I have a parent/child structure in 3 levels. Let's say:
Company -> Employee -> Availability
Since Availability (and also Employee) is frequently updated here, I choose using parent/child structure against nested. And search function works fine (all documents in correct shards).
Now I want to sort those results. Sorting them by meta data from company (1st level) is easy. But I need to sort also by 3rd level (availability).
I want list of companies which are sorted by:
For example:
Company A is 5 miles away, has rating 4 and soonest one of their employees is available in 20 hours Company B is also 5 miles away, also has rating 4 but soonest one of their employee is available in 5 hours.
Therefore sort result needs to be B, A.
I would like to append special weight to each of this data, so I started writing aggregations which I could later use in my custom_score script.
Full gist for creating index, importing data and searching
Now, I've managed to write a query which actually returns back result, but availability aggregation bucket is empty. However, I'm also getting results back too structured, I would like to flatten them.
Currently I get back:
Company IDS -> Employee IDS -> first availability
I would like to have aggregation like:
Company IDS -> first availability
This way I'm able to do my custom_score
script to calculate score and sort them properly.
More simplified question:
How can one sort/aggregate by multi level (grand)children and possibly flatten the result.
Elasticsearch Aggregations provide you with the ability to group and perform calculations and statistics (such as sums and averages) on your data by using a simple search query. An aggregation can be viewed as a working unit that builds analytical information across a set of documents.
Bucket aggregations don't calculate metrics over fields like the metrics aggregations do, but instead, they create buckets of documents. Each bucket is associated with a criterion (depending on the aggregation type) which determines whether or not a document in the current context "falls" into it.
Elasticsearch organizes aggregations into three categories: Metric aggregations that calculate metrics, such as a sum or average, from field values. Bucket aggregations that group documents into buckets, also called bins, based on field values, ranges, or other criteria.
Create an aggregation-based visualization paneledit Choose the type of visualization you want to create, then use the editor to configure the options. On the dashboard, click All types > Aggregation based. Select the visualization type you want to create. Select the data source you want to visualize.
You don't need aggregations to do this:
These are the sort criteria:
If you ignore #3, then you can run a relatively simple company query like this:
GET /companies/company/_search { "query": { "match_all" : {} }, "sort": { "_script": { "params": { "lat": 51.5186, "lon": -0.1347 }, "lang": "groovy", "type": "number", "order": "asc", "script": "doc['location'].distanceInMiles(lat,lon)" }, "rating_value": { "order": "desc" } } }
#3 is tricky because you need to reach down and find the availability ( company > employee > availability ) for each company closest to the time of the request and use that duration as a third sort criterion.
We're going to use a function_score
query at the grandchild level to take the time difference between the request time and each availability in the hit _score
. (Then we'll use the _score
as the third sort criterion).
To reach the grandchildren we need to use a has_child
query inside a has_child
query.
For each company we want the soonest available Employee (and of course their closest Availability). Elasticsearch 2.0 will give us a "score_mode": "min"
for cases like this, but for now, since we're limited to "score_mode": "max"
we'll make the grandchild _score
be the reciprocal of the time-difference.
"function_score": { "filter": { "range": { "start": { "gt": "2014-12-22T10:34:18+01:00" } } }, "functions": [ { "script_score": { "lang": "groovy", "params": { "requested": "2014-12-22T10:34:18+01:00", "millisPerHour": 3600000 }, "script": "1 / ((doc['availability.start'].value - new DateTime(requested).getMillis()) / millisPerHour)" } } ] }
So now the _score
for each grandchild (Availability) will be 1 / number-of-hours-until-available
(so that we can use the maximum reciprocal time until available per Employee, and the maximum reciprocal(ly?) available Employee per Company).
Putting it all together, we continue to query company but use company > employee > availabilty to generate the _score
to use as the #3 sort criterion:
GET /companies/company/_search { "query": { "has_child" : { "type" : "employee", "score_mode" : "max", "query": { "has_child" : { "type" : "availability", "score_mode" : "max", "query": { "function_score": { "filter": { "range": { "start": { "gt": "2014-12-22T10:34:18+01:00" } } }, "functions": [ { "script_score": { "lang": "groovy", "params": { "requested": "2014-12-22T10:34:18+01:00", "millisPerHour": 3600000 }, "script": "1/((doc['availability.start'].value - new DateTime(requested).getMillis()) / millisPerHour)" } } ] } } } } } }, "sort": { "_script": { "params": { "lat": 51.5186, "lon": -0.1347 }, "lang": "groovy", "type": "number", "order": "asc", "script": "doc['location'].distanceInMiles(lat,lon)" }, "rating_value": { "order": "desc" }, "_score": { "order": "asc" } } }
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