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SQL | Statements | Full Syntax Diagram | Below is the full syntax diagram of the `SELECT` statement:
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|
SQL | Statements | SET and RESET Statements | The `SET` statement modifies the provided DuckDB configuration option at the specified scope. | 17 | [
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|
SQL | Statements | Examples | Update the `memory_limit` configuration value:
```sql
SET memory_limit = '10GB';
```
Configure the system to use `1` thread:
```sql
SET threads = 1;
```
Or use the `TO` keyword:
```sql
SET threads TO 1;
```
Change configuration option to default value:
```sql
RESET threads;
```
Retrieve configuration value:
```sql
SELECT current_setting('threads');
```
Set the default catalog search path globally:
```sql
SET GLOBAL search_path = 'db1,db2'
```
Set the default collation for the session:
```sql
SET SESSION default_collation = 'nocase';
``` | 147 | [
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|
SQL | Statements | Syntax | <div id="rrdiagram1"></div>
`SET` updates a DuckDB configuration option to the provided value. | 25 | [
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|
SQL | Statements | `RESET` | <div id="rrdiagram2"></div>
The `RESET` statement changes the given DuckDB configuration option to the default value. | 28 | [
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|
SQL | Statements | Scopes | Configuration options can have different scopes:
* `GLOBAL`: Configuration value is used (or reset) across the entire DuckDB instance.
* `SESSION`: Configuration value is used (or reset) only for the current session attached to a DuckDB instance.
* `LOCAL`: Not yet implemented.
When not specified, the default scope for the configuration option is used. For most options this is `GLOBAL`. | 82 | [
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|
SQL | Statements | Configuration | See the [Configuration](#docs:configuration:overview) page for the full list of configuration options. | 21 | [
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|
SQL | Statements | `SET VARIABLE` | The `SET VARIABLE` statement assigns a value to a variable, which can be accessed using the `getvariable` call:
```sql
SET VARIABLE my_var = 30;
SELECT 20 + getvariable('my_var') AS total;
```
| total |
|------:|
| 50 |
If `SET VARIABLE` is invoked on an existing variable, it will overwrite its value:
```sql
SET VARIABLE my_var = 30;
SET VARIABLE my_var = 100;
SELECT 20 + getvariable('my_var') AS total;
```
| total |
|------:|
| 120 |
Variables can have different types:
```sql
SET VARIABLE my_date = DATE '2018-07-13';
SET VARIABLE my_string = 'Hello world';
SET VARIABLE my_map = MAP {'k1': 10, 'k2': 20};
```
If the variable is not set, the `getvariable` function returns `NULL`:
```sql
SELECT getvariable('undefined_var') AS result;
```
| result |
|--------|
| NULL |
The `getvariable` function can also be used in a [`COLUMNS` expression](#docs:sql:expressions:star::columns-expression):
```sql
SET VARIABLE column_to_exclude = 'col1';
CREATE TABLE tbl AS SELECT 12 AS col0, 34 AS col1, 56 AS col2;
SELECT COLUMNS(c -> c != getvariable('column_to_exclude')) FROM tbl;
```
| col0 | col2 |
|-----:|-----:|
| 12 | 56 |
#### Syntax {#docs:sql:statements:set_variable::syntax}
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|
SQL | Statements | `RESET VARIABLE` | The `RESET VARIABLE` statement unsets a variable.
```sql
SET VARIABLE my_var = 30;
RESET VARIABLE my_var;
SELECT getvariable('my_var') AS my_var;
```
| my_var |
|--------|
| NULL |
#### Syntax {#docs:sql:statements:set_variable::syntax}
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|
SQL | Statements | SUMMARIZE Statement | The `SUMMARIZE` statement returns summary statistics for a table, view or a query. | 19 | [
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|
SQL | Statements | Usage | ```sql
SUMMARIZE tbl;
```
In order to summarize a query, prepend `SUMMARIZE` to a query.
```sql
SUMMARIZE SELECT * FROM tbl;
``` | 40 | [
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|
SQL | Statements | See Also | For more examples, see the [guide on `SUMMARIZE`](#docs:guides:meta:summarize). | 26 | [
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|
SQL | Statements | Transaction Management | DuckDB supports [ACID database transactions](https://en.wikipedia.org/wiki/Database_transaction).
Transactions provide isolation, i.e., changes made by a transaction are not visible from concurrent transactions until it is committed.
A transaction can also be aborted, which discards any changes it made so far. | 60 | [
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|
SQL | Statements | Statements | DuckDB provides the following statements for transaction management.
#### Starting a Transaction {#docs:sql:statements:transactions::starting-a-transaction}
To start a transaction, run:
```sql
BEGIN TRANSACTION;
```
#### Committing a Transaction {#docs:sql:statements:transactions::committing-a-transaction}
You can commit a transaction to make it visible to other transactions and to write it to persistent storage (if using DuckDB in persistent mode).
To commit a transaction, run:
```sql
COMMIT;
```
If you are not in an active transaction, the `COMMIT` statement will fail.
#### Rolling Back a Transaction {#docs:sql:statements:transactions::rolling-back-a-transaction}
You can abort a transaction.
This operation, also known as rolling back, will discard any changes the transaction made to the database.
To abort a transaction, run:
```sql
ROLLBACK;
```
You can also use the abort command, which has an identical behavior:
```sql
ABORT;
```
If you are not in an active transaction, the `ROLLBACK` and `ABORT` statements will fail.
#### Example {#docs:sql:statements:transactions::example}
We illustrate the use of transactions through a simple example.
```sql
CREATE TABLE person (name VARCHAR, age BIGINT);
BEGIN TRANSACTION;
INSERT INTO person VALUES ('Ada', 52);
COMMIT;
BEGIN TRANSACTION;
DELETE FROM person WHERE name = 'Ada';
INSERT INTO person VALUES ('Bruce', 39);
ROLLBACK;
SELECT * FROM person;
```
The first transaction (inserting “Ada”) was committed but the second (deleting “Ada” and inserting “Bruce”) was aborted.
Therefore, the resulting table will only contain `<'Ada', 52>`. | 377 | [
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|
SQL | Statements | UNPIVOT Statement | The `UNPIVOT` statement allows multiple columns to be stacked into fewer columns.
In the basic case, multiple columns are stacked into two columns: a `NAME` column (which contains the name of the source column) and a `VALUE` column (which contains the value from the source column).
DuckDB implements both the SQL Standard `UNPIVOT` syntax and a simplified `UNPIVOT` syntax.
Both can utilize a [`COLUMNS` expression](#docs:sql:expressions:star::columns) to automatically detect the columns to unpivot.
`PIVOT_LONGER` may also be used in place of the `UNPIVOT` keyword.
For details on how the `UNPIVOT` statement is implemented, see the [Pivot Internals site](#docs:internals:pivot::unpivot).
> The [`PIVOT` statement](#docs:sql:statements:pivot) is the inverse of the `UNPIVOT` statement. | 210 | [
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|
SQL | Statements | Simplified `UNPIVOT` Syntax | The full syntax diagram is below, but the simplified `UNPIVOT` syntax can be summarized using spreadsheet pivot table naming conventions as:
```sql
UNPIVOT ⟨dataset⟩
ON ⟨column(s)⟩
INTO
NAME ⟨name-column-name⟩
VALUE ⟨value-column-name(s)⟩
ORDER BY ⟨column(s)-with-order-direction(s)⟩
LIMIT ⟨number-of-rows⟩;
```
#### Example Data {#docs:sql:statements:unpivot::example-data}
All examples use the dataset produced by the queries below:
```sql
CREATE OR REPLACE TABLE monthly_sales
(empid INTEGER, dept TEXT, Jan INTEGER, Feb INTEGER, Mar INTEGER, Apr INTEGER, May INTEGER, Jun INTEGER);
INSERT INTO monthly_sales VALUES
(1, 'electronics', 1, 2, 3, 4, 5, 6),
(2, 'clothes', 10, 20, 30, 40, 50, 60),
(3, 'cars', 100, 200, 300, 400, 500, 600);
```
```sql
FROM monthly_sales;
```
<div class="narrow_table"></div>
| empid | dept | Jan | Feb | Mar | Apr | May | Jun |
|------:|-------------|----:|----:|----:|----:|----:|----:|
| 1 | electronics | 1 | 2 | 3 | 4 | 5 | 6 |
| 2 | clothes | 10 | 20 | 30 | 40 | 50 | 60 |
| 3 | cars | 100 | 200 | 300 | 400 | 500 | 600 |
<!--
Easiest is to just unpivot all months into their own name/value pair manually.
Then show the columns-expr version.
Can also show the quarterly example. -->
#### `UNPIVOT` Manually {#docs:sql:statements:unpivot::unpivot-manually}
The most typical `UNPIVOT` transformation is to take already pivoted data and re-stack it into a column each for the name and value.
In this case, all months will be stacked into a `month` column and a `sales` column.
```sql
UNPIVOT monthly_sales
ON jan, feb, mar, apr, may, jun
INTO
NAME month
VALUE sales;
```
<div class="narrow_table"></div>
| empid | dept | month | sales |
|------:|-------------|-------|------:|
| 1 | electronics | Jan | 1 |
| 1 | electronics | Feb | 2 |
| 1 | electronics | Mar | 3 |
| 1 | electronics | Apr | 4 |
| 1 | electronics | May | 5 |
| 1 | electronics | Jun | 6 |
| 2 | clothes | Jan | 10 |
| 2 | clothes | Feb | 20 |
| 2 | clothes | Mar | 30 |
| 2 | clothes | Apr | 40 |
| 2 | clothes | May | 50 |
| 2 | clothes | Jun | 60 |
| 3 | cars | Jan | 100 |
| 3 | cars | Feb | 200 |
| 3 | cars | Mar | 300 |
| 3 | cars | Apr | 400 |
| 3 | cars | May | 500 |
| 3 | cars | Jun | 600 |
#### `UNPIVOT` Dynamically Using Columns Expression {#docs:sql:statements:unpivot::unpivot-dynamically-using-columns-expression}
In many cases, the number of columns to unpivot is not easy to predetermine ahead of time.
In the case of this dataset, the query above would have to change each time a new month is added.
The [`COLUMNS` expression](#docs:sql:expressions:star::columns-expression) can be used to select all columns that are not `empid` or `dept`.
This enables dynamic unpivoting that will work regardless of how many months are added.
The query below returns identical results to the one above.
```sql
UNPIVOT monthly_sales
ON COLUMNS(* EXCLUDE (empid, dept))
INTO
NAME month
VALUE sales;
```
<div class="narrow_table"></div>
| empid | dept | month | sales |
|------:|-------------|-------|------:|
| 1 | electronics | Jan | 1 |
| 1 | electronics | Feb | 2 |
| 1 | electronics | Mar | 3 |
| 1 | electronics | Apr | 4 |
| 1 | electronics | May | 5 |
| 1 | electronics | Jun | 6 |
| 2 | clothes | Jan | 10 |
| 2 | clothes | Feb | 20 |
| 2 | clothes | Mar | 30 |
| 2 | clothes | Apr | 40 |
| 2 | clothes | May | 50 |
| 2 | clothes | Jun | 60 |
| 3 | cars | Jan | 100 |
| 3 | cars | Feb | 200 |
| 3 | cars | Mar | 300 |
| 3 | cars | Apr | 400 |
| 3 | cars | May | 500 |
| 3 | cars | Jun | 600 |
#### `UNPIVOT` into Multiple Value Columns {#docs:sql:statements:unpivot::unpivot-into-multiple-value-columns}
The `UNPIVOT` statement has additional flexibility: more than 2 destination columns are supported.
This can be useful when the goal is to reduce the extent to which a dataset is pivoted, but not completely stack all pivoted columns.
To demonstrate this, the query below will generate a dataset with a separate column for the number of each month within the quarter (month 1, 2, or 3), and a separate row for each quarter.
Since there are fewer quarters than months, this does make the dataset longer, but not as long as the above.
To accomplish this, multiple sets of columns are included in the `ON` clause.
The `q1` and `q2` aliases are optional.
The number of columns in each set of columns in the `ON` clause must match the number of columns in the `VALUE` clause.
```sql
UNPIVOT monthly_sales
ON (jan, feb, mar) AS q1, (apr, may, jun) AS q2
INTO
NAME quarter
VALUE month_1_sales, month_2_sales, month_3_sales;
```
<div class="narrow_table"></div>
| empid | dept | quarter | month_1_sales | month_2_sales | month_3_sales |
|------:|-------------|---------|--------------:|--------------:|--------------:|
| 1 | electronics | q1 | 1 | 2 | 3 |
| 1 | electronics | q2 | 4 | 5 | 6 |
| 2 | clothes | q1 | 10 | 20 | 30 |
| 2 | clothes | q2 | 40 | 50 | 60 |
| 3 | cars | q1 | 100 | 200 | 300 |
| 3 | cars | q2 | 400 | 500 | 600 |
#### Using `UNPIVOT` within a `SELECT` Statement {#docs:sql:statements:unpivot::using-unpivot-within-a-select-statement}
The `UNPIVOT` statement may be included within a `SELECT` statement as a CTE ([a Common Table Expression, or WITH clause](#docs:sql:query_syntax:with)), or a subquery.
This allows for an `UNPIVOT` to be used alongside other SQL logic, as well as for multiple `UNPIVOT`s to be used in one query.
No `SELECT` is needed within the CTE, the `UNPIVOT` keyword can be thought of as taking its place.
```sql
WITH unpivot_alias AS (
UNPIVOT monthly_sales
ON COLUMNS(* EXCLUDE (empid, dept))
INTO
NAME month
VALUE sales
)
SELECT * FROM unpivot_alias;
```
An `UNPIVOT` may be used in a subquery and must be wrapped in parentheses.
Note that this behavior is different than the SQL Standard Unpivot, as illustrated in subsequent examples.
```sql
SELECT *
FROM (
UNPIVOT monthly_sales
ON COLUMNS(* EXCLUDE (empid, dept))
INTO
NAME month
VALUE sales
) unpivot_alias;
```
#### Expressions within `UNPIVOT` Statements {#docs:sql:statements:unpivot::expressions-within-unpivot-statements}
DuckDB allows expressions within the `UNPIVOT` statements, provided that they only involve a single column. These can be used to perform computations as well as [explicit casts](#docs:sql:data_types:typecasting::explicit-casting). For example:
```sql
UNPIVOT
(SELECT 42 AS col1, 'woot' AS col2)
ON
(col1 * 2)::VARCHAR,
col2;
```
| name | value |
|------|-------|
| col1 | 84 |
| col2 | woot |
#### Simplified `UNPIVOT` Full Syntax Diagram {#docs:sql:statements:unpivot::simplified-unpivot-full-syntax-diagram}
Below is the full syntax diagram of the `UNPIVOT` statement.
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|
SQL | Statements | SQL Standard `UNPIVOT` Syntax | The full syntax diagram is below, but the SQL Standard `UNPIVOT` syntax can be summarized as:
```sql
FROM [dataset]
UNPIVOT [INCLUDE NULLS] (
[value-column-name(s)]
FOR [name-column-name] IN [column(s)]
);
```
Note that only one column can be included in the `name-column-name` expression.
#### SQL Standard `UNPIVOT` Manually {#docs:sql:statements:unpivot::sql-standard-unpivot-manually}
To complete the basic `UNPIVOT` operation using the SQL standard syntax, only a few additions are needed.
```sql
FROM monthly_sales UNPIVOT (
sales
FOR month IN (jan, feb, mar, apr, may, jun)
);
```
<div class="narrow_table"></div>
| empid | dept | month | sales |
|------:|-------------|-------|------:|
| 1 | electronics | Jan | 1 |
| 1 | electronics | Feb | 2 |
| 1 | electronics | Mar | 3 |
| 1 | electronics | Apr | 4 |
| 1 | electronics | May | 5 |
| 1 | electronics | Jun | 6 |
| 2 | clothes | Jan | 10 |
| 2 | clothes | Feb | 20 |
| 2 | clothes | Mar | 30 |
| 2 | clothes | Apr | 40 |
| 2 | clothes | May | 50 |
| 2 | clothes | Jun | 60 |
| 3 | cars | Jan | 100 |
| 3 | cars | Feb | 200 |
| 3 | cars | Mar | 300 |
| 3 | cars | Apr | 400 |
| 3 | cars | May | 500 |
| 3 | cars | Jun | 600 |
#### SQL Standard `UNPIVOT` Dynamically Using the `COLUMNS` Expression {#docs:sql:statements:unpivot::sql-standard-unpivot-dynamically-using-the-columns-expression}
The [`COLUMNS` expression](#docs:sql:expressions:star::columns) can be used to determine the `IN` list of columns dynamically.
This will continue to work even if additional `month` columns are added to the dataset.
It produces the same result as the query above.
```sql
FROM monthly_sales UNPIVOT (
sales
FOR month IN (columns(* EXCLUDE (empid, dept)))
);
```
#### SQL Standard `UNPIVOT` into Multiple Value Columns {#docs:sql:statements:unpivot::sql-standard-unpivot-into-multiple-value-columns}
The `UNPIVOT` statement has additional flexibility: more than 2 destination columns are supported.
This can be useful when the goal is to reduce the extent to which a dataset is pivoted, but not completely stack all pivoted columns.
To demonstrate this, the query below will generate a dataset with a separate column for the number of each month within the quarter (month 1, 2, or 3), and a separate row for each quarter.
Since there are fewer quarters than months, this does make the dataset longer, but not as long as the above.
To accomplish this, multiple columns are included in the `value-column-name` portion of the `UNPIVOT` statement.
Multiple sets of columns are included in the `IN` clause.
The `q1` and `q2` aliases are optional.
The number of columns in each set of columns in the `IN` clause must match the number of columns in the `value-column-name` portion.
```sql
FROM monthly_sales
UNPIVOT (
(month_1_sales, month_2_sales, month_3_sales)
FOR quarter IN (
(jan, feb, mar) AS q1,
(apr, may, jun) AS q2
)
);
```
<div class="narrow_table"></div>
| empid | dept | quarter | month_1_sales | month_2_sales | month_3_sales |
|------:|-------------|---------|--------------:|--------------:|--------------:|
| 1 | electronics | q1 | 1 | 2 | 3 |
| 1 | electronics | q2 | 4 | 5 | 6 |
| 2 | clothes | q1 | 10 | 20 | 30 |
| 2 | clothes | q2 | 40 | 50 | 60 |
| 3 | cars | q1 | 100 | 200 | 300 |
| 3 | cars | q2 | 400 | 500 | 600 |
#### SQL Standard `UNPIVOT` Full Syntax Diagram {#docs:sql:statements:unpivot::sql-standard-unpivot-full-syntax-diagram}
Below is the full syntax diagram of the SQL Standard version of the `UNPIVOT` statement.
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|
SQL | Statements | UPDATE Statement | The `UPDATE` statement modifies the values of rows in a table. | 14 | [
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|
SQL | Statements | Examples | For every row where `i` is `NULL`, set the value to 0 instead:
```sql
UPDATE tbl
SET i = 0
WHERE i IS NULL;
```
Set all values of `i` to 1 and all values of `j` to 2:
```sql
UPDATE tbl
SET i = 1, j = 2;
``` | 79 | [
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|
SQL | Statements | Syntax | <div id="rrdiagram"></div>
`UPDATE` changes the values of the specified columns in all rows that satisfy the condition. Only the columns to be modified need be mentioned in the `SET` clause; columns not explicitly modified retain their previous values. | 53 | [
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|
SQL | Statements | Update from Other Table | A table can be updated based upon values from another table. This can be done by specifying a table in a `FROM` clause, or using a sub-select statement. Both approaches have the benefit of completing the `UPDATE` operation in bulk for increased performance.
```sql
CREATE OR REPLACE TABLE original AS
SELECT 1 AS key, 'original value' AS value
UNION ALL
SELECT 2 AS key, 'original value 2' AS value;
CREATE OR REPLACE TABLE new AS
SELECT 1 AS key, 'new value' AS value
UNION ALL
SELECT 2 AS key, 'new value 2' AS value;
SELECT *
FROM original;
```
<div class="narrow_table"></div>
| key | value |
|-----|------------------|
| 1 | original value |
| 2 | original value 2 |
```sql
UPDATE original
SET value = new.value
FROM new
WHERE original.key = new.key;
```
Or:
```sql
UPDATE original
SET value = (
SELECT
new.value
FROM new
WHERE original.key = new.key
);
```
```sql
SELECT *
FROM original;
```
<div class="narrow_table"></div>
| key | value |
|-----|-------------|
| 1 | new value |
| 2 | new value 2 | | 290 | [
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|
SQL | Statements | Update from Same Table | The only difference between this case and the above is that a different table alias must be specified on both the target table and the source table.
In this example `AS true_original` and `AS new` are both required.
```sql
UPDATE original AS true_original
SET value = (
SELECT
new.value || ' a change!' AS value
FROM original AS new
WHERE true_original.key = new.key
);
``` | 86 | [
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|
SQL | Statements | Update Using Joins | To select the rows to update, `UPDATE` statements can use the `FROM` clause and express joins via the `WHERE` clause. For example:
```sql
CREATE TABLE city (name VARCHAR, revenue BIGINT, country_code VARCHAR);
CREATE TABLE country (code VARCHAR, name VARCHAR);
INSERT INTO city VALUES ('Paris', 700, 'FR'), ('Lyon', 200, 'FR'), ('Brussels', 400, 'BE');
INSERT INTO country VALUES ('FR', 'France'), ('BE', 'Belgium');
```
To increase the revenue of all cities in France, join the `city` and the `country` tables, and filter on the latter:
```sql
UPDATE city
SET revenue = revenue + 100
FROM country
WHERE city.country_code = country.code
AND country.name = 'France';
```
```sql
SELECT *
FROM city;
```
| name | revenue | country_code |
|----------|--------:|--------------|
| Paris | 800 | FR |
| Lyon | 300 | FR |
| Brussels | 400 | BE | | 235 | [
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|
SQL | Statements | Upsert (Insert or Update) | See the [Insert documentation](#docs:sql:statements:insert::on-conflict-clause) for details. | 24 | [
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|
SQL | Statements | USE Statement | The `USE` statement selects a database and optional schema to use as the default. | 17 | [
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|
SQL | Statements | Examples | ```sql
--- Sets the 'memory' database as the default
USE memory;
--- Sets the 'duck.main' database and schema as the default
USE duck.main;
``` | 36 | [
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|
SQL | Statements | Syntax | <div id="rrdiagram1"></div>
The `USE` statement sets a default database or database/schema combination to use for
future operations. For instance, tables created without providing a fully qualified
table name will be created in the default database. | 52 | [
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|
SQL | Statements | VACUUM Statement | The `VACUUM` statement alone does nothing and is at present provided for PostgreSQL-compatibility.
The `VACUUM ANALYZE` statement recomputes table statistics if they have become stale due to table updates or deletions. | 50 | [
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|
SQL | Statements | Examples | No-op:
```sql
VACUUM;
```
Rebuild database statistics:
```sql
VACUUM ANALYZE;
```
Rebuild statistics for the table and column:
```sql
VACUUM ANALYZE memory.main.my_table(my_column);
```
Not supported:
```sql
VACUUM FULL; -- error
``` | 79 | [
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|
SQL | Statements | Reclaiming Space | The `VACUUM` statement does not reclaim space.
For instruction on reclaiming space, refer to the [“Reclaiming space” page](#docs:operations_manual:footprint_of_duckdb:reclaiming_space). | 50 | [
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|
SQL | Statements | `INSTALL` | The `INSTALL` statement downloads an extension so it can be loaded into a DuckDB session.
#### Examples {#docs:sql:statements:load_and_install::examples}
Install the [`httpfs`](#docs:extensions:httpfs:overview) extension:
```sql
INSTALL httpfs;
```
Install the `h3` [Community Extension](#docs:extensions:community_extensions):
```sql
INSTALL h3 FROM community;
```
#### Syntax {#docs:sql:statements:load_and_install::syntax}
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|
SQL | Statements | `LOAD` | The `LOAD` statement loads an installed DuckDB extension into the current session.
#### Examples {#docs:sql:statements:load_and_install::examples}
Load the [`httpfs`](#docs:extensions:httpfs:overview) extension:
```sql
LOAD httpfs;
```
Load the [`spatial`](#docs:extensions:spatial:overview) extension:
```sql
LOAD spatial;
```
#### Syntax {#docs:sql:statements:load_and_install::syntax}
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|
SQL | Query Syntax | SELECT Clause | The `SELECT` clause specifies the list of columns that will be returned by the query. While it appears first in the clause, *logically* the expressions here are executed only at the end. The `SELECT` clause can contain arbitrary expressions that transform the output, as well as aggregates and window functions. | 62 | [
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|
SQL | Query Syntax | Examples | Select all columns from the table called `table_name`:
```sql
SELECT * FROM table_name;
```
Perform arithmetic on the columns in a table, and provide an alias:
```sql
SELECT col1 + col2 AS res, sqrt(col1) AS root FROM table_name;
```
Select all unique cities from the `addresses` table:
```sql
SELECT DISTINCT city FROM addresses;
```
Return the total number of rows in the `addresses` table:
```sql
SELECT count(*) FROM addresses;
```
Select all columns except the city column from the `addresses` table:
```sql
SELECT * EXCLUDE (city) FROM addresses;
```
Select all columns from the `addresses` table, but replace `city` with `lower(city)`:
```sql
SELECT * REPLACE (lower(city) AS city) FROM addresses;
```
Select all columns matching the given regular expression from the table:
```sql
SELECT COLUMNS('number\d+') FROM addresses;
```
Compute a function on all given columns of a table:
```sql
SELECT min(COLUMNS(*)) FROM addresses;
```
To select columns with spaces or special characters, use double quotes (` "`):
```sql
SELECT "Some Column Name" FROM tbl;
``` | 263 | [
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|
SQL | Query Syntax | `SELECT` List | The `SELECT` clause contains a list of expressions that specify the result of a query. The select list can refer to any columns in the `FROM` clause, and combine them using expressions. As the output of a SQL query is a table – every expression in the `SELECT` clause also has a name. The expressions can be explicitly named using the `AS` clause (e.g., `expr AS name`). If a name is not provided by the user the expressions are named automatically by the system.
> Column names are case-insensitive. See the [Rules for Case Sensitivity](#docs:sql:dialect:keywords_and_identifiers::rules-for-case-sensitivity) for more details.
#### Star Expressions {#docs:sql:query_syntax:select::star-expressions}
Select all columns from the table called `table_name`:
```sql
SELECT *
FROM table_name;
```
Select all columns matching the given regular expression from the table:
```sql
SELECT COLUMNS('number\d+')
FROM addresses;
```
The [star expression](#docs:sql:expressions:star) is a special expression that expands to *multiple expressions* based on the contents of the `FROM` clause. In the simplest case, `*` expands to **all** expressions in the `FROM` clause. Columns can also be selected using regular expressions or lambda functions. See the [star expression page](#docs:sql:expressions:star) for more details.
#### `DISTINCT` Clause {#docs:sql:query_syntax:select::distinct-clause}
Select all unique cities from the addresses table:
```sql
SELECT DISTINCT city
FROM addresses;
```
The `DISTINCT` clause can be used to return **only** the unique rows in the result – so that any duplicate rows are filtered out.
> Queries starting with `SELECT DISTINCT` run deduplication, which is an expensive operation. Therefore, only use `DISTINCT` if necessary.
#### `DISTINCT ON` Clause {#docs:sql:query_syntax:select::distinct-on-clause}
Select only the highest population city for each country:
```sql
SELECT DISTINCT ON(country) city, population
FROM cities
ORDER BY population DESC;
```
The `DISTINCT ON` clause returns only one row per unique value in the set of expressions as defined in the `ON` clause. If an `ORDER BY` clause is present, the row that is returned is the first row that is encountered as per the `ORDER BY` criteria. If an `ORDER BY` clause is not present, the first row that is encountered is not defined and can be any row in the table.
> When querying large data sets, using `DISTINCT` on all columns can be expensive. Therefore, consider using `DISTINCT ON` on a column (or a set of columns) which guaranetees a sufficient degree of uniqueness for your results. For example, using `DISTINCT ON` on the key column(s) of a table guarantees full uniqueness.
#### Aggregates {#docs:sql:query_syntax:select::aggregates}
Return the total number of rows in the addresses table:
```sql
SELECT count(*)
FROM addresses;
```
Return the total number of rows in the addresses table grouped by city:
```sql
SELECT city, count(*)
FROM addresses
GROUP BY city;
```
[Aggregate functions](#docs:sql:functions:aggregates) are special functions that *combine* multiple rows into a single value. When aggregate functions are present in the `SELECT` clause, the query is turned into an aggregate query. In an aggregate query, **all** expressions must either be part of an aggregate function, or part of a group (as specified by the [`GROUP BY clause`](#docs:sql:query_syntax:groupby)).
#### Window Functions {#docs:sql:query_syntax:select::window-functions}
Generate a `row_number` column containing incremental identifiers for each row:
```sql
SELECT row_number() OVER ()
FROM sales;
```
Compute the difference between the current amount, and the previous amount, by order of time:
```sql
SELECT amount - lag(amount) OVER (ORDER BY time)
FROM sales;
```
[Window functions](#docs:sql:functions:window_functions) are special functions that allow the computation of values relative to *other rows* in a result. Window functions are marked by the `OVER` clause which contains the *window specification*. The window specification defines the frame or context in which the window function is computed. See the [window functions page](#docs:sql:functions:window_functions) for more information.
#### `unnest` Function {#docs:sql:query_syntax:select::unnest-function}
Unnest an array by one level:
```sql
SELECT unnest([1, 2, 3]);
```
Unnest a struct by one level:
```sql
SELECT unnest({'a': 42, 'b': 84});
```
The [`unnest`](#docs:sql:query_syntax:unnest) function is a special function that can be used together with [arrays](#docs:sql:data_types:array), [lists](#docs:sql:data_types:list), or [structs](#docs:sql:data_types:struct). The unnest function strips one level of nesting from the type. For example, `INTEGER[]` is transformed into `INTEGER`. `STRUCT(a INTEGER, b INTEGER)` is transformed into `a INTEGER, b INTEGER`. The unnest function can be used to transform nested types into regular scalar types, which makes them easier to operate on. | 1,190 | [
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|
SQL | Query Syntax | FROM and JOIN Clauses | The `FROM` clause specifies the *source* of the data on which the remainder of the query should operate. Logically, the `FROM` clause is where the query starts execution. The `FROM` clause can contain a single table, a combination of multiple tables that are joined together using `JOIN` clauses, or another `SELECT` query inside a subquery node. DuckDB also has an optional `FROM`-first syntax which enables you to also query without a `SELECT` statement. | 101 | [
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|
SQL | Query Syntax | Examples | Select all columns from the table called `table_name`:
```sql
SELECT *
FROM table_name;
```
Select all columns from the table using the `FROM`-first syntax:
```sql
FROM table_name
SELECT *;
```
Select all columns using the `FROM`-first syntax and omitting the `SELECT` clause:
```sql
FROM table_name;
```
Select all columns from the table called `table_name` through an alias `tn`:
```sql
SELECT tn.*
FROM table_name tn;
```
Select all columns from the table `table_name` in the schema `schema_name`:
```sql
SELECT *
FROM schema_name.table_name;
```
Select the column `i` from the table function `range`, where the first column of the range function is renamed to `i`:
```sql
SELECT t.i
FROM range(100) AS t(i);
```
Select all columns from the CSV file called `test.csv`:
```sql
SELECT *
FROM 'test.csv';
```
Select all columns from a subquery:
```sql
SELECT *
FROM (SELECT * FROM table_name);
```
Select the entire row of the table as a struct:
```sql
SELECT t
FROM t;
```
Select the entire row of the subquery as a struct (i.e., a single column):
```sql
SELECT t
FROM (SELECT unnest(generate_series(41, 43)) AS x, 'hello' AS y) t;
```
Join two tables together:
```sql
SELECT *
FROM table_name
JOIN other_table
ON table_name.key = other_table.key;
```
Select a 10% sample from a table:
```sql
SELECT *
FROM table_name
TABLESAMPLE 10%;
```
Select a sample of 10 rows from a table:
```sql
SELECT *
FROM table_name
TABLESAMPLE 10 ROWS;
```
Use the `FROM`-first syntax with `WHERE` clause and aggregation:
```sql
FROM range(100) AS t(i)
SELECT sum(t.i)
WHERE i % 2 = 0;
``` | 445 | [
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|
SQL | Query Syntax | Joins | Joins are a fundamental relational operation used to connect two tables or relations horizontally.
The relations are referred to as the _left_ and _right_ sides of the join
based on how they are written in the join clause.
Each result row has the columns from both relations.
A join uses a rule to match pairs of rows from each relation.
Often this is a predicate, but there are other implied rules that may be specified.
#### Outer Joins {#docs:sql:query_syntax:from::outer-joins}
Rows that do not have any matches can still be returned if an `OUTER` join is specified.
Outer joins can be one of:
* `LEFT` (All rows from the left relation appear at least once)
* `RIGHT` (All rows from the right relation appear at least once)
* `FULL` (All rows from both relations appear at least once)
A join that is not `OUTER` is `INNER` (only rows that get paired are returned).
When an unpaired row is returned, the attributes from the other table are set to `NULL`.
#### Cross Product Joins (Cartesian Product) {#docs:sql:query_syntax:from::cross-product-joins-cartesian-product}
The simplest type of join is a `CROSS JOIN`.
There are no conditions for this type of join,
and it just returns all the possible pairs.
Return all pairs of rows:
```sql
SELECT a.*, b.*
FROM a
CROSS JOIN b;
```
This is equivalent to omitting the `JOIN` clause:
```sql
SELECT a.*, b.*
FROM a, b;
```
#### Conditional Joins {#docs:sql:query_syntax:from::conditional-joins}
Most joins are specified by a predicate that connects
attributes from one side to attributes from the other side.
The conditions can be explicitly specified using an `ON` clause
with the join (clearer) or implied by the `WHERE` clause (old-fashioned).
We use the `l_regions` and the `l_nations` tables from the TPC-H schema:
```sql
CREATE TABLE l_regions (
r_regionkey INTEGER NOT NULL PRIMARY KEY,
r_name CHAR(25) NOT NULL,
r_comment VARCHAR(152)
);
CREATE TABLE l_nations (
n_nationkey INTEGER NOT NULL PRIMARY KEY,
n_name CHAR(25) NOT NULL,
n_regionkey INTEGER NOT NULL,
n_comment VARCHAR(152),
FOREIGN KEY (n_regionkey) REFERENCES l_regions(r_regionkey)
);
```
Return the regions for the nations:
```sql
SELECT n.*, r.*
FROM l_nations n
JOIN l_regions r ON (n_regionkey = r_regionkey);
```
If the column names are the same and are required to be equal,
then the simpler `USING` syntax can be used:
```sql
CREATE TABLE l_regions (regionkey INTEGER NOT NULL PRIMARY KEY,
name CHAR(25) NOT NULL,
comment VARCHAR(152));
CREATE TABLE l_nations (nationkey INTEGER NOT NULL PRIMARY KEY,
name CHAR(25) NOT NULL,
regionkey INTEGER NOT NULL,
comment VARCHAR(152),
FOREIGN KEY (regionkey) REFERENCES l_regions(regionkey));
```
Return the regions for the nations:
```sql
SELECT n.*, r.*
FROM l_nations n
JOIN l_regions r USING (regionkey);
```
The expressions do not have to be equalities – any predicate can be used:
Return the pairs of jobs where one ran longer but cost less:
```sql
SELECT s1.t_id, s2.t_id
FROM west s1, west s2
WHERE s1.time > s2.time
AND s1.cost < s2.cost;
```
#### Natural Joins {#docs:sql:query_syntax:from::natural-joins}
Natural joins join two tables based on attributes that share the same name.
For example, take the following example with cities, airport codes and airport names. Note that both tables are intentionally incomplete, i.e., they do not have a matching pair in the other table.
```sql
CREATE TABLE city_airport (city_name VARCHAR, iata VARCHAR);
CREATE TABLE airport_names (iata VARCHAR, airport_name VARCHAR);
INSERT INTO city_airport VALUES
('Amsterdam', 'AMS'),
('Rotterdam', 'RTM'),
('Eindhoven', 'EIN'),
('Groningen', 'GRQ');
INSERT INTO airport_names VALUES
('AMS', 'Amsterdam Airport Schiphol'),
('RTM', 'Rotterdam The Hague Airport'),
('MST', 'Maastricht Aachen Airport');
```
To join the tables on their shared [`IATA`](https://en.wikipedia.org/wiki/IATA_airport_code) attributes, run:
```sql
SELECT *
FROM city_airport
NATURAL JOIN airport_names;
```
This produces the following result:
| city_name | iata | airport_name |
|-----------|------|-----------------------------|
| Amsterdam | AMS | Amsterdam Airport Schiphol |
| Rotterdam | RTM | Rotterdam The Hague Airport |
Note that only rows where the same `iata` attribute was present in both tables were included in the result.
We can also express query using the vanilla `JOIN` clause with the `USING` keyword:
```sql
SELECT *
FROM city_airport
JOIN airport_names
USING (iata);
```
#### Semi and Anti Joins {#docs:sql:query_syntax:from::semi-and-anti-joins}
Semi joins return rows from the left table that have at least one match in the right table.
Anti joins return rows from the left table that have _no_ matches in the right table.
When using a semi or anti join the result will never have more rows than the left hand side table.
Semi joins provide the same logic as the [`IN` operator](#docs:sql:expressions:in) statement.
Anti joins provide the same logic as the `NOT IN` operator, except anti joins ignore `NULL` values from the right table. | 1,285 | [
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|
SQL | Query Syntax | Joins | Semi Join Example | Return a list of city–airport code pairs from the `city_airport` table where the airport name **is available** in the `airport_names` table:
```sql
SELECT *
FROM city_airport
SEMI JOIN airport_names
USING (iata);
```
| city_name | iata |
|-----------|------|
| Amsterdam | AMS |
| Rotterdam | RTM |
This query is equivalent with:
```sql
SELECT *
FROM city_airport
WHERE iata IN (SELECT iata FROM airport_names);
``` | 114 | [
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SQL | Query Syntax | Joins | Anti Join Example | Return a list of city–airport code pairs from the `city_airport` table where the airport name **is not available** in the `airport_names` table:
```sql
SELECT *
FROM city_airport
ANTI JOIN airport_names
USING (iata);
```
| city_name | iata |
|-----------|------|
| Eindhoven | EIN |
| Groningen | GRQ |
This query is equivalent with:
```sql
SELECT *
FROM city_airport
WHERE iata NOT IN (SELECT iata FROM airport_names WHERE iata IS NOT NULL);
```
#### Lateral Joins {#docs:sql:query_syntax:from::lateral-joins}
The `LATERAL` keyword allows subqueries in the `FROM` clause to refer to previous subqueries. This feature is also known as a _lateral join_.
```sql
SELECT *
FROM range(3) t(i), LATERAL (SELECT i + 1) t2(j);
```
<div class="center_aligned_header_table"></div>
| i | j |
|--:|--:|
| 0 | 1 |
| 2 | 3 |
| 1 | 2 |
Lateral joins are a generalization of correlated subqueries, as they can return multiple values per input value rather than only a single value.
```sql
SELECT *
FROM
generate_series(0, 1) t(i),
LATERAL (SELECT i + 10 UNION ALL SELECT i + 100) t2(j);
```
<div class="center_aligned_header_table"></div>
| i | j |
|--:|----:|
| 0 | 10 |
| 1 | 11 |
| 0 | 100 |
| 1 | 101 |
It may be helpful to think about `LATERAL` as a loop where we iterate through the rows of the first subquery and use it as input to the second (` LATERAL`) subquery.
In the examples above, we iterate through table `t` and refer to its column `i` from the definition of table `t2`. The rows of `t2` form column `j` in the result.
It is possible to refer to multiple attributes from the `LATERAL` subquery. Using the table from the first example:
```sql
CREATE TABLE t1 AS
SELECT *
FROM range(3) t(i), LATERAL (SELECT i + 1) t2(j);
SELECT *
FROM t1, LATERAL (SELECT i + j) t2(k)
ORDER BY ALL;
```
<div class="center_aligned_header_table"></div>
| i | j | k |
|--:|--:|--:|
| 0 | 1 | 1 |
| 1 | 2 | 3 |
| 2 | 3 | 5 |
> DuckDB detects when `LATERAL` joins should be used, making the use of the `LATERAL` keyword optional.
#### Positional Joins {#docs:sql:query_syntax:from::positional-joins}
When working with data frames or other embedded tables of the same size,
the rows may have a natural correspondence based on their physical order.
In scripting languages, this is easily expressed using a loop:
```cpp
for (i = 0; i < n; i++) {
f(t1.a[i], t2.b[i]);
}
```
It is difficult to express this in standard SQL because
relational tables are not ordered, but imported tables such as [data frames](#docs:api:python:data_ingestion::pandas-dataframes-–-object-columns)
or disk files (like [CSVs](#docs:data:csv:overview) or [Parquet files](#docs:data:parquet:overview)) do have a natural ordering.
Connecting them using this ordering is called a _positional join:_
```sql
CREATE TABLE t1 (x INTEGER);
CREATE TABLE t2 (s VARCHAR);
INSERT INTO t1 VALUES (1), (2), (3);
INSERT INTO t2 VALUES ('a'), ('b');
SELECT *
FROM t1
POSITIONAL JOIN t2;
```
<div class="center_aligned_header_table"></div>
| x | s |
|--:|------|
| 1 | a |
| 2 | b |
| 3 | NULL |
Positional joins are always `FULL OUTER` joins, i.e., missing values (the last values in the shorter column) are set to `NULL`.
#### As-Of Joins {#docs:sql:query_syntax:from::as-of-joins}
A common operation when working with temporal or similarly-ordered data
is to find the nearest (first) event in a reference table (such as prices).
This is called an _as-of join:_
Attach prices to stock trades:
```sql
SELECT t.*, p.price
FROM trades t
ASOF JOIN prices p
ON t.symbol = p.symbol AND t.when >= p.when;
```
The `ASOF` join requires at least one inequality condition on the ordering field.
The inequality can be any inequality condition (` >=`, `>`, `<=`, `<`)
on any data type, but the most common form is `>=` on a temporal type.
Any other conditions must be equalities (or `NOT DISTINCT`).
This means that the left/right order of the tables is significant.
`ASOF` joins each left side row with at most one right side row.
It can be specified as an `OUTER` join to find unpaired rows
(e.g., trades without prices or prices which have no trades.)
Attach prices or NULLs to stock trades:
```sql
SELECT *
FROM trades t
ASOF LEFT JOIN prices p
ON t.symbol = p.symbol
AND t.when >= p.when;
```
`ASOF` joins can also specify join conditions on matching column names with the `USING` syntax,
but the *last* attribute in the list must be the inequality,
which will be greater than or equal to (` >=`):
```sql
SELECT *
FROM trades t
ASOF JOIN prices p USING (symbol, "when");
```
Returns symbol, trades.when, price (but NOT prices.when):
If you combine `USING` with a `SELECT *` like this,
the query will return the left side (probe) column values for the matches,
not the right side (build) column values.
To get the `prices` times in the example, you will need to list the columns explicitly:
```sql
SELECT t.symbol, t.when AS trade_when, p.when AS price_when, price
FROM trades t
ASOF LEFT JOIN prices p USING (symbol, "when");
```
#### Self-Joins {#docs:sql:query_syntax:from::self-joins}
DuckDB allows self-joins for all types of joins.
Note that tables need to be aliased, using the same table name without aliases will result in an error:
```sql
CREATE TABLE t(x int);
SELECT * FROM t JOIN t USING(x);
```
```console
Binder Error: Duplicate alias "t" in query!
```
Adding the aliases allows the query to parse successfully:
```sql
SELECT * FROM t AS t t1 JOIN t t2 USING(x);
``` | 1,558 | [
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SQL | Query Syntax | `FROM`-First Syntax | DuckDB's SQL supports the `FROM`-first syntax, i.e., it allows putting the `FROM` clause before the `SELECT` clause or completely omitting the `SELECT` clause. We use the following example to demonstrate it:
```sql
CREATE TABLE tbl AS
SELECT *
FROM (VALUES ('a'), ('b')) t1(s), range(1, 3) t2(i);
```
#### `FROM`-First Syntax with a `SELECT` Clause {#docs:sql:query_syntax:from::from-first-syntax-with-a-select-clause}
The following statement demonstrates the use of the `FROM`-first syntax:
```sql
FROM tbl
SELECT i, s;
```
This is equivalent to:
```sql
SELECT i, s
FROM tbl;
```
<div class="center_aligned_header_table"></div>
| i | s |
|--:|---|
| 1 | a |
| 2 | a |
| 1 | b |
| 2 | b |
#### `FROM`-First Syntax without a `SELECT` Clause {#docs:sql:query_syntax:from::from-first-syntax-without-a-select-clause}
The following statement demonstrates the use of the optional `SELECT` clause:
```sql
FROM tbl;
```
This is equivalent to:
```sql
SELECT *
FROM tbl;
```
<div class="center_aligned_header_table"></div>
| s | i |
|---|--:|
| a | 1 |
| a | 2 |
| b | 1 |
| b | 2 | | 334 | [
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|
SQL | Query Syntax | WHERE Clause | The `WHERE` clause specifies any filters to apply to the data. This allows you to select only a subset of the data in which you are interested. Logically the `WHERE` clause is applied immediately after the `FROM` clause. | 48 | [
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|
SQL | Query Syntax | Examples | Select all rows that where the `id` is equal to 3:
```sql
SELECT *
FROM table_name
WHERE id = 3;
```
Select all rows that match the given case-insensitive `LIKE` expression:
```sql
SELECT *
FROM table_name
WHERE name ILIKE '%mark%';
```
Select all rows that match the given composite expression:
```sql
SELECT *
FROM table_name
WHERE id = 3 OR id = 7;
``` | 100 | [
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|
SQL | Query Syntax | GROUP BY Clause | The `GROUP BY` clause specifies which grouping columns should be used to perform any aggregations in the `SELECT` clause.
If the `GROUP BY` clause is specified, the query is always an aggregate query, even if no aggregations are present in the `SELECT` clause.
When a `GROUP BY` clause is specified, all tuples that have matching data in the grouping columns (i.e., all tuples that belong to the same group) will be combined.
The values of the grouping columns themselves are unchanged, and any other columns can be combined using an [aggregate function](#docs:sql:functions:aggregates) (such as `count`, `sum`, `avg`, etc). | 143 | [
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|
SQL | Query Syntax | `GROUP BY ALL` | Use `GROUP BY ALL` to `GROUP BY` all columns in the `SELECT` statement that are not wrapped in aggregate functions.
This simplifies the syntax by allowing the columns list to be maintained in a single location, and prevents bugs by keeping the `SELECT` granularity aligned to the `GROUP BY` granularity (e.g., it prevents duplication).
See examples below and additional examples in the [“Friendlier SQL with DuckDB” blog post](https://duckdb.org/2022/05/04/friendlier-sql#group-by-all). | 113 | [
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|
SQL | Query Syntax | Multiple Dimensions | Normally, the `GROUP BY` clause groups along a single dimension.
Using the [`GROUPING SETS`, `CUBE` or `ROLLUP` clauses](#docs:sql:query_syntax:grouping_sets) it is possible to group along multiple dimensions.
See the [`GROUPING SETS`](#docs:sql:query_syntax:grouping_sets) page for more information. | 79 | [
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|
SQL | Query Syntax | Examples | Count the number of entries in the `addresses` table that belong to each different city:
```sql
SELECT city, count(*)
FROM addresses
GROUP BY city;
```
Compute the average income per city per street_name:
```sql
SELECT city, street_name, avg(income)
FROM addresses
GROUP BY city, street_name;
```
#### `GROUP BY ALL` Examples {#docs:sql:query_syntax:groupby::group-by-all-examples}
Group by city and street_name to remove any duplicate values:
```sql
SELECT city, street_name
FROM addresses
GROUP BY ALL;
```
Compute the average income per city per street_name. Since income is wrapped in an aggregate function, do not include it in the `GROUP BY`:
```sql
SELECT city, street_name, avg(income)
FROM addresses
GROUP BY ALL;
-- GROUP BY city, street_name:
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|
SQL | Query Syntax | GROUPING SETS | `GROUPING SETS`, `ROLLUP` and `CUBE` can be used in the `GROUP BY` clause to perform a grouping over multiple dimensions within the same query.
Note that this syntax is not compatible with [`GROUP BY ALL`](#docs:sql:query_syntax:groupby::group-by-all). | 65 | [
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|
SQL | Query Syntax | Examples | Compute the average income along the provided four different dimensions:
```sql
-- the syntax () denotes the empty set (i.e., computing an ungrouped aggregate)
SELECT city, street_name, avg(income)
FROM addresses
GROUP BY GROUPING SETS ((city, street_name), (city), (street_name), ());
```
Compute the average income along the same dimensions:
```sql
SELECT city, street_name, avg(income)
FROM addresses
GROUP BY CUBE (city, street_name);
```
Compute the average income along the dimensions `(city, street_name)`, `(city)` and `()`:
```sql
SELECT city, street_name, avg(income)
FROM addresses
GROUP BY ROLLUP (city, street_name);
``` | 157 | [
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|
SQL | Query Syntax | Description | `GROUPING SETS` perform the same aggregate across different `GROUP BY clauses` in a single query.
```sql
CREATE TABLE students (course VARCHAR, type VARCHAR);
INSERT INTO students (course, type)
VALUES
('CS', 'Bachelor'), ('CS', 'Bachelor'), ('CS', 'PhD'), ('Math', 'Masters'),
('CS', NULL), ('CS', NULL), ('Math', NULL);
```
```sql
SELECT course, type, count(*)
FROM students
GROUP BY GROUPING SETS ((course, type), course, type, ());
```
| course | type | count_star() |
|--------|----------|-------------:|
| Math | NULL | 1 |
| NULL | NULL | 7 |
| CS | PhD | 1 |
| CS | Bachelor | 2 |
| Math | Masters | 1 |
| CS | NULL | 2 |
| Math | NULL | 2 |
| CS | NULL | 5 |
| NULL | NULL | 3 |
| NULL | Masters | 1 |
| NULL | Bachelor | 2 |
| NULL | PhD | 1 |
In the above query, we group across four different sets: `course, type`, `course`, `type` and `()` (the empty group). The result contains `NULL` for a group which is not in the grouping set for the result, i.e., the above query is equivalent to the following UNION statement:
Group by course, type:
```sql
SELECT course, type, count(*)
FROM students
GROUP BY course, type
UNION ALL
```
Group by type:
```sql
SELECT NULL AS course, type, count(*)
FROM students
GROUP BY type
UNION ALL
```
Group by course:
```sql
SELECT course, NULL AS type, count(*)
FROM students
GROUP BY course
UNION ALL
```
Group by nothing:
```sql
SELECT NULL AS course, NULL AS type, count(*)
FROM students;
```
`CUBE` and `ROLLUP` are syntactic sugar to easily produce commonly used grouping sets.
The `ROLLUP` clause will produce all “sub-groups” of a grouping set, e.g., `ROLLUP (country, city, zip)` produces the grouping sets `(country, city, zip), (country, city), (country), ()`. This can be useful for producing different levels of detail of a group by clause. This produces `n+1` grouping sets where n is the amount of terms in the `ROLLUP` clause.
`CUBE` produces grouping sets for all combinations of the inputs, e.g., `CUBE (country, city, zip)` will produce `(country, city, zip), (country, city), (country, zip), (city, zip), (country), (city), (zip), ()`. This produces `2^n` grouping sets. | 643 | [
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|
SQL | Query Syntax | Identifying Grouping Sets with `GROUPING_ID()` | The super-aggregate rows generated by `GROUPING SETS`, `ROLLUP` and `CUBE` can often be identified by `NULL`-values returned for the respective column in the grouping. But if the columns used in the grouping can themselves contain actual `NULL`-values, then it can be challenging to distinguish whether the value in the resultset is a “real” `NULL`-value coming out of the data itself, or a `NULL`-value generated by the grouping construct. The `GROUPING_ID()` or `GROUPING()` function is designed to identify which groups generated the super-aggregate rows in the result.
`GROUPING_ID()` is an aggregate function that takes the column expressions that make up the grouping(s). It returns a `BIGINT` value. The return value is `0` for the rows that are not super-aggregate rows. But for the super-aggregate rows, it returns an integer value that identifies the combination of expressions that make up the group for which the super-aggregate is generated. At this point, an example might help. Consider the following query:
```sql
WITH days AS (
SELECT
year("generate_series") AS y,
quarter("generate_series") AS q,
month("generate_series") AS m
FROM generate_series(DATE '2023-01-01', DATE '2023-12-31', INTERVAL 1 DAY)
)
SELECT y, q, m, GROUPING_ID(y, q, m) AS "grouping_id()"
FROM days
GROUP BY GROUPING SETS (
(y, q, m),
(y, q),
(y),
()
)
ORDER BY y, q, m;
```
These are the results:
| y | q | m | grouping_id() |
|-----:|-----:|-----:|--------------:|
| 2023 | 1 | 1 | 0 |
| 2023 | 1 | 2 | 0 |
| 2023 | 1 | 3 | 0 |
| 2023 | 1 | NULL | 1 |
| 2023 | 2 | 4 | 0 |
| 2023 | 2 | 5 | 0 |
| 2023 | 2 | 6 | 0 |
| 2023 | 2 | NULL | 1 |
| 2023 | 3 | 7 | 0 |
| 2023 | 3 | 8 | 0 |
| 2023 | 3 | 9 | 0 |
| 2023 | 3 | NULL | 1 |
| 2023 | 4 | 10 | 0 |
| 2023 | 4 | 11 | 0 |
| 2023 | 4 | 12 | 0 |
| 2023 | 4 | NULL | 1 |
| 2023 | NULL | NULL | 3 |
| NULL | NULL | NULL | 7 |
In this example, the lowest level of grouping is at the month level, defined by the grouping set `(y, q, m)`. Result rows corresponding to that level are simply aggregate rows and the `GROUPING_ID(y, q, m)` function returns `0` for those. The grouping set `(y, q)` results in super-aggregate rows over the month level, leaving a `NULL`-value for the `m` column, and for which `GROUPING_ID(y, q, m)` returns `1`. The grouping set `(y)` results in super-aggregate rows over the quarter level, leaving `NULL`-values for the `m` and `q` column, for which `GROUPING_ID(y, q, m)` returns `3`. Finally, the `()` grouping set results in one super-aggregate row for the entire resultset, leaving `NULL`-values for `y`, `q` and `m` and for which `GROUPING_ID(y, q, m)` returns `7`.
To understand the relationship between the return value and the grouping set, you can think of `GROUPING_ID(y, q, m)` writing to a bitfield, where the first bit corresponds to the last expression passed to `GROUPING_ID()`, the second bit to the one-but-last expression passed to `GROUPING_ID()`, and so on. This may become clearer by casting `GROUPING_ID()` to `BIT`:
```sql
WITH days AS (
SELECT
year("generate_series") AS y,
quarter("generate_series") AS q,
month("generate_series") AS m
FROM generate_series(DATE '2023-01-01', DATE '2023-12-31', INTERVAL 1 DAY)
)
SELECT
y, q, m,
GROUPING_ID(y, q, m) AS "grouping_id(y, q, m)",
right(GROUPING_ID(y, q, m)::BIT::VARCHAR, 3) AS "y_q_m_bits"
FROM days
GROUP BY GROUPING SETS (
(y, q, m),
(y, q),
(y),
()
)
ORDER BY y, q, m;
```
Which returns these results:
| y | q | m | grouping_id(y, q, m) | y_q_m_bits |
|-----:|-----:|-----:|---------------------:|------------|
| 2023 | 1 | 1 | 0 | 000 |
| 2023 | 1 | 2 | 0 | 000 |
| 2023 | 1 | 3 | 0 | 000 |
| 2023 | 1 | NULL | 1 | 001 |
| 2023 | 2 | 4 | 0 | 000 |
| 2023 | 2 | 5 | 0 | 000 |
| 2023 | 2 | 6 | 0 | 000 |
| 2023 | 2 | NULL | 1 | 001 |
| 2023 | 3 | 7 | 0 | 000 |
| 2023 | 3 | 8 | 0 | 000 |
| 2023 | 3 | 9 | 0 | 000 |
| 2023 | 3 | NULL | 1 | 001 |
| 2023 | 4 | 10 | 0 | 000 |
| 2023 | 4 | 11 | 0 | 000 |
| 2023 | 4 | 12 | 0 | 000 |
| 2023 | 4 | NULL | 1 | 001 |
| 2023 | NULL | NULL | 3 | 011 |
| NULL | NULL | NULL | 7 | 111 |
Note that the number of expressions passed to `GROUPING_ID()`, or the order in which they are passed is independent from the actual group definitions appearing in the `GROUPING SETS`-clause (or the groups implied by `ROLLUP` and `CUBE`). As long as the expressions passed to `GROUPING_ID()` are expressions that appear some where in the `GROUPING SETS`-clause, `GROUPING_ID()` will set a bit corresponding to the position of the expression whenever that expression is rolled up to a super-aggregate. | 1,644 | [
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|
SQL | Query Syntax | HAVING Clause | The `HAVING` clause can be used after the `GROUP BY` clause to provide filter criteria *after* the grouping has been completed. In terms of syntax the `HAVING` clause is identical to the `WHERE` clause, but while the `WHERE` clause occurs before the grouping, the `HAVING` clause occurs after the grouping. | 71 | [
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|
SQL | Query Syntax | Examples | Count the number of entries in the `addresses` table that belong to each different `city`, filtering out cities with a count below 50:
```sql
SELECT city, count(*)
FROM addresses
GROUP BY city
HAVING count(*) >= 50;
```
Compute the average income per city per `street_name`, filtering out cities with an average `income` bigger than twice the median `income`:
```sql
SELECT city, street_name, avg(income)
FROM addresses
GROUP BY city, street_name
HAVING avg(income) > 2 * median(income);
``` | 123 | [
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|
SQL | Query Syntax | ORDER BY Clause | `ORDER BY` is an output modifier. Logically it is applied near the very end of the query (just prior to [`LIMIT`](#docs:sql:query_syntax:limit) or [`OFFSET`](#docs:sql:query_syntax:limit), if present).
The `ORDER BY` clause sorts the rows on the sorting criteria in either ascending or descending order.
In addition, every order clause can specify whether `NULL` values should be moved to the beginning or to the end.
The `ORDER BY` clause may contain one or more expressions, separated by commas.
An error will be thrown if no expressions are included, since the `ORDER BY` clause should be removed in that situation.
The expressions may begin with either an arbitrary scalar expression (which could be a column name), a column position number (where the indexing starts from 1), or the keyword `ALL`.
Each expression can optionally be followed by an order modifier (` ASC` or `DESC`, default is `ASC`), and/or a `NULL` order modifier (` NULLS FIRST` or `NULLS LAST`, default is `NULLS LAST`). | 228 | [
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|
SQL | Query Syntax | `ORDER BY ALL` | The `ALL` keyword indicates that the output should be sorted by every column in order from left to right.
The direction of this sort may be modified using either `ORDER BY ALL ASC` or `ORDER BY ALL DESC` and/or `NULLS FIRST` or `NULLS LAST`.
Note that `ALL` may not be used in combination with other expressions in the `ORDER BY` clause – it must be by itself.
See examples below. | 90 | [
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|
SQL | Query Syntax | `NULL` Order Modifier | By default, DuckDB sorts `ASC` and `NULLS LAST`, i.e., the values are sorted in ascending order and `NULL` values are placed last.
This is identical to the default sort order of PostgreSQL.
The default sort order can be changed with the following configuration options.
Use the `default_null_order` option to change the default `NULL` sorting order to either `NULLS_FIRST`, `NULLS_LAST`, `NULLS_FIRST_ON_ASC_LAST_ON_DESC` or `NULLS_LAST_ON_ASC_FIRST_ON_DESC`:
```sql
SET default_null_order = 'NULLS_FIRST';
```
Use the `default_order` to change the direction of the default sorting order to either `DESC` or `ASC`:
```sql
SET default_order = 'DESC';
``` | 161 | [
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SQL | Query Syntax | Collations | Text is sorted using the binary comparison collation by default, which means values are sorted on their binary UTF-8 values.
While this works well for ASCII text (e.g., for English language data), the sorting order can be incorrect for other languages.
For this purpose, DuckDB provides collations.
For more information on collations, see the [Collation page](#docs:sql:expressions:collations). | 86 | [
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|
SQL | Query Syntax | Examples | All examples use this example table:
```sql
CREATE OR REPLACE TABLE addresses AS
SELECT '123 Quack Blvd' AS address, 'DuckTown' AS city, '11111' AS zip
UNION ALL
SELECT '111 Duck Duck Goose Ln', 'DuckTown', '11111'
UNION ALL
SELECT '111 Duck Duck Goose Ln', 'Duck Town', '11111'
UNION ALL
SELECT '111 Duck Duck Goose Ln', 'Duck Town', '11111-0001';
```
Select the addresses, ordered by city name using the default null order and default order:
```sql
SELECT *
FROM addresses
ORDER BY city;
```
Select the addresses, ordered by city name in descending order with nulls at the end:
```sql
SELECT *
FROM addresses
ORDER BY city DESC NULLS LAST;
```
Order by city and then by zip code, both using the default orderings:
```sql
SELECT *
FROM addresses
ORDER BY city, zip;
```
Order by city using German collation rules:
```sql
SELECT *
FROM addresses
ORDER BY city COLLATE DE;
```
#### `ORDER BY ALL` Examples {#docs:sql:query_syntax:orderby::order-by-all-examples}
Order from left to right (by address, then by city, then by zip) in ascending order:
```sql
SELECT *
FROM addresses
ORDER BY ALL;
```
<div class="narrow_table"></div>
| address | city | zip |
|------------------------|-----------|------------|
| 111 Duck Duck Goose Ln | Duck Town | 11111 |
| 111 Duck Duck Goose Ln | Duck Town | 11111-0001 |
| 111 Duck Duck Goose Ln | DuckTown | 11111 |
| 123 Quack Blvd | DuckTown | 11111 |
Order from left to right (by address, then by city, then by zip) in descending order:
```sql
SELECT *
FROM addresses
ORDER BY ALL DESC;
```
<div class="narrow_table"></div>
| address | city | zip |
|------------------------|-----------|------------|
| 123 Quack Blvd | DuckTown | 11111 |
| 111 Duck Duck Goose Ln | DuckTown | 11111 |
| 111 Duck Duck Goose Ln | Duck Town | 11111-0001 |
| 111 Duck Duck Goose Ln | Duck Town | 11111 | | 537 | [
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|
SQL | Query Syntax | LIMIT and OFFSET Clauses | `LIMIT` is an output modifier. Logically it is applied at the very end of the query. The `LIMIT` clause restricts the amount of rows fetched. The `OFFSET` clause indicates at which position to start reading the values, i.e., the first `OFFSET` values are ignored.
Note that while `LIMIT` can be used without an `ORDER BY` clause, the results might not be deterministic without the `ORDER BY` clause. This can still be useful, however, for example when you want to inspect a quick snapshot of the data. | 115 | [
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|
SQL | Query Syntax | Examples | Select the first 5 rows from the addresses table:
```sql
SELECT *
FROM addresses
LIMIT 5;
```
Select the 5 rows from the addresses table, starting at position 5 (i.e., ignoring the first 5 rows):
```sql
SELECT *
FROM addresses
LIMIT 5
OFFSET 5;
```
Select the top 5 cities with the highest population:
```sql
SELECT city, count(*) AS population
FROM addresses
GROUP BY city
ORDER BY population DESC
LIMIT 5;
``` | 111 | [
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|
SQL | Query Syntax | SAMPLE Clause | The `SAMPLE` clause allows you to run the query on a sample from the base table. This can significantly speed up processing of queries, at the expense of accuracy in the result. Samples can also be used to quickly see a snapshot of the data when exploring a data set. The sample clause is applied right after anything in the `FROM` clause (i.e., after any joins, but before the `WHERE` clause or any aggregates). See the [`SAMPLE`](#docs:sql:samples) page for more information. | 106 | [
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|
SQL | Query Syntax | Examples | Select a sample of 1% of the addresses table using default (system) sampling:
```sql
SELECT *
FROM addresses
USING SAMPLE 1%;
```
Select a sample of 1% of the addresses table using bernoulli sampling:
```sql
SELECT *
FROM addresses
USING SAMPLE 1% (bernoulli);
```
Select a sample of 10 rows from the subquery:
```sql
SELECT *
FROM (SELECT * FROM addresses)
USING SAMPLE 10 ROWS;
``` | 108 | [
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|
SQL | Query Syntax | Examples | Unnest a list, generating 3 rows (1, 2, 3):
```sql
SELECT unnest([1, 2, 3]);
```
Unnesting a struct, generating two columns (a, b):
```sql
SELECT unnest({'a': 42, 'b': 84});
```
Recursive unnest of a list of structs:
```sql
SELECT unnest([{'a': 42, 'b': 84}, {'a': 100, 'b': NULL}], recursive := true);
```
Limit depth of recursive unnest using `max_depth`:
```sql
SELECT unnest([[[1, 2], [3, 4]], [[5, 6], [7, 8, 9], []], [[10, 11]]], max_depth := 2);
```
The `unnest` special function is used to unnest lists or structs by one level. The function can be used as a regular scalar function, but only in the `SELECT` clause. Invoking `unnest` with the `recursive` parameter will unnest lists and structs of multiple levels. The depth of unnesting can be limited using the `max_depth` parameter (which assumes `recursive` unnesting by default).
#### Unnesting Lists {#docs:sql:query_syntax:unnest::unnesting-lists}
Unnest a list, generating 3 rows (1, 2, 3):
```sql
SELECT unnest([1, 2, 3]);
```
Unnest a scalar list, generating 3 rows ((1, 10), (2, 11), (3, NULL)):
```sql
SELECT unnest([1, 2, 3]), unnest([10, 11]);
```
Unnest a scalar list, generating 3 rows ((1, 10), (2, 10), (3, 10)):
```sql
SELECT unnest([1, 2, 3]), 10;
```
Unnest a list column generated from a subquery:
```sql
SELECT unnest(l) + 10 FROM (VALUES ([1, 2, 3]), ([4, 5])) tbl(l);
```
Empty result:
```sql
SELECT unnest([]);
```
Empty result:
```sql
SELECT unnest(NULL);
```
Using `unnest` on a list will emit one tuple per entry in the list. When `unnest` is combined with regular scalar expressions, those expressions are repeated for every entry in the list. When multiple lists are unnested in the same `SELECT` clause, the lists are unnested side-by-side. If one list is longer than the other, the shorter list will be padded with `NULL` values.
An empty list and a `NULL` list will both unnest to zero elements.
#### Unnesting Structs {#docs:sql:query_syntax:unnest::unnesting-structs}
Unnesting a struct, generating two columns (a, b):
```sql
SELECT unnest({'a': 42, 'b': 84});
```
Unnesting a struct, generating two columns (a, b):
```sql
SELECT unnest({'a': 42, 'b': {'x': 84}});
```
`unnest` on a struct will emit one column per entry in the struct.
#### Recursive Unnest {#docs:sql:query_syntax:unnest::recursive-unnest}
Unnesting a list of lists recursively, generating 5 rows (1, 2, 3, 4, 5):
```sql
SELECT unnest([[1, 2, 3], [4, 5]], recursive := true);
```
Unnesting a list of structs recursively, generating two rows of two columns (a, b):
```sql
SELECT unnest([{'a': 42, 'b': 84}, {'a': 100, 'b': NULL}], recursive := true);
```
Unnesting a struct, generating two columns (a, b):
```sql
SELECT unnest({'a': [1, 2, 3], 'b': 88}, recursive := true);
```
Calling `unnest` with the `recursive` setting will fully unnest lists, followed by fully unnesting structs. This can be useful to fully flatten columns that contain lists within lists, or lists of structs. Note that lists *within* structs are not unnested.
#### Setting the Maximum Depth of Unnesting {#docs:sql:query_syntax:unnest::setting-the-maximum-depth-of-unnesting}
The `max_depth` parameter allows limiting the maximum depth of recursive unnesting (which is assumed by default and does not have to be specified separately).
For example, unnestig to `max_depth` of 2 yields the following:
```sql
SELECT unnest([[[1, 2], [3, 4]], [[5, 6], [7, 8, 9], []], [[10, 11]]], max_depth := 2) AS x;
```
| x |
|-----------|
| [1, 2] |
| [3, 4] |
| [5, 6] |
| [7, 8, 9] |
| [] |
| [10, 11] |
Meanwhile, unnesting to `max_depth` of 3 results in:
```sql
SELECT unnest([[[1, 2], [3, 4]], [[5, 6], [7, 8, 9], []], [[10, 11]]], max_depth := 3) AS x;
```
| x |
|---:|
| 1 |
| 2 |
| 3 |
| 4 |
| 5 |
| 6 |
| 7 |
| 8 |
| 9 |
| 10 |
| 11 |
#### Keeping Track of List Entry Positions {#docs:sql:query_syntax:unnest::keeping-track-of-list-entry-positions}
To keep track of each entry's position within the original list, `unnest` may be combined with [`generate_subscripts`](#docs:sql:functions:list::generate_subscripts):
```sql
SELECT unnest(l) AS x, generate_subscripts(l, 1) AS index
FROM (VALUES ([1, 2, 3]), ([4, 5])) tbl(l);
```
| x | index |
|--:|------:|
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
| 4 | 1 |
| 5 | 2 | | 1,448 | [
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|
SQL | Query Syntax | WITH Clause | The `WITH` clause allows you to specify common table expressions (CTEs).
Regular (non-recursive) common-table-expressions are essentially views that are limited in scope to a particular query.
CTEs can reference each-other and can be nested. [Recursive CTEs](#::recursive-ctes) can reference themselves. | 67 | [
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|
SQL | Query Syntax | Basic CTE Examples | Create a CTE called `cte` and use it in the main query:
```sql
WITH cte AS (SELECT 42 AS x)
SELECT * FROM cte;
```
| x |
|---:|
| 42 |
Create two CTEs `cte1` and `cte2`, where the second CTE references the first CTE:
```sql
WITH
cte1 AS (SELECT 42 AS i),
cte2 AS (SELECT i * 100 AS x FROM cte1)
SELECT * FROM cte2;
```
| x |
|-----:|
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|
SQL | Query Syntax | CTE Materialization | DuckDB can employ CTE materialization, i.e., inlining CTEs into the main query.
This is performed using heuristics: if the CTE performs a grouped aggregation and is queried more than once, it is materialized.
Materialization can be explicitly activated by defining the CTE using `AS MATERIALIZED` and disabled by using `AS NOT MATERIALIZED`.
Take the following query for example, which invokes the same CTE three times:
```sql
WITH t(x) AS (⟨complex_query⟩)
SELECT *
FROM
t AS t1,
t AS t2,
t AS t3;
```
Inlining duplicates the definition of `t` for each reference which results in the following query:
```sql
SELECT *
FROM
(⟨complex_query⟩) AS t1(x),
(⟨complex_query⟩) AS t2(x),
(⟨complex_query⟩) AS t3(x);
```
If `⟨complex_query⟩` is expensive, materializing it with the `MATERIALIZED` keyword can improve performance. In this case, `⟨complex_query⟩` is evaluated only once.
```sql
WITH t(x) AS MATERIALIZED (⟨complex_query⟩)
SELECT *
FROM
t AS t1,
t AS t2,
t AS t3;
```
If one wants to disable materialization, use `NOT MATERIALIZED`:
```sql
WITH t(x) AS NOT MATERIALIZED (⟨complex_query⟩)
SELECT *
FROM
t AS t1,
t AS t2,
t AS t3;
``` | 335 | [
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|
SQL | Query Syntax | Recursive CTEs | `WITH RECURSIVE` allows the definition of CTEs which can refer to themselves. Note that the query must be formulated in a way that ensures termination, otherwise, it may run into an infinite loop.
#### Example: Fibonacci Sequence {#docs:sql:query_syntax:with::example-fibonacci-sequence}
`WITH RECURSIVE` can be used to make recursive calculations. For example, here is how `WITH RECURSIVE` could be used to calculate the first ten Fibonacci numbers:
```sql
WITH RECURSIVE FibonacciNumbers (RecursionDepth, FibonacciNumber, NextNumber) AS (
-- Base case
SELECT
0 AS RecursionDepth,
0 AS FibonacciNumber,
1 AS NextNumber
UNION ALL
-- Recursive step
SELECT
fib.RecursionDepth + 1 AS RecursionDepth,
fib.NextNumber AS FibonacciNumber,
fib.FibonacciNumber + fib.NextNumber AS NextNumber
FROM
FibonacciNumbers fib
WHERE
fib.RecursionDepth + 1 < 10
)
SELECT
fn.RecursionDepth AS FibonacciNumberIndex,
fn.FibonacciNumber
FROM
FibonacciNumbers fn;
```
| FibonacciNumberIndex | FibonacciNumber |
|---------------------:|----------------:|
| 0 | 0 |
| 1 | 1 |
| 2 | 1 |
| 3 | 2 |
| 4 | 3 |
| 5 | 5 |
| 6 | 8 |
| 7 | 13 |
| 8 | 21 |
| 9 | 34 |
#### Example: Tree Traversal {#docs:sql:query_syntax:with::example-tree-traversal}
`WITH RECURSIVE` can be used to traverse trees. For example, take a hierarchy of tags:
<img alt="Example tree" src="/images/examples/with-recursive-tree-example.svg" style="width: 700px; text-align: center">
```sql
CREATE TABLE tag (id INTEGER, name VARCHAR, subclassof INTEGER);
INSERT INTO tag VALUES
(1, 'U2', 5),
(2, 'Blur', 5),
(3, 'Oasis', 5),
(4, '2Pac', 6),
(5, 'Rock', 7),
(6, 'Rap', 7),
(7, 'Music', 9),
(8, 'Movies', 9),
(9, 'Art', NULL);
```
The following query returns the path from the node `Oasis` to the root of the tree (` Art`).
```sql
WITH RECURSIVE tag_hierarchy(id, source, path) AS (
SELECT id, name, [name] AS path
FROM tag
WHERE subclassof IS NULL
UNION ALL
SELECT tag.id, tag.name, list_prepend(tag.name, tag_hierarchy.path)
FROM tag, tag_hierarchy
WHERE tag.subclassof = tag_hierarchy.id
)
SELECT path
FROM tag_hierarchy
WHERE source = 'Oasis';
```
| path |
|---------------------------|
| [Oasis, Rock, Music, Art] |
#### Graph Traversal {#docs:sql:query_syntax:with::graph-traversal}
The `WITH RECURSIVE` clause can be used to express graph traversal on arbitrary graphs. However, if the graph has cycles, the query must perform cycle detection to prevent infinite loops.
One way to achieve this is to store the path of a traversal in a [list](#docs:sql:data_types:list) and, before extending the path with a new edge, check whether its endpoint has been visited before (see the example later).
Take the following directed graph from the [LDBC Graphalytics benchmark](https://arxiv.org/pdf/2011.15028.pdf):
<img alt="Example graph" src="/images/examples/with-recursive-graph-example.svg" style="width: 700px; text-align: center">
```sql
CREATE TABLE edge (node1id INTEGER, node2id INTEGER);
INSERT INTO edge VALUES
(1, 3), (1, 5), (2, 4), (2, 5), (2, 10), (3, 1),
(3, 5), (3, 8), (3, 10), (5, 3), (5, 4), (5, 8),
(6, 3), (6, 4), (7, 4), (8, 1), (9, 4);
```
Note that the graph contains directed cycles, e.g., between nodes 1, 2, and 5. | 1,005 | [
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|
SQL | Query Syntax | Recursive CTEs | Enumerate All Paths from a Node | The following query returns **all paths** starting in node 1:
```sql
WITH RECURSIVE paths(startNode, endNode, path) AS (
SELECT -- Define the path as the first edge of the traversal
node1id AS startNode,
node2id AS endNode,
[node1id, node2id] AS path
FROM edge
WHERE startNode = 1
UNION ALL
SELECT -- Concatenate new edge to the path
paths.startNode AS startNode,
node2id AS endNode,
array_append(path, node2id) AS path
FROM paths
JOIN edge ON paths.endNode = node1id
-- Prevent adding a repeated node to the path.
-- This ensures that no cycles occur.
WHERE list_position(paths.path, node2id) IS NULL
)
SELECT startNode, endNode, path
FROM paths
ORDER BY length(path), path;
```
| startNode | endNode | path |
|----------:|--------:|---------------|
| 1 | 3 | [1, 3] |
| 1 | 5 | [1, 5] |
| 1 | 5 | [1, 3, 5] |
| 1 | 8 | [1, 3, 8] |
| 1 | 10 | [1, 3, 10] |
| 1 | 3 | [1, 5, 3] |
| 1 | 4 | [1, 5, 4] |
| 1 | 8 | [1, 5, 8] |
| 1 | 4 | [1, 3, 5, 4] |
| 1 | 8 | [1, 3, 5, 8] |
| 1 | 8 | [1, 5, 3, 8] |
| 1 | 10 | [1, 5, 3, 10] |
Note that the result of this query is not restricted to shortest paths, e.g., for node 5, the results include paths `[1, 5]` and `[1, 3, 5]`. | 498 | [
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SQL | Query Syntax | Recursive CTEs | Enumerate Unweighted Shortest Paths from a Node | In most cases, enumerating all paths is not practical or feasible. Instead, only the **(unweighted) shortest paths** are of interest. To find these, the second half of the `WITH RECURSIVE` query should be adjusted such that it only includes a node if it has not yet been visited. This is implemented by using a subquery that checks if any of the previous paths includes the node:
```sql
WITH RECURSIVE paths(startNode, endNode, path) AS (
SELECT -- Define the path as the first edge of the traversal
node1id AS startNode,
node2id AS endNode,
[node1id, node2id] AS path
FROM edge
WHERE startNode = 1
UNION ALL
SELECT -- Concatenate new edge to the path
paths.startNode AS startNode,
node2id AS endNode,
array_append(path, node2id) AS path
FROM paths
JOIN edge ON paths.endNode = node1id
-- Prevent adding a node that was visited previously by any path.
-- This ensures that (1) no cycles occur and (2) only nodes that
-- were not visited by previous (shorter) paths are added to a path.
WHERE NOT EXISTS (
FROM paths previous_paths
WHERE list_contains(previous_paths.path, node2id)
)
)
SELECT startNode, endNode, path
FROM paths
ORDER BY length(path), path;
```
| startNode | endNode | path |
|----------:|--------:|------------|
| 1 | 3 | [1, 3] |
| 1 | 5 | [1, 5] |
| 1 | 8 | [1, 3, 8] |
| 1 | 10 | [1, 3, 10] |
| 1 | 4 | [1, 5, 4] |
| 1 | 8 | [1, 5, 8] | | 430 | [
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SQL | Query Syntax | Recursive CTEs | Enumerate Unweighted Shortest Paths between Two Nodes | `WITH RECURSIVE` can also be used to find **all (unweighted) shortest paths between two nodes**. To ensure that the recursive query is stopped as soon as we reach the end node, we use a [window function](#docs:sql:functions:window_functions) which checks whether the end node is among the newly added nodes.
The following query returns all unweighted shortest paths between nodes 1 (start node) and 8 (end node):
```sql
WITH RECURSIVE paths(startNode, endNode, path, endReached) AS (
SELECT -- Define the path as the first edge of the traversal
node1id AS startNode,
node2id AS endNode,
[node1id, node2id] AS path,
(node2id = 8) AS endReached
FROM edge
WHERE startNode = 1
UNION ALL
SELECT -- Concatenate new edge to the path
paths.startNode AS startNode,
node2id AS endNode,
array_append(path, node2id) AS path,
max(CASE WHEN node2id = 8 THEN 1 ELSE 0 END)
OVER (ROWS BETWEEN UNBOUNDED PRECEDING
AND UNBOUNDED FOLLOWING) AS endReached
FROM paths
JOIN edge ON paths.endNode = node1id
WHERE NOT EXISTS (
FROM paths previous_paths
WHERE list_contains(previous_paths.path, node2id)
)
AND paths.endReached = 0
)
SELECT startNode, endNode, path
FROM paths
WHERE endNode = 8
ORDER BY length(path), path;
```
| startNode | endNode | path |
|----------:|--------:|-----------|
| 1 | 8 | [1, 3, 8] |
| 1 | 8 | [1, 5, 8] | | 391 | [
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SQL | Query Syntax | Limitations | DuckDB does not support mutually recursive CTEs. See the [related issue and discussion in the DuckDB repository](https://github.com/duckdb/duckdb/issues/14716#issuecomment-2467952456). | 48 | [
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|
SQL | Query Syntax | WINDOW Clause | The `WINDOW` clause allows you to specify named windows that can be used within [window functions](#docs:sql:functions:window_functions). These are useful when you have multiple window functions, as they allow you to avoid repeating the same window clause. | 52 | [
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|
SQL | Query Syntax | QUALIFY Clause | The `QUALIFY` clause is used to filter the results of [`WINDOW` functions](#docs:sql:functions:window_functions). This filtering of results is similar to how a [`HAVING` clause](#docs:sql:query_syntax:having) filters the results of aggregate functions applied based on the [`GROUP BY` clause](#docs:sql:query_syntax:groupby).
The `QUALIFY` clause avoids the need for a subquery or [`WITH` clause](#docs:sql:query_syntax:with) to perform this filtering (much like `HAVING` avoids a subquery). An example using a `WITH` clause instead of `QUALIFY` is included below the `QUALIFY` examples.
Note that this is filtering based on [`WINDOW` functions](#docs:sql:functions:window_functions), not necessarily based on the [`WINDOW` clause](#docs:sql:query_syntax:window). The `WINDOW` clause is optional and can be used to simplify the creation of multiple `WINDOW` function expressions.
The position of where to specify a `QUALIFY` clause is following the [`WINDOW` clause](#docs:sql:query_syntax:window) in a `SELECT` statement (` WINDOW` does not need to be specified), and before the [`ORDER BY`](#docs:sql:query_syntax:orderby). | 280 | [
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|
SQL | Query Syntax | Examples | Each of the following examples produce the same output, located below.
Filter based on a window function defined in the `QUALIFY` clause:
```sql
SELECT
schema_name,
function_name,
-- In this example the function_rank column in the select clause is for reference
row_number() OVER (PARTITION BY schema_name ORDER BY function_name) AS function_rank
FROM duckdb_functions()
QUALIFY
row_number() OVER (PARTITION BY schema_name ORDER BY function_name) < 3;
```
Filter based on a window function defined in the `SELECT` clause:
```sql
SELECT
schema_name,
function_name,
row_number() OVER (PARTITION BY schema_name ORDER BY function_name) AS function_rank
FROM duckdb_functions()
QUALIFY
function_rank < 3;
```
Filter based on a window function defined in the `QUALIFY` clause, but using the `WINDOW` clause:
```sql
SELECT
schema_name,
function_name,
-- In this example the function_rank column in the select clause is for reference
row_number() OVER my_window AS function_rank
FROM duckdb_functions()
WINDOW
my_window AS (PARTITION BY schema_name ORDER BY function_name)
QUALIFY
row_number() OVER my_window < 3;
```
Filter based on a window function defined in the `SELECT` clause, but using the `WINDOW` clause:
```sql
SELECT
schema_name,
function_name,
row_number() OVER my_window AS function_rank
FROM duckdb_functions()
WINDOW
my_window AS (PARTITION BY schema_name ORDER BY function_name)
QUALIFY
function_rank < 3;
```
Equivalent query based on a `WITH` clause (without a `QUALIFY` clause):
```sql
WITH ranked_functions AS (
SELECT
schema_name,
function_name,
row_number() OVER (PARTITION BY schema_name ORDER BY function_name) AS function_rank
FROM duckdb_functions()
)
SELECT
*
FROM ranked_functions
WHERE
function_rank < 3;
```
<div class="narrow_table"></div>
| schema_name | function_name | function_rank |
|:---|:---|:---|
| main | !__postfix | 1 |
| main | !~~ | 2 |
| pg_catalog | col_description | 1 |
| pg_catalog | format_pg_type | 2 | | 500 | [
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|
SQL | Query Syntax | VALUES Clause | The `VALUES` clause is used to specify a fixed number of rows. The `VALUES` clause can be used as a stand-alone statement, as part of the `FROM` clause, or as input to an `INSERT INTO` statement. | 49 | [
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|
SQL | Query Syntax | Examples | Generate two rows and directly return them:
```sql
VALUES ('Amsterdam', 1), ('London', 2);
```
Generate two rows as part of a `FROM` clause, and rename the columns:
```sql
SELECT *
FROM (VALUES ('Amsterdam', 1), ('London', 2)) cities(name, id);
```
Generate two rows and insert them into a table:
```sql
INSERT INTO cities
VALUES ('Amsterdam', 1), ('London', 2);
```
Create a table directly from a `VALUES` clause:
```sql
CREATE TABLE cities AS
SELECT *
FROM (VALUES ('Amsterdam', 1), ('London', 2)) cities(name, id);
``` | 152 | [
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|
SQL | Query Syntax | FILTER Clause | The `FILTER` clause may optionally follow an aggregate function in a `SELECT` statement. This will filter the rows of data that are fed into the aggregate function in the same way that a `WHERE` clause filters rows, but localized to the specific aggregate function. `FILTER`s are not currently able to be used when the aggregate function is in a windowing context.
There are multiple types of situations where this is useful, including when evaluating multiple aggregates with different filters, and when creating a pivoted view of a dataset. `FILTER` provides a cleaner syntax for pivoting data when compared with the more traditional `CASE WHEN` approach discussed below.
Some aggregate functions also do not filter out null values, so using a `FILTER` clause will return valid results when at times the `CASE WHEN` approach will not. This occurs with the functions `first` and `last`, which are desirable in a non-aggregating pivot operation where the goal is to simply re-orient the data into columns rather than re-aggregate it. `FILTER` also improves null handling when using the `list` and `array_agg` functions, as the `CASE WHEN` approach will include null values in the list result, while the `FILTER` clause will remove them. | 255 | [
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|
SQL | Query Syntax | Examples | Return the following:
* The total number of rows.
* The number of rows where `i <= 5`
* The number of rows where `i` is odd
```sql
SELECT
count(*) AS total_rows,
count(*) FILTER (i <= 5) AS lte_five,
count(*) FILTER (i % 2 = 1) AS odds
FROM generate_series(1, 10) tbl(i);
```
<div class="narrow_table monospace_table"></div>
| total_rows | lte_five | odds |
|:---|:---|:---|
| 10 | 5 | 5 |
Different aggregate functions may be used, and multiple `WHERE` expressions are also permitted:
```sql
SELECT
sum(i) FILTER (i <= 5) AS lte_five_sum,
median(i) FILTER (i % 2 = 1) AS odds_median,
median(i) FILTER (i % 2 = 1 AND i <= 5) AS odds_lte_five_median
FROM generate_series(1, 10) tbl(i);
```
<div class="narrow_table monospace_table"></div>
| lte_five_sum | odds_median | odds_lte_five_median |
|:---|:---|:---|
| 15 | 5.0 | 3.0 |
The `FILTER` clause can also be used to pivot data from rows into columns. This is a static pivot, as columns must be defined prior to runtime in SQL. However, this kind of statement can be dynamically generated in a host programming language to leverage DuckDB's SQL engine for rapid, larger than memory pivoting.
First generate an example dataset:
```sql
CREATE TEMP TABLE stacked_data AS
SELECT
i,
CASE WHEN i <= rows * 0.25 THEN 2022
WHEN i <= rows * 0.5 THEN 2023
WHEN i <= rows * 0.75 THEN 2024
WHEN i <= rows * 0.875 THEN 2025
ELSE NULL
END AS year
FROM (
SELECT
i,
count(*) OVER () AS rows
FROM generate_series(1, 100_000_000) tbl(i)
) tbl;
```
“Pivot” the data out by year (move each year out to a separate column):
```sql
SELECT
count(i) FILTER (year = 2022) AS "2022",
count(i) FILTER (year = 2023) AS "2023",
count(i) FILTER (year = 2024) AS "2024",
count(i) FILTER (year = 2025) AS "2025",
count(i) FILTER (year IS NULL) AS "NULLs"
FROM stacked_data;
```
This syntax produces the same results as the `FILTER` clauses above:
```sql
SELECT
count(CASE WHEN year = 2022 THEN i END) AS "2022",
count(CASE WHEN year = 2023 THEN i END) AS "2023",
count(CASE WHEN year = 2024 THEN i END) AS "2024",
count(CASE WHEN year = 2025 THEN i END) AS "2025",
count(CASE WHEN year IS NULL THEN i END) AS "NULLs"
FROM stacked_data;
```
<div class="narrow_table monospace_table"></div>
| 2022 | 2023 | 2024 | 2025 | NULLs |
|:---|:---|:---|:---|:---|
| 25000000 | 25000000 | 25000000 | 12500000 | 12500000 |
However, the `CASE WHEN` approach will not work as expected when using an aggregate function that does not ignore `NULL` values. The `first` function falls into this category, so `FILTER` is preferred in this case.
“Pivot” the data out by year (move each year out to a separate column):
```sql
SELECT
first(i) FILTER (year = 2022) AS "2022",
first(i) FILTER (year = 2023) AS "2023",
first(i) FILTER (year = 2024) AS "2024",
first(i) FILTER (year = 2025) AS "2025",
first(i) FILTER (year IS NULL) AS "NULLs"
FROM stacked_data;
```
<div class="narrow_table monospace_table"></div>
| 2022 | 2023 | 2024 | 2025 | NULLs |
|:---|:---|:---|:---|:---|
| 1474561 | 25804801 | 50749441 | 76431361 | 87500001 |
This will produce `NULL` values whenever the first evaluation of the `CASE WHEN` clause returns a `NULL`:
```sql
SELECT
first(CASE WHEN year = 2022 THEN i END) AS "2022",
first(CASE WHEN year = 2023 THEN i END) AS "2023",
first(CASE WHEN year = 2024 THEN i END) AS "2024",
first(CASE WHEN year = 2025 THEN i END) AS "2025",
first(CASE WHEN year IS NULL THEN i END) AS "NULLs"
FROM stacked_data;
```
<div class="narrow_table monospace_table"></div>
| 2022 | 2023 | 2024 | 2025 | NULLs |
|:---|:---|:---|:---|:---|
| 1228801 | NULL | NULL | NULL | NULL | | 1,222 | [
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|
SQL | Query Syntax | Set Operations | Set operations allow queries to be combined according to [set operation semantics](https://en.wikipedia.org/wiki/Set_(mathematics)#Basic_operations). Set operations refer to the [`UNION [ALL]`](#union), [`INTERSECT [ALL]`](#intersect) and [`EXCEPT [ALL]`](#except) clauses. The vanilla variants use set semantics, i.e., they eliminate duplicates, while the variants with `ALL` use bag semantics.
Traditional set operations unify queries **by column position**, and require the to-be-combined queries to have the same number of input columns. If the columns are not of the same type, casts may be added. The result will use the column names from the first query.
DuckDB also supports [`UNION [ALL] BY NAME`](#union-all-by-name), which joins columns by name instead of by position. `UNION BY NAME` does not require the inputs to have the same number of columns. `NULL` values will be added in case of missing columns. | 212 | [
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|
SQL | Query Syntax | `UNION` | The `UNION` clause can be used to combine rows from multiple queries. The queries are required to return the same number of columns. [Implicit casting](https://duckdb.org/docs/sql/data_types/typecasting#implicit-casting) to one of the returned types is performed to combine columns of different types where necessary. If this is not possible, the `UNION` clause throws an error.
#### Vanilla `UNION` (Set Semantics) {#docs:sql:query_syntax:setops::vanilla-union-set-semantics}
The vanilla `UNION` clause follows set semantics, therefore it performs duplicate elimination, i.e., only unique rows will be included in the result.
```sql
SELECT * FROM range(2) t1(x)
UNION
SELECT * FROM range(3) t2(x);
```
| x |
|--:|
| 2 |
| 1 |
| 0 |
#### `UNION ALL` (Bag Semantics) {#docs:sql:query_syntax:setops::union-all-bag-semantics}
`UNION ALL` returns all rows of both queries following bag semantics, i.e., *without* duplicate elimination.
```sql
SELECT * FROM range(2) t1(x)
UNION ALL
SELECT * FROM range(3) t2(x);
```
| x |
|--:|
| 0 |
| 1 |
| 0 |
| 1 |
| 2 |
#### `UNION [ALL] BY NAME` {#docs:sql:query_syntax:setops::union-all-by-name}
The `UNION [ALL] BY NAME` clause can be used to combine rows from different tables by name, instead of by position. `UNION BY NAME` does not require both queries to have the same number of columns. Any columns that are only found in one of the queries are filled with `NULL` values for the other query.
Take the following tables for example:
```sql
CREATE TABLE capitals (city VARCHAR, country VARCHAR);
INSERT INTO capitals VALUES
('Amsterdam', 'NL'),
('Berlin', 'Germany');
CREATE TABLE weather (city VARCHAR, degrees INTEGER, date DATE);
INSERT INTO weather VALUES
('Amsterdam', 10, '2022-10-14'),
('Seattle', 8, '2022-10-12');
```
```sql
SELECT * FROM capitals
UNION BY NAME
SELECT * FROM weather;
```
| city | country | degrees | date |
|-----------|---------|--------:|------------|
| Seattle | NULL | 8 | 2022-10-12 |
| Amsterdam | NL | NULL | NULL |
| Berlin | Germany | NULL | NULL |
| Amsterdam | NULL | 10 | 2022-10-14 |
`UNION BY NAME` follows set semantics (therefore it performs duplicate elimination), whereas `UNION ALL BY NAME` follows bag semantics. | 626 | [
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|
SQL | Query Syntax | `INTERSECT` | The `INTERSECT` clause can be used to select all rows that occur in the result of **both** queries.
#### Vanilla `INTERSECT` (Set Semantics) {#docs:sql:query_syntax:setops::vanilla-intersect-set-semantics}
Vanilla `INTERSECT` performs duplicate elimination, so only unique rows are returned.
```sql
SELECT * FROM range(2) t1(x)
INTERSECT
SELECT * FROM range(6) t2(x);
```
| x |
|--:|
| 0 |
| 1 |
#### `INTERSECT ALL` (Bag Semantics) {#docs:sql:query_syntax:setops::intersect-all-bag-semantics}
`INTERSECT ALL` follows bag semantics, so duplicates are returned.
```sql
SELECT unnest([5, 5, 6, 6, 6, 6, 7, 8]) AS x
INTERSECT ALL
SELECT unnest([5, 6, 6, 7, 7, 9]);
```
| x |
|--:|
| 5 |
| 6 |
| 6 |
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|
SQL | Query Syntax | `EXCEPT` | The `EXCEPT` clause can be used to select all rows that **only** occur in the left query.
#### Vanilla `EXCEPT` (Set Semantics) {#docs:sql:query_syntax:setops::vanilla-except-set-semantics}
Vanilla `EXCEPT` follows set semantics, therefore, it performs duplicate elimination, so only unique rows are returned.
```sql
SELECT * FROM range(5) t1(x)
EXCEPT
SELECT * FROM range(2) t2(x);
```
| x |
|--:|
| 2 |
| 3 |
| 4 |
#### `EXCEPT ALL` (Bag Semantics) {#docs:sql:query_syntax:setops::except-all-bag-semantics}
`EXCEPT ALL` uses bag semantics:
```sql
SELECT unnest([5, 5, 6, 6, 6, 6, 7, 8]) AS x
EXCEPT ALL
SELECT unnest([5, 6, 6, 7, 7, 9]);
```
| x |
|--:|
| 5 |
| 8 |
| 6 |
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|
SQL | Query Syntax | Prepared Statements | DuckDB supports prepared statements where parameters are substituted when the query is executed.
This can improve readability and is useful for preventing [SQL injections](https://en.wikipedia.org/wiki/SQL_injection). | 40 | [
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