nested terms aggregation you should use the Composite aggregation
which allows to paginate over all possible terms rather than setting a
size greater than the cardinality of the field in the terms
aggregation. The terms aggregation is meant to return the top terms
and does not allow pagination.
I have an index of documents connected with some product_id. And I would like to find all documents for specific ids (around 100 000 product_ids to be found and 100 million are in total in index).
Would the filter query be the fastest and best option in that case?
"query": {
"bool": {
"filter": {"terms": {"product_id": product_ids}
}
}
Or is it better to chunkify ids and use just terms query or smth else?
The question is probably kind of a duplicate, but I would be very grateful for the best practice advice (and a bit of reasoning).
After some testing and more reading I found an answer:
Filter query works much much faster as chunks with just terms query.
But making really big filter can slower getting the result a lot.
In my case, using filter query with chunks of 10 000 ids is 10 times faster, than using filter query with all 100 000 ids at once (btw, this number is already restricted in Elasticsearch 6).
Also from official elasticsearch documentation:
Potentially the amount of ids specified in the terms filter can be a lot. In this scenario it makes sense to use the terms filter’s terms lookup mechanism.
The only disadvantage to be taken into account is that filter query is stored in cache. (The cache implements an LRU eviction policy: when a cache becomes full, the least recently used data is evicted to make way for new data.)
P.S. In all cases I always used scroll.
you can use "paging" or "scrolling" feature of elastic search query for very large result sets.
Use "from - to" query : https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-from-size.html
or "scroll" query:
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-scroll.html
I think that "From / To" is a more efficient way to go unless you want to return thousands of results each time (which could be many many MB of data so you probably don't want that)
Edit:
You can make a query like this in bulks:
GET my_index/_search
{
"query": {
"terms": {
"_id": [ "1", "2", "3", .... "10000" ] // tune for the best array length
}
}
}
If your document Id is sequential or some other number form that you could easily order by, and have a field available you can do a "range query"
GET _search
{
"query": {
"range" : {
"document_id_that_is_a_number" : {
"gte" : 0, // bump this on each query by "lte" step factor
"lte" : 10000 // find a good number here
}
}
}
}
In a data flow of mine, I am trying to retrieve a subset of documents from a previous terms aggregation, but hitting the maxClauseCount limit within my ES cluster. The follow up query is along these lines:
GET dataset/_search
{
"size": 2000,
"query": {
"bool": {
"must": [
(a filter or two)...,
{
"terms":{
"otherid":[
"789e18f2-bacb-4e38-9800-bf8e4c65c206",
"8e6967aa-5b98-483e-b50f-c681c7396a6a",
...
]
}
}
]}
}
}
In my research I've come across a lookup - which sadly we can't use - as well as the ids query.
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-terms-query.html
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-ids-query.html
From experimentation, it appears that the ids query doesn't share the limit the terms query has (potentially it's not converted into terms clauses). Do any of you know if there's a good way to achieve similar functionality to the ids query without using the ids fields.
My version of ES is 5.0.
Thanks!
instead of using terms use the Terms filter it will solve the issue
OR
index.query.bool.max_clause_count: increase to higher value(*Not Recommended)
http://george-stathis.com/2013/10/18/setting-the-booleanquery-maxclausecount-in-elasticsearch/
I wonder how Elastic search is sorting (on what field) when no search query is specified (I just filter on documents) and no sort option specified. It looks like sorting is than random ... Default sort order is _score, but score is always 1 when you do not specify a search query ...
You got it right. Its then more or less random with score being 1. You still get consistent results as far as I remember. You have the "same" when you get results in SQL but don't specify ORDER BY.
Just in case someone may see this post even it posted over 6 yrs ago..
When you wanna know how elasticsearch calculate its own score known as _score, you can use the explain option.
I suppose that your query(with filter & without search) might like this more or less (but the point is making the explain option true) :
POST /goods/_search
{
"explain": true,
"query": {
"bool": {
"must": {
"match_all": {}
},
"filter": {
"term": {
"maker_name": "nike"
}
}
}
}
}
As running this, you will notice that the _explaination of each hits describes as below :
"_explanation" : {
"value" : 1.0,
"description" : "ConstantScore(maker_name:nike)",
"details" : [ ]
}
which means ES gave constant score to all of the hits.
So to answer the question, "yes".
The results are sorted kinda randomly because all the filtered results have same (constant) score without any search query.
By the way, enabling an explain option is more helpful when you use search queries. You will see how ES calculates the score and will understand the reason why it returns in that order.
Score is mainly used for sorting, Score is calculated by lucene score calculating using several constraints,For more info refer here .