Table joins in Manticore Search enable you to combine documents from two tables by matching related columns. This functionality allows for more complex queries and enhanced data retrieval across multiple tables.
SELECT
select_expr [, select_expr] ...
FROM tbl_name
{INNER | LEFT} JOIN tbl2_name
ON join_condition
[...other select options]
join_condition: {
left_table.attr = right_table.attr
| left_table.json_attr.string_id = string(right_table.json_attr.string_id)
| left_table.json_attr.int_id = int(right_table.json_attr.int_id)
}
For more information on select options, refer to the SELECT section.
When joining by a value from a JSON attribute, you need to explicitly specify the value's type using the int() or string() function.
SELECT ... ON left_table.json_attr.string_id = string(right_table.json_attr.string_id)
SELECT ... ON left_table.json_attr.int_id = int(right_table.json_attr.int_id)
POST /search
{
"table": "table_name",
"query": {
<optional full-text query against the left table>
},
"join": [
{
"type": "inner" | "left",
"table": "joined_table_name",
"query": {
<optional full-text query against the right table>
},
"on": [
{
"left": {
"table": "left_table_name",
"field": "field_name",
"type": "<common field's type when joining using json attributes>"
},
"operator": "eq",
"right": {
"table": "right_table_name",
"field": "field_name"
}
}
]
}
],
"options": {
...
}
}
on.type: {
int
| string
}
Note, there is the type field in the left operand section which you should use when joining two tables using json attributes. The allowed values are string and int.
Manticore Search supports two types of joins:
- INNER JOIN: Returns only the rows where there is a match in both tables. For example, the query performs an INNER JOIN between the
orders and customers tables, including only the orders that have matching customers.
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;
POST /search
{
"table": "orders",
"join": [
{
"type": "inner",
"table": "customers",
"query": {
"query_string": "maple"
},
"on": [
{
"left": {
"table": "orders",
"field": "customer_id"
},
"operator": "eq",
"right": {
"table": "customers",
"field": "id"
}
}
]
}
],
"_source": ["product", "customers.email", "customers.name", "customers.address"],
"sort": [{"customers.email": "asc"}]
}
+---------+-------------------+----------------+-------------------+
| product | customers.email | customers.name | customers.address |
+---------+-------------------+----------------+-------------------+
| Laptop | alice@example.com | Alice Johnson | 123 Maple St |
| Tablet | alice@example.com | Alice Johnson | 123 Maple St |
+---------+-------------------+----------------+-------------------+
2 rows in set (0.00 sec)
{
"took": 0,
"timed_out": false,
"hits": {
"total": 2,
"total_relation": "eq",
"hits": [
{
"_id": 1,
"_score": 1,
"_source": {
"product": "Laptop",
"customers.email": "alice@example.com",
"customers.name": "Alice Johnson",
"customers.address": "123 Maple St"
}
},
{
"_id": 3,
"_score": 1,
"_source": {
"product": "Tablet",
"customers.email": "alice@example.com",
"customers.name": "Alice Johnson",
"customers.address": "123 Maple St"
}
}
]
}
}
- LEFT JOIN: Returns all rows from the left table and the matched rows from the right table. If there is no match, NULL values are returned for the right table's columns. For example, this query retrieves all customers along with their corresponding orders using a LEFT JOIN. If no corresponding order exists, NULL values will appear. The results are sorted by the customer's email, and only the customer's name and the order quantity are selected.
SELECT
name, orders.quantity
FROM customers
LEFT JOIN orders
ON orders.customer_id = customers.id
ORDER BY email ASC;
POST /search
{
"table": "customers",
"_source": ["name", "orders.quantity"],
"join": [
{
"type": "left",
"table": "orders",
"on": [
{
"left": {
"table": "orders",
"field": "customer_id"
},
"operator": "eq",
"right": {
"table": "customers",
"field": "id"
}
}
]
}
],
"sort": [{"email": "asc"}]
}
+---------------+-----------------+-------------------+
| name | orders.quantity | @int_attr_email |
+---------------+-----------------+-------------------+
| Alice Johnson | 1 | alice@example.com |
| Alice Johnson | 1 | alice@example.com |
| Bob Smith | 2 | bob@example.com |
| Carol White | 1 | carol@example.com |
| John Smith | NULL | john@example.com |
+---------------+-----------------+-------------------+
5 rows in set (0.00 sec)
{
"took": 0,
"timed_out": false,
"hits": {
"total": 5,
"total_relation": "eq",
"hits": [
{
"_id": 1,
"_score": 1,
"_source": {
"name": "Alice Johnson",
"address": "123 Maple St",
"email": "alice@example.com",
"orders.id": 3,
"orders.customer_id": 1,
"orders.quantity": 1,
"orders.order_date": "2023-01-03",
"orders.tags": [
101,
104
],
"orders.details": {
"price": 450,
"warranty": "1 year"
},
"orders.product": "Tablet"
}
},
{
"_id": 1,
"_score": 1,
"_source": {
"name": "Alice Johnson",
"address": "123 Maple St",
"email": "alice@example.com",
"orders.id": 1,
"orders.customer_id": 1,
"orders.quantity": 1,
"orders.order_date": "2023-01-01",
"orders.tags": [
101,
102
],
"orders.details": {
"price": 1200,
"warranty": "2 years"
},
"orders.product": "Laptop"
}
},
{
"_id": 2,
"_score": 1,
"_source": {
"name": "Bob Smith",
"address": "456 Oak St",
"email": "bob@example.com",
"orders.id": 2,
"orders.customer_id": 2,
"orders.quantity": 2,
"orders.order_date": "2023-01-02",
"orders.tags": [
103
],
"orders.details": {
"price": 800,
"warranty": "1 year"
},
"orders.product": "Phone"
}
},
{
"_id": 3,
"_score": 1,
"_source": {
"name": "Carol White",
"address": "789 Pine St",
"email": "carol@example.com",
"orders.id": 4,
"orders.customer_id": 3,
"orders.quantity": 1,
"orders.order_date": "2023-01-04",
"orders.tags": [
105
],
"orders.details": {
"price": 300,
"warranty": "1 year"
},
"orders.product": "Monitor"
}
},
{
"_id": 4,
"_score": 1,
"_source": {
"name": "John Smith",
"address": "15 Barclays St",
"email": "john@example.com",
"orders.id": 0,
"orders.customer_id": 0,
"orders.quantity": 0,
"orders.order_date": "",
"orders.tags": [],
"orders.details": null,
"orders.product": ""
}
}
]
}
}
One of the powerful features of table joins in Manticore Search is the ability to perform full-text searches on both the left and right tables simultaneously. This allows you to create complex queries that filter based on text content in multiple tables.
You can use separate MATCH() functions for each table in your JOIN query. The query filters results based on text content in both tables.
SELECT t1.f, t2.f
FROM t1
LEFT JOIN t2 ON t1.id = t2.id
WHERE MATCH('hello', t1) AND MATCH('goodbye', t2);
POST /search
{
"table": "t1",
"query": {
"query_string": "hello"
},
"join": [
{
"type": "left",
"table": "t2",
"query": {
"query_string": "goodbye"
},
"on": [
{
"left": {
"table": "t1",
"field": "id"
},
"operator": "eq",
"right": {
"table": "t2",
"field": "id"
}
}
]
}
],
"_source": ["f", "t2.f"]
}
+-------------+---------------+
| f | t2.f |
+-------------+---------------+
| hello world | goodbye world |
+-------------+---------------+
1 row in set (0.00 sec)
{
"took": 1,
"timed_out": false,
"hits": {
"total": 1,
"total_relation": "eq",
"hits": [
{
"_id": 2,
"_score": 1680,
"t2._score": 1680,
"_source": {
"f": "hello world",
"t2.f": "goodbye world"
}
}
]
}
}
In JSON API queries, table-specific full-text matching is structured differently than SQL:
Main table query: The "query" field at the root level applies to the main table (specified in "table").
Joined table query: Each join definition can include its own "query" field that applies specifically to that joined table.
POST /search
{
"table": "t1",
"query": {
"query_string": "hello"
},
"join": [
{
"type": "left",
"table": "t2",
"query": {
"match": {
"*": "goodbye"
}
},
"on": [
{
"left": {
"table": "t1",
"field": "id"
},
"operator": "eq",
"right": {
"table": "t2",
"field": "id"
}
}
]
}
]
}
{
"took": 1,
"timed_out": false,
"hits": {
"total": 1,
"total_relation": "eq",
"hits": [
{
"_id": 1,
"_score": 1680,
"t2._score": 1680,
"_source": {
"f": "hello world",
"t2.id": 1,
"t2.f": "goodbye world"
}
}
]
}
}
1. Query on main table only: Returns all matching rows from the main table. For unmatched joined records (LEFT JOIN), SQL returns NULL values while JSON API returns default values (0 for numbers, empty strings for text).
SELECT * FROM t1
LEFT JOIN t2 ON t1.id = t2.id
WHERE MATCH('database', t1);
POST /search
{
"table": "t1",
"query": {
"query_string": "database"
},
"join": [
{
"type": "left",
"table": "t2",
"on": [
{
"left": {
"table": "t1",
"field": "id"
},
"operator": "eq",
"right": {
"table": "t2",
"field": "id"
}
}
]
}
]
}
+------+-----------------+-------+------+
| id | f | t2.id | t2.f |
+------+-----------------+-------+------+
| 3 | database search | NULL | NULL |
+------+-----------------+-------+------+
1 row in set (0.00 sec)
{
"took": 0,
"timed_out": false,
"hits": {
"total": 1,
"total_relation": "eq",
"hits": [
{
"_id": 3,
"_score": 1680,
"t2._score": 0,
"_source": {
"f": "database search",
"t2.id": 0,
"t2.f": ""
}
}
]
}
}
2. Query on joined table acts as filter: When a joined table has a query, only records matching both the join condition AND the query condition are returned.
POST /search
{
"table": "t1",
"query": {
"query_string": "database"
},
"join": [
{
"type": "left",
"table": "t2",
"query": {
"query_string": "nonexistent"
},
"on": [
{
"left": {
"table": "t1",
"field": "id"
},
"operator": "eq",
"right": {
"table": "t2",
"field": "id"
}
}
]
}
]
}
{
"took": 0,
"timed_out": false,
"hits": {
"total": 0,
"total_relation": "eq",
"hits": []
}
}
3. JOIN type affects filtering: INNER JOIN requires both join and query conditions to be satisfied, while LEFT JOIN returns matching left table rows even when right table conditions fail.
When using full-text matching with joins, keep these points in mind:
-
Table-specific matching:
- SQL: Each
MATCH() function should specify which table to search in: MATCH('term', table_name)
- JSON: Use the root-level
"query" for the main table and "query" within each join definition for joined tables
-
Query syntax flexibility: JSON API supports both "query_string" and "match" syntaxes for full-text queries
-
Performance implications: Full-text matching on both tables may impact query performance, especially with large datasets. Consider using appropriate indexes and batch sizes.
-
NULL/default value handling: With LEFT JOIN, if there's no matching record in the right table, the query optimizer decides whether to evaluate full-text conditions or filtering conditions first based on performance. SQL returns NULL values while JSON API returns default values (0 for numbers, empty strings for text).
-
Filtering behavior: Queries on joined tables act as filters - they restrict results to records that satisfy both join and query conditions.
-
Full-text operator support: All full-text operators are supported in JOIN queries, including phrase, proximity, field search, NEAR, quorum matching, and advanced operators.
-
Score calculation: Each table maintains its own relevance score, accessible via table_name.weight() in SQL or table_name._score in JSON responses.
Building on the previous examples, let's explore a more advanced scenario where we combine table joins with faceting and full-text matching across multiple tables. This demonstrates the full power of Manticore's JOIN capabilities with complex filtering and aggregation.
This query demonstrates full-text matching across both the customers and orders tables, combined with range filtering and faceting. It searches for customers named "Alice" or "Bob" and their orders containing "laptop", "phone", or "tablet" with prices above $500. The results are ordered by order ID and faceted by warranty terms.
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 | tablet', orders)
AND MATCH('alice | bob', customers)
ORDER BY orders.id ASC
FACET orders.details.warranty;
POST /search
{
"table": "customers",
"query": {
"query_string": "alice | bob"
},
"join": [
{
"type": "left",
"table": "orders",
"query": {
"bool": {
"must": [
{
"range": {
"details.price": {
"gt": 500
}
}
},
{
"query_string": "laptop | phone | tablet"
}
]
}
},
"on": [
{
"left": {
"table": "customers",
"field": "id"
},
"operator": "eq",
"right": {
"table": "orders",
"field": "customer_id"
}
}
]
}
],
"_source": ["orders.product", "name", "orders.details.price", "orders.tags"],
"sort": [{"orders.id": "asc"}],
"aggs": {
"warranty_facet": {
"terms": {
"field": "orders.details.warranty"
}
}
}
}
+-----------------+---------------+----------------------+-------------+
| orders.product | name | orders.details.price | orders.tags |
+-----------------+---------------+----------------------+-------------+
| Laptop Computer | Alice Johnson | 1200 | 101,102 |
| Smart Phone | Bob Smith | 800 | 103 |
+-----------------+---------------+----------------------+-------------+
2 rows in set (0.00 sec)
+-------------------------+----------+
| orders.details.warranty | count(*) |
+-------------------------+----------+
| 2 years | 1 |
| 1 year | 1 |
+-------------------------+----------+
2 rows in set (0.00 sec)
{
"took": 0,
"timed_out": false,
"hits": {
"total": 3,
"total_relation": "eq",
"hits": [
{
"_id": 1,
"_score": 1,
"orders._score": 1565,
"_source": {
"name": "Alice Johnson",
"orders.tags": [
101,
102
],
"orders.product": "Laptop Computer"
}
},
{
"_id": 2,
"_score": 1,
"orders._score": 1565,
"_source": {
"name": "Bob Smith",
"orders.tags": [
103
],
"orders.product": "Smart Phone"
}
},
{
"_id": 1,
"_score": 1,
"orders._score": 1565,
"_source": {
"name": "Alice Johnson",
"orders.tags": [
101,
104
],
"orders.product": "Tablet Device"
}
}
]
},
"aggregations": {
"warranty_facet": {
"buckets": [
{
"key": "2 years",
"doc_count": 1
},
{
"key": "1 year",
"doc_count": 2
}
]
}
}
}
Separate options can be specified for queries in a join: for the left table and the right table. The syntax is OPTION(<table_name>) for SQL queries and one or more subobjects under "options" for JSON queries.
Here's an example of how to specify different field weights for a full-text query on the right table. To retrieve match weights via SQL, use the <table_name>.weight() expression.
In JSON queries, this weight is represented as <table_name>._score.
SELECT product, customers.email, customers.name, customers.address, customers.weight()
FROM orders
INNER JOIN customers
ON customers.id = orders.customer_id
WHERE MATCH('maple', customers)
OPTION(customers) field_weights=(address=1500);
POST /search
{
"table": "orders",
"options": {
"customers": {
"field_weights": {
"address": 1500
}
}
},
"join": [
{
"type": "inner",
"table": "customers",
"query": {
"query_string": "maple"
},
"on": [
{
"left": {
"table": "orders",
"field": "customer_id"
},
"operator": "eq",
"right": {
"table": "customers",
"field": "id"
}
}
]
}
],
"_source": ["product", "customers.email", "customers.name", "customers.address"]
}
+---------+-------------------+----------------+-------------------+--------------------+
| product | customers.email | customers.name | customers.address | customers.weight() |
+---------+-------------------+----------------+-------------------+--------------------+
| Laptop | alice@example.com | Alice Johnson | 123 Maple St | 1500680 |
| Tablet | alice@example.com | Alice Johnson | 123 Maple St | 1500680 |
+---------+-------------------+----------------+-------------------+--------------------+
2 rows in set (0.00 sec)
{
"took": 0,
"timed_out": false,
"hits": {
"total": 2,
"total_relation": "eq",
"hits": [
{
"_id": 1,
"_score": 1,
"customers._score": 15000680,
"_source": {
"product": "Laptop",
"customers.email": "alice@example.com",
"customers.name": "Alice Johnson",
"customers.address": "123 Maple St"
}
},
{
"_id": 3,
"_score": 1,
"customers._score": 15000680,
"_source": {
"product": "Tablet",
"customers.email": "alice@example.com",
"customers.name": "Alice Johnson",
"customers.address": "123 Maple St"
}
}
]
}
}
When performing table joins, Manticore Search processes the results in batches to optimize performance and resource usage. Here's how it works:
To further optimize join operations, Manticore Search employs a caching mechanism for queries executed on the right table. Here's what you need to know:
-
How Caching Works:
- Each query on the right table is defined by the
JOIN ON conditions.
- If the same
JOIN ON conditions are repeated across multiple queries, the results are cached and reused.
- This avoids redundant queries and speeds up subsequent join operations.
-
Configuring Cache Size:
- The size of the join cache can be configured using the join_cache_size option in the
searchd section of the configuration file.
- The default cache size is
20MB, but you can adjust it based on your workload and available memory.
- Setting
join_cache_size=0 disables caching entirely.
-
Memory Considerations:
- Each thread maintains its own cache, so the total memory usage depends on the number of threads and the cache size.
- Ensure your server has sufficient memory to accommodate the cache, especially for high-concurrency environments.
Distributed tables consisting only of local tables are supported on both the left and right sides of a join query. However, distributed tables that include remote tables are not supported.
When using JOINs in Manticore Search, keep the following points in mind:
-
Field selection: When selecting fields from two tables in a JOIN, do not prefix fields from the left table, but do prefix fields from the right table. For example:
SELECT field_name, right_table.field_name FROM ...
-
JOIN conditions: Always explicitly specify the table names in your JOIN conditions:
JOIN ON table_name.some_field = another_table_name.some_field
-
Expressions with JOINs: When using expressions that combine fields from both joined tables, alias the result of the expression:
SELECT *, (nums2.n + 3) AS x, x * n FROM nums LEFT JOIN nums2 ON nums2.id = nums.num2_id
-
Filtering on aliased expressions: You cannot use aliases for expressions involving fields from both tables in the WHERE clause.
-
JSON attributes: When joining on JSON attributes, you must explicitly cast the values to the appropriate type:
-- Correct:
SELECT * FROM t1 LEFT JOIN t2 ON int(t1.json_attr.id) = t2.json_attr.id
-- Incorrect:
SELECT * FROM t1 LEFT JOIN t2 ON t1.json_attr.id = t2.json_attr.id
-
NULL handling: You can use IS NULL and IS NOT NULL conditions on joined fields:
SELECT * FROM t1 LEFT JOIN t2 ON t1.id = t2.id WHERE t2.name IS NULL
SELECT * FROM t1 LEFT JOIN t2 ON t1.id = t2.id WHERE t2.name IS NOT NULL
-
Using ANY with MVA: When using the ANY() function with multi-valued attributes in JOINs, alias the multi-valued attribute from the joined table:
SELECT *, t2.m AS alias
FROM t
LEFT JOIN t2 ON t.id = t2.t_id
WHERE ANY(alias) IN (3, 5)
By following these guidelines, you can effectively use JOINs in Manticore Search to combine data from multiple indexes and perform complex queries.