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SQL | Functions | Scalar Utility Functions | `current_catalog()` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Return the name of the currently active catalog. Default is memory. |
| **Example** | `current_catalog()` |
| **Result** | `memory` | | 60 | [
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SQL | Functions | Scalar Utility Functions | `current_schema()` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Return the name of the currently active schema. Default is main. |
| **Example** | `current_schema()` |
| **Result** | `main` | | 60 | [
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SQL | Functions | Scalar Utility Functions | `current_schemas(boolean)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Return list of schemas. Pass a parameter of `true` to include implicit schemas. |
| **Example** | `current_schemas(true)` |
| **Result** | `['temp', 'main', 'pg_catalog']` | | 75 | [
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SQL | Functions | Scalar Utility Functions | `current_setting('setting_name')` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Return the current value of the configuration setting. |
| **Example** | `current_setting('access_mode')` |
| **Result** | `automatic` | | 60 | [
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SQL | Functions | Scalar Utility Functions | `currval('sequence_name')` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Return the current value of the sequence. Note that `nextval` must be called at least once prior to calling `currval`. |
| **Example** | `currval('my_sequence_name')` |
| **Result** | `1` | | 79 | [
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] |
SQL | Functions | Scalar Utility Functions | `error(message)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Throws the given error `message`. |
| **Example** | `error('access_mode')` | | 48 | [
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] | [
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SQL | Functions | Scalar Utility Functions | `force_checkpoint(database)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Synchronize WAL with file for (optional) database interrupting transactions. |
| **Example** | `force_checkpoint(my_db)` |
| **Result** | success Boolean | | 62 | [
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] |
SQL | Functions | Scalar Utility Functions | `gen_random_uuid()` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Alias of `uuid`. Return a random UUID similar to this: `eeccb8c5-9943-b2bb-bb5e-222f4e14b687`. |
| **Example** | `gen_random_uuid()` |
| **Result** | various | | 84 | [
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SQL | Functions | Scalar Utility Functions | `getenv(var)` | | | |
|:--|:--------|
| **Description** |Returns the value of the environment variable `var`. Only available in the [command line client](#docs:api:cli:overview). |
| **Example** | `getenv('HOME')` |
| **Result** | `/path/to/user/home` | | 71 | [
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SQL | Functions | Scalar Utility Functions | `hash(value)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns a `UBIGINT` with the hash of the `value`. |
| **Example** | `hash('🦆')` |
| **Result** | `2595805878642663834` | | 72 | [
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SQL | Functions | Scalar Utility Functions | `icu_sort_key(string, collator)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Surrogate key used to sort special characters according to the specific locale. Collator parameter is optional. Valid only when ICU extension is installed. |
| **Example** | `icu_sort_key('ö', 'DE')` |
| **Result** | `460145960106` | | 86 | [
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SQL | Functions | Scalar Utility Functions | `if(a, b, c)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Ternary conditional operator; returns b if a, else returns c. Equivalent to `CASE WHEN a THEN b ELSE c END`. |
| **Example** | `if(2 > 1, 3, 4)` |
| **Result** | `3` | | 84 | [
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SQL | Functions | Scalar Utility Functions | `ifnull(expr, other)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |A two-argument version of coalesce. |
| **Example** | `ifnull(NULL, 'default_string')` |
| **Result** | `default_string` | | 63 | [
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SQL | Functions | Scalar Utility Functions | `md5(string)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the MD5 hash of the `string` as a `VARCHAR`. |
| **Example** | `md5('123')` |
| **Result** | `202cb962ac59075b964b07152d234b70` | | 79 | [
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] |
SQL | Functions | Scalar Utility Functions | `md5_number(string)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the MD5 hash of the `string` as a `HUGEINT`. |
| **Example** | `md5_number('123')` |
| **Result** | `149263671248412135425768892945843956768` | | 80 | [
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SQL | Functions | Scalar Utility Functions | `md5_number_lower(string)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the MD5 hash of the `string` as a `BIGINT`. |
| **Example** | `md5_number_lower('123')` |
| **Result** | `8091599832034528150` | | 74 | [
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SQL | Functions | Scalar Utility Functions | `md5_number_higher(string)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the MD5 hash of the `string` as a `BIGINT`. |
| **Example** | `md5_number_higher('123')` |
| **Result** | `6559309979213966368` | | 75 | [
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SQL | Functions | Scalar Utility Functions | `nextval('sequence_name')` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Return the following value of the sequence. |
| **Example** | `nextval('my_sequence_name')` |
| **Result** | `2` | | 60 | [
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SQL | Functions | Scalar Utility Functions | `nullif(a, b)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Return null if a = b, else return a. Equivalent to `CASE WHEN a = b THEN NULL ELSE a END`. |
| **Example** | `nullif(1+1, 2)` |
| **Result** | `NULL` | | 79 | [
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SQL | Functions | Scalar Utility Functions | `pg_typeof(expression)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the lower case name of the data type of the result of the expression. For PostgreSQL compatibility. |
| **Example** | `pg_typeof('abc')` |
| **Result** | `varchar` | | 70 | [
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SQL | Functions | Scalar Utility Functions | `query(` *`query_string_literal`*`)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Table function that parses and executes the query defined in *`query_string_literal`*. Only literal strings are allowed. Warning: this function allows invoking arbitrary queries, potentially altering the database state. |
| **Example** | `query('SELECT 42 AS x')` |
| **Result** | `42` | | 91 | [
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SQL | Functions | Scalar Utility Functions | `query_table(` *`tbl_name`*`)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Table function that returns the table given in *`tbl_name`*. |
| **Example** | `query(t1)` |
| **Result** | (the rows of `t1`) | | 67 | [
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SQL | Functions | Scalar Utility Functions | `query_table(` *`tbl_names`*`, [`*`by_name`*`])` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Table function that returns the union of tables given in *`tbl_names`*. If the optional *`by_name`* parameter is set to `true`, it uses [`UNION ALL BY NAME`](#..:..:sql:query_syntax:setops::union-all-by-name) semantics. |
| **Example** | `query(['t1', 't2'])` |
| **Result** | (the union of the two tables) | | 119 | [
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SQL | Functions | Scalar Utility Functions | `read_blob(source)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the content from `source` (a filename, a list of filenames, or a glob pattern) as a `BLOB`. See the [`read_blob` guide](#docs:guides:file_formats:read_file::read_blob) for more details. |
| **Example** | `read_blob('hello.bin')` |
| **Result** | `hello\x0A` | | 108 | [
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SQL | Functions | Scalar Utility Functions | `read_text(source)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the content from `source` (a filename, a list of filenames, or a glob pattern) as a `VARCHAR`. The file content is first validated to be valid UTF-8. If `read_text` attempts to read a file with invalid UTF-8 an error is thrown suggesting to use `read_blob` instead. See the [`read_text` guide](#docs:guides:file_formats:read_file::read_text) for more details. |
| **Example** | `read_text('hello.txt')` |
| **Result** | `hello\n` | | 146 | [
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SQL | Functions | Scalar Utility Functions | `sha256(value)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns a `VARCHAR` with the SHA-256 hash of the `value`. |
| **Example** | `sha256('🦆')` |
| **Result** | `d7a5c5e0d1d94c32218539e7e47d4ba9c3c7b77d61332fb60d633dde89e473fb` | | 109 | [
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] |
SQL | Functions | Scalar Utility Functions | `stats(expression)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns a string with statistics about the expression. Expression can be a column, constant, or SQL expression. |
| **Example** | `stats(5)` |
| **Result** | `'[Min: 5, Max: 5][Has Null: false]'` | | 84 | [
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SQL | Functions | Scalar Utility Functions | `txid_current()` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the current transaction's identifier, a `BIGINT` value. It will assign a new one if the current transaction does not have one already. |
| **Example** | `txid_current()` |
| **Result** | various | | 76 | [
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SQL | Functions | Scalar Utility Functions | `typeof(expression)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns the name of the data type of the result of the expression. |
| **Example** | `typeof('abc')` |
| **Result** | `VARCHAR` | | 63 | [
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SQL | Functions | Scalar Utility Functions | `uuid()` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Return a random UUID similar to this: `eeccb8c5-9943-b2bb-bb5e-222f4e14b687`. |
| **Example** | `uuid()` |
| **Result** | various | | 77 | [
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] |
SQL | Functions | Scalar Utility Functions | `version()` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Return the currently active version of DuckDB in this format. |
| **Example** | `version()` |
| **Result** | various | | 56 | [
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] |
SQL | Functions | Utility Table Functions | A table function is used in place of a table in a `FROM` clause.
<div class="narrow_table"></div>
| Name | Description |
|:--|:-------|
| [`glob(search_path)`](#::globsearch_path) | Return filenames found at the location indicated by the *search_path* in a single column named `file`. The *search_path* may contain [glob pattern matching syntax](#docs:sql:functions:pattern_matching). |
| [`repeat_row(varargs, num_rows)`](#::repeat_rowvarargs-num_rows) | Returns a table with `num_rows` rows, each containing the fields defined in `varargs`. | | 141 | [
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|
SQL | Functions | Utility Table Functions | `glob(search_path)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Return filenames found at the location indicated by the *search_path* in a single column named `file`. The *search_path* may contain [glob pattern matching syntax](#docs:sql:functions:pattern_matching). |
| **Example** | `glob('*')` |
| **Result** | (table of filenames) | | 94 | [
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SQL | Functions | Utility Table Functions | `repeat_row(varargs, num_rows)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns a table with `num_rows` rows, each containing the fields defined in `varargs`. |
| **Example** | `repeat_row(1, 2, 'foo', num_rows = 3)` |
| **Result** | 3 rows of `1, 2, 'foo'` | | 92 | [
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SQL | Functions | Window Functions | <!-- markdownlint-disable MD001 -->
DuckDB supports [window functions](https://en.wikipedia.org/wiki/Window_function_(SQL)), which can use multiple rows to calculate a value for each row.
Window functions are [blocking operators](#docs:guides:performance:how_to_tune_workloads::blocking-operators), i.e., they require their entire input to be buffered, making them one of the most memory-intensive operators in SQL.
Window function are available in SQL since [SQL:2003](https://en.wikipedia.org/wiki/SQL:2003) and are supported by major SQL database systems. | 126 | [
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|
SQL | Functions | Examples | Generate a `row_number` column with containing incremental identifiers for each row:
```sql
SELECT row_number() OVER ()
FROM sales;
```
Generate a `row_number` column, by order of time:
```sql
SELECT row_number() OVER (ORDER BY time)
FROM sales;
```
Generate a `row_number` column, by order of time partitioned by region:
```sql
SELECT row_number() OVER (PARTITION BY region ORDER BY time)
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;
```
Compute the percentage of the total amount of sales per region for each row:
```sql
SELECT amount / sum(amount) OVER (PARTITION BY region)
FROM sales;
``` | 177 | [
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|
SQL | Functions | Syntax | <div id="rrdiagram"></div>
Window functions can only be used in the `SELECT` clause. To share `OVER` specifications between functions, use the statement's `WINDOW` clause and use the `OVER ⟨window-name⟩` syntax. | 52 | [
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|
SQL | Functions | General-Purpose Window Functions | The table below shows the available general window functions.
| Name | Description |
|:--|:-------|
| [`cume_dist()`](#::cume_dist) | The cumulative distribution: (number of partition rows preceding or peer with current row) / total partition rows. |
| [`dense_rank()`](#::dense_rank) | The rank of the current row *without gaps;* this function counts peer groups. |
| [`first_value(expr[ IGNORE NULLS])`](#first_valueexpr-ignore-nulls) | Returns `expr` evaluated at the row that is the first row (with a non-null value of `expr` if `IGNORE NULLS` is set) of the window frame. |
| [`lag(expr[, offset[, default]][ IGNORE NULLS])`](#lagexpr-offset-default-ignore-nulls) | Returns `expr` evaluated at the row that is `offset` rows (among rows with a non-null value of `expr` if `IGNORE NULLS` is set) before the current row within the window frame; if there is no such row, instead return `default` (which must be of the Same type as `expr`). Both `offset` and `default` are evaluated with respect to the current row. If omitted, `offset` defaults to `1` and default to `NULL`. |
| [`last_value(expr[ IGNORE NULLS])`](#last_valueexpr-ignore-nulls) | Returns `expr` evaluated at the row that is the last row (among rows with a non-null value of `expr` if `IGNORE NULLS` is set) of the window frame. |
| [`lead(expr[, offset[, default]][ IGNORE NULLS])`](#leadexpr-offset-default-ignore-nulls) | Returns `expr` evaluated at the row that is `offset` rows after the current row (among rows with a non-null value of `expr` if `IGNORE NULLS` is set) within the window frame; if there is no such row, instead return `default` (which must be of the Same type as `expr`). Both `offset` and `default` are evaluated with respect to the current row. If omitted, `offset` defaults to `1` and default to `NULL`. |
| [`nth_value(expr, nth[ IGNORE NULLS])`](#nth_valueexpr-nth-ignore-nulls) | Returns `expr` evaluated at the nth row (among rows with a non-null value of `expr` if `IGNORE NULLS` is set) of the window frame (counting from 1); `NULL` if no such row. |
| [`ntile(num_buckets)`](#::ntilenum_buckets) | An integer ranging from 1 to `num_buckets`, dividing the partition as equally as possible. |
| [`percent_rank()`](#::percent_rank) | The relative rank of the current row: `(rank() - 1) / (total partition rows - 1)`. |
| [`rank_dense()`](#::rank_dense) | The rank of the current row *without gaps. |
| [`rank()`](#::rank) | The rank of the current row *with gaps;* same as `row_number` of its first peer. |
| [`row_number()`](#::row_number) | The number of the current row within the partition, counting from 1. | | 693 | [
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|
SQL | Functions | General-Purpose Window Functions | `cume_dist()` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The cumulative distribution: (number of partition rows preceding or peer with current row) / total partition rows. |
| **Return Type** | `DOUBLE` |
| **Example** | `cume_dist()` | | 70 | [
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0.02390380948781967,
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SQL | Functions | General-Purpose Window Functions | `dense_rank()` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The rank of the current row *without gaps;* this function counts peer groups. |
| **Return Type** | `BIGINT` |
| **Example** | `dense_rank()` |
| **Aliases** | `rank_dense()` | | 76 | [
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] |
SQL | Functions | General-Purpose Window Functions | `first_value(expr[ IGNORE NULLS])` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns `expr` evaluated at the row that is the first row (with a non-null value of `expr` if `IGNORE NULLS` is set) of the window frame. |
| **Return Type** | Same type as `expr` |
| **Example** | `first_value(column)` | | 89 | [
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SQL | Functions | General-Purpose Window Functions | `lag(expr[, offset[, default]][ IGNORE NULLS])` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns `expr` evaluated at the row that is `offset` rows (among rows with a non-null value of `expr` if `IGNORE NULLS` is set) before the current row within the window frame; if there is no such row, instead return `default` (which must be of the Same type as `expr`). Both `offset` and `default` are evaluated with respect to the current row. If omitted, `offset` defaults to `1` and default to `NULL`. |
| **Return Type** | Same type as `expr` |
| **Aliases** | `lag(column, 3, 0)` | | 159 | [
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] |
SQL | Functions | General-Purpose Window Functions | `last_value(expr[ IGNORE NULLS])` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns `expr` evaluated at the row that is the last row (among rows with a non-null value of `expr` if `IGNORE NULLS` is set) of the window frame. |
| **Return Type** | Same type as `expr` |
| **Example** | `last_value(column)` | | 91 | [
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SQL | Functions | General-Purpose Window Functions | `lead(expr[, offset[, default]][ IGNORE NULLS])` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns `expr` evaluated at the row that is `offset` rows after the current row (among rows with a non-null value of `expr` if `IGNORE NULLS` is set) within the window frame; if there is no such row, instead return `default` (which must be of the Same type as `expr`). Both `offset` and `default` are evaluated with respect to the current row. If omitted, `offset` defaults to `1` and default to `NULL`. |
| **Return Type** | Same type as `expr` |
| **Aliases** | `lead(column, 3, 0)` | | 159 | [
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SQL | Functions | General-Purpose Window Functions | `nth_value(expr, nth[ IGNORE NULLS])` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |Returns `expr` evaluated at the nth row (among rows with a non-null value of `expr` if `IGNORE NULLS` is set) of the window frame (counting from 1); `NULL` if no such row. |
| **Return Type** | Same type as `expr` |
| **Aliases** | `nth_value(column, 2)` | | 104 | [
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] |
SQL | Functions | General-Purpose Window Functions | `ntile(num_buckets)` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |An integer ranging from 1 to `num_buckets`, dividing the partition as equally as possible. |
| **Return Type** | `BIGINT` |
| **Example** | `ntile(4)` | | 70 | [
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0.01533113420009613,
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0.02390380948781967,
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0.03417108580470085,
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0.025638282299041748,
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0.02561834640800953,
0.021830419078469276,
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0.06040745973587036,
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SQL | Functions | General-Purpose Window Functions | `percent_rank()` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The relative rank of the current row: `(rank() - 1) / (total partition rows - 1)`. |
| **Return Type** | `DOUBLE` |
| **Example** | `percent_rank()` | | 73 | [
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SQL | Functions | General-Purpose Window Functions | `rank_dense()` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The rank of the current row *without gaps*. |
| **Return Type** | `BIGINT` |
| **Example** | `rank_dense()` |
| **Aliases** | `dense_rank()` | | 69 | [
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0.040229085832834244,
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] |
SQL | Functions | General-Purpose Window Functions | `rank()` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The rank of the current row *with gaps;* same as `row_number` of its first peer. |
| **Return Type** | `BIGINT` |
| **Example** | `rank()` | | 70 | [
0.11890102177858353,
0.01533113420009613,
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0.02390380948781967,
0.0552239827811718,
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0.009833657182753086,
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0.0029007543344050646,
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0.09728990495204926,
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0.005303097423166037,
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0.025638282299041748,
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0.03379229083657265,
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0.02561834640800953,
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0.014433993957936764,
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0.056220803409814835,
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] |
SQL | Functions | General-Purpose Window Functions | `row_number()` | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Description** |The number of the current row within the partition, counting from 1. |
| **Return Type** | `BIGINT` |
| **Example** | `row_number()` | | 64 | [
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SQL | Functions | Aggregate Window Functions | All [aggregate functions](#docs:sql:functions:aggregates) can be used in a windowing context, including the optional [`FILTER` clause](#docs:sql:query_syntax:filter).
The `first` and `last` aggregate functions are shadowed by the respective general-purpose window functions, with the minor consequence that the `FILTER` clause is not available for these but `IGNORE NULLS` is. | 87 | [
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|
SQL | Functions | Nulls | All [general-purpose window functions](#::general-purpose-window-functions) that accept `IGNORE NULLS` respect nulls by default. This default behavior can optionally be made explicit via `RESPECT NULLS`.
In contrast, all [aggregate window functions](#::aggregate-window-functions) (except for `list` and its aliases, which can be made to ignore nulls via a `FILTER`) ignore nulls and do not accept `RESPECT NULLS`. For example, `sum(column) OVER (ORDER BY time) AS cumulativeColumn` computes a cumulative sum where rows with a `NULL` value of `column` have the same value of `cumulativeColumn` as the row that precedes them. | 145 | [
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|
SQL | Functions | Evaluation | Windowing works by breaking a relation up into independent *partitions*,
*ordering* those partitions,
and then computing a new column for each row as a function of the nearby values.
Some window functions depend only on the partition boundary and the ordering,
but a few (including all the aggregates) also use a *frame*.
Frames are specified as a number of rows on either side (*preceding* or *following*) of the *current row*.
The distance can either be specified as a number of *rows* or a *range* of values
using the partition's ordering value and a distance.
The full syntax is shown in the diagram at the top of the page,
and this diagram visually illustrates computation environment:
<img src="/images/blog/windowing/framing.svg" alt="The Window Computation Environment" title="Figure 1: The Window Computation Environment" style="max-width:90%;width:90%;height:auto"/>
#### Partition and Ordering {#docs:sql:functions:window_functions::partition-and-ordering}
Partitioning breaks the relation up into independent, unrelated pieces.
Partitioning is optional, and if none is specified then the entire relation is treated as a single partition.
Window functions cannot access values outside of the partition containing the row they are being evaluated at.
Ordering is also optional, but without it the results are not well-defined.
Each partition is ordered using the same ordering clause.
Here is a table of power generation data, available as a CSV file ([`power-plant-generation-history.csv`](https://duckdb.org/data/power-plant-generation-history.csv)). To load the data, run:
```sql
CREATE TABLE "Generation History" AS
FROM 'power-plant-generation-history.csv';
```
After partitioning by plant and ordering by date, it will have this layout:
<div class="narrow_table"></div>
| Plant | Date | MWh |
|:---|:---|---:|
| Boston | 2019-01-02 | 564337 |
| Boston | 2019-01-03 | 507405 |
| Boston | 2019-01-04 | 528523 |
| Boston | 2019-01-05 | 469538 |
| Boston | 2019-01-06 | 474163 |
| Boston | 2019-01-07 | 507213 |
| Boston | 2019-01-08 | 613040 |
| Boston | 2019-01-09 | 582588 |
| Boston | 2019-01-10 | 499506 |
| Boston | 2019-01-11 | 482014 |
| Boston | 2019-01-12 | 486134 |
| Boston | 2019-01-13 | 531518 |
| Worcester | 2019-01-02 | 118860 |
| Worcester | 2019-01-03 | 101977 |
| Worcester | 2019-01-04 | 106054 |
| Worcester | 2019-01-05 | 92182 |
| Worcester | 2019-01-06 | 94492 |
| Worcester | 2019-01-07 | 99932 |
| Worcester | 2019-01-08 | 118854 |
| Worcester | 2019-01-09 | 113506 |
| Worcester | 2019-01-10 | 96644 |
| Worcester | 2019-01-11 | 93806 |
| Worcester | 2019-01-12 | 98963 |
| Worcester | 2019-01-13 | 107170 |
In what follows,
we shall use this table (or small sections of it) to illustrate various pieces of window function evaluation.
The simplest window function is `row_number()`.
This function just computes the 1-based row number within the partition using the query:
```sql
SELECT
"Plant",
"Date",
row_number() OVER (PARTITION BY "Plant" ORDER BY "Date") AS "Row"
FROM "Generation History"
ORDER BY 1, 2;
```
The result will be the following:
<div class="narrow_table"></div>
| Plant | Date | Row |
|:---|:---|---:|
| Boston | 2019-01-02 | 1 |
| Boston | 2019-01-03 | 2 |
| Boston | 2019-01-04 | 3 |
| ... | ... | ... |
| Worcester | 2019-01-02 | 1 |
| Worcester | 2019-01-03 | 2 |
| Worcester | 2019-01-04 | 3 |
| ... | ... | ... |
Note that even though the function is computed with an `ORDER BY` clause,
the result does not have to be sorted,
so the `SELECT` also needs to be explicitly sorted if that is desired.
#### Framing {#docs:sql:functions:window_functions::framing}
Framing specifies a set of rows relative to each row where the function is evaluated.
The distance from the current row is given as an expression either `PRECEDING` or `FOLLOWING` the current row.
This distance can either be specified as an integral number of `ROWS`
or as a `RANGE` delta expression from the value of the ordering expression.
For a `RANGE` specification, there must be only one ordering expression,
and it has to support addition and subtraction (i.e., numbers or `INTERVAL`s).
The default values for frames are from `UNBOUNDED PRECEDING` to `CURRENT ROW`.
It is invalid for a frame to start after it ends.
Using the [`EXCLUDE` clause](#::exclude-clause), rows around the current row can be excluded from the frame. | 1,232 | [
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|
SQL | Functions | Evaluation | `ROW` Framing | Here is a simple `ROW` frame query, using an aggregate function:
```sql
SELECT points,
sum(points) OVER (
ROWS BETWEEN 1 PRECEDING
AND 1 FOLLOWING) we
FROM results;
```
This query computes the `sum` of each point and the points on either side of it:
<img src="/images/blog/windowing/moving-sum.jpg" alt="Moving SUM of three values" title="Figure 2: A moving SUM of three values" style="max-width:90%;width:90%;height:auto"/>
Notice that at the edge of the partition, there are only two values added together.
This is because frames are cropped to the edge of the partition. | 146 | [
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SQL | Functions | Evaluation | `RANGE` Framing | Returning to the power data, suppose the data is noisy.
We might want to compute a 7 day moving average for each plant to smooth out the noise.
To do this, we can use this window query:
```sql
SELECT "Plant", "Date",
avg("MWh") OVER (
PARTITION BY "Plant"
ORDER BY "Date" ASC
RANGE BETWEEN INTERVAL 3 DAYS PRECEDING
AND INTERVAL 3 DAYS FOLLOWING)
AS "MWh 7-day Moving Average"
FROM "Generation History"
ORDER BY 1, 2;
```
This query partitions the data by `Plant` (to keep the different power plants' data separate),
orders each plant's partition by `Date` (to put the energy measurements next to each other),
and uses a `RANGE` frame of three days on either side of each day for the `avg`
(to handle any missing days).
This is the result:
<div class="narrow_table"></div>
| Plant | Date | MWh 7-day Moving Average |
|:---|:---|---:|
| Boston | 2019-01-02 | 517450.75 |
| Boston | 2019-01-03 | 508793.20 |
| Boston | 2019-01-04 | 508529.83 |
| ... | ... | ... |
| Boston | 2019-01-13 | 499793.00 |
| Worcester | 2019-01-02 | 104768.25 |
| Worcester | 2019-01-03 | 102713.00 |
| Worcester | 2019-01-04 | 102249.50 |
| ... | ... | ... | | 358 | [
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SQL | Functions | Evaluation | `EXCLUDE` Clause | The `EXCLUDE` clause allows rows around the current row to be excluded from the frame. It has the following options:
* `EXCLUDE NO OTHERS`: exclude nothing (default)
* `EXCLUDE CURRENT ROW`: exclude the current row from the window frame
* `EXCLUDE GROUP`: exclude the current row and all its peers (according to the columns specified by `ORDER BY`) from the window frame
* `EXCLUDE TIES`: exclude only the current row's peers from the window frame
#### `WINDOW` Clauses {#docs:sql:functions:window_functions::window-clauses}
Multiple different `OVER` clauses can be specified in the same `SELECT`, and each will be computed separately.
Often, however, we want to use the same layout for multiple window functions.
The `WINDOW` clause can be used to define a *named* window that can be shared between multiple window functions:
```sql
SELECT "Plant", "Date",
min("MWh") OVER seven AS "MWh 7-day Moving Minimum",
avg("MWh") OVER seven AS "MWh 7-day Moving Average",
max("MWh") OVER seven AS "MWh 7-day Moving Maximum"
FROM "Generation History"
WINDOW seven AS (
PARTITION BY "Plant"
ORDER BY "Date" ASC
RANGE BETWEEN INTERVAL 3 DAYS PRECEDING
AND INTERVAL 3 DAYS FOLLOWING)
ORDER BY 1, 2;
```
The three window functions will also share the data layout, which will improve performance.
Multiple windows can be defined in the same `WINDOW` clause by comma-separating them:
```sql
SELECT "Plant", "Date",
min("MWh") OVER seven AS "MWh 7-day Moving Minimum",
avg("MWh") OVER seven AS "MWh 7-day Moving Average",
max("MWh") OVER seven AS "MWh 7-day Moving Maximum",
min("MWh") OVER three AS "MWh 3-day Moving Minimum",
avg("MWh") OVER three AS "MWh 3-day Moving Average",
max("MWh") OVER three AS "MWh 3-day Moving Maximum"
FROM "Generation History"
WINDOW
seven AS (
PARTITION BY "Plant"
ORDER BY "Date" ASC
RANGE BETWEEN INTERVAL 3 DAYS PRECEDING
AND INTERVAL 3 DAYS FOLLOWING),
three AS (
PARTITION BY "Plant"
ORDER BY "Date" ASC
RANGE BETWEEN INTERVAL 1 DAYS PRECEDING
AND INTERVAL 1 DAYS FOLLOWING)
ORDER BY 1, 2;
```
The queries above do not use a number of clauses commonly found in select statements, like
`WHERE`, `GROUP BY`, etc. For more complex queries you can find where `WINDOW` clauses fall in
the canonical order of the [`SELECT statement`](#docs:sql:statements:select).
#### Filtering the Results of Window Functions Using `QUALIFY` {#docs:sql:functions:window_functions::filtering-the-results-of-window-functions-using-qualify}
Window functions are executed after the [`WHERE`](#docs:sql:query_syntax:where) and [`HAVING`](#docs:sql:query_syntax:having) clauses have been already evaluated, so it's not possible to use these clauses to filter the results of window functions
The [`QUALIFY` clause](#docs:sql:query_syntax:qualify) avoids the need for a subquery or [`WITH` clause](#docs:sql:query_syntax:with) to perform this filtering.
#### Box and Whisker Queries {#docs:sql:functions:window_functions::box-and-whisker-queries}
All aggregates can be used as windowing functions, including the complex statistical functions.
These function implementations have been optimised for windowing,
and we can use the window syntax to write queries that generate the data for moving box-and-whisker plots:
```sql
SELECT "Plant", "Date",
min("MWh") OVER seven AS "MWh 7-day Moving Minimum",
quantile_cont("MWh", [0.25, 0.5, 0.75]) OVER seven
AS "MWh 7-day Moving IQR",
max("MWh") OVER seven AS "MWh 7-day Moving Maximum",
FROM "Generation History"
WINDOW seven AS (
PARTITION BY "Plant"
ORDER BY "Date" ASC
RANGE BETWEEN INTERVAL 3 DAYS PRECEDING
AND INTERVAL 3 DAYS FOLLOWING)
ORDER BY 1, 2;
``` | 946 | [
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SQL | Constraints | In SQL, constraints can be specified for tables. Constraints enforce certain properties over data that is inserted into a table. Constraints can be specified along with the schema of the table as part of the [`CREATE TABLE` statement](#docs:sql:statements:create_table). In certain cases, constraints can also be added to a table using the [`ALTER TABLE` statement](#docs:sql:statements:alter_table), but this is not currently supported for all constraints.
> **Warning. ** Constraints have a strong impact on performance: they slow down loading and updates but speed up certain queries. Please consult the [Performance Guide](#docs:guides:performance:schema::constraints) for details. | 142 | [
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SQL | Constraints | Syntax | <div id="rrdiagram"></div> | 9 | [
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|
SQL | Constraints | Check Constraint | Check constraints allow you to specify an arbitrary Boolean expression. Any columns that *do not* satisfy this expression violate the constraint. For example, we could enforce that the `name` column does not contain spaces using the following `CHECK` constraint.
```sql
CREATE TABLE students (name VARCHAR CHECK (NOT contains(name, ' ')));
INSERT INTO students VALUES ('this name contains spaces');
```
```console
Constraint Error: CHECK constraint failed: students
``` | 93 | [
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|
SQL | Constraints | Not Null Constraint | A not-null constraint specifies that the column cannot contain any `NULL` values. By default, all columns in tables are nullable. Adding `NOT NULL` to a column definition enforces that a column cannot contain `NULL` values.
```sql
CREATE TABLE students (name VARCHAR NOT NULL);
INSERT INTO students VALUES (NULL);
```
```console
Constraint Error: NOT NULL constraint failed: students.name
``` | 84 | [
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|
SQL | Constraints | Primary Key and Unique Constraint | Primary key or unique constraints define a column, or set of columns, that are a unique identifier for a row in the table. The constraint enforces that the specified columns are *unique* within a table, i.e., that at most one row contains the given values for the set of columns.
```sql
CREATE TABLE students (id INTEGER PRIMARY KEY, name VARCHAR);
INSERT INTO students VALUES (1, 'Student 1');
INSERT INTO students VALUES (1, 'Student 2');
```
```console
Constraint Error: Duplicate key "id: 1" violates primary key constraint
```
```sql
CREATE TABLE students (id INTEGER, name VARCHAR, PRIMARY KEY (id, name));
INSERT INTO students VALUES (1, 'Student 1');
INSERT INTO students VALUES (1, 'Student 2');
INSERT INTO students VALUES (1, 'Student 1');
```
```console
Constraint Error: Duplicate key "id: 1, name: Student 1" violates primary key constraint
```
In order to enforce this property efficiently, an [ART index is automatically created](#docs:sql:indexes) for every primary key or unique constraint that is defined in the table.
Primary key constraints and unique constraints are identical except for two points:
* A table can only have one primary key constraint defined, but many unique constraints
* A primary key constraint also enforces the keys to not be `NULL`.
```sql
CREATE TABLE students(id INTEGER PRIMARY KEY, name VARCHAR, email VARCHAR UNIQUE);
INSERT INTO students VALUES (1, 'Student 1', '[email protected]');
INSERT INTO students values (2, 'Student 2', '[email protected]');
```
```console
Constraint Error: Duplicate key "email: [email protected]" violates unique constraint.
```
```sql
INSERT INTO students(id, name) VALUES (3, 'Student 3');
INSERT INTO students(name, email) VALUES ('Student 3', '[email protected]');
```
```console
Constraint Error: NOT NULL constraint failed: students.id
```
> **Warning. ** Indexes have certain limitations that might result in constraints being evaluated too eagerly, leading to constraint errors such as `violates primary key constraint` and `violates unique constraint`. See the [indexes section for more details](#docs:sql:indexes::index-limitations). | 493 | [
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|
SQL | Constraints | Foreign Keys | Foreign keys define a column, or set of columns, that refer to a primary key or unique constraint from *another* table. The constraint enforces that the key exists in the other table.
```sql
CREATE TABLE students (id INTEGER PRIMARY KEY, name VARCHAR);
CREATE TABLE exams (exam_id INTEGER REFERENCES students(id), grade INTEGER);
INSERT INTO students VALUES (1, 'Student 1');
INSERT INTO exams VALUES (1, 10);
INSERT INTO exams VALUES (2, 10);
```
```console
Constraint Error: Violates foreign key constraint because key "id: 2" does not exist in the referenced table
```
In order to enforce this property efficiently, an [ART index is automatically created](#docs:sql:indexes) for every foreign key constraint that is defined in the table.
> **Warning. ** Indexes have certain limitations that might result in constraints being evaluated too eagerly, leading to constraint errors such as `violates primary key constraint` and `violates unique constraint`. See the [indexes section for more details](#docs:sql:indexes::index-limitations). | 229 | [
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|
SQL | Indexes | Index Types | DuckDB has two built-in index types. Indexes can also be defined via [extensions](#docs:extensions:overview).
#### Min-Max Index (Zonemap) {#docs:sql:indexes::min-max-index-zonemap}
A [min-max index](https://en.wikipedia.org/wiki/Block_Range_Index) (also known as zonemap and block range index) is automatically created for columns of all [general-purpose data types](#docs:sql:data_types:overview).
#### Adaptive Radix Tree (ART) {#docs:sql:indexes::adaptive-radix-tree-art}
An [Adaptive Radix Tree (ART)](https://db.in.tum.de/~leis/papers/ART.pdf) is mainly used to ensure primary key constraints and to speed up point and very highly selective (i.e., < 0.1%) queries. ART indexes are automatically created for columns with a `UNIQUE` or `PRIMARY KEY` constraint and can be defined using `CREATE INDEX`.
> **Warning. ** ART indexes must currently be able to fit in-memory. Avoid creating ART indexes if the index does not fit in memory.
#### Indexes Defined by Extensions {#docs:sql:indexes::indexes-defined-by-extensions}
[Starting with version 1.1.0](https://duckdb.org/2024/09/09/announcing-duckdb-110#r-tree), DuckDB supports [R-trees for spatial indexing](#docs:extensions:spatial:r-tree_indexes) via the `spatial` extension. | 332 | [
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|
SQL | Indexes | Persistence | Both min-max indexes and ART indexes are persisted on disk. | 12 | [
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|
SQL | Indexes | `CREATE INDEX` and `DROP INDEX` | To create an index, use the [`CREATE INDEX` statement](#docs:sql:statements:create_index::create-index).
To drop an index, use the [`DROP INDEX` statement](#docs:sql:statements:create_index::drop-index). | 50 | [
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|
SQL | Indexes | Limitations of ART Indexes | ART indexes create a secondary copy of the data in a second location – this complicates processing, particularly when combined with transactions. Certain limitations apply when it comes to modifying data that is also stored in secondary indexes.
> As expected, indexes have a strong effect on performance, slowing down loading and updates, but speeding up certain queries. Please consult the [Performance Guide](#docs:guides:performance:indexing) for details.
#### Updates Become Deletes and Inserts {#docs:sql:indexes::updates-become-deletes-and-inserts}
When an update statement is executed on a column that is present in an index, the statement is transformed into a *delete* of the original row followed by an *insert*.
This has certain performance implications, particularly for wide tables, as entire rows are rewritten instead of only the affected columns.
#### Over-Eager Unique Constraint Checking {#docs:sql:indexes::over-eager-unique-constraint-checking}
Due to the presence of transactions, data can only be removed from the index after (1) the transaction that performed the delete is committed, and (2) no further transactions exist that refer to the old entry still present in the index. As a result of this – transactions that perform *deletions followed by insertions* may trigger unexpected unique constraint violations, as the deleted tuple has not actually been removed from the index yet. For example:
```sql
CREATE TABLE students (id INTEGER, name VARCHAR);
INSERT INTO students VALUES (1, 'John Doe');
CREATE UNIQUE INDEX students_id ON students (id);
BEGIN; -- start transaction
DELETE FROM students WHERE id = 1;
INSERT INTO students VALUES (1, 'Jane Doe');
```
The last statement fails with the following error:
```console
Constraint Error: Duplicate key "id: 1" violates unique constraint. If this is an unexpected constraint violation please double check with the known index limitations section in our documentation (https://duckdb.org/docs/sql/indexes).
```
This, combined with the fact that updates are turned into deletions and insertions within the same transaction, means that updating rows in the presence of unique or primary key constraints can often lead to unexpected unique constraint violations. For example, in the following query, `SET id = 1` causes a `Constraint Error` to occur.
```sql
CREATE TABLE students (id INTEGER PRIMARY KEY, name VARCHAR);
INSERT INTO students VALUES (1, 'John Doe');
UPDATE students SET id = 1 WHERE id = 1;
```
```console
Constraint Error: Duplicate key "id: 1" violates primary key constraint.
If this is an unexpected constraint violation please double check with the known index limitations section in our documentation (https://duckdb.org/docs/sql/indexes).
```
Currently, this is an expected limitation of DuckDB – although we aim to resolve this in the future. | 592 | [
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|
SQL | Meta Queries | Information Schema | The views in the `information_schema` are SQL-standard views that describe the catalog entries of the database. These views can be filtered to obtain information about a specific column or table.
DuckDB's implementation is based on [PostgreSQL's information schema](https://www.postgresql.org/docs/16/infoschema-columns.html). | 66 | [
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|
SQL | Meta Queries | Tables | #### `character_sets`: Character Sets {#docs:sql:meta:information_schema::character_sets-character-sets}
| Column | Description | Type | Example |
|--------|-------------|------|---------|
| `character_set_catalog` | Currently not implemented – always `NULL`. | `VARCHAR` | `NULL` |
| `character_set_schema` | Currently not implemented – always `NULL`. | `VARCHAR` | `NULL` |
| `character_set_name` | Name of the character set, currently implemented as showing the name of the database encoding. | `VARCHAR` | `'UTF8'` |
| `character_repertoire` | Character repertoire, showing `UCS` if the encoding is `UTF8`, else just the encoding name. | `VARCHAR` | `'UCS'` |
| `form_of_use` | Character encoding form, same as the database encoding. | `VARCHAR` | `'UTF8'` |
| `default_collate_catalog`| Name of the database containing the default collation (always the current database). | `VARCHAR` | `'my_db'` |
| `default_collate_schema` | Name of the schema containing the default collation. | `VARCHAR` | `'pg_catalog'` |
| `default_collate_name` | Name of the default collation. | `VARCHAR` | `'ucs_basic'` |
#### `columns`: Columns {#docs:sql:meta:information_schema::columns-columns}
The view that describes the catalog information for columns is `information_schema.columns`. It lists the column present in the database and has the following layout:
| Column | Description | Type | Example |
|:--|:---|:-|:-|
| `table_catalog` | Name of the database containing the table (always the current database). | `VARCHAR` | `'my_db'` |
| `table_schema` | Name of the schema containing the table. | `VARCHAR` | `'main'` |
| `table_name` | Name of the table. | `VARCHAR` | `'widgets'` |
| `column_name` | Name of the column. | `VARCHAR` | `'price'` |
| `ordinal_position` | Ordinal position of the column within the table (count starts at 1). | `INTEGER` | `5` |
| `column_default` | Default expression of the column. |`VARCHAR`| `1.99` |
| `is_nullable` | `YES` if the column is possibly nullable, `NO` if it is known not nullable. |`VARCHAR`| `'YES'` |
| `data_type` | Data type of the column. |`VARCHAR`| `'DECIMAL(18, 2)'` |
| `character_maximum_length` | If `data_type` identifies a character or bit string type, the declared maximum length; null for all other data types or if no maximum length was declared. |`INTEGER`| `255` |
| `character_octet_length` | If `data_type` identifies a character type, the maximum possible length in octets (bytes) of a datum; null for all other data types. The maximum octet length depends on the declared character maximum length (see above) and the character encoding. |`INTEGER`| `1073741824` |
| `numeric_precision` | If `data_type` identifies a numeric type, this column contains the (declared or implicit) precision of the type for this column. The precision indicates the number of significant digits. For all other data types, this column is null. |`INTEGER`| `18` |
| `numeric_scale` | If `data_type` identifies a numeric type, this column contains the (declared or implicit) scale of the type for this column. The precision indicates the number of significant digits. For all other data types, this column is null. |`INTEGER`| `2` |
| `datetime_precision` | If `data_type` identifies a date, time, timestamp, or interval type, this column contains the (declared or implicit) fractional seconds precision of the type for this column, that is, the number of decimal digits maintained following the decimal point in the seconds value. No fractional seconds are currently supported in DuckDB. For all other data types, this column is null. |`INTEGER`| `0` |
#### `constraint_column_usage`: Constraint Column Usage {#docs:sql:meta:information_schema::constraint_column_usage-constraint-column-usage}
This view describes all columns in the current database that are used by some constraint. For a check constraint, this view identifies the columns that are used in the check expression. For a not-null constraint, this view identifies the column that the constraint is defined on. For a foreign key constraint, this view identifies the columns that the foreign key references. For a unique or primary key constraint, this view identifies the constrained columns.
| Column | Description | Type | Example |
|--------|-------------|------|---------|
| `table_catalog` | Name of the database that contains the table that contains the column that is used by some constraint (always the current database) |`VARCHAR`| `'my_db'` |
| `table_schema` | Name of the schema that contains the table that contains the column that is used by some constraint |`VARCHAR`| `'main'` |
| `table_name` | Name of the table that contains the column that is used by some constraint |`VARCHAR`| `'widgets'` |
| `column_name` | Name of the column that is used by some constraint |`VARCHAR`| `'price'` |
| `constraint_catalog` | Name of the database that contains the constraint (always the current database) |`VARCHAR`| `'my_db'` |
| `constraint_schema` | Name of the schema that contains the constraint |`VARCHAR`| `'main'` |
| `constraint_name` | Name of the constraint |`VARCHAR`| `'exam_id_students_id_fkey'` |
#### `key_column_usage`: Key Column Usage {#docs:sql:meta:information_schema::key_column_usage-key-column-usage}
| Column | Description | Type | Example |
|--------|-------------|------|---------|
| `constraint_catalog` | Name of the database that contains the constraint (always the current database). | `VARCHAR` | `'my_db'` |
| `constraint_schema` | Name of the schema that contains the constraint. | `VARCHAR` | `'main'` |
| `constraint_name` | Name of the constraint. | `VARCHAR` | `'exams_exam_id_fkey'` |
| `table_catalog` | Name of the database that contains the table that contains the column that is restricted by this constraint (always the current database). | `VARCHAR` | `'my_db'` |
| `table_schema` | Name of the schema that contains the table that contains the column that is restricted by this constraint. | `VARCHAR` | `'main'` |
| `table_name` | Name of the table that contains the column that is restricted by this constraint. | `VARCHAR` | `'exams'` |
| `column_name` | Name of the column that is restricted by this constraint. | `VARCHAR` | `'exam_id'` |
| `ordinal_position` | Ordinal position of the column within the constraint key (count starts at 1). | `INTEGER` | `1` |
| `position_in_unique_constraint` | For a foreign-key constraint, ordinal position of the referenced column within its unique constraint (count starts at `1`); otherwise `NULL`. | `INTEGER` | `1` |
#### `referential_constraints`: Referential Constraints {#docs:sql:meta:information_schema::referential_constraints-referential-constraints}
| Column | Description | Type | Example |
|--------|-------------|------|---------|
| `constraint_catalog` | Name of the database containing the constraint (always the current database). | `VARCHAR` | `'my_db'` |
| `constraint_schema` | Name of the schema containing the constraint. | `VARCHAR` | `main` |
| `constraint_name` | Name of the constraint. | `VARCHAR` | `exam_id_students_id_fkey` |
| `unique_constraint_catalog` | Name of the database that contains the unique or primary key constraint that the foreign key constraint references. | `VARCHAR` | `'my_db'` |
| `unique_constraint_schema` | Name of the schema that contains the unique or primary key constraint that the foreign key constraint references. | `VARCHAR` | `'main'` |
| `unique_constraint_name` | Name of the unique or primary key constraint that the foreign key constraint references. | `VARCHAR` | `'students_id_pkey'` |
| `match_option` | Match option of the foreign key constraint. Always `NONE`. | `VARCHAR` | `NONE` |
| `update_rule` | Update rule of the foreign key constraint. Always `NO ACTION`. | `VARCHAR` | `NO ACTION` |
| `delete_rule` | Delete rule of the foreign key constraint. Always `NO ACTION`. | `VARCHAR` | `NO ACTION` |
#### `schemata`: Database, Catalog and Schema {#docs:sql:meta:information_schema::schemata-database-catalog-and-schema}
The top level catalog view is `information_schema.schemata`. It lists the catalogs and the schemas present in the database and has the following layout:
| Column | Description | Type | Example |
|:--|:---|:-|:-|
| `catalog_name` | Name of the database that the schema is contained in. | `VARCHAR` | `'my_db'` |
| `schema_name` | Name of the schema. | `VARCHAR` | `'main'` |
| `schema_owner` | Name of the owner of the schema. Not yet implemented. | `VARCHAR` | `'duckdb'` |
| `default_character_set_catalog` | Applies to a feature not available in DuckDB. | `VARCHAR` | `NULL` |
| `default_character_set_schema` | Applies to a feature not available in DuckDB. | `VARCHAR` | `NULL` |
| `default_character_set_name` | Applies to a feature not available in DuckDB. | `VARCHAR` | `NULL` |
| `sql_path` | The file system location of the database. Currently unimplemented. | `VARCHAR` | `NULL` |
#### `tables`: Tables and Views {#docs:sql:meta:information_schema::tables-tables-and-views}
The view that describes the catalog information for tables and views is `information_schema.tables`. It lists the tables present in the database and has the following layout:
| Column | Description | Type | Example |
|:--|:---|:-|:-|
| `table_catalog` | The catalog the table or view belongs to. | `VARCHAR` | `'my_db'` |
| `table_schema` | The schema the table or view belongs to. | `VARCHAR` | `'main'` |
| `table_name` | The name of the table or view. | `VARCHAR` | `'widgets'` |
| `table_type` | The type of table. One of: `BASE TABLE`, `LOCAL TEMPORARY`, `VIEW`. | `VARCHAR` | `'BASE TABLE'` |
| `self_referencing_column_name` | Applies to a feature not available in DuckDB. | `VARCHAR` | `NULL` |
| `reference_generation` | Applies to a feature not available in DuckDB. | `VARCHAR` | `NULL` |
| `user_defined_type_catalog` | If the table is a typed table, the name of the database that contains the underlying data type (always the current database), else null. Currently unimplemented. | `VARCHAR` | `NULL` |
| `user_defined_type_schema` | If the table is a typed table, the name of the schema that contains the underlying data type, else null. Currently unimplemented. | `VARCHAR` | `NULL` |
| `user_defined_type_name` | If the table is a typed table, the name of the underlying data type, else null. Currently unimplemented. | `VARCHAR` | `NULL` |
| `is_insertable_into` | `YES` if the table is insertable into, `NO` if not (Base tables are always insertable into, views not necessarily.)| `VARCHAR` | `'YES'` |
| `is_typed` | `YES` if the table is a typed table, `NO` if not. | `VARCHAR` | `'NO'` |
| `commit_action` | Not yet implemented. | `VARCHAR` | `'NO'` |
#### `table_constraints`: Table Constraints {#docs:sql:meta:information_schema::table_constraints-table-constraints}
| Column | Description | Type | Example |
|--------|-------------|------|---------|
| `constraint_catalog` | Name of the database that contains the constraint (always the current database). | `VARCHAR` | `'my_db'` |
| `constraint_schema` | Name of the schema that contains the constraint. | `VARCHAR` | `'main'` |
| `constraint_name` | Name of the constraint. | `VARCHAR` | `'exams_exam_id_fkey'` |
| `table_catalog` | Name of the database that contains the table (always the current database). | `VARCHAR` | `'my_db'` |
| `table_schema` | Name of the schema that contains the table. | `VARCHAR` | `'main'` |
| `table_name` | Name of the table. | `VARCHAR` | `'exams'` |
| `constraint_type` | Type of the constraint: `CHECK`, `FOREIGN KEY`, `PRIMARY KEY`, or `UNIQUE`. | `VARCHAR` | `'FOREIGN KEY'` |
| `is_deferrable` | `YES` if the constraint is deferrable, `NO` if not. | `VARCHAR` | `'NO'` |
| `initially_deferred` | `YES` if the constraint is deferrable and initially deferred, `NO` if not. | `VARCHAR` | `'NO'` |
| `enforced` | Always `YES`. | `VARCHAR` | `'YES'` |
| `nulls_distinct` | If the constraint is a unique constraint, then `YES` if the constraint treats nulls as distinct or `NO` if it treats nulls as not distinct, otherwise `NULL` for other types of constraints. | `VARCHAR` | `'YES'` | | 3,063 | [
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|
SQL | Meta Queries | Catalog Functions | Several functions are also provided to see details about the catalogs and schemas that are configured in the database.
| Function | Description | Example | Result |
|:--|:---|:--|:--|
| `current_catalog()` | Return the name of the currently active catalog. Default is memory. | `current_catalog()` | `'memory'` |
| `current_schema()` | Return the name of the currently active schema. Default is main. | `current_schema()` | `'main'` |
| `current_schemas(boolean)` | Return list of schemas. Pass a parameter of `true` to include implicit schemas. | `current_schemas(true)` | `['temp', 'main', 'pg_catalog']` | | 149 | [
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|
SQL | Meta Queries | DuckDB_% Metadata Functions | DuckDB offers a collection of table functions that provide metadata about the current database. These functions reside in the `main` schema and their names are prefixed with `duckdb_`.
The resultset returned by a `duckdb_` table function may be used just like an ordinary table or view. For example, you can use a `duckdb_` function call in the `FROM` clause of a `SELECT` statement, and you may refer to the columns of its returned resultset elsewhere in the statement, for example in the `WHERE` clause.
Table functions are still functions, and you should write parenthesis after the function name to call it to obtain its returned resultset:
```sql
SELECT * FROM duckdb_settings();
```
Alternatively, you may execute table functions also using the `CALL`-syntax:
```sql
CALL duckdb_settings();
```
In this case too, the parentheses are mandatory.
> For some of the `duckdb_%` functions, there is also an identically named view available, which also resides in the `main` schema. Typically, these views do a `SELECT` on the `duckdb_` table function with the same name, while filtering out those objects that are marked as internal. We mention it here, because if you accidentally omit the parentheses in your `duckdb_` table function call, you might still get a result, but from the identically named view.
Example:
The `duckdb_views()` _table function_ returns all views, including those marked internal:
```sql
SELECT * FROM duckdb_views();
```
The `duckdb_views` _view_ returns views that are not marked as internal:
```sql
SELECT * FROM duckdb_views;
``` | 362 | [
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|
SQL | Meta Queries | `duckdb_columns` | The `duckdb_columns()` function provides metadata about the columns available in the DuckDB instance.
| Column | Description | Type |
|:-|:---|:-|
| `database_name` | The name of the database that contains the column object. | `VARCHAR` |
| `database_oid` | Internal identifier of the database that contains the column object. | `BIGINT` |
| `schema_name` | The SQL name of the schema that contains the table object that defines this column. | `VARCHAR` |
| `schema_oid` | Internal identifier of the schema object that contains the table of the column. | `BIGINT` |
| `table_name` | The SQL name of the table that defines the column. | `VARCHAR` |
| `table_oid` | Internal identifier (name) of the table object that defines the column. | `BIGINT` |
| `column_name` | The SQL name of the column. | `VARCHAR` |
| `column_index` | The unique position of the column within its table. | `INTEGER` |
| `internal` | `true` if this column built-in, `false` if it is user-defined. | `BOOLEAN` |
| `column_default` | The default value of the column (expressed in SQL)| `VARCHAR` |
| `is_nullable` | `true` if the column can hold `NULL` values; `false` if the column cannot hold `NULL`-values. | `BOOLEAN` |
| `data_type` | The name of the column datatype. | `VARCHAR` |
| `data_type_id` | The internal identifier of the column data type. | `BIGINT` |
| `character_maximum_length` | Always `NULL`. DuckDB [text types](#docs:sql:data_types:text) do not enforce a value length restriction based on a length type parameter. | `INTEGER` |
| `numeric_precision` | The number of units (in the base indicated by `numeric_precision_radix`) used for storing column values. For integral and approximate numeric types, this is the number of bits. For decimal types, this is the number of digits positions. | `INTEGER` |
| `numeric_precision_radix` | The number-base of the units in the `numeric_precision` column. For integral and approximate numeric types, this is `2`, indicating the precision is expressed as a number of bits. For the `decimal` type this is `10`, indicating the precision is expressed as a number of decimal positions. | `INTEGER` |
| `numeric_scale` | Applicable to `decimal` type. Indicates the maximum number of fractional digits (i.e., the number of digits that may appear after the decimal separator). | `INTEGER` |
The [`information_schema.columns`](#docs:sql:meta:information_schema::columns-columns) system view provides a more standardized way to obtain metadata about database columns, but the `duckdb_columns` function also returns metadata about DuckDB internal objects. (In fact, `information_schema.columns` is implemented as a query on top of `duckdb_columns()`) | 640 | [
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|
SQL | Meta Queries | `duckdb_constraints` | The `duckdb_constraints()` function provides metadata about the constraints available in the DuckDB instance.
| Column | Description | Type |
|:-|:---|:-|
| `database_name` | The name of the database that contains the constraint. | `VARCHAR` |
| `database_oid` | Internal identifier of the database that contains the constraint. | `BIGINT` |
| `schema_name` | The SQL name of the schema that contains the table on which the constraint is defined. | `VARCHAR` |
| `schema_oid` | Internal identifier of the schema object that contains the table on which the constraint is defined. | `BIGINT` |
| `table_name` | The SQL name of the table on which the constraint is defined. | `VARCHAR` |
| `table_oid` | Internal identifier (name) of the table object on which the constraint is defined. | `BIGINT` |
| `constraint_index` | Indicates the position of the constraint as it appears in its table definition. | `BIGINT` |
| `constraint_type` | Indicates the type of constraint. Applicable values are `CHECK`, `FOREIGN KEY`, `PRIMARY KEY`, `NOT NULL`, `UNIQUE`. | `VARCHAR` |
| `constraint_text` | The definition of the constraint expressed as a SQL-phrase. (Not necessarily a complete or syntactically valid DDL-statement.)| `VARCHAR` |
| `expression` | If constraint is a check constraint, the definition of the condition being checked, otherwise `NULL`. | `VARCHAR` |
| `constraint_column_indexes` | An array of table column indexes referring to the columns that appear in the constraint definition. | `BIGINT[]` |
| `constraint_column_names` | An array of table column names appearing in the constraint definition. | `VARCHAR[]` |
The [`information_schema.referential_constraints`](#docs:sql:meta:information_schema::referential_constraints-referential-constraints) and [`information_schema.table_constraints`](#docs:sql:meta:information_schema::table_constraints-table-constraints) system views provide a more standardized way to obtain metadata about constraints, but the `duckdb_constraints` function also returns metadata about DuckDB internal objects. (In fact, `information_schema.referential_constraints` and `information_schema.table_constraints` are implemented as a query on top of `duckdb_constraints()`) | 492 | [
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|
SQL | Meta Queries | `duckdb_databases` | The `duckdb_databases()` function lists the databases that are accessible from within the current DuckDB process.
Apart from the database associated at startup, the list also includes databases that were [attached](#docs:sql:statements:attach) later on to the DuckDB process
| Column | Description | Type |
|:-|:---|:-|
| `database_name` | The name of the database, or the alias if the database was attached using an ALIAS-clause. | `VARCHAR` |
| `database_oid` | The internal identifier of the database. | `VARCHAR` |
| `path` | The file path associated with the database. | `VARCHAR` |
| `internal` | `true` indicates a system or built-in database. False indicates a user-defined database. | `BOOLEAN` |
| `type` | The type indicates the type of RDBMS implemented by the attached database. For DuckDB databases, that value is `duckdb`. | `VARCHAR` | | 205 | [
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|
SQL | Meta Queries | `duckdb_dependencies` | The `duckdb_dependencies()` function provides metadata about the dependencies available in the DuckDB instance.
| Column | Description | Type |
|:--|:------|:-|
| `classid` | Always 0| `BIGINT` |
| `objid` | The internal id of the object. | `BIGINT` |
| `objsubid` | Always 0| `INTEGER` |
| `refclassid` | Always 0| `BIGINT` |
| `refobjid` | The internal id of the dependent object. | `BIGINT` |
| `refobjsubid` | Always 0| `INTEGER` |
| `deptype` | The type of dependency. Either regular (n) or automatic (a). | `VARCHAR` | | 164 | [
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|
SQL | Meta Queries | `duckdb_extensions` | The `duckdb_extensions()` function provides metadata about the extensions available in the DuckDB instance.
| Column | Description | Type |
|:--|:------|:-|
| `extension_name` | The name of the extension. | `VARCHAR` |
| `loaded` | `true` if the extension is loaded, `false` if it's not loaded. | `BOOLEAN` |
| `installed` | `true` if the extension is installed, `false` if it's not installed. | `BOOLEAN` |
| `install_path` | `(BUILT-IN)` if the extension is built-in, otherwise, the filesystem path where binary that implements the extension resides. | `VARCHAR` |
| `description` | Human readable text that describes the extension's functionality. | `VARCHAR` |
| `aliases` | List of alternative names for this extension. | `VARCHAR[]` | | 184 | [
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|
SQL | Meta Queries | `duckdb_functions` | The `duckdb_functions()` function provides metadata about the functions (including macros) available in the DuckDB instance.
| Column | Description | Type |
|:-|:---|:-|
| `database_name` | The name of the database that contains this function. | `VARCHAR` |
| `schema_name` | The SQL name of the schema where the function resides. | `VARCHAR` |
| `function_name` | The SQL name of the function. | `VARCHAR` |
| `function_type` | The function kind. Value is one of: `table`,`scalar`,`aggregate`,`pragma`,`macro`| `VARCHAR` |
| `description` | Description of this function (always `NULL`)| `VARCHAR` |
| `return_type` | The logical data type name of the returned value. Applicable for scalar and aggregate functions. | `VARCHAR` |
| `parameters` | If the function has parameters, the list of parameter names. | `VARCHAR[]` |
| `parameter_types` | If the function has parameters, a list of logical data type names corresponding to the parameter list. | `VARCHAR[]` |
| `varargs` | The name of the data type in case the function has a variable number of arguments, or `NULL` if the function does not have a variable number of arguments. | `VARCHAR` |
| `macro_definition` | If this is a [macro](#docs:sql:statements:create_macro), the SQL expression that defines it. | `VARCHAR` |
| `has_side_effects` | `false` if this is a pure function. `true` if this function changes the database state (like sequence functions `nextval()` and `curval()`). | `BOOLEAN` |
| `function_oid` | The internal identifier for this function | `BIGINT` | | 375 | [
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|
SQL | Meta Queries | `duckdb_indexes` | The `duckdb_indexes()` function provides metadata about secondary indexes available in the DuckDB instance.
| Column | Description | Type |
|:-|:---|:-|
| `database_name` | The name of the database that contains this index. | `VARCHAR` |
| `database_oid` | Internal identifier of the database containing the index. | `BIGINT` |
| `schema_name` | The SQL name of the schema that contains the table with the secondary index. | `VARCHAR` |
| `schema_oid` | Internal identifier of the schema object. | `BIGINT` |
| `index_name` | The SQL name of this secondary index. | `VARCHAR` |
| `index_oid` | The object identifier of this index. | `BIGINT` |
| `table_name` | The name of the table with the index. | `VARCHAR` |
| `table_oid` | Internal identifier (name) of the table object. | `BIGINT` |
| `is_unique` | `true` if the index was created with the `UNIQUE` modifier, `false` if it was not. | `BOOLEAN` |
| `is_primary` | Always `false`| `BOOLEAN` |
| `expressions` | Always `NULL`| `VARCHAR` |
| `sql` | The definition of the index, expressed as a `CREATE INDEX` SQL statement. | `VARCHAR` |
Note that `duckdb_indexes` only provides metadata about secondary indexes, i.e., those indexes created by explicit [`CREATE INDEX`](#docs:sql:indexes::create-index) statements. Primary keys, foreign keys, and `UNIQUE` constraints are maintained using indexes, but their details are included in the `duckdb_constraints()` function. | 363 | [
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] |
|
SQL | Meta Queries | `duckdb_keywords` | The `duckdb_keywords()` function provides metadata about DuckDB's keywords and reserved words.
<div class="narrow_table"></div>
| Column | Description | Type |
|:-|:---|:-|
| `keyword_name` | The keyword. | `VARCHAR` |
| `keyword_category` | Indicates the category of the keyword. Values are `column_name`, `reserved`, `type_function` and `unreserved`. | `VARCHAR` | | 94 | [
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|
SQL | Meta Queries | `duckdb_memory` | The `duckdb_memory()` function provides metadata about DuckDB's buffer manager.
| Column | Description | Type |
|:-|:---|:-|
| `tag` | The memory tag. It has one of the following values: `BASE_TABLE`, `HASH_TABLE`, `PARQUET_READER`, `CSV_READER`, `ORDER_BY`, `ART_INDEX`, `COLUMN_DATA`, `METADATA`, `OVERFLOW_STRINGS`, `IN_MEMORY_TABLE`, `ALLOCATOR`, `EXTENSION`. | `VARCHAR` |
| `memory_usage_bytes` | The memory used (in bytes). | `BIGINT` |
| `temporary_storage_bytes` | The disk storage used (in bytes). | `BIGINT` | | 150 | [
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] |
|
SQL | Meta Queries | `duckdb_optimizers` | The `duckdb_optimizers()` function provides metadata about the optimization rules (e.g., `expression_rewriter`, `filter_pushdown`) available in the DuckDB instance.
These can be selectively turned off using [`PRAGMA disabled_optimizers`](#docs:configuration:pragmas::selectively-disabling-optimizers).
<div class="narrow_table"></div>
| Column | Description | Type |
|:-|:---|:-|
| `name` | The name of the optimization rule. | `VARCHAR` | | 111 | [
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|
SQL | Meta Queries | `duckdb_schemas` | The `duckdb_schemas()` function provides metadata about the schemas available in the DuckDB instance.
| Column | Description | Type |
|:-|:---|:-|
| `oid` | Internal identifier of the schema object. | `BIGINT` |
| `database_name` | The name of the database that contains this schema. | `VARCHAR` |
| `database_oid` | Internal identifier of the database containing the schema. | `BIGINT` |
| `schema_name` | The SQL name of the schema. | `VARCHAR` |
| `internal` | `true` if this is an internal (built-in) schema, `false` if this is a user-defined schema. | `BOOLEAN` |
| `sql` | Always `NULL`| `VARCHAR` |
The [`information_schema.schemata`](#docs:sql:meta:information_schema::schemata-database-catalog-and-schema) system view provides a more standardized way to obtain metadata about database schemas. | 206 | [
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|
SQL | Meta Queries | `duckdb_secrets` | The `duckdb_secrets()` function provides metadata about the secrets available in the DuckDB instance.
| Column | Description | Type |
|:-|:---|:-|
| `name` | The name of the secret. | `VARCHAR` |
| `type` | The type of the secret, e.g., `S3`, `GCS`, `R2`, `AZURE`. | `VARCHAR` |
| `provider` | The provider of the secret. | `VARCHAR` |
| `persistent` | Denotes whether the secret is persisent. | `BOOLEAN` |
| `storage` | The backend for storing the secret. | `VARCHAR` |
| `scope` | The scope of the secret. | `VARCHAR[]` |
| `secret_string` | Returns the content of the secret as a string. Sensitive pieces of information, e.g., they access key, are redacted. | `VARCHAR` | | 195 | [
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|
SQL | Meta Queries | `duckdb_sequences` | The `duckdb_sequences()` function provides metadata about the sequences available in the DuckDB instance.
| Column | Description | Type |
|:-|:---|:-|
| `database_name` | The name of the database that contains this sequence | `VARCHAR` |
| `database_oid` | Internal identifier of the database containing the sequence. | `BIGINT` |
| `schema_name` | The SQL name of the schema that contains the sequence object. | `VARCHAR` |
| `schema_oid` | Internal identifier of the schema object that contains the sequence object. | `BIGINT` |
| `sequence_name` | The SQL name that identifies the sequence within the schema. | `VARCHAR` |
| `sequence_oid` | The internal identifier of this sequence object. | `BIGINT` |
| `temporary` | Whether this sequence is temporary. Temporary sequences are transient and only visible within the current connection. | `BOOLEAN` |
| `start_value` | The initial value of the sequence. This value will be returned when `nextval()` is called for the very first time on this sequence. | `BIGINT` |
| `min_value` | The minimum value of the sequence. | `BIGINT` |
| `max_value` | The maximum value of the sequence. | `BIGINT` |
| `increment_by` | The value that is added to the current value of the sequence to draw the next value from the sequence. | `BIGINT` |
| `cycle` | Whether the sequence should start over when drawing the next value would result in a value outside the range. | `BOOLEAN` |
| `last_value` | `null` if no value was ever drawn from the sequence using `nextval(...)`. `1` if a value was drawn. | `BIGINT` |
| `sql` | The definition of this object, expressed as SQL DDL-statement. | `VARCHAR` |
Attributes like `temporary`, `start_value` etc. correspond to the various options available in the [`CREATE SEQUENCE`](#docs:sql:statements:create_sequence) statement and are documented there in full. Note that the attributes will always be filled out in the `duckdb_sequences` resultset, even if they were not explicitly specified in the `CREATE SEQUENCE` statement.
> 1. The column name `last_value` suggests that it contains the last value that was drawn from the sequence, but that is not the case. It's either `null` if a value was never drawn from the sequence, or `1` (when there was a value drawn, ever, from the sequence).
>
> 2. If the sequence cycles, then the sequence will start over from the boundary of its range, not necessarily from the value specified as start value. | 574 | [
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|
SQL | Meta Queries | `duckdb_settings` | The `duckdb_settings()` function provides metadata about the settings available in the DuckDB instance.
<div class="narrow_table"></div>
| Column | Description | Type |
|:-|:---|:-|
| `name` | Name of the setting. | `VARCHAR` |
| `value` | Current value of the setting. | `VARCHAR` |
| `description` | A description of the setting. | `VARCHAR` |
| `input_type` | The logical datatype of the setting's value. | `VARCHAR` |
The various settings are described in the [configuration page](#docs:configuration:overview). | 131 | [
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|
SQL | Meta Queries | `duckdb_tables` | The `duckdb_tables()` function provides metadata about the base tables available in the DuckDB instance.
| Column | Description | Type |
|:-|:---|:-|
| `database_name` | The name of the database that contains this table | `VARCHAR` |
| `database_oid` | Internal identifier of the database containing the table. | `BIGINT` |
| `schema_name` | The SQL name of the schema that contains the base table. | `VARCHAR` |
| `schema_oid` | Internal identifier of the schema object that contains the base table. | `BIGINT` |
| `table_name` | The SQL name of the base table. | `VARCHAR` |
| `table_oid` | Internal identifier of the base table object. | `BIGINT` |
| `internal` | `false` if this is a user-defined table. | `BOOLEAN` |
| `temporary` | Whether this is a temporary table. Temporary tables are not persisted and only visible within the current connection. | `BOOLEAN` |
| `has_primary_key` | `true` if this table object defines a `PRIMARY KEY`. | `BOOLEAN` |
| `estimated_size` | The estimated number of rows in the table. | `BIGINT` |
| `column_count` | The number of columns defined by this object. | `BIGINT` |
| `index_count` | The number of indexes associated with this table. This number includes all secondary indexes, as well as internal indexes generated to maintain `PRIMARY KEY` and/or `UNIQUE` constraints. | `BIGINT` |
| `check_constraint_count` | The number of check constraints active on columns within the table. | `BIGINT` |
| `sql` | The definition of this object, expressed as SQL [`CREATE TABLE`-statement](#docs:sql:statements:create_table). | `VARCHAR` |
The [`information_schema.tables`](#docs:sql:meta:information_schema::tables-tables-and-views) system view provides a more standardized way to obtain metadata about database tables that also includes views. But the resultset returned by `duckdb_tables` contains a few columns that are not included in `information_schema.tables`. | 456 | [
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] |
|
SQL | Meta Queries | `duckdb_temporary_files` | The `duckdb_temporary_files()` function provides metadata about the temporary files DuckDB has written to disk, to offload data from memory. This function mostly exists for debugging and testing purposes.
<div class="narrow_table"></div>
| Column | Description | Type |
|:-|:---|:-|
| `path` | The name of the temporary file. | `VARCHAR` |
| `size` | The size in bytes of the temporary file. | `BIGINT` | | 102 | [
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|
SQL | Meta Queries | `duckdb_types` | The `duckdb_types()` function provides metadata about the data types available in the DuckDB instance.
| Column | Description | Type |
|:-|:---|:-|
| `database_name` | The name of the database that contains this schema. | `VARCHAR` |
| `database_oid` | Internal identifier of the database that contains the data type. | `BIGINT` |
| `schema_name` | The SQL name of the schema containing the type definition. Always `main`. | `VARCHAR` |
| `schema_oid` | Internal identifier of the schema object. | `BIGINT` |
| `type_name` | The name or alias of this data type. | `VARCHAR` |
| `type_oid` | The internal identifier of the data type object. If `NULL`, then this is an alias of the type (as identified by the value in the `logical_type` column). | `BIGINT` |
| `type_size` | The number of bytes required to represent a value of this type in memory. | `BIGINT` |
| `logical_type` | The 'canonical' name of this data type. The same `logical_type` may be referenced by several types having different `type_name`s. | `VARCHAR` |
| `type_category` | The category to which this type belongs. Data types within the same category generally expose similar behavior when values of this type are used in expression. For example, the `NUMERIC` type_category includes integers, decimals, and floating point numbers. | `VARCHAR` |
| `internal` | Whether this is an internal (built-in) or a user object. | `BOOLEAN` | | 342 | [
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|
SQL | Meta Queries | `duckdb_variables` | The `duckdb_variables()` function provides metadata about the variables available in the DuckDB instance.
| Column | Description | Type |
|:-|:---|:-|
| `name` | The name of the variable, e.g., `x`. | `VARCHAR` |
| `value` | The value of the variable, e.g. `12`. | `VARCHAR` |
| `type` | The type of the variable, e.g., `INTEGER`. | `VARCHAR` | | 101 | [
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|
SQL | Meta Queries | `duckdb_views` | The `duckdb_views()` function provides metadata about the views available in the DuckDB instance.
| Column | Description | Type |
|:-|:---|:-|
| `database_name` | The name of the database that contains this view. | `VARCHAR` |
| `database_oid` | Internal identifier of the database that contains this view. | `BIGINT` |
| `schema_name` | The SQL name of the schema where the view resides. | `VARCHAR` |
| `schema_oid` | Internal identifier of the schema object that contains the view. | `BIGINT` |
| `view_name` | The SQL name of the view object. | `VARCHAR` |
| `view_oid` | The internal identifier of this view object. | `BIGINT` |
| `internal` | `true` if this is an internal (built-in) view, `false` if this is a user-defined view. | `BOOLEAN` |
| `temporary` | `true` if this is a temporary view. Temporary views are not persistent and are only visible within the current connection. | `BOOLEAN` |
| `column_count` | The number of columns defined by this view object. | `BIGINT` |
| `sql` | The definition of this object, expressed as SQL DDL-statement. | `VARCHAR` |
The [`information_schema.tables`](#docs:sql:meta:information_schema::tables-tables-and-views) system view provides a more standardized way to obtain metadata about database views that also includes base tables. But the resultset returned by `duckdb_views` contains also definitions of internal view objects as well as a few columns that are not included in `information_schema.tables`. | 354 | [
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|
SQL | DuckDB's SQL Dialect | Overview | DuckDB's SQL dialect is based on PostgreSQL.
DuckDB tries to closely match PostgreSQL's semantics, however, some use cases require slightly different behavior.
For example, interchangeability with data frame libraries necessitates [order preservation of inserts](#docs:sql:dialect:order_preservation) to be supported by default.
These differences are documented in the pages below. | 76 | [
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|
SQL | DuckDB's SQL Dialect | Examples | The index origin is 1 for strings, lists, etc.
```sql
SELECT list[1] AS element
FROM (SELECT ['first', 'second', 'third'] AS list);
```
```text
┌─────────┐
│ element │
│ varchar │
├─────────┤
│ first │
└─────────┘
```
The index origin is 0 for JSON objects.
```sql
SELECT json[1] AS element
FROM (SELECT '["first", "second", "third"]'::JSON AS json);
```
```text
┌──────────┐
│ element │
│ json │
├──────────┤
│ "second" │
└──────────┘
``` | 166 | [
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SQL | DuckDB's SQL Dialect | Friendly SQL | DuckDB offers several advanced SQL features and syntactic sugar to make SQL queries more concise. We refer to these colloquially as “friendly SQL”.
> Several of these features are also supported in other systems while some are (currently) exclusive to DuckDB. | 54 | [
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|
SQL | DuckDB's SQL Dialect | Clauses | * Creating tables and inserting data:
* [`CREATE OR REPLACE TABLE`](#docs:sql:statements:create_table::create-or-replace): avoid `DROP TABLE IF EXISTS` statements in scripts.
* [`CREATE TABLE ... AS SELECT` (CTAS)](#docs:sql:statements:create_table::create-table--as-select-ctas): create a new table from the output of a table without manually defining a schema.
* [`INSERT INTO ... BY NAME`](#docs:sql:statements:insert::insert-into--by-name): this variant of the `INSERT` statement allows using column names instead of positions.
* [`INSERT OR IGNORE INTO ...`](#docs:sql:statements:insert::insert-or-ignore-into): insert the rows that do not result in a conflict due to `UNIQUE` or `PRIMARY KEY` constraints.
* [`INSERT OR REPLACE INTO ...`](#docs:sql:statements:insert::insert-or-replace-into): insert the rows that do not result in a conflict due to `UNIQUE` or `PRIMARY KEY` constraints. For those that result in a conflict, replace the columns of the existing row to the new values of the to-be-inserted row.
* Describing tables and computing statistics:
* [`DESCRIBE`](#docs:guides:meta:describe): provides a succinct summary of the schema of a table or query.
* [`SUMMARIZE`](#docs:guides:meta:summarize): returns summary statistics for a table or query.
* Making SQL clauses more compact:
* [`FROM`-first syntax with an optional `SELECT` clause](#docs:sql:query_syntax:from::from-first-syntax): DuckDB allows queries in the form of `FROM tbl` which selects all columns (performing a `SELECT *` statement).
* [`GROUP BY ALL`](#docs:sql:query_syntax:groupby::group-by-all): omit the group-by columns by inferring them from the list of attributes in the `SELECT` clause.
* [`ORDER BY ALL`](#docs:sql:query_syntax:orderby::order-by-all): shorthand to order on all columns (e.g., to ensure deterministic results).
* [`SELECT * EXCLUDE`](#docs:sql:expressions:star::exclude-clause): the `EXCLUDE` option allows excluding specific columns from the `*` expression.
* [`SELECT * REPLACE`](#docs:sql:expressions:star::replace-clause): the `REPLACE` option allows replacing specific columns with different expressions in a `*` expression.
* [`UNION BY NAME`](#docs:sql:query_syntax:setops::union-all-by-name): perform the `UNION` operation along the names of columns (instead of relying on positions).
* Transforming tables:
* [`PIVOT`](#docs:sql:statements:pivot) to turn long tables to wide tables.
* [`UNPIVOT`](#docs:sql:statements:unpivot) to turn wide tables to long tables.
* Defining SQL-level variables:
* [`SET VARIABLE`](#docs:sql:statements:set::set-variable)
* [`RESET VARIABLE`](#docs:sql:statements:set::reset-variable) | 659 | [
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|
SQL | DuckDB's SQL Dialect | Query Features | * [Column aliases in `WHERE`, `GROUP BY`, and `HAVING`](https://duckdb.org/2022/05/04/friendlier-sql#column-aliases-in-where--group-by--having)
* [`COLUMNS()` expression](#docs:sql:expressions:star::columns-expression) can be used to execute the same expression on multiple columns:
* [with regular expressions](https://duckdb.org/2023/08/23/even-friendlier-sql#columns-with-regular-expressions)
* [with `EXCLUDE` and `REPLACE`](https://duckdb.org/2023/08/23/even-friendlier-sql#columns-with-exclude-and-replace)
* [with lambda functions](https://duckdb.org/2023/08/23/even-friendlier-sql#columns-with-lambda-functions)
* Reusable column aliases, e.g.: `SELECT i + 1 AS j, j + 2 AS k FROM range(0, 3) t(i)`
* Advanced aggregation features for analytical (OLAP) queries:
* [`FILTER` clause](#docs:sql:query_syntax:filter)
* [`GROUPING SETS`, `GROUP BY CUBE`, `GROUP BY ROLLUP` clauses](#docs:sql:query_syntax:grouping_sets)
* [`count()` shorthand](#docs:sql:functions:aggregates) for `count(*)` | 303 | [
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|
SQL | DuckDB's SQL Dialect | Literals and Identifiers | * [Case-insensitivity while maintaining case of entities in the catalog](#docs:sql:dialect:keywords_and_identifiers::case-sensitivity-of-identifiers)
* [Deduplicating identifiers](#docs:sql:dialect:keywords_and_identifiers::deduplicating-identifiers)
* [Underscores as digit separators in numeric literals](#docs:sql:dialect:keywords_and_identifiers::numeric-literals) | 92 | [
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|
SQL | DuckDB's SQL Dialect | Data Types | * [`MAP` data type](#docs:sql:data_types:map)
* [`UNION` data type](#docs:sql:data_types:union) | 33 | [
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|
SQL | DuckDB's SQL Dialect | Data Import | * [Auto-detecting the headers and schema of CSV files](#docs:data:csv:auto_detection)
* Directly querying [CSV files](#docs:data:csv:overview) and [Parquet files](#docs:data:parquet:overview)
* Loading from files using the syntax `FROM 'my.csv'`, `FROM 'my.csv.gz'`, `FROM 'my.parquet'`, etc.
* [Filename expansion (globbing)](#docs:sql:functions:pattern_matching::globbing), e.g.: `FROM 'my-data/part-*.parquet'` | 121 | [
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|
SQL | DuckDB's SQL Dialect | Functions and Expressions | * [Dot operator for function chaining](#docs:sql:functions:overview::function-chaining-via-the-dot-operator): `SELECT ('hello').upper()`
* String formatters:
the [`format()` function with the `fmt` syntax](#docs:sql:functions:char::fmt-syntax) and
the [`printf() function`](#docs:sql:functions:char::printf-syntax)
* [List comprehensions](https://duckdb.org/2023/08/23/even-friendlier-sql#list-comprehensions)
* [List slicing](https://duckdb.org/2022/05/04/friendlier-sql#string-slicing)
* [String slicing](https://duckdb.org/2022/05/04/friendlier-sql#string-slicing)
* [`STRUCT.*` notation](https://duckdb.org/2022/05/04/friendlier-sql#struct-dot-notation)
* [Simple `LIST` and `STRUCT` creation](https://duckdb.org/2022/05/04/friendlier-sql#simple-list-and-struct-creation) | 236 | [
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|
SQL | DuckDB's SQL Dialect | Join Types | * [`ASOF` joins](#docs:sql:query_syntax:from::as-of-joins)
* [`LATERAL` joins](#docs:sql:query_syntax:from::lateral-joins)
* [`POSITIONAL` joins](#docs:sql:query_syntax:from::positional-joins) | 70 | [
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-0.015503146685659885,
-0.00035804545041173697,
-0.010833236388862133,
-0.022169601172208786,
-0.0216653011739254,
0.009380885399878025,
-0.05374061316251755,
-0.0017703507328405976,
-0.005103739444166422,
0.014520001597702503,
-0.02523028664290905,
-0.006131501868367195,
0.03106022998690605,
-0.037383195012807846,
0.033202219754457474,
-0.007089233957231045,
-0.035599347203969955,
-0.008142001926898956
] |
Subsets and Splits