Grouping search results is often helpful for obtaining per-group match counts or other aggregations. For example, it's useful for creating a graph illustrating the number of matching blog posts per month or grouping web search results by site or forum posts by author, etc.
Manticore supports the grouping of search results by single or multiple columns and computed expressions. The results can:
- Be sorted within a group
- Return more than one row per group
- Have groups filtered
- Have groups sorted
- Be aggregated using the aggregation functions
- SQL
- JSON
General syntax
SELECT {* | SELECT_expr [, SELECT_expr ...]}
...
GROUP BY {field_name | alias } [, ...]
[HAVING where_condition]
[WITHIN GROUP ORDER BY field_name {ASC | DESC} [, ...]]
...
SELECT_expr: { field_name | function_name(...) }
where_condition: {aggregation expression alias | COUNT(*)}Grouping is quite simple - just add "GROUP BY smth" to the end of your SELECT query. The something can be:
- Any non-full-text field from the table: integer, float, string, MVA (multi-value attribute)
- Or, if you used an alias in the
SELECTlist, you can GROUP BY it too
You can omit any aggregation functions in the SELECT list and it will still work:
- SQL
SELECT release_year FROM films GROUP BY release_year LIMIT 5;+--------------+
| release_year |
+--------------+
| 2004 |
| 2002 |
| 2001 |
| 2005 |
| 2000 |
+--------------+In most cases, however, you'll want to obtain some aggregated data for each group, such as:
COUNT(*)to simply get the number of elements in each group- or
AVG(field)to calculate the average value of the field within the group
- SQL1
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SELECT release_year, count(*) FROM films GROUP BY release_year LIMIT 5;+--------------+----------+
| release_year | count(*) |
+--------------+----------+
| 2004 | 108 |
| 2002 | 108 |
| 2001 | 91 |
| 2005 | 93 |
| 2000 | 97 |
+--------------+----------+By default, groups are not sorted, and the next thing you typically want to do is order them by something, like the field you're grouping by:
- SQL
SELECT release_year, count(*) from films GROUP BY release_year ORDER BY release_year asc limit 5;+--------------+----------+
| release_year | count(*) |
+--------------+----------+
| 2000 | 97 |
| 2001 | 91 |
| 2002 | 108 |
| 2003 | 106 |
| 2004 | 108 |
+--------------+----------+Alternatively, you can sort by the aggregation:
- by
count(*)to display groups with the most elements first - by
avg(rental_rate)to show the highest-rated movies first. Note that in the example, it's done via an alias:avg(rental_rate)is first mapped toavgin theSELECTlist, and then we simply doORDER BY avg
- SQL1
- SQL2
SELECT release_year, count(*) FROM films GROUP BY release_year ORDER BY count(*) desc LIMIT 5;+--------------+----------+
| release_year | count(*) |
+--------------+----------+
| 2004 | 108 |
| 2002 | 108 |
| 2003 | 106 |
| 2006 | 103 |
| 2008 | 102 |
+--------------+----------+In some cases, you might want to group not just by a single field, but by multiple fields at once, such as a movie's category and year:
- SQL
- JSON
SELECT category_id, release_year, count(*) FROM films GROUP BY category_id, release_year ORDER BY category_id ASC, release_year ASC;+-------------+--------------+----------+
| category_id | release_year | count(*) |
+-------------+--------------+----------+
| 1 | 2000 | 5 |
| 1 | 2001 | 2 |
| 1 | 2002 | 6 |
| 1 | 2003 | 6 |
| 1 | 2004 | 5 |
| 1 | 2005 | 10 |
| 1 | 2006 | 4 |
| 1 | 2007 | 5 |
| 1 | 2008 | 7 |
| 1 | 2009 | 14 |
| 2 | 2000 | 10 |
| 2 | 2001 | 5 |
| 2 | 2002 | 6 |
| 2 | 2003 | 6 |
| 2 | 2004 | 10 |
| 2 | 2005 | 4 |
| 2 | 2006 | 5 |
| 2 | 2007 | 8 |
| 2 | 2008 | 8 |
| 2 | 2009 | 4 |
+-------------+--------------+----------+Sometimes it's useful to see not just a single element per group, but multiple. This can be easily achieved with the help of GROUP N BY. For example, in the following case, we get two movies for each year rather than just one, which a simple GROUP BY release_year would have returned.
- SQL
SELECT release_year, title FROM films GROUP 2 BY release_year ORDER BY release_year DESC LIMIT 6;+--------------+-----------------------------+
| release_year | title |
+--------------+-----------------------------+
| 2009 | ALICE FANTASIA |
| 2009 | ALIEN CENTER |
| 2008 | AMADEUS HOLY |
| 2008 | ANACONDA CONFESSIONS |
| 2007 | ANGELS LIFE |
| 2007 | ARACHNOPHOBIA ROLLERCOASTER |
+--------------+-----------------------------+Another crucial analytics requirement is to sort elements within a group. To achieve this, use the WITHIN GROUP ORDER BY ... {ASC|DESC} clause. For example, let's get the highest-rated film for each year. Note that it works in parallel with just ORDER BY:
WITHIN GROUP ORDER BYsorts results inside a group- while just
GROUP BYsorts the groups themselves
These two work entirely independently.
- SQL
SELECT release_year, title, rental_rate FROM films GROUP BY release_year WITHIN GROUP ORDER BY rental_rate DESC ORDER BY release_year DESC LIMIT 5;+--------------+------------------+-------------+
| release_year | title | rental_rate |
+--------------+------------------+-------------+
| 2009 | AMERICAN CIRCUS | 4.990000 |
| 2008 | ANTHEM LUKE | 4.990000 |
| 2007 | ATTACKS HATE | 4.990000 |
| 2006 | ALADDIN CALENDAR | 4.990000 |
| 2005 | AIRPLANE SIERRA | 4.990000 |
+--------------+------------------+-------------+HAVING expression is a helpful clause for filtering groups. While WHERE is applied before grouping, HAVING works with the groups. For example, let's keep only those years when the average rental rate of the films for that year was higher than 3. We get only four years:
- SQL
SELECT release_year, avg(rental_rate) avg FROM films GROUP BY release_year HAVING avg > 3;+--------------+------------+
| release_year | avg |
+--------------+------------+
| 2002 | 3.08259249 |
| 2001 | 3.09989142 |
| 2000 | 3.17556739 |
| 2006 | 3.26184368 |
+--------------+------------+There is a function GROUPBY() which returns the key of the current group. It's useful in many cases, especially when you GROUP BY an MVA or a JSON value.
It can also be used in HAVING, for example, to keep only years 2000 and 2002.
Note that GROUPBY()is not recommended for use when you GROUP BY multiple fields at once. It will still work, but since the group key in this case is a compound of field values, it may not appear the way you expect.
- SQL
SELECT release_year, count(*) FROM films GROUP BY release_year HAVING GROUPBY() IN (2000, 2002);+--------------+----------+
| release_year | count(*) |
+--------------+----------+
| 2002 | 108 |
| 2000 | 97 |
+--------------+----------+Manticore supports grouping by MVA. To demonstrate how it works, let's create a table "shoes" with MVA "sizes" and insert a few documents into it:
create table shoes(title text, sizes multi);
insert into shoes values(0,'nike',(40,41,42)),(0,'adidas',(41,43)),(0,'reebook',(42,43));so we have:
SELECT * FROM shoes;
+---------------------+----------+---------+
| id | sizes | title |
+---------------------+----------+---------+
| 1657851069130080265 | 40,41,42 | nike |
| 1657851069130080266 | 41,43 | adidas |
| 1657851069130080267 | 42,43 | reebook |
+---------------------+----------+---------+If we now GROUP BY "sizes", it will process all our multi-value attributes and return an aggregation for each, in this case just the count:
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SELECT groupby() gb, count(*) FROM shoes GROUP BY sizes ORDER BY gb asc;+------+----------+
| gb | count(*) |
+------+----------+
| 40 | 1 |
| 41 | 2 |
| 42 | 2 |
| 43 | 2 |
+------+----------+If you have a field of type JSON, you can GROUP BY any node from it. To demonstrate this, let's create a table "products" with a few documents, each having a color in the "meta" JSON field:
create table products(title text, meta json);
insert into products values(0,'nike','{"color":"red"}'),(0,'adidas','{"color":"red"}'),(0,'puma','{"color":"green"}');This gives us:
SELECT * FROM products;
+---------------------+-------------------+--------+
| id | meta | title |
+---------------------+-------------------+--------+
| 1657851069130080268 | {"color":"red"} | nike |
| 1657851069130080269 | {"color":"red"} | adidas |
| 1657851069130080270 | {"color":"green"} | puma |
+---------------------+-------------------+--------+To group the products by color, we can simply use GROUP BY meta.color, and to display the corresponding group key in the SELECT list, we can use GROUPBY():
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SELECT groupby() color, count(*) from products GROUP BY meta.color;+-------+----------+
| color | count(*) |
+-------+----------+
| red | 2 |
| green | 1 |
+-------+----------+Besides COUNT(*), which returns the number of elements in each group, you can use various other aggregation functions:
While COUNT(*) returns the number of all elements in the group, COUNT(DISTINCT field) returns the number of unique values of the field in the group, which may be completely different from the total count. For instance, you can have 100 elements in the group, but all with the same value for a certain field. COUNT(DISTINCT field) helps to determine that. To demonstrate this, let's create a table "students" with the student's name, age, and major:
CREATE TABLE students(name text, age int, major string);
INSERT INTO students values(0,'John',21,'arts'),(0,'William',22,'business'),(0,'Richard',21,'cs'),(0,'Rebecca',22,'cs'),(0,'Monica',21,'arts');so we have:
MySQL [(none)]> SELECT * from students;
+---------------------+------+----------+---------+
| id | age | major | name |
+---------------------+------+----------+---------+
| 1657851069130080271 | 21 | arts | John |
| 1657851069130080272 | 22 | business | William |
| 1657851069130080273 | 21 | cs | Richard |
| 1657851069130080274 | 22 | cs | Rebecca |
| 1657851069130080275 | 21 | arts | Monica |
+---------------------+------+----------+---------+In the example, you can see that if we GROUP BY major and display both COUNT(*) and COUNT(DISTINCT age), it becomes clear that there are two students who chose the major "cs" with two unique ages, but for the major "arts", there are also two students, yet only one unique age.
There can be at most one COUNT(DISTINCT) per query.
By default, counts are approximate
Actually, some of them are exact, while others are approximate. More on that below.
Manticore supports two algorithms for computing counts of distinct values. One is a legacy algorithm that uses a lot of memory and is usually slow. It collects {group; value} pairs, sorts them, and periodically discards duplicates. The benefit of this approach is that it guarantees exact counts within a plain table. You can enable it by setting the distinct_precision_threshold option to 0.
The other algorithm (enabled by default) loads counts into a hash table and returns its size. If the hash table becomes too large, its contents are moved into a HyperLogLog. This is where the counts become approximate since HyperLogLog is a probabilistic algorithm. The advantage is that the maximum memory usage per group is fixed and depends on the accuracy of the HyperLogLog. The overall memory usage also depends on the max_matches setting, which reflects the number of groups.
The distinct_precision_threshold option sets the threshold below which counts are guaranteed to be exact. The HyperLogLog accuracy setting and the threshold for the "hash table to HyperLogLog" conversion are derived from this setting. It's important to use this option with caution because doubling it will double the maximum memory required for count calculations. The maximum memory usage can be roughly estimated using this formula: 64 * max_matches * distinct_precision_threshold. Note that this is the worst-case scenario, and in most cases, count calculations will use significantly less RAM.
COUNT(DISTINCT) against a distributed table or a real-time table consisting of multiple disk chunks may return inaccurate results, but the result should be accurate for a distributed table consisting of local plain or real-time tables with the same schema (identical set/order of fields, but may have different tokenization settings).
- SQL
SELECT major, count(*), count(distinct age) FROM students GROUP BY major;+----------+----------+---------------------+
| major | count(*) | count(distinct age) |
+----------+----------+---------------------+
| arts | 2 | 1 |
| business | 1 | 1 |
| cs | 2 | 2 |
+----------+----------+---------------------+Often, you want to better understand the contents of each group. You can use GROUP N BY for that, but it would return additional rows you might not want in the output. GROUP_CONCAT() enriches your grouping by concatenating values of a specific field in the group. Let's take the previous example and improve it by displaying all the ages in each group.
GROUP_CONCAT(field) returns the list as comma-separated values.
- SQL
SELECT major, count(*), count(distinct age), group_concat(age) FROM students GROUP BY major+----------+----------+---------------------+-------------------+
| major | count(*) | count(distinct age) | group_concat(age) |
+----------+----------+---------------------+-------------------+
| arts | 2 | 1 | 21,21 |
| business | 1 | 1 | 22 |
| cs | 2 | 2 | 21,22 |
+----------+----------+---------------------+-------------------+- SQL
SELECT release_year year, sum(rental_rate) sum, min(rental_rate) min, max(rental_rate) max, avg(rental_rate) avg FROM films GROUP BY release_year ORDER BY year asc LIMIT 5;+------+------------+----------+----------+------------+
| year | sum | min | max | avg |
+------+------------+----------+----------+------------+
| 2000 | 308.030029 | 0.990000 | 4.990000 | 3.17556739 |
| 2001 | 282.090118 | 0.990000 | 4.990000 | 3.09989142 |
| 2002 | 332.919983 | 0.990000 | 4.990000 | 3.08259249 |
| 2003 | 310.940063 | 0.990000 | 4.990000 | 2.93339682 |
| 2004 | 300.920044 | 0.990000 | 4.990000 | 2.78629661 |
+------+------------+----------+----------+------------+Grouping is done in fixed memory, which depends on the max_matches setting. If max_matches allows for storage of all found groups, the results will be 100% accurate. However, if the value of max_matches is lower, the results will be less accurate.
When parallel processing is involved, it can become more complicated. When pseudo_sharding is enabled and/or when using an RT table with several disk chunks, each chunk or pseudo shard gets a result set that is no larger than max_matches. This can lead to inaccuracies in aggregates and group counts when the result sets from different threads are merged. To fix this, either a larger max_matches value or disabling parallel processing can be used.
Manticore will try to increase max_matches up to max_matches_increase_threshold if it detects that groupby may return inaccurate results. Detection is based on the number of unique values of the groupby attribute, which is retrieved from secondary indexes (if present).
To ensure accurate aggregates and/or group counts when using RT tables or pseudo_sharding, accurate_aggregation can be enabled. This will try to increase max_matches up to the threshold, and if the threshold is not high enough, Manticore will disable parallel processing for the query.
- SQL
MySQL [(none)]> SELECT release_year year, count(*) FROM films GROUP BY year limit 5;
+------+----------+
| year | count(*) |
+------+----------+
| 2004 | 108 |
| 2002 | 108 |
| 2001 | 91 |
| 2005 | 93 |
| 2000 | 97 |
+------+----------+
MySQL [(none)]> SELECT release_year year, count(*) FROM films GROUP BY year limit 5 option max_matches=1;
+------+----------+
| year | count(*) |
+------+----------+
| 2004 | 76 |
+------+----------+
MySQL [(none)]> SELECT release_year year, count(*) FROM films GROUP BY year limit 5 option max_matches=2;
+------+----------+
| year | count(*) |
+------+----------+
| 2004 | 76 |
| 2002 | 74 |
+------+----------+
MySQL [(none)]> SELECT release_year year, count(*) FROM films GROUP BY year limit 5 option max_matches=3;
+------+----------+
| year | count(*) |
+------+----------+
| 2004 | 108 |
| 2002 | 108 |
| 2001 | 91 |
+------+----------+Faceted search is as crucial to a modern search application as autocomplete, spell correction, and search keywords highlighting, especially in e-commerce products.
Faceted search comes in handy when dealing with large quantities of data and various interconnected properties, such as size, color, manufacturer, or other factors. When querying vast amounts of data, search results frequently include numerous entries that don't match the user's expectations. Faceted search enables the end user to explicitly define the criteria they want their search results to satisfy.
In Manticore Search, there's an optimization that maintains the result set of the original query and reuses it for each facet calculation. Since the aggregations are applied to an already calculated subset of documents, they're fast, and the total execution time can often be only slightly longer than the initial query. Facets can be added to any query, and the facet can be any attribute or expression. A facet result includes the facet values and the facet counts. Facets can be accessed using the SQL SELECT statement by declaring them at the very end of the query.
The facet values can originate from an attribute, a JSON property within a JSON attribute, or an expression. Facet values can also be aliased, but the alias must be unique across all result sets (main query result set and other facets result sets). The facet value is derived from the aggregated attribute/expression, but it can also come from another attribute/expression.
FACET {expr_list} [BY {expr_list} ] [DISTINCT {field_name}] [ORDER BY {expr | FACET()} {ASC | DESC}] [LIMIT [offset,] count]Multiple facet declarations must be separated by a whitespace.
Facets can be defined in the aggs node:
"aggs" :
{
"group name" :
{
"terms" :
{
"field":"attribute name",
"size": 1000
}
"sort": [ {"attribute name": { "order":"asc" }} ]
}
}where:
group nameis an alias assigned to the aggregationfieldvalue must contain the name of the attribute or expression being faceted- optional
sizespecifies the maximum number of buckets to include in the result. When not specified, it inherits the main query's limit. More details can be found in the Size of facet result section. - optional
sortspecifies an array of attributes and/or additional properties using the same syntax as the "sort" parameter in the main query.
The result set will contain an aggregations node with the returned facets, where key is the aggregated value and doc_count is the aggregation count.
"aggregations": {
"group name": {
"buckets": [
{
"key": 10,
"doc_count": 1019
},
{
"key": 9,
"doc_count": 954
},
{
"key": 8,
"doc_count": 1021
},
{
"key": 7,
"doc_count": 1011
},
{
"key": 6,
"doc_count": 997
}
]
}
} - SQL
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SELECT *, price AS aprice FROM facetdemo LIMIT 10 FACET price LIMIT 10 FACET brand_id LIMIT 5;+------+-------+----------+---------------------+------------+-------------+---------------------------------------+------------+--------+
| id | price | brand_id | title | brand_name | property | j | categories | aprice |
+------+-------+----------+---------------------+------------+-------------+---------------------------------------+------------+--------+
| 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 | 306 |
| 2 | 400 | 10 | Product Three One | Brand Ten | Four_Three | {"prop1":69,"prop2":19,"prop3":"One"} | 13,14 | 400 |
...
| 9 | 560 | 6 | Product Two Five | Brand Six | Eight_Two | {"prop1":90,"prop2":84,"prop3":"One"} | 13,14 | 560 |
| 10 | 229 | 9 | Product Three Eight | Brand Nine | Seven_Three | {"prop1":84,"prop2":39,"prop3":"One"} | 12,13 | 229 |
+------+-------+----------+---------------------+------------+-------------+---------------------------------------+------------+--------+
10 rows in set (0.00 sec)
+-------+----------+
| price | count(*) |
+-------+----------+
| 306 | 7 |
| 400 | 13 |
...
| 229 | 9 |
| 595 | 10 |
+-------+----------+
10 rows in set (0.00 sec)
+----------+----------+
| brand_id | count(*) |
+----------+----------+
| 1 | 1013 |
| 10 | 998 |
| 5 | 1007 |
| 8 | 1033 |
| 7 | 965 |
+----------+----------+
5 rows in set (0.00 sec)Data can be faceted by aggregating another attribute or expression. For example if the documents contain both the brand id and name, we can return in facet the brand names, but aggregate the brand ids. This can be done by using FACET {expr1} BY {expr2}
- SQL
SELECT * FROM facetdemo FACET brand_name by brand_id;+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| id | price | brand_id | title | brand_name | property | j | categories |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 |
| 2 | 400 | 10 | Product Three One | Brand Ten | Four_Three | {"prop1":69,"prop2":19,"prop3":"One"} | 13,14 |
....
| 19 | 855 | 1 | Product Seven Two | Brand One | Eight_Seven | {"prop1":63,"prop2":78,"prop3":"One"} | 10,11,12 |
| 20 | 31 | 9 | Product Four One | Brand Nine | Ten_Four | {"prop1":79,"prop2":42,"prop3":"One"} | 12,13,14 |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
20 rows in set (0.00 sec)
+-------------+----------+
| brand_name | count(*) |
+-------------+----------+
| Brand One | 1013 |
| Brand Ten | 998 |
| Brand Five | 1007 |
| Brand Nine | 944 |
| Brand Two | 990 |
| Brand Six | 1039 |
| Brand Three | 1016 |
| Brand Four | 994 |
| Brand Eight | 1033 |
| Brand Seven | 965 |
+-------------+----------+
10 rows in set (0.00 sec)If you need to remove duplicates from the buckets returned by FACET, you can use DISTINCT field_name, where field_name is the field by which you want to perform deduplication. It can also be id (which is the default) if you make a FACET query against a distributed table and are not sure whether you have unique ids in the tables (the tables should be local and have the same schema).
If you have multiple FACET declarations in your query, field_name should be the same in all of them.
DISTINCT returns an additional column count(distinct ...) before the column count(*), allowing you to obtain both results without needing to make another query.
- SQL
SELECT brand_name, property FROM facetdemo FACET brand_name distinct property;+-------------+----------+
| brand_name | property |
+-------------+----------+
| Brand Nine | Four |
| Brand Ten | Four |
| Brand One | Five |
| Brand Seven | Nine |
| Brand Seven | Seven |
| Brand Three | Seven |
| Brand Nine | Five |
| Brand Three | Eight |
| Brand Two | Eight |
| Brand Six | Eight |
| Brand Ten | Four |
| Brand Ten | Two |
| Brand Four | Ten |
| Brand One | Nine |
| Brand Four | Eight |
| Brand Nine | Seven |
| Brand Four | Five |
| Brand Three | Four |
| Brand Four | Two |
| Brand Four | Eight |
+-------------+----------+
20 rows in set (0.00 sec)
+-------------+--------------------------+----------+
| brand_name | count(distinct property) | count(*) |
+-------------+--------------------------+----------+
| Brand Nine | 3 | 3 |
| Brand Ten | 2 | 3 |
| Brand One | 2 | 2 |
| Brand Seven | 2 | 2 |
| Brand Three | 3 | 3 |
| Brand Two | 1 | 1 |
| Brand Six | 1 | 1 |
| Brand Four | 4 | 5 |
+-------------+--------------------------+----------+
8 rows in set (0.00 sec)Facets can aggregate over expressions. A classic example is the segmentation of prices by specific ranges:
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SELECT * FROM facetdemo FACET INTERVAL(price,200,400,600,800) AS price_range ;+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+
| id | price | brand_id | title | brand_name | property | j | categories | price_range |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+
| 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 | 1 |
...
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+
20 rows in set (0.00 sec)
+-------------+----------+
| price_range | count(*) |
+-------------+----------+
| 0 | 1885 |
| 3 | 1973 |
| 4 | 2100 |
| 2 | 1999 |
| 1 | 2043 |
+-------------+----------+
5 rows in set (0.01 sec)Facets can aggregate over multi-level grouping, with the result set being the same as if the query performed a multi-level grouping:
- SQL
SELECT *,INTERVAL(price,200,400,600,800) AS price_range FROM facetdemo
FACET price_range AS price_range,brand_name ORDER BY brand_name asc;+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+
| id | price | brand_id | title | brand_name | property | j | categories | price_range |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+
| 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 | 1 |
...
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+-------------+
20 rows in set (0.00 sec)
+--------------+-------------+----------+
| fprice_range | brand_name | count(*) |
+--------------+-------------+----------+
| 1 | Brand Eight | 197 |
| 4 | Brand Eight | 235 |
| 3 | Brand Eight | 203 |
| 2 | Brand Eight | 201 |
| 0 | Brand Eight | 197 |
| 4 | Brand Five | 230 |
| 2 | Brand Five | 197 |
| 1 | Brand Five | 204 |
| 3 | Brand Five | 193 |
| 0 | Brand Five | 183 |
| 1 | Brand Four | 195 |
...Facets can aggregate over histogram values by constructing fixed-size buckets over the values. The key function is:
key_of_the_bucket = interval + offset * floor ( ( value - offset ) / interval )The histogram argument interval must be positive, and the histogram argument offset must be positive and less than interval. By default, the buckets are returned as an array. The histogram argument keyed makes the response a dictionary with the bucket keys.
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- JSON 2
SELECT COUNT(*), HISTOGRAM(price, {hist_interval=100}) as price_range FROM facets GROUP BY price_range ORDER BY price_range ASC;+----------+-------------+
| count(*) | price_range |
+----------+-------------+
| 5 | 0 |
| 5 | 100 |
| 1 | 300 |
| 4 | 400 |
| 1 | 500 |
| 3 | 700 |
| 1 | 900 |
+----------+-------------+Facets can aggregate over histogram date values, which is similar to the normal histogram. The difference is that the interval is specified using a date or time expression. Such expressions require special support because the intervals are not always of fixed length. Values are rounded to the closest bucket using the following key function:
key_of_the_bucket = interval * floor ( value / interval )The histogram parameter calendar_interval understands months to have different amounts of days. The accepted intervals are described in the date_histogram expression. By default, the buckets are returned as an array. The histogram argument keyed makes the response a dictionary with the bucket keys.
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SELECT count(*), DATE_HISTOGRAM(tm, {calendar_interval='month'}) AS months FROM idx_dates GROUP BY months ORDER BY months ASC+----------+------------+
| count(*) | months |
+----------+------------+
| 442 | 1485907200 |
| 744 | 1488326400 |
| 720 | 1491004800 |
| 230 | 1493596800 |
+----------+------------+Facets can aggregate over a set of ranges. The values are checked against the bucket range, where each bucket includes the from value and excludes the to value from the range.
Setting the keyed property to true makes the response a dictionary with the bucket keys rather than an array.
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SELECT COUNT(*), RANGE(price, {range_to=150},{range_from=150,range_to=300},{range_from=300}) price_range FROM facets GROUP BY price_range ORDER BY price_range ASC;+----------+-------------+
| count(*) | price_range |
+----------+-------------+
| 8 | 0 |
| 2 | 1 |
| 10 | 2 |
+----------+-------------+Facets can aggregate over a set of date ranges, which is similar to the normal range. The difference is that the from and to values can be expressed in Date math expressions. This aggregation includes the from value and excludes the to value for each range. Setting the keyed property to true makes the response a dictionary with the bucket keys rather than an array.
- SQL
- JSON
SELECT COUNT(*), DATE_RANGE(tm, {range_to='2017||+2M/M'},{range_from='2017||+2M/M',range_to='2017||+5M/M'},{range_from='2017||+5M/M'}) AS points FROM idx_dates GROUP BY points ORDER BY points ASC;+----------+--------+
| count(*) | points |
+----------+--------+
| 442 | 0 |
| 1464 | 1 |
| 230 | 2 |
+----------+--------+Facets support the ORDER BY clause just like a standard query. Each facet can have its own ordering, and the facet ordering doesn't affect the main result set's ordering, which is determined by the main query's ORDER BY. Sorting can be done on attribute name, count (using COUNT(*)), or the special FACET() function, which provides the aggregated data values.
- SQL
- JSON
SELECT * FROM facetdemo
FACET brand_name BY brand_id ORDER BY FACET() ASC
FACET brand_name BY brand_id ORDER BY brand_name ASC
FACET brand_name BY brand_id order BY COUNT(*) DESC;+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| id | price | brand_id | title | brand_name | property | j | categories |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 |
...
| 20 | 31 | 9 | Product Four One | Brand Nine | Ten_Four | {"prop1":79,"prop2":42,"prop3":"One"} | 12,13,14 |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
20 rows in set (0.01 sec)
+-------------+----------+
| brand_name | count(*) |
+-------------+----------+
| Brand One | 1013 |
| Brand Two | 990 |
| Brand Three | 1016 |
| Brand Four | 994 |
| Brand Five | 1007 |
| Brand Six | 1039 |
| Brand Seven | 965 |
| Brand Eight | 1033 |
| Brand Nine | 944 |
| Brand Ten | 998 |
+-------------+----------+
10 rows in set (0.01 sec)
+-------------+----------+
| brand_name | count(*) |
+-------------+----------+
| Brand Eight | 1033 |
| Brand Five | 1007 |
| Brand Four | 994 |
| Brand Nine | 944 |
| Brand One | 1013 |
| Brand Seven | 965 |
| Brand Six | 1039 |
| Brand Ten | 998 |
| Brand Three | 1016 |
| Brand Two | 990 |
+-------------+----------+
10 rows in set (0.01 sec)
+-------------+----------+
| brand_name | count(*) |
+-------------+----------+
| Brand Six | 1039 |
| Brand Eight | 1033 |
| Brand Three | 1016 |
| Brand One | 1013 |
| Brand Five | 1007 |
| Brand Ten | 998 |
| Brand Four | 994 |
| Brand Two | 990 |
| Brand Seven | 965 |
| Brand Nine | 944 |
+-------------+----------+
10 rows in set (0.01 sec)By default, each facet result set is limited to 20 values. The number of facet values can be controlled with the LIMIT clause individually for each facet by providing either a number of values to return in the format LIMIT count or with an offset as LIMIT offset, count.
The maximum facet values that can be returned is limited by the query's max_matches setting. If you want to implement dynamic max_matches (limiting max_matches to offset + per page for better performance), it must be taken into account that a too low max_matches value can affect the number of facet values. In this case, a minimum max_matches value should be used that is sufficient to cover the number of facet values.
- SQL
- JSON
- PHP
- Python
- Javascript
- Java
- C#
- TypeScript
- Go
SELECT * FROM facetdemo
FACET brand_name BY brand_id ORDER BY FACET() ASC LIMIT 0,1
FACET brand_name BY brand_id ORDER BY brand_name ASC LIMIT 2,4
FACET brand_name BY brand_id order BY COUNT(*) DESC LIMIT 4;+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| id | price | brand_id | title | brand_name | property | j | categories |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 |
...
| 20 | 31 | 9 | Product Four One | Brand Nine | Ten_Four | {"prop1":79,"prop2":42,"prop3":"One"} | 12,13,14 |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
20 rows in set (0.01 sec)
+-------------+----------+
| brand_name | count(*) |
+-------------+----------+
| Brand One | 1013 |
+-------------+----------+
1 rows in set (0.01 sec)
+-------------+----------+
| brand_name | count(*) |
+-------------+----------+
| Brand Four | 994 |
| Brand Nine | 944 |
| Brand One | 1013 |
| Brand Seven | 965 |
+-------------+----------+
4 rows in set (0.01 sec)
+-------------+----------+
| brand_name | count(*) |
+-------------+----------+
| Brand Six | 1039 |
| Brand Eight | 1033 |
| Brand Three | 1016 |
+-------------+----------+
3 rows in set (0.01 sec)When using SQL, a search with facets returns multiple result sets. The MySQL client/library/connector used must support multiple result sets in order to access the facet result sets.
Internally, the FACET is a shorthand for executing a multi-query where the first query contains the main search query and the rest of the queries in the batch have each a clustering. As in the case of multi-query, the common query optimization can kick in for a faceted search, meaning the search query is executed only once, and the facets operate on the search query result, with each facet adding only a fraction of time to the total query time.
To check if the faceted search ran in an optimized mode, you can look in the query log, where all logged queries will contain an xN string, where N is the number of queries that ran in the optimized group. Alternatively, you can check the output of the SHOW META statement, which will display a multiplier metric:
- SQL
SELECT * FROM facetdemo FACET brand_id FACET price FACET categories;
SHOW META LIKE 'multiplier';+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| id | price | brand_id | title | brand_name | property | j | categories |
+------+-------+----------+---------------------+-------------+-------------+---------------------------------------+------------+
| 1 | 306 | 1 | Product Ten Three | Brand One | Six_Ten | {"prop1":66,"prop2":91,"prop3":"One"} | 10,11 |
...
+----------+----------+
| brand_id | count(*) |
+----------+----------+
| 1 | 1013 |
...
+-------+----------+
| price | count(*) |
+-------+----------+
| 306 | 7 |
...
+------------+----------+
| categories | count(*) |
+------------+----------+
| 10 | 2436 |
...
+---------------+-------+
| Variable_name | Value |
+---------------+-------+
| multiplier | 4 |
+---------------+-------+
1 row in set (0.00 sec)One of the greatest features of Manticore Search is the ability to combine full-text searching with geo-location. For example, a retailer can offer a search where a user looks for a product, and the result set can indicate the closest shop that has the product in stock, so the user can go in-store and pick it up. A travel site can provide results based on a search limited to a certain area and have the results sorted by the distance from a point (for example, 'search museums near a hotel').
To perform geo-searching, a document needs to contain pairs of latitude/longitude coordinates. The coordinates can be stored as float attributes. If the document has multiple locations, it may be convenient to use a JSON attribute to store coordinate pairs.
table myrt
{
...
rt_attr_float = lat
rt_attr_float = lon
...
}The coordinates can be stored as degrees or radians.
To find out the distance between two points, the GEODIST() function can be used. GEODIST requires two pairs of coordinates as its first four parameters.
The 5th parameter in a simplified JSON format can configure certain aspects of the function. By default, GEODIST expects coordinates to be in radians, but in=degrees can be added to allow using degrees as input. The coordinates for which we perform the geo distance must have the same type (degrees or radians) as the ones stored in the table; otherwise, results will be misleading.
The calculated distance is by default in meters, but with the out option, it can be transformed to kilometers, feet, or miles. Lastly, by default, a calculation method called adaptive is used. An alternative method based on the haversine algorithm is available; however, this one is slower and less precise.
The result of the function - the distance - can be used in theORDER BY clause to sort the results:
SELECT *, GEODIST(40.7643929, -73.9997683, lat, lon, {in=degrees, out=miles}) AS distance FROM myindex WHERE MATCH('...') ORDER BY distance ASC, WEIGHT() DESC;Or to limit the results to a radial area around the point:
SELECT *,GEODIST(40.7643929, -73.9997683, lat,lon, {in=degrees, out=miles}) AS distance FROM myindex WHERE MATCH('...') AND distance <1000 ORDER BY WEIGHT(), DISTANCE ASC;Another geo search feature is the ability to determine if a location is within a specified area. A special function constructs a polygon object, which is then used by another function to test whether a set of coordinates is contained within that polygon or not.
There are two functions available for creating the polygon:
- GEOPOLY2D() - creates a polygon that takes into account the Earth's curvature
- POLY2D() - creates a simple polygon in flat space
POLY2D is suitable for geo searches when the area has sides shorter than 500km (for polygons with 3-4 sides; for polygons with more sides, lower values should be considered). For areas with longer sides, using GEOPOLY2D is required to maintain accurate results. GEOPOLY2D expects coordinates as latitude/longitude pairs in degrees; using radians will yield results in flat space (similar to POLY2D).
CONTAINS() takes a polygon and a set of coordinates as input and outputs 1 if the point is inside the polygon or 0 otherwise.
SELECT *,CONTAINS(GEOPOLY2D(40.76439, -73.9997, 42.21211, -73.999, 42.21211, -76.123, 40.76439, -76.123), 41.5445, -74.973) AS inside FROM myindex WHERE MATCH('...') AND inside=1;