The JSON facet API uses the t-digest approach to estimate the percentiles.
You can also use Solr Math Expressions to take a random sample from a field
and estimate the median from the sample. Here is the Streaming Expression:
let(a=random(collection1, q="*:*", fl="filesize_d", rows="25000"),
b=col(a, filesize_d),
median=percentile(b, 50))
The example above takes a random sample and sets it to variable "a".
Then the filesize_d field from the sample (in variable "a") are copied to a
vector and set to variable "b".
Then the percentile function is called on the vector and the results are
set to variable "median".
The results look like this:
{ "result-set": { "docs": [ { "median": 39980.53459335005 }, { "EOF": true,
"RESPONSE_TIME": 365 } ] } }
You can adjust the sample size to see how it effects the estimate.
Here is the link to Solr Math Expressions in the User Guide:
https://lucene.apache.org/solr/guide/7_5/math-expressions.html
Joel Bernstein
http://joelsolr.blogspot.com/
On Wed, Nov 14, 2018 at 8:21 AM Toke Eskildsen <t...@kb.dk> wrote:
> On Wed, 2018-11-14 at 17:53 +0530, Anil wrote:
> > I don;t see median aggregation in JSON facet api documentation.
>
> It's the 50 percentile:
>
>
>
> https://lucene.apache.org/solr/guide/7_5/json-facet-api.html#metrics-example
>
> - Toke Eskildsen, Royal Danish Library
>
>
>
