Besides simple SFW (`SELECT-FROM-WHERE`) queries, the SQL plugin supports complex queries such as subquery, join, union, and minus. These queries operate on more than one OpenSearch index. To examine how these queries execute behind the scenes, use the `explain` operation.
## Joins
OpenSearch SQL supports inner joins, cross joins, and left outer joins.
### Constraints
Joins have a number of constraints:
1. You can only join two indices.
1. You must use aliases for indices (e.g. `people p`).
1. Within an ON clause, you can only use AND conditions.
1. In a WHERE statement, don't combine trees that contain multiple indices. For example, the following statement works:
```
WHERE (a.type1 > 3 OR a.type1 <0)AND(b.type2> 4 OR b.type2 <-1)
```
The following statement does not:
```
WHERE (a.type1 > 3 OR b.type2 <0)AND(a.type1> 4 OR b.type2 <-1)
```
1. You can't use GROUP BY or ORDER BY for results.
1. LIMIT with OFFSET (e.g. `LIMIT 25 OFFSET 25`) is not supported.
### Description
The `JOIN` clause combines columns from one or more indices using values common to each.
Inner join creates a new result set by combining columns of two indices based on your join predicates. It iterates the two indices and compares each document to find the ones that satisfy the join predicates. You can optionally precede the `JOIN` clause with an `INNER` keyword.
The join predicate(s) is specified by the ON clause.
SQL query:
```sql
SELECT
a.account_number, a.firstname, a.lastname,
e.id, e.name
FROM accounts a
JOIN employees_nested e
ON a.account_number = e.id
```
Explain:
The `explain` output is complicated, because a `JOIN` clause is associated with two OpenSearch DSL queries that execute in separate query planner frameworks. You can interpret it by examining the `Physical Plan` and `Logical Plan` objects.
"Scroll [ employees_nested as e, pageSize=10000 ]" : {
"request" : {
"size" : 200,
"from" : 0,
"_source" : {
"excludes" : [ ],
"includes" : [
"id",
"name"
]
}
}
},
"Scroll [ accounts as a, pageSize=10000 ]" : {
"request" : {
"size" : 200,
"from" : 0,
"_source" : {
"excludes" : [ ],
"includes" : [
"account_number",
"firstname",
"lastname"
]
}
}
},
"useTermsFilterOptimization" : false
}
}
}
},
"description" : "Hash Join algorithm builds hash table based on result of first query, and then probes hash table to find matched rows for each row returned by second query",
Cross join, also known as cartesian join, combines each document from the first index with each document from the second.
The result set is the the cartesian product of documents of both indices.
This operation is similar to the inner join without the `ON` clause that specifies the join condition.
It's risky to perform cross join on two indices of large or even medium size. It might trigger a circuit breaker that terminates the query to avoid running out of memory.
"description" : "Hash Join algorithm builds hash table based on result of first query, and then probes hash table to find matched rows for each row returned by second query",
