JSON Facet API

Facet & Analytics Module

The new Facet & Analytics Module exposed via the JSON Facet API is a rewrite of Solr’s previous faceting capabilities, with the following goals:

  • First class native JSON API to control faceting and analytics

    • The structured nature of nested sub-facets are more naturally expressed in JSON rather than the flat namespace provided by normal query parameters.

  • First class integrated analytics support

  • Nest any facet type under any other facet type (such as range facet, field facet, query facet)

  • Ability to sort facet buckets by any calculated metric

  • Easier programmatic construction of complex nested facet commands

  • Support a more canonical response format that is easier for clients to parse

  • Support a cleaner way to implement distributed faceting

  • Support better integration with other search features

  • Full integration with the JSON Request API

Faceted search is about aggregating data and calculating metrics about that data.

There are two main types of facets:

  • Facets that partition or categorize data (the domain) into multiple buckets

  • Facets that calculate data for a given bucket (normally a metric, statistic or analytic function)

Metrics Example

By default, the domain for facets starts with all documents that match the base query and any filters. Here’s an example that requests various metrics about the root domain:

curl http://localhost:8983/solr/techproducts/query -d '
q=memory&
fq=inStock:true&
json.facet={
  "avg_price" : "avg(price)",
  "num_suppliers" : "unique(manu_exact)",
  "median_weight" : "percentile(weight,50)"
}'

The response to the facet request above will start with documents matching the root domain (docs containing "memory" with inStock:true) then calculate and return the requested metrics:

 "facets" : {
    "count" : 4,
    "avg_price" : 109.9950008392334,
    "num_suppliers" : 3,
    "median_weight" : 352.0
  }

Bucketing Facet Example

Here’s an example of a bucketing facet, that partitions documents into bucket based on the cat field (short for category), and returns the top 3 buckets:

curl

curl http://localhost:8983/solr/techproducts/query -d 'q=*:*&
json.facet={
  categories : {
    type : terms,
    field : cat,    // bucket documents based on the "cat" field
    limit : 3       // retrieve the top 3 buckets ranked by the number of docs in each bucket
  }
}'

SolrJ

Unresolved directive in json-facet-api.adoc - include::../../solrj/src/test/org/apache/solr/client/ref_guide_examples/JsonRequestApiTest.java[tag=solrj-json-simple-terms-facet]

The response below shows us that 32 documents match the default root domain. and 12 documents have cat:electronics, 4 documents have cat:currency, etc.

[...]
  "facets":{
    "count":32,
    "categories":{
      "buckets":[{
          "val":"electronics",
          "count":12},
        {
          "val":"currency",
          "count":4},
        {
          "val":"memory",
          "count":3},
      ]
    }
  }

Making a Facet Request

In this guide, we will often just present the facet command block:

{
  x: "average(mul(price,popularity))"
}

To execute a facet command block such as this, you’ll need to use the json.facet parameter, and provide at least a base query such as q=*:*

curl http://localhost:8983/solr/techproducts/query -d 'q=*:*&json.facet=
{
  x: "avg(mul(price,popularity))"
}
'

Another option is to use the JSON Request API to provide the entire request in JSON:

curl

curl http://localhost:8983/solr/techproducts/query -d '
{
  query: "*:*",                        // this is the base query
  filter: [ "inStock:true" ],          // a list of filters
  facet: {
    x: "avg(mul(price,popularity))"    // and our funky metric of average of price * popularity
 }
}
'

SolrJ

Unresolved directive in json-facet-api.adoc - include::../../solrj/src/test/org/apache/solr/client/ref_guide_examples/JsonRequestApiTest.java[tag=solrj-json-terms-facet2]

JSON Extensions

The Noggit JSON parser that is used by Solr accepts a number of JSON extensions such as,

  • bare words can be left unquoted

  • single line comments using either // or #

  • Multi-line comments using C style /* comments in here */

  • Single quoted strings

  • Allow backslash escaping of any character

  • Allow trailing commas and extra commas. Example: [9,4,3,]

  • Handle nbsp (non-break space, \u00a0) as whitespace.

Terms Facet

The terms facet (or field facet) buckets the domain based on the unique terms / values of a field.

curl http://localhost:8983/solr/techproducts/query -d 'q=*:*&
json.facet={
  categories:{
    terms: {
      field : cat,    // bucket documents based on the "cat" field
      limit : 5       // retrieve the top 5 buckets ranked by the number of docs in each bucket
    }
  }
}'
Parameter Description

field

The field name to facet over.

offset

Used for paging, this skips the first N buckets. Defaults to 0.

limit

Limits the number of buckets returned. Defaults to 10.

sort

Specifies how to sort the buckets produced.

“count” specifies document count, “index” sorts by the index (natural) order of the bucket value. One can also sort by any facet function / statistic that occurs in the bucket. The default is “count desc”. This parameter may also be specified in JSON like sort:{count:desc}. The sort order may either be “asc” or “desc”

overrequest

Number of buckets beyond the limit to internally request from shards during a distributed search.

Larger values can increase the accuracy of the final "Top Terms" returned when the individual shards have very diff top terms.

The default of -1 causes a hueristic to be applied based on the other options specified.

refine

If true, turns on distributed facet refining. This uses a second phase to retrieve any buckets needed for the final result from shards that did not include those buckets in their initial internal results, so that every shard contributes to every returned bucket in this facet and any sub-facets. This makes counts & stats for returned buckets exact.

overrefine

Number of buckets beyond the limit to consider internally during a distributed search when determining which buckets to refine.

Larger values can increase the accuracy of the final "Top Terms" returned when the individual shards have very diff top terms, and the current sort option can result in refinement pushing terms lower down the sorted list (ex: sort:"count asc")

The default of -1 causes a hueristic to be applied based on other options specified.

mincount

Only return buckets with a count of at least this number. Defaults to 1.

missing

A boolean that specifies if a special “missing” bucket should be returned that is defined by documents without a value in the field. Defaults to false.

numBuckets

A boolean. If true, adds “numBuckets” to the response, an integer representing the number of buckets for the facet (as opposed to the number of buckets returned). Defaults to false.

allBuckets

A boolean. If true, adds an “allBuckets” bucket to the response, representing the union of all of the buckets. For multi-valued fields, this is different than a bucket for all of the documents in the domain since a single document can belong to multiple buckets. Defaults to false.

prefix

Only produce buckets for terms starting with the specified prefix.

facet

Aggregations, metrics or nested facets that will be calculated for every returned bucket

method

This parameter indicates the facet algorithm to use:

  • "dv" DocValues, collect into ordinal array

  • "uif" UnInvertedField, collect into ordinal array

  • "dvhash" DocValues, collect into hash - improves efficiency over high cardinality fields

  • "enum" TermsEnum then intersect DocSet (stream-able)

  • "stream" Presently equivalent to "enum"

  • "smart" Pick the best method for the field type (this is the default)

prelim_sort

An optional parameter for specifying an approximation of the final sort to use during initial collection of top buckets when the sort parameter is very costly.

Query Facet

The query facet produces a single bucket of documents that match the domain as well as the specified query.

An example of the simplest form of the query facet is "query":"query string".

{
  high_popularity: {query: "popularity:[8 TO 10]" }
}

An expanded form allows for more parameters and a facet command block to specify sub-facets (either nested facets or metrics):

{
  high_popularity : {
    type: query,
    q : "popularity:[8 TO 10]",
    facet : { average_price : "avg(price)" }
  }
}

Example response:

"high_popularity" : {
  "count" : 36,
  "average_price" : 36.75
}

Range Facet

The range facet produces multiple buckets over a date field or numeric field.

Example:

{
  prices : {
    type: range,
    field : price,
    start : 0,
    end : 100,
    gap : 20
  }
}
"prices":{
  "buckets":[
    {
      "val":0.0,  // the bucket value represents the start of each range.  This bucket covers 0-20
      "count":5},
    {
      "val":20.0,
      "count":3},
    {
      "val":40.0,
      "count":2},
    {
      "val":60.0,
      "count":1},
    {
      "val":80.0,
      "count":1}
  ]
}

Range Facet Parameters

To ease migration, the range facet parameter names and semantics largely mirror facet.range query-parameter style faceting. For example "start" here corresponds to "facet.range.start" in a facet.range command.

Parameter Description

field

The numeric field or date field to produce range buckets from.

start

Lower bound of the ranges.

end

Upper bound of the ranges.

gap

Size of each range bucket produced.

hardend

A boolean, which if true means that the last bucket will end at “end” even if it is less than “gap” wide. If false, the last bucket will be “gap” wide, which may extend past “end”.

other

This parameter indicates that in addition to the counts for each range constraint between start and end, counts should also be computed for…

  • "before" all records with field values lower then lower bound of the first range

  • "after" all records with field values greater then the upper bound of the last range

  • "between" all records with field values between the start and end bounds of all ranges

  • "none" compute none of this information

  • "all" shortcut for before, between, and after

include

By default, the ranges used to compute range faceting between start and end are inclusive of their lower bounds and exclusive of the upper bounds. The “before” range is exclusive and the “after” range is inclusive. This default, equivalent to "lower" below, will not result in double counting at the boundaries. The include parameter may be any combination of the following options:

  • "lower" all gap based ranges include their lower bound

  • "upper" all gap based ranges include their upper bound

  • "edge" the first and last gap ranges include their edge bounds (i.e., lower for the first one, upper for the last one) even if the corresponding upper/lower option is not specified

  • "outer" the “before” and “after” ranges will be inclusive of their bounds, even if the first or last ranges already include those boundaries.

  • "all" shorthand for lower, upper, edge, outer

facet

Aggregations, metrics, or nested facets that will be calculated for every returned bucket

Heatmap Facet

The heatmap facet generates a 2D grid of facet counts for documents having spatial data in each grid cell.

This feature is primarily documented in the spatial section of the reference guide. The key parameters are type to specify heatmap and field to indicate a spatial RPT field. The rest of the parameter names use the same names and semantics mirroring facet.heatmap query-parameter style faceting, albeit without the "facet.heatmap." prefix. For example geom here corresponds to facet.heatmap.geom in a facet.heatmap command.

Note
Unlike other facets that partition the domain into buckets, heatmap facets do not currently support Nested Facets.

Here’s an example query:

{
  hm : {
    type : heatmap,
    field : points_srpt,
    geom : "[-49.492,-180 TO 64.701,73.125]",
    distErrPct : 0.5
  }
}

And the facet response will look like:

{
"facets":{
    "count":145725,
    "hm":{
      "gridLevel":1,
      "columns":6,
      "rows":4,
      "minX":-180.0,
      "maxX":90.0,
      "minY":-90.0,
      "maxY":90.0,
      "counts_ints2D":[[68,1270,459,5359,39456,1713],[123,10472,13620,7777,18376,6239],[88,6,3898,989,1314,255],[0,0,30,1,0,1]]
      }}}

Aggregation Functions

Unlike all the facets discussed so far, Aggregation functions (also called facet functions, analytic functions, or metrics) do not partition data into buckets. Instead, they calculate something over all the documents in the domain.

Aggregation Example Description

sum

sum(sales)

summation of numeric values

avg

avg(popularity)

average of numeric values

min

min(salary)

minimum value

max

max(mul(price,popularity))

maximum value

unique

unique(author)

number of unique values of the given field. Beyond 100 values it yields not exact estimate

uniqueBlock

uniqueBlock(_root_)

same as above with smaller footprint strictly for counting the number of Block Join blocks. The given field must be unique across blocks, and only singlevalued string fields are supported, docValues are recommended.

hll

hll(author)

distributed cardinality estimate via hyper-log-log algorithm

percentile

percentile(salary,50,75,99,99.9)

Percentile estimates via t-digest algorithm. When sorting by this metric, the first percentile listed is used as the sort value.

sumsq

sumsq(rent)

sum of squares of field or function

variance

variance(rent)

variance of numeric field or function

stddev

stddev(rent)

standard deviation of field or function

relatedness

relatedness('popularity:[100 TO *]','inStock:true')

A function for computing a relatedness score of the documents in the domain to a Foreground set, relative to a Background set (both defined as queries). This is primarily for use when building Semantic Knowledge Graphs.

Numeric aggregation functions such as avg can be on any numeric field, or on a nested function of multiple numeric fields such as avg(div(popularity,price)).

The most common way of requesting an aggregation function is as a simple containing the expression you wish to compute:

{
  "average_roi": "avg(div(popularity,price))"
}

An expanded form allows for Local Parameters to be specified. These may be used explicitly by some specialized aggregations such as relatedness(), but can also be used as parameter references to make aggregation expressions more readable, with out needing to use (global) request parameters:

{
  "average_roi" : {
    "type": "func",
    "func": "avg(div($numer,$denom))",
    "numer": "mul(popularity,rating)",
    "denom": "mul(price,size)"
  }
}

Nested Facets

Nested facets, or sub-facets, allow one to nest facet commands under any facet command that partitions the domain into buckets (i.e., terms, range, query). These sub-facets are then evaluated against the domains defined by the set of all documents in each bucket of their parent.

The syntax is identical to top-level facets - just add a facet command to the facet command block of the parent facet. Technically, every facet command is actually a sub-facet since we start off with a single facet bucket with a domain defined by the main query and filters.

Nested Facet Example

Let’s start off with a simple non-nested terms facet on the genre field:

 top_genres:{
    type: terms
    field: genre,
    limit: 5
  }

Now if we wanted to add a nested facet to find the top 2 authors for each genre bucket:

  top_genres:{
    type: terms,
    field: genre,
    limit: 5,
    facet:{
      top_authors:{
        type: terms, // nested terms facet on author will be calculated for each parent bucket (genre)
        field: author,
        limit: 2
      }
    }
  }

And the response will look something like:

  "facets":{
    "top_genres":{
      "buckets":[
        {
          "val":"Fantasy",
          "count":5432,
          "top_authors":{  // these are the top authors in the "Fantasy" genre
            "buckets":[{
                "val":"Mercedes Lackey",
                "count":121},
              {
                "val":"Piers Anthony",
                "count":98}
            ]
          }
        },
        {
          "val":"Mystery",
          "count":4322,
          "top_authors":{  // these are the top authors in the "Mystery" genre
            "buckets":[{
                "val":"James Patterson",
                "count":146},
              {
                "val":"Patricia Cornwell",
                "count":132}
            ]
          }
        }

By default "top authors" is defined by simple document count descending, but we could use our aggregation functions to sort by more interesting metrics.

Sorting Facets By Nested Functions

The default sort for a field or terms facet is by bucket count descending. We can optionally sort ascending or descending by any facet function that appears in each bucket.

{
  categories:{
    type : terms,     // terms facet creates a bucket for each indexed term in the field
    field : cat,
    sort : "x desc",  // can also use sort:{x:desc}
    facet : {
      x : "avg(price)",     // x = average price for each facet bucket
      y : "max(popularity)" // y = max popularity value in each facet bucket
    }
  }
}

In some situations the desired sort may be an aggregation function that is very costly to compute for every bucket. A prelim_sort option can be used to specify an approximation of the sort, for initially ranking the buckets to determine the top candidates (based on the limit and overrequest). Only after the top candidate buckets have been refined, will the actual sort be used.

{
  categories:{
    type : terms,
    field : cat,
    refine: true,
    limit: 10,
    overrequest: 100,
    prelim_sort: "sales_rank desc",
    sort : "prod_quality desc",
    facet : {
      prod_quality : "avg(div(prod(rating,sales_rank),prod(num_returns,price)))"
      sales_rank : "sum(sales_rank)"
    }
  }
}

Changing the Domain

As discussed above, facets compute buckets or statistics based on a "domain" which is typically implicit:

  • By default, facets use the set of all documents matching the main query as their domain.

  • Nested "sub-facets" are computed for every bucket of their parent facet, using a domain containing all documents in that bucket.

But users can also override the "domain" of a facet that partitions data, using an explicit domain attribute whose value is a JSON Object that can support various options for restricting, expanding, or completely changing the original domain before the buckets are computed for the associated facet.

Note

domain changes can only be specified on individual facets that do data partitioning — not statistical/metric facets, or groups of facets.

A *:* query facet with a domain change can be used to group multiple sub-facets of any type, for the purpose of applying a common domain change.

Adding Domain Filters

The simplest example of a domain change is to specify an additional filter which will be applied to the existing domain. This can be done via the filter keyword in the domain block of the facet.

Example:

{
  categories: {
     type: terms,
     field: cat,
     domain: {filter: "popularity:[5 TO 10]" }
   }
}

The value of filter can be a single query to treat as a filter, or a JSON list of filter queries. Each query can be:

  • a string containing a query in Solr query syntax

  • a reference to a request parameter containing Solr query syntax, of the form: {param : <request_param_name>}

When a filter option is combined with other domain changing options, the filtering is applied after the other domain changes take place.

Filter Exclusions

Domains can also exclude the top-level query or filters via the excludeTags keywords in the domain block of the facet, expanding the existing domain.

Example:

&q={!tag=top}"running shorts"
&fq={!tag=COLOR}color:Blue
&json={
   filter:"{!tag=BRAND}brand:Bosco"
   facet:{
      sizes:{type:terms, field:size},
      colors:{type:terms, field:color, domain:{excludeTags:COLOR} },
      brands:{type:terms, field:brand, domain:{excludeTags:"BRAND,top"} }
   }
}

The value of excludeTags can be a single string tag, array of string tags or comma-separated tags in the single string.

When an excludeTags option is combined with other domain changing options, it expands the domain before any other domain changes take place.

See also the section on multi-select faceting.

Arbitrary Domain Query

A query domain can be specified when you wish to compute a facet against an arbitrary set of documents, regardless of the original domain. The most common use case would be to compute a top level facet against a specific subset of the collection, regardless of the main query. But it can also be useful on nested facets when building Semantic Knowledge Graphs.

Example:

{
  "categories": {
     "type": "terms",
     "field": "cat",
     "domain": {"query": "*:*" }
   }
}

The value of query can be a single query, or a JSON list of queries. Each query can be:

  • a string containing a query in Solr query syntax

  • a reference to a request parameter containing Solr query syntax, of the form: {param: <request_param_name>}

Note
While a query domain can be combined with an additional domain filter, It is not possible to also use excludeTags, because the tags would be meaningless: The query domain already completely ignores the top-level query and all previous filters.

Block Join Domain Changes

When a collection contains Block Join child documents, the blockChildren or blockParent domain options can be used transform an existing domain containing one type of document, into a domain containing the documents with the specified relationship (child or parent of) to the documents from the original domain.

Both of these options work similar to the corresponding Block Join Query Parsers by taking in a single String query that exclusively matches all parent documents in the collection. If blockParent is used, then the resulting domain will contain all parent documents of the children from the original domain. If If blockChildren is used, then the resulting domain will contain all child documents of the parents from the original domain.

Example:

{
  "colors": {                         (1)
    "type": "terms",
    "field": "sku_color",             (2)
    "facet" : {
      "brands" : {
        "type": "terms",
        "field": "product_brand",     (3)
        "domain": {
          "blockParent": "doc_type:product"
        }
      }}}}
  1. This example assumes we parent documents corresponding to Products, with child documents corresponding to individual SKUs with unique colors, and that our original query was against SKU documents.

  2. The colors facet will be computed against all of the original SKU documents matching our search.

  3. For each bucket in the colors facet, the set of all matching SKU documents will be transformed into the set of corresponding parent Product documents. The resulting brands sub-facet will count how many Product documents (that have SKUs with the associated color) exist for each Brand.

Join Query Domain Changes

A join domain change option can be used to specify arbitrary from and to fields to use in transforming from the existing domain to a related set of documents.

This works very similar to the Join Query Parser, and has the same limitations when dealing with multi-shard collections.

Example:

{
  "colors": {
    "type": "terms",
    "field": "sku_color",
    "facet": {
      "brands": {
        "type": "terms",
        "field": "product_brand",
        "domain" : {
          "join" : {
            "from": "product_id_of_this_sku",
            "to": "id"
          },
          "filter": "doc_type:product"
        }
      }
    }
  }
}

Graph Traversal Domain Changes

A graph domain change option works similarly to the join domain option, but can do traversal multiple hops from the existing domain to other documents.

This works very similar to the Graph Query Parser, supporting all of it’s optional parameters, and has the same limitations when dealing with multi-shard collections.

Example:

{
  "related_brands": {
    "type": "terms",
    "field": "brand",
    "domain": {
      "graph": {
        "from": "related_product_ids",
        "to": "id",
        "maxDepth": 3
      }
    }
  }
}

Block Join Counts

When a collection contains Block Join child documents, the blockChildren and blockParent domain changes mentioned above can be useful when searching for parent documents and you want to compute stats against all of the affected children documents (or vice versa). But in the situation where the count of all the blocks that exist in the current domain is sufficient, a more efficient option is the uniqueBlock() aggregate function.

Block Join Counts Example

Suppose we have products with multiple SKUs, and we want to count products for each color.

{
  "id": "1", "type": "product", "name": "Solr T-Shirt",
  "_childDocuments_": [
    { "id": "11", "type": "SKU", "color": "Red",  "size": "L" },
    { "id": "12", "type": "SKU", "color": "Blue", "size": "L" },
    { "id": "13", "type": "SKU", "color": "Red",  "size": "M" }
  ]
},
{
  "id": "2", "type": "product", "name": "Solr T-Shirt",
  "_childDocuments_": [
    { "id": "21", "type": "SKU", "color": "Blue", "size": "S" }
  ]
}

When searching against a set of SKU documents, we can ask for a facet on color, with a nested statistic counting all the "blocks" — aka: products:

color: {
  type: terms,
  field: color,
  limit: -1,
  facet: {
    productsCount: "uniqueBlock(_root_)"
  }
}

and get:

color:{
   buckets:[
      { val:Blue, count:2, productsCount:2 },
      { val:Red, count:2, productsCount:1 }
   ]
}

Please notice that _root_ is an internal field added by Lucene to each child document to reference on parent one. Aggregation uniqueBlock(_root_) is functionally equivalent to unique(_root_), but is optimized for nested documents block structure. It’s recommended to define limit: -1 for uniqueBlock calculation, like in above example, since default value of limit parameter is 10, while uniqueBlock is supposed to be much faster with -1.

Semantic Knowledge Graphs

The relatedness(…​) aggregation function allows for sets of documents to be scored relative to Foreground and Background sets of documents, for the purposes of finding ad-hoc relationships that make up a "Semantic Knowledge Graph":

At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes.

— Grainger et al.
The Semantic Knowledge Graph

The relatedness(…​) function is used to "score" these relationships, relative to "Foreground" and "Background" sets of documents, specified in the function params as queries.

Unlike most aggregation functions, the relatedness(…​) function is aware of whether and how it’s used in Nested Facets. It evaluates the query defining the current bucket independently from it’s parent/ancestor buckets, and intersects those documents with a "Foreground Set" defined by the foreground query combined with the ancestor buckets. The result is then compared to a similar intersection done against the "Background Set" (defined exclusively by background query) to see if there is a positive, or negative, correlation between the current bucket and the Foreground Set, relative to the Background Set.

Note
While it’s very common to define the Background Set as *:*, or some other super-set of the Foreground Query, it is not strictly required. The relatedness(…​) function can be used to compare the statistical relatedness of sets of documents to orthogonal foreground/background queries.

relatedness() Options

When using the extended type:func syntax for specifying a relatedness() aggregation, an opional min_popularity (float) option can be used to specify a lower bound on the foreground_popularity and background_popularity values, that must be met in order for the relatedness score to be valid — If this min_popularity is not met, then the relatedness score will be -Infinity.

{ "type": "func",
  "func": "relatedness($fore,$back)",
  "min_popularity": 0.001,
}

This can be particularly useful when using a descending sorting on relatedness() with foreground and background queries that are disjoint, to ensure the "top buckets" are all relevant to both sets.

Tip

When sorting on relatedness(…​) requests can be processed much more quickly by adding a prelim_sort: "count desc" option. Increasing the overrequest can help improve the accuracy of the top buckets.

Semantic Knowledge Graph Example

Sample Documents
curl -sS -X POST 'http://localhost:8983/solr/gettingstarted/update?commit=true' -d '[
{"id":"01",age:15,"state":"AZ","hobbies":["soccer","painting","cycling"]},
{"id":"02",age:22,"state":"AZ","hobbies":["swimming","darts","cycling"]},
{"id":"03",age:27,"state":"AZ","hobbies":["swimming","frisbee","painting"]},
{"id":"04",age:33,"state":"AZ","hobbies":["darts"]},
{"id":"05",age:42,"state":"AZ","hobbies":["swimming","golf","painting"]},
{"id":"06",age:54,"state":"AZ","hobbies":["swimming","golf"]},
{"id":"07",age:67,"state":"AZ","hobbies":["golf","painting"]},
{"id":"08",age:71,"state":"AZ","hobbies":["painting"]},
{"id":"09",age:14,"state":"CO","hobbies":["soccer","frisbee","skiing","swimming","skating"]},
{"id":"10",age:23,"state":"CO","hobbies":["skiing","darts","cycling","swimming"]},
{"id":"11",age:26,"state":"CO","hobbies":["skiing","golf"]},
{"id":"12",age:35,"state":"CO","hobbies":["golf","frisbee","painting","skiing"]},
{"id":"13",age:47,"state":"CO","hobbies":["skiing","darts","painting","skating"]},
{"id":"14",age:51,"state":"CO","hobbies":["skiing","golf"]},
{"id":"15",age:64,"state":"CO","hobbies":["skating","cycling"]},
{"id":"16",age:73,"state":"CO","hobbies":["painting"]},
]'
Example Query
curl -sS -X POST http://localhost:8983/solr/gettingstarted/query -d 'rows=0&q=*:*
&back=*:*                                  (1)
&fore=age:[35 TO *]                        (2)
&json.facet={
  hobby : {
    type : terms,
    field : hobbies,
    limit : 5,
    sort : { r1: desc },                   (3)
    facet : {
      r1 : "relatedness($fore,$back)",     (4)
      location : {
        type : terms,
        field : state,
        limit : 2,
        sort : { r2: desc },               (3)
        facet : {
          r2 : "relatedness($fore,$back)"  (4)
        }
      }
    }
  }
}'
  1. Use the entire collection as our "Background Set"

  2. Use a query for "age >= 35" to define our (initial) "Foreground Set"

  3. For both the top level hobbies facet & the sub-facet on state we will be sorting on the relatedness(…​) values

  4. In both calls to the relatedness(…​) function, we use Parameter Variables to refer to the previously defined fore and back queries.

The Facet Response
"facets":{
  "count":16,
  "hobby":{
    "buckets":[{
        "val":"golf",
        "count":6,                                (1)
        "r1":{
          "relatedness":0.01225,
          "foreground_popularity":0.3125,         (2)
          "background_popularity":0.375},         (3)
        "location":{
          "buckets":[{
              "val":"az",
              "count":3,
              "r2":{
                "relatedness":0.00496,            (4)
                "foreground_popularity":0.1875,   (6)
                "background_popularity":0.5}},    (7)
            {
              "val":"co",
              "count":3,
              "r2":{
                "relatedness":-0.00496,           (5)
                "foreground_popularity":0.125,
                "background_popularity":0.5}}]}},
      {
        "val":"painting",
        "count":8,                                (1)
        "r1":{
          "relatedness":0.01097,
          "foreground_popularity":0.375,
          "background_popularity":0.5},
        "location":{
          "buckets":[{
            ...
  1. Even though hobbies:golf has a lower total facet count then hobbies:painting, it has a higher relatedness score, indicating that relative to the Background Set (the entire collection) Golf has a stronger correlation to our Foreground Set (people age 35+) then Painting.

  2. The number of documents matching age:[35 TO *] and hobbies:golf is 31.25% of the total number of documents in the Background Set

  3. 37.5% of the documents in the Background Set match hobbies:golf

  4. The state of Arizona (AZ) has a positive relatedness correlation with the nested Foreground Set (people ages 35+ who play Golf) compared to the Background Set — i.e., "People in Arizona are statistically more likely to be '35+ year old Golfers' then the country as a whole."

  5. The state of Colorado (CO) has a negative correlation with the nested Foreground Set — i.e., "People in Colorado are statistically less likely to be '35+ year old Golfers' then the country as a whole."

  6. The number documents matching age:[35 TO *] and hobbies:golf and state:AZ is 18.75% of the total number of documents in the Background Set

  7. 50% of the documents in the Background Set match state:AZ