Filters

WHERE

WHERE is an SQL clause which works for both fulltext matching and additional filtering. The following operators are available:

MATCH('query') is supported and maps to fulltext query.

{col_name | expr_alias} [NOT] IN @uservar condition syntax is supported. Refer to SET syntax for a description of global user variables.

HTTP

If you prefer HTTP JSON interface you can also do filtering. It looks more complex that in SQL, but can be recommended for the cases when you need to prepare a query in a programmatic manner, for example as a result of a form in your application filled out by the user.

Here's an example of several filters in a bool query.

This is a fulltext query that matches all the documents containing product in any field. These documents must have a price greater or equal than 500 (gte) and less or equal than 1000 (lte). All of these documents must not have a revision less than 15 (lt).

‹›
  • Example
Example
📋
{
  "index": "test1",
  "query": {
    "bool": {
      "must": [
        { "match" : { "_all" : "product" } },
        { "range": { "price": { "gte": 500, "lte": 1000 } } },
      ],
      "must_not": {
        "range": { "revision": { "lt": 15 } }
      }
    }
  }
}

bool query

bool query matches documents matching boolean combinations of other queries and/or filters. Queries and filters must be specified in must, should or must_not sections and can be nested.

‹›
  • Example
Example
📋
{
  "index":"test",
  "query": {
    "bool": {
      "must": [
        { "match": {"_all":"keyword"} },
        { "range": { "int_col": { "gte": 14 } } }
      ]
    }
  }
}

must

Queries and filters specified in the must section must match the documents. If several fulltext queries or filters are specified, all of them. This is the equivalent of AND queries in SQL.

should

Queries and filters specified in the should section should match the documents. If some queries are specified in must or must_not, should queries are ignored. On the other hand, if there are no queries other than should, then at least one of these queries must match a document for it to match the bool query. This is the equivalent of OR queries.

must_not

Queries and filters specified in the must_not section must not match the documents. If several queries are specified under must_not, the document matches if none of them match.

‹›
  • Example
Example
📋
{
  "index":"t",
  "query": {
    "bool": {
      "should": [
        {
          "equals": {
            "b": 1
          }
        },
        {
          "equals": {
            "b": 3
          }
        }
      ],
      "must": [
        {
          "equals": {
            "a": 1
          }
        }
      ],
      "must_not": {
        "equals": {
          "b": 2
        }
      }
    }
  }
}

Nested bool query

A bool query can be nested inside another bool so you can make more complex queries. To make a nested boolean query just use another bool instead of must, should or must_not. Here is how this query:

a = 2 and (a = 10 or b = 0)

should be presented in JSON.

‹›
  • a = 2 and (a = 10 or b = 0)
a = 2 and (a = 10 or b = 0)
📋
{
  "index":"t",
  "query": {
    "bool": {
      "must": [
        {
          "equals": {
            "a": 2
          }
        },
        {
          "bool": {
            "should": [
              {
                "equals": {
                  "a": 10
                }
              },
              {
                "equals": {
                  "b": 0
                }
              }
            ]
          }
        }
      ]
    }
  }
}

More complex query:

(a = 1 and b = 1) or (a = 10 and b = 2) or (b = 0)
‹›
  • (a = 1 and b = 1) or (a = 10 and b = 2) or (b = 0)
(a = 1 and b = 1) or (a = 10 and b = 2) or (b = 0)
📋
{
  "index":"t",
  "query": {
    "bool": {
      "should": [
        {
          "bool": {
            "must": [
              {
                "equals": {
                  "a": 1
                }
              },
              {
                "equals": {
                  "b": 1
                }
              }
            ]
          }
        },
        {
          "bool": {
            "must": [
              {
                "equals": {
                  "a": 10
                }
              },
              {
                "equals": {
                  "b": 2
                }
              }
            ]
          }
        },
        {
          "bool": {
            "must": [
              {
                "equals": {
                  "b": 0
                }
              }
            ]
          }

        }
      ]
    }
  }
}

Queries in SQL format

Queries in SQL format (query_string) can also be used in bool queries.

‹›
  • Example
Example
📋
{
  "index": "test1",
  "query": {
    "bool": {
      "must": [
        { "query_string" : "product" },
        { "query_string" : "good" }
      ]
    }
  }
}

Various filters

Equality filters

Equality filters are the simplest filters that work with integer, float and string attributes.

‹›
  • Example
Example
📋
{
  "index":"test1",
  "query": {
    "equals": { "price": 500 }
  }
}

Set filters

Set filters check if attribute value is equal to any of the values in the specified set.

Set filters support integer, string and multi-value attributes.

‹›
  • Example
Example
📋
{
  "index":"test1",
  "query": {
    "in": {
      "price": [1,10,100]
    }
  }
}

Range filters

Range filters match documents that have attribute values within a specified range.

Range filters support the following properties:

  • gte: greater than or equal to
  • gt: greater than
  • lte: less than or equal to
  • lt: less than
‹›
  • Example
Example
📋
{
  "index":"test1",
  "query": {
    "range": {
      "price": {
        "gte": 500,
        "lte": 1000
      }
    }
  }
}

Geo distance filters

geo_distance filters are used to filter the documents that are within a specific distance from a geo location.

location_anchor

Specifies the pin location, in degrees. Distances are calculated from this point.

location_source

Specifies the attributes that contain latitude and longitude.

distance_type

Specifies distance calculation function. Can be either adaptive or haversine. adaptive is faster and more precise, for more details see GEODIST(). Optional, defaults to adaptive.

distance

Specifies the maximum distance from the pin locations. All documents within this distance match. The distance can be specified in various units. If no unit is specified, the distance is assumed to be in meters. Here is a list of supported distance units:

  • Meter: m or meters
  • Kilometer: km or kilometers
  • Centimeter: cm or centimeters
  • Millimeter: mm or millimeters
  • Mile: mi or miles
  • Yard: yd or yards
  • Feet: ft or feet
  • Inch: in or inch
  • Nautical mile: NM, nmi or nauticalmiles

location_anchor and location_source properties accept the following latitude/longitude formats:

  • an object with lat and lon keys: { "lat": "attr_lat", "lon": "attr_lon" }
  • a string of the following structure: "attr_lat, attr_lon"
  • an array with the latitude and longitude in the following order: [attr_lon, attr_lat]

Latitude and longitude are specified in degrees.

‹›
  • Basic example
  • Advanced example
📋
{
  "index":"test",
  "query": {
    "geo_distance": {
      "location_anchor": {"lat":49, "lon":15},
      "location_source": {"attr_lat, attr_lon"},
      "distance_type": "adaptive",
      "distance":"100 km"
    }
  }
}