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;+------+------+--------+
| 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;++--------+--------------+----------------+
| 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;+------+------+
| 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;+------+------+------+
| 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');ERROR 1064 (42000): index idx: query error: no field 'surname' found in schemaVia 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 searchtimed_out: whether the query timed out or nothits: search results, with the following properties:total: total number of matching documentshits: 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:
{
"index":"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
POST /search
{
"index": "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
POST /search
{
"index":"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
POST /search
{
"index":"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
a = 2 and (a = 10 or b = 0)
POST /search
{
"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)- JSON
(a = 1 and b = 1) or (a = 10 and b = 2) or (b = 0)
POST /search
{
"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 (query_string) can also be used in bool queries.
- JSON
POST /search
{
"index": "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
POST /search
{
"index":"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
POST /search
{
"index":"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
POST /search
{
"index":"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
POST /search
{
"index":"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 togt: greater thanlte: less than or equal tolt: less than
- JSON
POST /search
{
"index":"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:
mormeters - Kilometer:
kmorkilometers - Centimeter:
cmorcentimeters - Millimeter:
mmormillimeters - Mile:
miormiles - Yard:
ydoryards - Feet:
ftorfeet - Inch:
inorinch - Nautical mile:
NM,nmiornauticalmiles
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
{
"index":"test",
"query": {
"geo_distance": {
"location_anchor": {"lat":49, "lon":15},
"location_source": {"attr_lat, attr_lon"},
"distance_type": "adaptive",
"distance":"100 km"
}
}
}WARNING: This functionality is in beta stage. Use it with caution.
The JOIN SQL statement in Manticore Search allows combining rows from two tables based on a related column between them.
This query retrieves all products from the orders table along with the corresponding email, name, and address of customers from the customers table. It performs a LEFT JOIN, ensuring that all records from the orders table are included even if there is no matching customer. The query also filters the results to include only those customers whose data matches the term 'maple' in the joined customers table. The results are ordered by the email address of the customers in ascending order.
- Example
SELECT product, customers.email, customers.name, customers.address
FROM orders
LEFT JOIN customers
ON customers.id = orders.customer_id
WHERE MATCH('maple', customers)
ORDER BY customers.email ASC;+----------+-------------------+----------------+-------------------+
| product | customers.email | customers.name | customers.address |
+----------+-------------------+----------------+-------------------+
| Phone | NULL | NULL | NULL |
| Monitor | NULL | NULL | NULL |
| Keyboard | NULL | NULL | NULL |
| Laptop | [email protected] | Alice Johnson | 123 Maple St |
| Tablet | [email protected] | Alice Johnson | 123 Maple St |
+----------+-------------------+----------------+-------------------+
5 rows in set (0.00 sec)This query does the same as the previous one, but it performs an INNER JOIN, which means only the orders with matching customers are included in the result.
- Example
SELECT product, customers.email, customers.name, customers.address
FROM orders
INNER JOIN customers
ON customers.id = orders.customer_id
WHERE MATCH('maple', customers)
ORDER BY customers.email asc;+---------+-------------------+----------------+-------------------+
| product | customers.email | customers.name | customers.address |
+---------+-------------------+----------------+-------------------+
| Laptop | [email protected] | Alice Johnson | 123 Maple St |
| Tablet | [email protected] | Alice Johnson | 123 Maple St |
+---------+-------------------+----------------+-------------------+
2 rows in set (0.00 sec)This query retrieves products, customer names, product prices, and product tags from the orders table and customers table. It performs a LEFT JOIN, ensuring all customers are included even if they have not made an order. The query filters to include only those orders with a price greater than 500 and matches the products to the terms 'laptop', 'phone', or 'monitor'. The results are ordered by the id of the orders in ascending order. Additionally, the query facets the results based on the warranty details from the JSON attributes of the joined orders table.
- Example
SELECT orders.product, name, orders.details.price, orders.tags
FROM customers
LEFT JOIN orders
ON customers.id = orders.customer_id
WHERE orders.details.price > 500
AND MATCH('laptop|phone|monitor', orders)
ORDER BY orders.id ASC
FACET orders.details.warranty;+----------------+---------------+----------------------+-------------+
| orders.product | name | orders.details.price | orders.tags |
+----------------+---------------+----------------------+-------------+
| Laptop | Alice Johnson | 1200 | 101,102 |
| Phone | Bob Smith | 800 | 103 |
+----------------+---------------+----------------------+-------------+
2 rows in set (0.01 sec)
--- 2 out of 2 results in 0ms ---
+-------------------------+----------+
| orders.details.warranty | count(*) |
+-------------------------+----------+
| 2 years | 1 |
| 1 year | 1 |
+-------------------------+----------+
2 rows in set (0.01 sec)- Field selection: When selecting fields from two tables in a
JOIN, you must not prefix fields from the left table but must prefix fields from the right table. Correct usage isSELECT field_name, right_table.field_name FROM ..., notSELECT left_table.field_name, right_table.field_name FROM .... - JOIN conditions: Always explicitly specify the table names in your
JOINconditions. Use the formatJOIN ON table_name.some_field = another_table_name.some_field. Omitting table names from the join condition is not supported. - Using expressions with JOINs: When using expressions that combine fields from both joined tables, you must alias the result of the expression. For instance, instead of
SELECT *, (nums2.n + 3) * n FROM nums LEFT JOIN nums2 ON nums2.id = nums.num2_id, you should writeSELECT *, (nums2.n + 3) AS x, x * n FROM nums LEFT JOIN nums2 ON nums2.id = nums.num2_id. - Filtering on aliased expressions: When you alias an expression involving fields from both tables (e.g.,
expr(field_from_left_table, field_from_right_table) AS some_alias), note that you cannot use this alias for filtering in theWHEREclause. - Using ANY with MVA: The
ANY()function with multi-valued attributes requires specific handling when used with JOINs. Direct filtering on multi-valued attributes in a WHERE clause is not supported when performing a JOIN. Instead, you must alias the multi-valued attribute from the joined table and use this alias for theANY()condition. For example:SELECT *, t2.m AS alias FROM t LEFT JOIN t2 ON t.id = t2.t_id WHERE ANY(alias) IN (3, 5)