421 lines
12 KiB
Markdown
421 lines
12 KiB
Markdown
---
|
|
layout: default
|
|
title: Complex Queries
|
|
parent: SQL
|
|
nav_order: 6
|
|
---
|
|
|
|
# Complex queries
|
|
|
|
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.
|
|
|
|
### Syntax
|
|
|
|
Rule `tableSource`:
|
|
|
|
|
|
|
|
Rule `joinPart`:
|
|
|
|
|
|
|
|
### Example 1: Inner join
|
|
|
|
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.
|
|
|
|
```json
|
|
{
|
|
"Physical Plan" : {
|
|
"Project [ columns=[a.account_number, a.firstname, a.lastname, e.name, e.id] ]" : {
|
|
"Top [ count=200 ]" : {
|
|
"BlockHashJoin[ conditions=( a.account_number = e.id ), type=JOIN, blockSize=[FixedBlockSize with size=10000] ]" : {
|
|
"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",
|
|
"Logical Plan" : {
|
|
"Project [ columns=[a.account_number, a.firstname, a.lastname, e.name, e.id] ]" : {
|
|
"Top [ count=200 ]" : {
|
|
"Join [ conditions=( a.account_number = e.id ) type=JOIN ]" : {
|
|
"Group" : [
|
|
{
|
|
"Project [ columns=[a.account_number, a.firstname, a.lastname] ]" : {
|
|
"TableScan" : {
|
|
"tableAlias" : "a",
|
|
"tableName" : "accounts"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"Project [ columns=[e.name, e.id] ]" : {
|
|
"TableScan" : {
|
|
"tableAlias" : "e",
|
|
"tableName" : "employees_nested"
|
|
}
|
|
}
|
|
}
|
|
]
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
```
|
|
|
|
Result set:
|
|
|
|
| a.account_number | a.firstname | a.lastname | e.id | e.name
|
|
:--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :---
|
|
6 | Hattie | Bond | 6 | Jane Smith
|
|
|
|
### Example 2: Cross join
|
|
|
|
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.
|
|
{: .warning }
|
|
|
|
SQL query:
|
|
|
|
```sql
|
|
SELECT
|
|
a.account_number, a.firstname, a.lastname,
|
|
e.id, e.name
|
|
FROM accounts a
|
|
JOIN employees_nested e
|
|
```
|
|
|
|
Result set:
|
|
|
|
| a.account_number | a.firstname | a.lastname | e.id | e.name
|
|
:--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :---
|
|
1 | Amber | Duke | 3 | Bob Smith
|
|
1 | Amber | Duke | 4 | Susan Smith
|
|
1 | Amber | Duke | 6 | Jane Smith
|
|
6 | Hattie | Bond | 3 | Bob Smith
|
|
6 | Hattie | Bond | 4 | Susan Smith
|
|
6 | Hattie | Bond | 6 | Jane Smith
|
|
13 | Nanette | Bates | 3 | Bob Smith
|
|
13 | Nanette | Bates | 4 | Susan Smith
|
|
13 | Nanette | Bates | 6 | Jane Smith
|
|
18 | Dale | Adams | 3 | Bob Smith
|
|
18 | Dale | Adams | 4 | Susan Smith
|
|
18 | Dale | Adams | 6 | Jane Smith
|
|
|
|
### Example 3: Left outer join
|
|
|
|
Use left outer join to retain rows from the first index if it does not satisfy the join predicate. The keyword `OUTER` is optional.
|
|
|
|
SQL query:
|
|
|
|
```sql
|
|
SELECT
|
|
a.account_number, a.firstname, a.lastname,
|
|
e.id, e.name
|
|
FROM accounts a
|
|
LEFT JOIN employees_nested e
|
|
ON a.account_number = e.id
|
|
```
|
|
|
|
Result set:
|
|
|
|
| a.account_number | a.firstname | a.lastname | e.id | e.name
|
|
:--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :---
|
|
1 | Amber | Duke | null | null
|
|
6 | Hattie | Bond | 6 | Jane Smith
|
|
13 | Nanette | Bates | null | null
|
|
18 | Dale | Adams | null | null
|
|
|
|
## Subquery
|
|
|
|
A subquery is a complete `SELECT` statement used within another statement and enclosed in parenthesis.
|
|
From the explain output, you can see that some subqueries are actually transformed to an equivalent join query to execute.
|
|
|
|
### Example 1: Table subquery
|
|
|
|
SQL query:
|
|
|
|
```sql
|
|
SELECT a1.firstname, a1.lastname, a1.balance
|
|
FROM accounts a1
|
|
WHERE a1.account_number IN (
|
|
SELECT a2.account_number
|
|
FROM accounts a2
|
|
WHERE a2.balance > 10000
|
|
)
|
|
```
|
|
|
|
Explain:
|
|
|
|
```json
|
|
{
|
|
"Physical Plan" : {
|
|
"Project [ columns=[a1.balance, a1.firstname, a1.lastname] ]" : {
|
|
"Top [ count=200 ]" : {
|
|
"BlockHashJoin[ conditions=( a1.account_number = a2.account_number ), type=JOIN, blockSize=[FixedBlockSize with size=10000] ]" : {
|
|
"Scroll [ accounts as a2, pageSize=10000 ]" : {
|
|
"request" : {
|
|
"size" : 200,
|
|
"query" : {
|
|
"bool" : {
|
|
"filter" : [
|
|
{
|
|
"bool" : {
|
|
"adjust_pure_negative" : true,
|
|
"must" : [
|
|
{
|
|
"bool" : {
|
|
"adjust_pure_negative" : true,
|
|
"must" : [
|
|
{
|
|
"bool" : {
|
|
"adjust_pure_negative" : true,
|
|
"must_not" : [
|
|
{
|
|
"bool" : {
|
|
"adjust_pure_negative" : true,
|
|
"must_not" : [
|
|
{
|
|
"exists" : {
|
|
"field" : "account_number",
|
|
"boost" : 1
|
|
}
|
|
}
|
|
],
|
|
"boost" : 1
|
|
}
|
|
}
|
|
],
|
|
"boost" : 1
|
|
}
|
|
},
|
|
{
|
|
"range" : {
|
|
"balance" : {
|
|
"include_lower" : false,
|
|
"include_upper" : true,
|
|
"from" : 10000,
|
|
"boost" : 1,
|
|
"to" : null
|
|
}
|
|
}
|
|
}
|
|
],
|
|
"boost" : 1
|
|
}
|
|
}
|
|
],
|
|
"boost" : 1
|
|
}
|
|
}
|
|
],
|
|
"adjust_pure_negative" : true,
|
|
"boost" : 1
|
|
}
|
|
},
|
|
"from" : 0
|
|
}
|
|
},
|
|
"Scroll [ accounts as a1, pageSize=10000 ]" : {
|
|
"request" : {
|
|
"size" : 200,
|
|
"from" : 0,
|
|
"_source" : {
|
|
"excludes" : [ ],
|
|
"includes" : [
|
|
"firstname",
|
|
"lastname",
|
|
"balance",
|
|
"account_number"
|
|
]
|
|
}
|
|
}
|
|
},
|
|
"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",
|
|
"Logical Plan" : {
|
|
"Project [ columns=[a1.balance, a1.firstname, a1.lastname] ]" : {
|
|
"Top [ count=200 ]" : {
|
|
"Join [ conditions=( a1.account_number = a2.account_number ) type=JOIN ]" : {
|
|
"Group" : [
|
|
{
|
|
"Project [ columns=[a1.balance, a1.firstname, a1.lastname, a1.account_number] ]" : {
|
|
"TableScan" : {
|
|
"tableAlias" : "a1",
|
|
"tableName" : "accounts"
|
|
}
|
|
}
|
|
},
|
|
{
|
|
"Project [ columns=[a2.account_number] ]" : {
|
|
"Filter [ conditions=[AND ( AND account_number ISN null, AND balance GT 10000 ) ] ]" : {
|
|
"TableScan" : {
|
|
"tableAlias" : "a2",
|
|
"tableName" : "accounts"
|
|
}
|
|
}
|
|
}
|
|
}
|
|
]
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
```
|
|
|
|
Result set:
|
|
|
|
| a1.firstname | a1.lastname | a1.balance
|
|
:--- | :--- | :--- | :--- | :--- | :---
|
|
Amber | Duke | 39225
|
|
Nanette | Bates | 32838
|
|
|
|
### Example 2: From subquery
|
|
|
|
SQL query:
|
|
|
|
```sql
|
|
SELECT a.f, a.l, a.a
|
|
FROM (
|
|
SELECT firstname AS f, lastname AS l, age AS a
|
|
FROM accounts
|
|
WHERE age > 30
|
|
) AS a
|
|
```
|
|
|
|
Explain:
|
|
|
|
```json
|
|
{
|
|
"from" : 0,
|
|
"size" : 200,
|
|
"query" : {
|
|
"bool" : {
|
|
"filter" : [
|
|
{
|
|
"bool" : {
|
|
"must" : [
|
|
{
|
|
"range" : {
|
|
"age" : {
|
|
"from" : 30,
|
|
"to" : null,
|
|
"include_lower" : false,
|
|
"include_upper" : true,
|
|
"boost" : 1.0
|
|
}
|
|
}
|
|
}
|
|
],
|
|
"adjust_pure_negative" : true,
|
|
"boost" : 1.0
|
|
}
|
|
}
|
|
],
|
|
"adjust_pure_negative" : true,
|
|
"boost" : 1.0
|
|
}
|
|
},
|
|
"_source" : {
|
|
"includes" : [
|
|
"firstname",
|
|
"lastname",
|
|
"age"
|
|
],
|
|
"excludes" : [ ]
|
|
}
|
|
}
|
|
```
|
|
|
|
Result set:
|
|
|
|
| f | l | a
|
|
:--- | :--- | :---
|
|
Amber | Duke | 32
|
|
Dale | Adams | 33
|
|
Hattie | Bond | 36
|