Manticore Search Manual: Searching

Queries can be automatically optimized if OPTION boolean_simplify=1 is specified. Some transformations performed by this optimization include:

Excess brackets: ((A | B) | C) becomes (A | B | C); ((A B) C) becomes (A B C)
Excess AND NOT: ((A !N1) !N2) becomes (A !(N1 | N2))
Common NOT: ((A !N) | (B !N)) becomes ((A | B) !N)
Common Compound NOT: ((A !(N AA)) | (B !(N BB))) becomes (((A | B) !N) | (A !AA) | (B !BB)) if the cost of evaluating N is greater than the sum of evaluating A and B
Common subterm: ((A (N | AA)) | (B (N | BB))) becomes (((A | B) N) | (A AA) | (B BB)) if the cost of evaluating N is greater than the sum of evaluating A and B
Common keywords: (A | "A B"~N) becomes A; ("A B" | "A B C") becomes "A B"; ("A B"~N | "A B C"~N) becomes ("A B"~N)
Common phrase: ("X A B" | "Y A B") becomes ("("X"|"Y") A B")
Common AND NOT: ((A !X) | (A !Y) | (A !Z)) becomes (A !(X Y Z))
Common OR NOT: ((A !(N | N1)) | (B !(N | N2))) becomes (( (A !N1) | (B !N2) ) !N) Note that optimizing queries consumes CPU time, so for simple queries or hand-optimized queries, you'll achieve better results with the default boolean_simplify=0 value. Simplifications often benefit complex queries or algorithmically generated queries.

Queries like -dog, which could potentially include all documents from the collection are not allowed by default. To allow them, you must specify not_terms_only_allowed=1 either as a global setting or as a search option.

When you run a query via SQL over the MySQL protocol, you receive the requested columns as a result or an empty result set if nothing is found.

‹›

SQL

📋

SELECT * FROM tbl;

‹›

Response

+------+------+--------+
| id   | age  | name   |
+------+------+--------+
|    1 |   25 | joe    |
|    2 |   25 | mary   |
|    3 |   33 | albert |
+------+------+--------+
3 rows in set (0.00 sec)

Additionally, you can use the SHOW META call to see extra meta-information about the latest query.

‹›

SQL

📋

SELECT id,story_author,comment_author FROM hn_small WHERE story_author='joe' LIMIT 3; SHOW META;

‹›

Response

++--------+--------------+----------------+
| id     | story_author | comment_author |
+--------+--------------+----------------+
| 152841 | joe          | SwellJoe       |
| 161323 | joe          | samb           |
| 163735 | joe          | jsjenkins168   |
+--------+--------------+----------------+
3 rows in set (0.01 sec)

+----------------+-------+
| Variable_name  | Value |
+----------------+-------+
| total          | 3     |
| total_found    | 20    |
| total_relation | gte   |
| time           | 0.010 |
+----------------+-------+
4 rows in set (0.00 sec)

In some cases, such as when performing a faceted search, you may receive multiple result sets as a response to your SQL query.

‹›

SQL

📋

SELECT * FROM tbl WHERE MATCH('joe') FACET age;

‹›

Response

+------+------+
| id   | age  |
+------+------+
|    1 |   25 |
+------+------+
1 row in set (0.00 sec)

+------+----------+
| age  | count(*) |
+------+----------+
|   25 |        1 |
+------+----------+
1 row in set (0.00 sec)

In case of a warning, the result set will include a warning flag, and you can see the warning using SHOW WARNINGS.

‹›

SQL

📋

SELECT * from tbl where match('"joe"/3'); show warnings;

‹›

Response

+------+------+------+
| id   | age  | name |
+------+------+------+
|    1 |   25 | joe  |
+------+------+------+
1 row in set, 1 warning (0.00 sec)

+---------+------+--------------------------------------------------------------------------------------------+
| Level   | Code | Message                                                                                    |
+---------+------+--------------------------------------------------------------------------------------------+
| warning | 1000 | quorum threshold too high (words=1, thresh=3); replacing quorum operator with AND operator |
+---------+------+--------------------------------------------------------------------------------------------+
1 row in set (0.00 sec)

If your query fails, you will receive an error:

‹›

SQL

📋

SELECT * from tbl where match('@surname joe');

‹›

Response

ERROR 1064 (42000): index idx: query error: no field 'surname' found in schema

Via the HTTP JSON interface, the query result is sent as a JSON document. Example:

{
  "took":10,
  "timed_out": false,
  "hits":
  {
    "total": 2,
    "hits":
    [
      {
        "_id": 1,
        "_score": 1,
        "_source": { "gid": 11 }
      },
      {
        "_id": 2,
        "_score": 1,
        "_source": { "gid": 12 }
      }
    ]
  }
}

took: time in milliseconds it took to execute the search
timed_out: whether the query timed out or not
hits: search results, with the following properties:
- total: total number of matching documents
- hits: an array containing matches

The query result can also include query profile information. See Query profile.

Each match in the hits array has the following properties:

_id: match id
_score: match weight, calculated by the ranker
_source: an array containing the attributes of this match

By default, all attributes are returned in the _source array. You can use the _source property in the request payload to select the fields you want to include in the result set. Example:

{
  "table":"test",
  "_source":"attr*",
  "query": { "match_all": {} }
}

You can specify the attributes you want to include in the query result as a string ("_source": "attr*") or as an array of strings ("_source": [ "attr1", "attri*" ]"). Each entry can be an attribute name or a wildcard (*, % and ? symbols are supported).

You can also explicitly specify which attributes you want to include and which to exclude from the result set using the includes and excludes properties:

"_source":
{
  "includes": [ "attr1", "attri*" ],
  "excludes": [ "*desc*" ]
}

An empty list of includes is interpreted as "include all attributes," while an empty list of excludes does not match anything. If an attribute matches both the includes and excludes, then the excludes win.

WHERE is an SQL clause that works for both full-text matching and additional filtering. The following operators are available:

Comparison operators <, >, <=, >=, =, <>, BETWEEN, IN, IS NULL
Boolean operators AND, OR, NOT

MATCH('query') is supported and maps to a full-text query.

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

If you prefer the HTTP JSON interface, you can also apply filtering. It might seem more complex than SQL, but it is recommended for cases when you need to prepare a query programmatically, such as when a user fills out a form in your application.

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

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

‹›

JSON

JSON

📋

⚙

POST /search
{
  "table": "test1",
  "query": {
    "bool": {
      "must": [
        { "match" : { "_all" : "product" } },
        { "range": { "price": { "gte": 500, "lte": 1000 } } }
      ],
      "must_not": {
        "range": { "revision": { "lt": 15 } }
      }
    }
  }
}

The bool query matches documents based on 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.

‹›

JSON

JSON

📋

⚙

POST /search
{
  "table":"test1",
  "query": {
    "bool": {
      "must": [
        { "match": {"_all":"keyword"} },
        { "range": { "revision": { "gte": 14 } } }
      ]
    }
  }
}

Queries and filters specified in the must section are required to match the documents. If multiple fulltext queries or filters are specified, all of them must match. This is the equivalent of AND queries in SQL. Note that if you want to match against an array (multi-value attribute), you can specify the attribute multiple times. The result will be positive only if all the queried values are found in the array, e.g.:

"must": [
  {"equals" : { "product_codes": 5 }},
  {"equals" : { "product_codes": 6 }}
]

Note also, it may be better in terms of performance to use:

  {"in" : { "all(product_codes)": [5,6] }}

(see details below).

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. Note, if you want to match against an array (multi-value attribute) you can specify the attribute multiple times, e.g.:

"should": [
  {"equals" : { "product_codes": 7 }},
  {"equals" : { "product_codes": 8 }}
]

Note also, it may be better in terms of performance to use:

  {"in" : { "any(product_codes)": [7,8] }}

(see details below).

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.

‹›

JSON

JSON

📋

⚙

POST /search
{
  "table":"t",
  "query": {
    "bool": {
      "should": [
        {
          "equals": {
            "b": 1
          }
        },
        {
          "equals": {
            "b": 3
          }
        }
      ],
      "must": [
        {
          "equals": {
            "a": 1
          }
        }
      ],
      "must_not": {
        "equals": {
          "b": 2
        }
      }
    }
  }
}

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.

‹›

JSON

JSON

📋

⚙

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

POST /search
{
  "table":"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)

‹›

JSON

JSON

📋

⚙

(a = 1 and b = 1) or (a = 10 and b = 2) or (b = 0)

POST /search
{
  "table":"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 (query_string) can also be used in bool queries.

‹›

JSON

JSON

📋

⚙

POST /search
{
  "table": "test1",
  "query": {
    "bool": {
      "must": [
        { "query_string" : "product" },
        { "query_string" : "good" }
      ]
    }
  }
}

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

‹›

JSON

JSON

📋

⚙

POST /search
{
  "table":"test1",
  "query": {
    "equals": { "price": 500 }
  }
}

Filter equals can be applied to a multi-value attribute and you can use:

any() which will be positive if the attribute has at least one value which equals to the queried value;
all() which will be positive if the attribute has a single value and it equals to the queried value

‹›

JSON

JSON

📋

⚙

POST /search
{
  "table":"test1",
  "query": {
    "equals": { "any(price)": 100 }
  }
}

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.

‹›

JSON

JSON

📋

⚙

POST /search
{
  "table":"test1",
  "query": {
    "in": {
      "price": [1,10,100]
    }
  }
}

When applied to a multi-value attribute you can use:

any() (equivalent to no function) which will be positive if there's at least one match between the attribute values and the queried values;
all() which will be positive if all the attribute values are in the queried set

‹›

JSON

JSON

📋

⚙

POST /search
{
  "table":"test1",
  "query": {
    "in": {
      "all(price)": [1,10]
    }
  }
}

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

‹›

JSON

JSON

📋

⚙

POST /search
{
  "table":"test1",
  "query": {
    "range": {
      "price": {
        "gte": 500,
        "lte": 1000
      }
    }
  }
}

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

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

Specifies the attributes that contain latitude and longitude.

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.

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

📋

POST /search
{
  "table":"test",
  "query": {
    "geo_distance": {
      "location_anchor": {"lat":49, "lon":15},
      "location_source": {"attr_lat, attr_lon"},
      "distance_type": "adaptive",
      "distance":"100 km"
    }
  }
}

Boolean optimization

Search results

SQL

HTTP

Source selection

Filters

WHERE

HTTP JSON

bool query

must

should

must_not

Nested bool query

Queries in SQL format

Various filters

Equality filters

Set filters

Range filters

Geo distance filters

location_anchor

location_source

distance_type

distance