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SQL | Statements | Options | <div class="narrow_table"></div>
| Name | Description | Type | Default value |
|-----------------------------|-----------------------------------------------------------------------------------------------------------------------------|-----------|---------------|
| `access_mode` | Access mode of the database (**AUTOMATIC**, **READ_ONLY**, or **READ_WRITE**) | `VARCHAR` | `automatic` |
| `type` | The file type (**DUCKDB** or **SQLITE**), or deduced from the input string literal (MySQL, PostgreSQL). | `VARCHAR` | `DUCKDB` |
| `block_size` | The block size of a new database file. Must be a power of two and within [16384, 262144]. Cannot be set for existing files. | `UBIGINT` | `262144` | | 170 | [
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|
SQL | Statements | Name Qualification | The fully qualified name of catalog objects contains the *catalog*, the *schema* and the *name* of the object. For example:
Attach the database `new_db`:
```sql
ATTACH 'new_db.db';
```
Create the schema `my_schema` in the database `new_db`:
```sql
CREATE SCHEMA new_db.my_schema;
```
Create the table `my_table` in the schema `my_schema`:
```sql
CREATE TABLE new_db.my_schema.my_table (col INTEGER);
```
Refer to the column `col` inside the table `my_table`:
```sql
SELECT new_db.my_schema.my_table.col FROM new_db.my_schema.my_table;
```
Note that often the fully qualified name is not required. When a name is not fully qualified, the system looks for which entries to reference using the *catalog search path*. The default catalog search path includes the system catalog, the temporary catalog and the initially attached database together with the `main` schema.
Also note the rules on [identifiers and database names in particular](#docs:sql:dialect:keywords_and_identifiers::database-names).
#### Default Database and Schema {#docs:sql:statements:attach::default-database-and-schema}
When a table is created without any qualifications, the table is created in the default schema of the default database. The default database is the database that is launched when the system is created – and the default schema is `main`.
Create the table `my_table` in the default database:
```sql
CREATE TABLE my_table (col INTEGER);
```
#### Changing the Default Database and Schema {#docs:sql:statements:attach::changing-the-default-database-and-schema}
The default database and schema can be changed using the `USE` command.
Set the default database schema to `new_db.main`:
```sql
USE new_db;
```
Set the default database schema to `new_db.my_schema`:
```sql
USE new_db.my_schema;
```
#### Resolving Conflicts {#docs:sql:statements:attach::resolving-conflicts}
When providing only a single qualification, the system can interpret this as *either* a catalog *or* a schema, as long as there are no conflicts. For example:
```sql
ATTACH 'new_db.db';
CREATE SCHEMA my_schema;
```
Creates the table `new_db.main.tbl`:
```sql
CREATE TABLE new_db.tbl (i INTEGER);
```
Creates the table `default_db.my_schema.tbl`:
```sql
CREATE TABLE my_schema.tbl (i INTEGER);
```
If we create a conflict (i.e., we have both a schema and a catalog with the same name) the system requests that a fully qualified path is used instead:
```sql
CREATE SCHEMA new_db;
CREATE TABLE new_db.tbl (i INTEGER);
```
```console
Error: Binder Error: Ambiguous reference to catalog or schema "new_db" -
use a fully qualified path like "memory.new_db"
```
#### Changing the Catalog Search Path {#docs:sql:statements:attach::changing-the-catalog-search-path}
The catalog search path can be adjusted by setting the `search_path` configuration option, which uses a comma-separated list of values that will be on the search path. The following example demonstrates searching in two databases:
```sql
ATTACH ':memory:' AS db1;
ATTACH ':memory:' AS db2;
CREATE table db1.tbl1 (i INTEGER);
CREATE table db2.tbl2 (j INTEGER);
```
Reference the tables using their fully qualified name:
```sql
SELECT * FROM db1.tbl1;
SELECT * FROM db2.tbl2;
```
Or set the search path and reference the tables using their name:
```sql
SET search_path = 'db1,db2';
SELECT * FROM tbl1;
SELECT * FROM tbl2;
``` | 812 | [
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|
SQL | Statements | Transactional Semantics | When running queries on multiple databases, the system opens separate transactions per database. The transactions are started *lazily* by default – when a given database is referenced for the first time in a query, a transaction for that database will be started. `SET immediate_transaction_mode = true` can be toggled to change this behavior to eagerly start transactions in all attached databases instead.
While multiple transactions can be active at a time – the system only supports *writing* to a single attached database in a single transaction. If you try to write to multiple attached databases in a single transaction the following error will be thrown:
```console
Attempting to write to database "db2" in a transaction that has already modified database "db1" -
a single transaction can only write to a single attached database.
```
The reason for this restriction is that the system does not maintain atomicity for transactions across attached databases. Transactions are only atomic *within* each database file. By restricting the global transaction to write to only a single database file the atomicity guarantees are maintained. | 215 | [
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|
SQL | Statements | CALL Statement | The `CALL` statement invokes the given table function and returns the results. | 15 | [
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] |
|
SQL | Statements | Examples | Invoke the 'duckdb_functions' table function:
```sql
CALL duckdb_functions();
```
Invoke the 'pragma_table_info' table function:
```sql
CALL pragma_table_info('pg_am');
```
Select only the functions where the name starts with `ST_`:
```sql
SELECT function_name, parameters, parameter_types, return_type
FROM duckdb_functions()
WHERE function_name LIKE 'ST_%';
``` | 88 | [
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|
SQL | Statements | CHECKPOINT Statement | The `CHECKPOINT` statement synchronizes data in the write-ahead log (WAL) to the database data file. For in-memory
databases this statement will succeed with no effect. | 38 | [
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|
SQL | Statements | Examples | Synchronize data in the default database:
```sql
CHECKPOINT;
```
Synchronize data in the specified database:
```sql
CHECKPOINT file_db;
```
Abort any in-progress transactions to synchronize the data:
```sql
FORCE CHECKPOINT;
``` | 55 | [
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|
SQL | Statements | Syntax | <div id="rrdiagram1"></div>
Checkpoint operations happen automatically based on the WAL size (see [Configuration](#docs:configuration:overview)). This
statement is for manual checkpoint actions. | 41 | [
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|
SQL | Statements | Behavior | The default `CHECKPOINT` command will fail if there are any running transactions. Including `FORCE` will abort any
transactions and execute the checkpoint operation.
Also see the related [`PRAGMA` option](#docs:configuration:pragmas::force-checkpoint) for further behavior modification.
#### Reclaiming Space {#docs:sql:statements:checkpoint::reclaiming-space}
When performing a checkpoint (automatic or otherwise), the space occupied by deleted rows is partially reclaimed. Note that this does not remove all deleted rows, but rather merges row groups that have a significant amount of deletes together. In the current implementation this requires ~25% of rows to be deleted in adjacent row groups.
When running in in-memory mode, checkpointing has no effect, hence it does not reclaim space after deletes in in-memory databases.
> **Warning. ** The [`VACUUM` statement](#docs:sql:statements:vacuum) does _not_ trigger vacuuming deletes and hence does not reclaim space. | 212 | [
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|
SQL | Statements | COMMENT ON Statement | The `COMMENT ON` statement allows adding metadata to catalog entries (tables, columns, etc.).
It follows the [PostgreSQL syntax](https://www.postgresql.org/docs/16/sql-comment.html). | 41 | [
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|
SQL | Statements | Examples | Create a comment on a `TABLE`:
```sql
COMMENT ON TABLE test_table IS 'very nice table';
```
Create a comment on a `COLUMN`:
```sql
COMMENT ON COLUMN test_table.test_table_column IS 'very nice column';
```
Create a comment on a `VIEW`:
```sql
COMMENT ON VIEW test_view IS 'very nice view';
```
Create a comment on an `INDEX`:
```sql
COMMENT ON INDEX test_index IS 'very nice index';
```
Create a comment on a `SEQUENCE`:
```sql
COMMENT ON SEQUENCE test_sequence IS 'very nice sequence';
```
Create a comment on a `TYPE`:
```sql
COMMENT ON TYPE test_type IS 'very nice type';
```
Create a comment on a `MACRO`:
```sql
COMMENT ON MACRO test_macro IS 'very nice macro';
```
Create a comment on a `MACRO TABLE`:
```sql
COMMENT ON MACRO TABLE test_table_macro IS 'very nice table macro';
```
To unset a comment, set it to `NULL`, e.g.:
```sql
COMMENT ON TABLE test_table IS NULL;
``` | 240 | [
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|
SQL | Statements | Reading Comments | Comments can be read by querying the `comment` column of the respective [metadata functions](#docs:sql:meta:duckdb_table_functions):
List comments on `TABLE`s:
```sql
SELECT comment FROM duckdb_tables();
```
List comments on `COLUMN`s:
```sql
SELECT comment FROM duckdb_columns();
```
List comments on `VIEW`s:
```sql
SELECT comment FROM duckdb_views();
```
List comments on `INDEX`s:
```sql
SELECT comment FROM duckdb_indexes();
```
List comments on `SEQUENCE`s:
```sql
SELECT comment FROM duckdb_sequences();
```
List comments on `TYPE`s:
```sql
SELECT comment FROM duckdb_types();
```
List comments on `MACRO`s:
```sql
SELECT comment FROM duckdb_functions();
```
List comments on `MACRO TABLE`s:
```sql
SELECT comment FROM duckdb_functions();
``` | 194 | [
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SQL | Statements | Limitations | The `COMMENT ON` statement currently has the following limitations:
* It is not possible to comment on schemas or databases.
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|
SQL | Statements | Examples | Read a CSV file into the `lineitem` table, using auto-detected CSV options:
```sql
COPY lineitem FROM 'lineitem.csv';
```
Read a CSV file into the `lineitem` table, using manually specified CSV options:
```sql
COPY lineitem FROM 'lineitem.csv' (DELIMITER '|');
```
Read a Parquet file into the `lineitem` table:
```sql
COPY lineitem FROM 'lineitem.pq' (FORMAT PARQUET);
```
Read a JSON file into the `lineitem` table, using auto-detected options:
```sql
COPY lineitem FROM 'lineitem.json' (FORMAT JSON, AUTO_DETECT true);
```
Read a CSV file into the `lineitem` table, using double quotes:
```sql
COPY lineitem FROM "lineitem.csv";
```
Read a CSV file into the `lineitem` table, omitting quotes:
```sql
COPY lineitem FROM lineitem.csv;
```
Write a table to a CSV file:
```sql
COPY lineitem TO 'lineitem.csv' (FORMAT CSV, DELIMITER '|', HEADER);
```
Write a table to a CSV file, using double quotes:
```sql
COPY lineitem TO "lineitem.csv";
```
Write a table to a CSV file, omitting quotes:
```sql
COPY lineitem TO lineitem.csv;
```
Write the result of a query to a Parquet file:
```sql
COPY (SELECT l_orderkey, l_partkey FROM lineitem) TO 'lineitem.parquet' (COMPRESSION ZSTD);
```
Copy the entire content of database `db1` to database `db2`:
```sql
COPY FROM DATABASE db1 TO db2;
```
Copy only the schema (catalog elements) but not any data:
```sql
COPY FROM DATABASE db1 TO db2 (SCHEMA);
``` | 401 | [
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|
SQL | Statements | Overview | `COPY` moves data between DuckDB and external files. `COPY ... FROM` imports data into DuckDB from an external file. `COPY ... TO` writes data from DuckDB to an external file. The `COPY` command can be used for `CSV`, `PARQUET` and `JSON` files. | 65 | [
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|
SQL | Statements | `COPY ... FROM` | `COPY ... FROM` imports data from an external file into an existing table. The data is appended to whatever data is in the table already. The amount of columns inside the file must match the amount of columns in the table `table_name`, and the contents of the columns must be convertible to the column types of the table. In case this is not possible, an error will be thrown.
If a list of columns is specified, `COPY` will only copy the data in the specified columns from the file. If there are any columns in the table that are not in the column list, `COPY ... FROM` will insert the default values for those columns
Copy the contents of a comma-separated file `test.csv` without a header into the table `test`:
```sql
COPY test FROM 'test.csv';
```
Copy the contents of a comma-separated file with a header into the `category` table:
```sql
COPY category FROM 'categories.csv' (HEADER);
```
Copy the contents of `lineitem.tbl` into the `lineitem` table, where the contents are delimited by a pipe character (` |`):
```sql
COPY lineitem FROM 'lineitem.tbl' (DELIMITER '|');
```
Copy the contents of `lineitem.tbl` into the `lineitem` table, where the delimiter, quote character, and presence of a header are automatically detected:
```sql
COPY lineitem FROM 'lineitem.tbl' (AUTO_DETECT true);
```
Read the contents of a comma-separated file `names.csv` into the `name` column of the `category` table. Any other columns of this table are filled with their default value:
```sql
COPY category(name) FROM 'names.csv';
```
Read the contents of a Parquet file `lineitem.parquet` into the `lineitem` table:
```sql
COPY lineitem FROM 'lineitem.parquet' (FORMAT PARQUET);
```
Read the contents of a newline-delimited JSON file `lineitem.ndjson` into the `lineitem` table:
```sql
COPY lineitem FROM 'lineitem.ndjson' (FORMAT JSON);
```
Read the contents of a JSON file `lineitem.json` into the `lineitem` table:
```sql
COPY lineitem FROM 'lineitem.json' (FORMAT JSON, ARRAY true);
```
#### Syntax {#docs:sql:statements:copy::syntax}
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|
SQL | Statements | `COPY ... TO` | `COPY ... TO` exports data from DuckDB to an external CSV or Parquet file. It has mostly the same set of options as `COPY ... FROM`, however, in the case of `COPY ... TO` the options specify how the file should be written to disk. Any file created by `COPY ... TO` can be copied back into the database by using `COPY ... FROM` with a similar set of options.
The `COPY ... TO` function can be called specifying either a table name, or a query. When a table name is specified, the contents of the entire table will be written into the resulting file. When a query is specified, the query is executed and the result of the query is written to the resulting file.
Copy the contents of the `lineitem` table to a CSV file with a header:
```sql
COPY lineitem TO 'lineitem.csv';
```
Copy the contents of the `lineitem` table to the file `lineitem.tbl`, where the columns are delimited by a pipe character (` |`), including a header line:
```sql
COPY lineitem TO 'lineitem.tbl' (DELIMITER '|');
```
Use tab separators to create a TSV file without a header:
```sql
COPY lineitem TO 'lineitem.tsv' (DELIMITER '\t', HEADER false);
```
Copy the l_orderkey column of the `lineitem` table to the file `orderkey.tbl`:
```sql
COPY lineitem(l_orderkey) TO 'orderkey.tbl' (DELIMITER '|');
```
Copy the result of a query to the file `query.csv`, including a header with column names:
```sql
COPY (SELECT 42 AS a, 'hello' AS b) TO 'query.csv' (DELIMITER ',');
```
Copy the result of a query to the Parquet file `query.parquet`:
```sql
COPY (SELECT 42 AS a, 'hello' AS b) TO 'query.parquet' (FORMAT PARQUET);
```
Copy the result of a query to the newline-delimited JSON file `query.ndjson`:
```sql
COPY (SELECT 42 AS a, 'hello' AS b) TO 'query.ndjson' (FORMAT JSON);
```
Copy the result of a query to the JSON file `query.json`:
```sql
COPY (SELECT 42 AS a, 'hello' AS b) TO 'query.json' (FORMAT JSON, ARRAY true);
```
#### `COPY ... TO` Options {#docs:sql:statements:copy::copy--to-options}
Zero or more copy options may be provided as a part of the copy operation. The `WITH` specifier is optional, but if any options are specified, the parentheses are required. Parameter values can be passed in with or without wrapping in single quotes.
Any option that is a Boolean can be enabled or disabled in multiple ways. You can write `true`, `ON`, or `1` to enable the option, and `false`, `OFF`, or `0` to disable it. The `BOOLEAN` value can also be omitted, e.g., by only passing `(HEADER)`, in which case `true` is assumed.
The below options are applicable to all formats written with `COPY`.
| Name | Description | Type | Default |
|:--|:-----|:-|:-|
| `FORMAT` | Specifies the copy function to use. The default is selected from the file extension (e.g., `.parquet` results in a Parquet file being written/read). If the file extension is unknown `CSV` is selected. Vanilla DuckDB provides `CSV`, `PARQUET` and `JSON` but additional copy functions can be added by [`extensions`](#docs:extensions:overview). | `VARCHAR` | `auto` |
| `USE_TMP_FILE` | Whether or not to write to a temporary file first if the original file exists (` target.csv.tmp`). This prevents overwriting an existing file with a broken file in case the writing is cancelled. | `BOOL` | `auto` |
| `OVERWRITE_OR_IGNORE` | Whether or not to allow overwriting files if they already exist. Only has an effect when used with `partition_by`. | `BOOL` | `false` |
| `OVERWRITE` | When set, all existing files inside targeted directories will be removed (not supported on remote filesystems). Only has an effect when used with `partition_by`. | `BOOL` | `false` |
| `APPEND` | When set, in the event a filename pattern is generated that already exists, the path will be regenerated to ensure no existing files are overwritten. Only has an effect when used with `partition_by`. | `BOOL` | `false` |
| `FILENAME_PATTERN` | Set a pattern to use for the filename, can optionally contain `{uuid}` to be filled in with a generated UUID or `{id}` which is replaced by an incrementing index. Only has an effect when used with `partition_by`. | `VARCHAR` | `auto` |
| `FILE_EXTENSION` | Set the file extension that should be assigned to the generated file(s). | `VARCHAR` | `auto` |
| `PER_THREAD_OUTPUT` | Generate one file per thread, rather than one file in total. This allows for faster parallel writing. | `BOOL` | `false` |
| `FILE_SIZE_BYTES` | If this parameter is set, the `COPY` process creates a directory which will contain the exported files. If a file exceeds the set limit (specified as bytes such as `1000` or in human-readable format such as `1k`), the process creates a new file in the directory. This parameter works in combination with `PER_THREAD_OUTPUT`. Note that the size is used as an approximation, and files can be occasionally slightly over the limit. | `VARCHAR` or `BIGINT` | (empty) |
| `PARTITION_BY` | The columns to partition by using a Hive partitioning scheme, see the [partitioned writes section](#docs:data:partitioning:partitioned_writes). | `VARCHAR[]` | (empty) |
| `RETURN_FILES` | Whether or not to include the created filepath(s) (as a `Files VARCHAR[]` column) in the query result. | `BOOL` | `false` |
| `WRITE_PARTITION_COLUMNS` | Whether or not to write partition columns into files. Only has an effect when used with `partition_by`. | `BOOL` | `false` |
#### Syntax {#docs:sql:statements:copy::syntax}
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|
SQL | Statements | `COPY FROM DATABASE ... TO` | The `COPY FROM DATABASE ... TO` statement copies the entire content from one attached database to another attached database. This includes the schema, including constraints, indexes, sequences, macros, and the data itself.
```sql
ATTACH 'db1.db' AS db1;
CREATE TABLE db1.tbl AS SELECT 42 AS x, 3 AS y;
CREATE MACRO db1.two_x_plus_y(x, y) AS 2 * x + y;
ATTACH 'db2.db' AS db2;
COPY FROM DATABASE db1 TO db2;
SELECT db2.two_x_plus_y(x, y) AS z FROM db2.tbl;
```
| z |
|---:|
| 87 |
To only copy the **schema** of `db1` to `db2` but omit copying the data, add `SCHEMA` to the statement:
```sql
COPY FROM DATABASE db1 TO db2 (SCHEMA);
```
#### Syntax {#docs:sql:statements:copy::syntax}
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|
SQL | Statements | Format-Specific Options | #### CSV Options {#docs:sql:statements:copy::csv-options}
The below options are applicable when writing CSV files.
| Name | Description | Type | Default |
|:--|:-----|:-|:-|
| `COMPRESSION` | The compression type for the file. By default this will be detected automatically from the file extension (e.g., `file.csv.gz` will use gzip, `file.csv` will use `none`). Options are `none`, `gzip`, `zstd`. | `VARCHAR` | `auto` |
| `DATEFORMAT` | Specifies the date format to use when writing dates. See [Date Format](#docs:sql:functions:dateformat) | `VARCHAR` | (empty) |
| `DELIM` or `SEP` | The character that is written to separate columns within each row. | `VARCHAR` | `,` |
| `ESCAPE` | The character that should appear before a character that matches the `quote` value. | `VARCHAR` | `"` |
| `FORCE_QUOTE` | The list of columns to always add quotes to, even if not required. | `VARCHAR[]` | `[]` |
| `HEADER` | Whether or not to write a header for the CSV file. | `BOOL` | `true` |
| `NULLSTR` | The string that is written to represent a `NULL` value. | `VARCHAR` | (empty) |
| `QUOTE` | The quoting character to be used when a data value is quoted. | `VARCHAR` | `"` |
| `TIMESTAMPFORMAT` | Specifies the date format to use when writing timestamps. See [Date Format](#docs:sql:functions:dateformat) | `VARCHAR` | (empty) |
#### Parquet Options {#docs:sql:statements:copy::parquet-options}
The below options are applicable when writing Parquet files.
| Name | Description | Type | Default |
|:--|:-----|:-|:-|
| `COMPRESSION` | The compression format to use (` uncompressed`, `snappy`, `gzip` or `zstd`). | `VARCHAR` | `snappy` |
| `COMPRESSION_LEVEL` | Compression level, set between 1 (lowest compression, fastest) and 22 (highest compression, slowest). Only supported for zstd compression. | `BIGINT` | `3` |
| `FIELD_IDS` | The `field_id` for each column. Pass `auto` to attempt to infer automatically. | `STRUCT` | (empty) |
| `ROW_GROUP_SIZE_BYTES` | The target size of each row group. You can pass either a human-readable string, e.g., `2MB`, or an integer, i.e., the number of bytes. This option is only used when you have issued `SET preserve_insertion_order = false;`, otherwise, it is ignored. | `BIGINT` | `row_group_size * 1024` |
| `ROW_GROUP_SIZE` | The target size, i.e., number of rows, of each row group. | `BIGINT` | 122880 |
| `ROW_GROUPS_PER_FILE` | Create a new Parquet file if the current one has a specified number of row groups. If multiple threads are active, the number of row groups in a file may slightly exceed the specified number of row groups to limit the amount of locking – similarly to the behaviour of `FILE_SIZE_BYTES`. However, if `per_thread_output` is set, only one thread writes to each file, and it becomes accurate again. | `BIGINT` | (empty) |
Some examples of `FIELD_IDS` are as follows.
Assign `field_ids` automatically:
```sql
COPY
(SELECT 128 AS i)
TO 'my.parquet'
(FIELD_IDS 'auto');
```
Sets the `field_id` of column `i` to 42:
```sql
COPY
(SELECT 128 AS i)
TO 'my.parquet'
(FIELD_IDS {i: 42});
```
Sets the `field_id` of column `i` to 42, and column `j` to 43:
```sql
COPY
(SELECT 128 AS i, 256 AS j)
TO 'my.parquet'
(FIELD_IDS {i: 42, j: 43});
```
Sets the `field_id` of column `my_struct` to 43, and column `i` (nested inside `my_struct`) to 43:
```sql
COPY
(SELECT {i: 128} AS my_struct)
TO 'my.parquet'
(FIELD_IDS {my_struct: {__duckdb_field_id: 42, i: 43}});
```
Sets the `field_id` of column `my_list` to 42, and column `element` (default name of list child) to 43:
```sql
COPY
(SELECT [128, 256] AS my_list)
TO 'my.parquet'
(FIELD_IDS {my_list: {__duckdb_field_id: 42, element: 43}});
```
Sets the `field_id` of column `my_map` to 42, and columns `key` and `value` (default names of map children) to 43 and 44:
```sql
COPY
(SELECT MAP {'key1' : 128, 'key2': 256} my_map)
TO 'my.parquet'
(FIELD_IDS {my_map: {__duckdb_field_id: 42, key: 43, value: 44}});
```
#### JSON Options {#docs:sql:statements:copy::json-options}
The below options are applicable when writing `JSON` files.
| Name | Description | Type | Default |
|:--|:-----|:-|:-|
| `ARRAY` | Whether to write a JSON array. If `true`, a JSON array of records is written, if `false`, newline-delimited JSON is written | `BOOL` | `false` |
| `COMPRESSION` | The compression type for the file. By default this will be detected automatically from the file extension (e.g., `file.json.gz` will use gzip, `file.json` will use `none`). Options are `none`, `gzip`, `zstd`. | `VARCHAR` | `auto` |
| `DATEFORMAT` | Specifies the date format to use when writing dates. See [Date Format](#docs:sql:functions:dateformat) | `VARCHAR` | (empty) |
| `TIMESTAMPFORMAT` | Specifies the date format to use when writing timestamps. See [Date Format](#docs:sql:functions:dateformat) | `VARCHAR` | (empty) | | 1,405 | [
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|
SQL | Statements | Limitations | `COPY` does not support copying between tables. To copy between tables, use an [`INSERT statement`](#docs:sql:statements:insert):
```sql
INSERT INTO tbl2
FROM tbl1;
``` | 44 | [
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|
SQL | Statements | CREATE MACRO Statement | The `CREATE MACRO` statement can create a scalar or table macro (function) in the catalog.
A macro may only be a single `SELECT` statement (similar to a `VIEW`), but it has the benefit of accepting parameters.
For a scalar macro, `CREATE MACRO` is followed by the name of the macro, and optionally parameters within a set of parentheses. The keyword `AS` is next, followed by the text of the macro. By design, a scalar macro may only return a single value.
For a table macro, the syntax is similar to a scalar macro except `AS` is replaced with `AS TABLE`. A table macro may return a table of arbitrary size and shape.
> If a `MACRO` is temporary, it is only usable within the same database connection and is deleted when the connection is closed. | 173 | [
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|
SQL | Statements | Examples | #### Scalar Macros {#docs:sql:statements:create_macro::scalar-macros}
Create a macro that adds two expressions (` a` and `b`):
```sql
CREATE MACRO add(a, b) AS a + b;
```
Create a macro for a case expression:
```sql
CREATE MACRO ifelse(a, b, c) AS CASE WHEN a THEN b ELSE c END;
```
Create a macro that does a subquery:
```sql
CREATE MACRO one() AS (SELECT 1);
```
Create a macro with a common table expression.
Note that parameter names get priority over column names. To work around this, disambiguate using the table name.
```sql
CREATE MACRO plus_one(a) AS (WITH cte AS (SELECT 1 AS a) SELECT cte.a + a FROM cte);
```
Macros are schema-dependent, and have an alias, `FUNCTION`:
```sql
CREATE FUNCTION main.my_avg(x) AS sum(x) / count(x);
```
Create a macro with default constant parameters:
```sql
CREATE MACRO add_default(a, b := 5) AS a + b;
```
Create a macro `arr_append` (with a functionality equivalent to `array_append`):
```sql
CREATE MACRO arr_append(l, e) AS list_concat(l, list_value(e));
```
#### Table Macros {#docs:sql:statements:create_macro::table-macros}
Create a table macro without parameters:
```sql
CREATE MACRO static_table() AS TABLE
SELECT 'Hello' AS column1, 'World' AS column2;
```
Create a table macro with parameters (that can be of any type):
```sql
CREATE MACRO dynamic_table(col1_value, col2_value) AS TABLE
SELECT col1_value AS column1, col2_value AS column2;
```
Create a table macro that returns multiple rows. It will be replaced if it already exists, and it is temporary (will be automatically deleted when the connection ends):
```sql
CREATE OR REPLACE TEMP MACRO dynamic_table(col1_value, col2_value) AS TABLE
SELECT col1_value AS column1, col2_value AS column2
UNION ALL
SELECT 'Hello' AS col1_value, 456 AS col2_value;
```
Pass an argument as a list:
```sql
CREATE MACRO get_users(i) AS TABLE
SELECT * FROM users WHERE uid IN (SELECT unnest(i));
```
An example for how to use the `get_users` table macro is the following:
```sql
CREATE TABLE users AS
SELECT *
FROM (VALUES (1, 'Ada'), (2, 'Bob'), (3, 'Carl'), (4, 'Dan'), (5, 'Eve')) t(uid, name);
SELECT * FROM get_users([1, 5]);
```
To define macros on arbitrary tables, use the [`query_table` function](#docs:guides:sql_features:query_and_query_table_functions). For example, the following macro computes a column-wise checksum on a table:
```sql
CREATE MACRO checksum(table_name) AS TABLE
SELECT bit_xor(md5_number(COLUMNS(*)::VARCHAR))
FROM query_table(table_name);
CREATE TABLE tbl AS SELECT unnest([42, 43]) AS x, 100 AS y;
SELECT * FROM checksum('tbl');
``` | 712 | [
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|
SQL | Statements | Overloading | It is possible to overload a macro based on the amount of parameters it takes, this works for both scalar and table macros.
By providing overloads we can have both `add_x(a, b)` and `add_x(a, b, c)` with different function bodies.
```sql
CREATE MACRO add_x
(a, b) AS a + b,
(a, b, c) AS a + b + c;
```
```sql
SELECT
add_x(21, 42) AS two_args,
add_x(21, 42, 21) AS three_args;
```
| two_args | three_args |
|----------|------------|
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|
SQL | Statements | Syntax | <div id="rrdiagram"></div>
Macros allow you to create shortcuts for combinations of expressions.
```sql
CREATE MACRO add(a) AS a + b;
```
```console
Binder Error: Referenced column "b" not found in FROM clause!
```
This works:
```sql
CREATE MACRO add(a, b) AS a + b;
```
Usage example:
```sql
SELECT add(1, 2) AS x;
```
| x |
|--:|
| 3 |
However, this fails:
```sql
SELECT add('hello', 3);
```
```console
Binder Error: Could not choose a best candidate function for the function call "+(STRING_LITERAL, INTEGER_LITERAL)". In order to select one, please add explicit type casts.
Candidate functions:
+(DATE, INTEGER) -> DATE
+(INTEGER, INTEGER) -> INTEGER
```
Macros can have default parameters.
Unlike some languages, default parameters must be named
when the macro is invoked.
`b` is a default parameter:
```sql
CREATE MACRO add_default(a, b := 5) AS a + b;
```
The following will result in 42:
```sql
SELECT add_default(37);
```
The following will throw an error:
```sql
SELECT add_default(40, 2);
```
```console
Binder Error: Macro function 'add_default(a)' requires a single positional argument, but 2 positional arguments were provided.
```
Default parameters must used by assigning them like the following:
```sql
SELECT add_default(40, b := 2) AS x;
```
| x |
|---:|
| 42 |
However, the following fails:
```sql
SELECT add_default(b := 2, 40);
```
```console
Binder Error: Positional parameters cannot come after parameters with a default value!
```
The order of default parameters does not matter:
```sql
CREATE MACRO triple_add(a, b := 5, c := 10) AS a + b + c;
```
```sql
SELECT triple_add(40, c := 1, b := 1) AS x;
```
| x |
|---:|
| 42 |
When macros are used, they are expanded (i.e., replaced with the original expression), and the parameters within the expanded expression are replaced with the supplied arguments. Step by step:
The `add` macro we defined above is used in a query:
```sql
SELECT add(40, 2) AS x;
```
Internally, add is replaced with its definition of `a + b`:
```sql
SELECT a + b; AS x
```
Then, the parameters are replaced by the supplied arguments:
```sql
SELECT 40 + 2 AS x;
``` | 600 | [
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|
SQL | Statements | Limitations | #### Using Named Parameters {#docs:sql:statements:create_macro::using-named-parameters}
Currently, positional macro parameters can only be used positionally, and named parameters can only be used by supplying their name. Therefore, the following will not work:
```sql
CREATE MACRO my_macro(a, b := 42) AS (a + b);
SELECT my_macro(32, 52);
```
```console
Error: Binder Error: Macro function 'my_macro(a)' requires a single positional argument, but 2 positional arguments were provided.
```
#### Using Subquery Macros {#docs:sql:statements:create_macro::using-subquery-macros}
If a `MACRO` is defined as a subquery, it cannot be invoked in a table function. DuckDB will return the following error:
```console
Binder Error: Table function cannot contain subqueries
```
#### Overloads {#docs:sql:statements:create_macro::overloads}
Overloads for macro functions have to be set at creation, it is not possible to define a macro by the same name twice without first removing the first definition. | 235 | [
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|
SQL | Statements | CREATE SCHEMA Statement | The `CREATE SCHEMA` statement creates a schema in the catalog. The default schema is `main`. | 21 | [
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|
SQL | Statements | Examples | Create a schema:
```sql
CREATE SCHEMA s1;
```
Create a schema if it does not exist yet:
```sql
CREATE SCHEMA IF NOT EXISTS s2;
```
Create table in the schemas:
```sql
CREATE TABLE s1.t (id INTEGER PRIMARY KEY, other_id INTEGER);
CREATE TABLE s2.t (id INTEGER PRIMARY KEY, j VARCHAR);
```
Compute a join between tables from two schemas:
```sql
SELECT *
FROM s1.t s1t, s2.t s2t
WHERE s1t.other_id = s2t.id;
``` | 125 | [
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|
SQL | Statements | CREATE SECRET Statement | The `CREATE SECRET` statement creates a new secret in the [Secrets Manager](#docs:configuration:secrets_manager).
#### Syntax for `CREATE SECRET` {#docs:sql:statements:create_secret::syntax-for-create-secret}
<div id="rrdiagram1"></div>
#### Syntax for `DROP SECRET` {#docs:sql:statements:create_secret::syntax-for-drop-secret}
<div id="rrdiagram2"></div> | 94 | [
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|
SQL | Statements | CREATE SEQUENCE Statement | The `CREATE SEQUENCE` statement creates a new sequence number generator.
#### Examples {#docs:sql:statements:create_sequence::examples}
Generate an ascending sequence starting from 1:
```sql
CREATE SEQUENCE serial;
```
Generate sequence from a given start number:
```sql
CREATE SEQUENCE serial START 101;
```
Generate odd numbers using `INCREMENT BY`:
```sql
CREATE SEQUENCE serial START WITH 1 INCREMENT BY 2;
```
Generate a descending sequence starting from 99:
```sql
CREATE SEQUENCE serial START WITH 99 INCREMENT BY -1 MAXVALUE 99;
```
By default, cycles are not allowed and will result in error, e.g.:
```console
Sequence Error: nextval: reached maximum value of sequence "serial" (10)
```
```sql
CREATE SEQUENCE serial START WITH 1 MAXVALUE 10;
```
`CYCLE` allows cycling through the same sequence repeatedly:
```sql
CREATE SEQUENCE serial START WITH 1 MAXVALUE 10 CYCLE;
```
#### Creating and Dropping Sequences {#docs:sql:statements:create_sequence::creating-and-dropping-sequences}
Sequences can be created and dropped similarly to other catalogue items.
Overwrite an existing sequence:
```sql
CREATE OR REPLACE SEQUENCE serial;
```
Only create sequence if no such sequence exists yet:
```sql
CREATE SEQUENCE IF NOT EXISTS serial;
```
Remove sequence:
```sql
DROP SEQUENCE serial;
```
Remove sequence if exists:
```sql
DROP SEQUENCE IF EXISTS serial;
```
#### Using Sequences for Primary Keys {#docs:sql:statements:create_sequence::using-sequences-for-primary-keys}
Sequences can provide an integer primary key for a table. For example:
```sql
CREATE SEQUENCE id_sequence START 1;
CREATE TABLE tbl (id INTEGER DEFAULT nextval('id_sequence'), s VARCHAR);
INSERT INTO tbl (s) VALUES ('hello'), ('world');
SELECT * FROM tbl;
```
The script results in the following table:
| id | s |
|---:|-------|
| 1 | hello |
| 2 | world |
Sequences can also be added using the [`ALTER TABLE` statement](#docs:sql:statements:alter_table). The following example adds an `id` column and fills it with values generated by the sequence:
```sql
CREATE TABLE tbl (s VARCHAR);
INSERT INTO tbl VALUES ('hello'), ('world');
CREATE SEQUENCE id_sequence START 1;
ALTER TABLE tbl ADD COLUMN id INTEGER DEFAULT nextval('id_sequence');
SELECT * FROM tbl;
```
This script results in the same table as the previous example.
#### Selecting the Next Value {#docs:sql:statements:create_sequence::selecting-the-next-value}
To select the next number from a sequence, use `nextval`:
```sql
CREATE SEQUENCE serial START 1;
SELECT nextval('serial') AS nextval;
```
| nextval |
|--------:|
| 1 |
Using this sequence in an `INSERT` command:
```sql
INSERT INTO distributors VALUES (nextval('serial'), 'nothing');
```
#### Selecting the Current Value {#docs:sql:statements:create_sequence::selecting-the-current-value}
You may also view the current number from the sequence. Note that the `nextval` function must have already been called before calling `currval`, otherwise a Serialization Error (` sequence is not yet defined in this session`) will be thrown.
```sql
CREATE SEQUENCE serial START 1;
SELECT nextval('serial') AS nextval;
SELECT currval('serial') AS currval;
```
| currval |
|--------:|
| 1 |
#### Syntax {#docs:sql:statements:create_sequence::syntax}
<div id="rrdiagram"></div>
`CREATE SEQUENCE` creates a new sequence number generator.
If a schema name is given then the sequence is created in the specified schema. Otherwise it is created in the current schema. Temporary sequences exist in a special schema, so a schema name may not be given when creating a temporary sequence. The sequence name must be distinct from the name of any other sequence in the same schema.
After a sequence is created, you use the function `nextval` to operate on the sequence. | 917 | [
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|
SQL | Statements | Parameters | | Name | Description |
|:--|:-----|
| `CYCLE` or `NO CYCLE` | The `CYCLE` option allows the sequence to wrap around when the `maxvalue` or `minvalue` has been reached by an ascending or descending sequence respectively. If the limit is reached, the next number generated will be the `minvalue` or `maxvalue`, respectively. If `NO CYCLE` is specified, any calls to `nextval` after the sequence has reached its maximum value will return an error. If neither `CYCLE` or `NO CYCLE` are specified, `NO CYCLE` is the default. |
| `increment` | The optional clause `INCREMENT BY increment` specifies which value is added to the current sequence value to create a new value. A positive value will make an ascending sequence, a negative one a descending sequence. The default value is 1. |
| `maxvalue` | The optional clause `MAXVALUE maxvalue` determines the maximum value for the sequence. If this clause is not supplied or `NO MAXVALUE` is specified, then default values will be used. The defaults are 2^63 - 1 and -1 for ascending and descending sequences, respectively. |
| `minvalue` | The optional clause `MINVALUE minvalue` determines the minimum value a sequence can generate. If this clause is not supplied or `NO MINVALUE` is specified, then defaults will be used. The defaults are 1 and -(2^63 - 1) for ascending and descending sequences, respectively. |
| `name` | The name (optionally schema-qualified) of the sequence to be created. |
| `start` | The optional clause `START WITH start` allows the sequence to begin anywhere. The default starting value is `minvalue` for ascending sequences and `maxvalue` for descending ones. |
| `TEMPORARY` or `TEMP` | If specified, the sequence object is created only for this session, and is automatically dropped on session exit. Existing permanent sequences with the same name are not visible (in this session) while the temporary sequence exists, unless they are referenced with schema-qualified names. |
> Sequences are based on `BIGINT` arithmetic, so the range cannot exceed the range of an eight-byte integer (-9223372036854775808 to 9223372036854775807). | 495 | [
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|
SQL | Statements | CREATE TABLE Statement | The `CREATE TABLE` statement creates a table in the catalog. | 13 | [
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|
SQL | Statements | Examples | Create a table with two integer columns (` i` and `j`):
```sql
CREATE TABLE t1 (i INTEGER, j INTEGER);
```
Create a table with a primary key:
```sql
CREATE TABLE t1 (id INTEGER PRIMARY KEY, j VARCHAR);
```
Create a table with a composite primary key:
```sql
CREATE TABLE t1 (id INTEGER, j VARCHAR, PRIMARY KEY (id, j));
```
Create a table with various different types and constraints:
```sql
CREATE TABLE t1 (
i INTEGER NOT NULL,
decimalnr DOUBLE CHECK (decimalnr < 10),
date DATE UNIQUE,
time TIMESTAMP
);
```
Create table with `CREATE TABLE ... AS SELECT` (CTAS):
```sql
CREATE TABLE t1 AS
SELECT 42 AS i, 84 AS j;
```
Create a table from a CSV file (automatically detecting column names and types):
```sql
CREATE TABLE t1 AS
SELECT *
FROM read_csv('path/file.csv');
```
We can use the `FROM`-first syntax to omit `SELECT *`:
```sql
CREATE TABLE t1 AS
FROM read_csv('path/file.csv');
```
Copy the schema of `t2` to `t1`:
```sql
CREATE TABLE t1 AS
FROM t2
LIMIT 0;
``` | 280 | [
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|
SQL | Statements | Temporary Tables | Temporary tables can be created using the `CREATE TEMP TABLE` or the `CREATE TEMPORARY TABLE` statement (see diagram below).
Temporary tables are session scoped (similar to PostgreSQL for example), meaning that only the specific connection that created them can access them, and once the connection to DuckDB is closed they will be automatically dropped.
Temporary tables reside in memory rather than on disk (even when connecting to a persistent DuckDB), but if the `temp_directory` [configuration](#docs:configuration:overview) is set when connecting or with a `SET` command, data will be spilled to disk if memory becomes constrained.
Create a temporary table from a CSV file (automatically detecting column names and types):
```sql
CREATE TEMP TABLE t1 AS
SELECT *
FROM read_csv('path/file.csv');
```
Allow temporary tables to off-load excess memory to disk:
```sql
SET temp_directory = '/path/to/directory/';
```
Temporary tables are part of the `temp.main` schema. While discouraged, their names can overlap with the names of the regular database tables. In these cases, use their fully qualified name, e.g., `temp.main.t1`, for disambiguation. | 247 | [
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|
SQL | Statements | `CREATE OR REPLACE` | The `CREATE OR REPLACE` syntax allows a new table to be created or for an existing table to be overwritten by the new table. This is shorthand for dropping the existing table and then creating the new one.
Create a table with two integer columns (i and j) even if t1 already exists:
```sql
CREATE OR REPLACE TABLE t1 (i INTEGER, j INTEGER);
``` | 80 | [
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|
SQL | Statements | `IF NOT EXISTS` | The `IF NOT EXISTS` syntax will only proceed with the creation of the table if it does not already exist. If the table already exists, no action will be taken and the existing table will remain in the database.
Create a table with two integer columns (` i` and `j`) only if `t1` does not exist yet:
```sql
CREATE TABLE IF NOT EXISTS t1 (i INTEGER, j INTEGER);
``` | 89 | [
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|
SQL | Statements | `CREATE TABLE ... AS SELECT` (CTAS) | DuckDB supports the `CREATE TABLE ... AS SELECT` syntax, also known as “CTAS”:
```sql
CREATE TABLE nums AS
SELECT i
FROM range(0, 3) t(i);
```
This syntax can be used in combination with the [CSV reader](#docs:data:csv:overview), the shorthand to read directly from CSV files without specifying a function, the [`FROM`-first syntax](#docs:sql:query_syntax:from), and the [HTTP(S) support](#docs:extensions:httpfs:https), yielding concise SQL commands such as the following:
```sql
CREATE TABLE flights AS
FROM 'https://duckdb.org/data/flights.csv';
```
The CTAS construct also works with the `OR REPLACE` modifier, yielding `CREATE OR REPLACE TABLE ... AS` statements:
```sql
CREATE OR REPLACE TABLE flights AS
FROM 'https://duckdb.org/data/flights.csv';
```
Note that it is not possible to create tables using CTAS statements with constraints (primary keys, check constraints, etc.). | 221 | [
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|
SQL | Statements | Check Constraints | A `CHECK` constraint is an expression that must be satisfied by the values of every row in the table.
```sql
CREATE TABLE t1 (
id INTEGER PRIMARY KEY,
percentage INTEGER CHECK (0 <= percentage AND percentage <= 100)
);
INSERT INTO t1 VALUES (1, 5);
INSERT INTO t1 VALUES (2, -1);
```
```console
Error: Constraint Error: CHECK constraint failed: t1
```
```sql
INSERT INTO t1 VALUES (3, 101);
```
```console
Error: Constraint Error: CHECK constraint failed: t1
```
```sql
CREATE TABLE t2 (id INTEGER PRIMARY KEY, x INTEGER, y INTEGER CHECK (x < y));
INSERT INTO t2 VALUES (1, 5, 10);
INSERT INTO t2 VALUES (2, 5, 3);
```
```console
Error: Constraint Error: CHECK constraint failed: t2
```
`CHECK` constraints can also be added as part of the `CONSTRAINTS` clause:
```sql
CREATE TABLE t3 (
id INTEGER PRIMARY KEY,
x INTEGER,
y INTEGER,
CONSTRAINT x_smaller_than_y CHECK (x < y)
);
INSERT INTO t3 VALUES (1, 5, 10);
INSERT INTO t3 VALUES (2, 5, 3);
```
```console
Error: Constraint Error: CHECK constraint failed: t3
``` | 294 | [
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|
SQL | Statements | Foreign Key Constraints | A `FOREIGN KEY` is a column (or set of columns) that references another table's primary key. Foreign keys check referential integrity, i.e., the referred primary key must exist in the other table upon insertion.
```sql
CREATE TABLE t1 (id INTEGER PRIMARY KEY, j VARCHAR);
CREATE TABLE t2 (
id INTEGER PRIMARY KEY,
t1_id INTEGER,
FOREIGN KEY (t1_id) REFERENCES t1 (id)
);
```
Example:
```sql
INSERT INTO t1 VALUES (1, 'a');
INSERT INTO t2 VALUES (1, 1);
INSERT INTO t2 VALUES (2, 2);
```
```console
Error: Constraint Error: Violates foreign key constraint because key "id: 2" does not exist in the referenced table
```
Foreign keys can be defined on composite primary keys:
```sql
CREATE TABLE t3 (id INTEGER, j VARCHAR, PRIMARY KEY (id, j));
CREATE TABLE t4 (
id INTEGER PRIMARY KEY, t3_id INTEGER, t3_j VARCHAR,
FOREIGN KEY (t3_id, t3_j) REFERENCES t3(id, j)
);
```
Example:
```sql
INSERT INTO t3 VALUES (1, 'a');
INSERT INTO t4 VALUES (1, 1, 'a');
INSERT INTO t4 VALUES (2, 1, 'b');
```
```console
Error: Constraint Error: Violates foreign key constraint because key "id: 1, j: b" does not exist in the referenced table
```
Foreign keys can also be defined on unique columns:
```sql
CREATE TABLE t5 (id INTEGER UNIQUE, j VARCHAR);
CREATE TABLE t6 (
id INTEGER PRIMARY KEY,
t5_id INTEGER,
FOREIGN KEY (t5_id) REFERENCES t5(id)
);
```
#### Limitations {#docs:sql:statements:create_table::limitations}
Foreign keys have the following limitations.
Foreign keys with cascading deletes (` FOREIGN KEY ... REFERENCES ... ON DELETE CASCADE`) are not supported.
Inserting into tables with self-referencing foreign keys is currently not supported and will result in the following error:
```console
Constraint Error: Violates foreign key constraint because key "..." does not exist in the referenced table.
``` | 473 | [
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|
SQL | Statements | Generated Columns | The `[type] [GENERATED ALWAYS] AS (expr) [VIRTUAL|STORED]` syntax will create a generated column. The data in this kind of column is generated from its expression, which can reference other (regular or generated) columns of the table. Since they are produced by calculations, these columns can not be inserted into directly.
DuckDB can infer the type of the generated column based on the expression's return type. This allows you to leave out the type when declaring a generated column. It is possible to explicitly set a type, but insertions into the referenced columns might fail if the type can not be cast to the type of the generated column.
Generated columns come in two varieties: `VIRTUAL` and `STORED`.
The data of virtual generated columns is not stored on disk, instead it is computed from the expression every time the column is referenced (through a select statement).
The data of stored generated columns is stored on disk and is computed every time the data of their dependencies change (through an `INSERT` / `UPDATE` / `DROP` statement).
Currently, only the `VIRTUAL` kind is supported, and it is also the default option if the last field is left blank.
The simplest syntax for a generated column:
The type is derived from the expression, and the variant defaults to `VIRTUAL`:
```sql
CREATE TABLE t1 (x FLOAT, two_x AS (2 * x));
```
Fully specifying the same generated column for completeness:
```sql
CREATE TABLE t1 (x FLOAT, two_x FLOAT GENERATED ALWAYS AS (2 * x) VIRTUAL);
``` | 333 | [
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|
SQL | Statements | CREATE VIEW Statement | The `CREATE VIEW` statement defines a new view in the catalog. | 14 | [
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|
SQL | Statements | Examples | Create a simple view:
```sql
CREATE VIEW v1 AS SELECT * FROM tbl;
```
Create a view or replace it if a view with that name already exists:
```sql
CREATE OR REPLACE VIEW v1 AS SELECT 42;
```
Create a view and replace the column names:
```sql
CREATE VIEW v1(a) AS SELECT 42;
```
The SQL query behind an existing view can be read using the [`duckdb_views()` function](#docs:sql:meta:duckdb_table_functions::duckdb_views) like this:
```sql
SELECT sql FROM duckdb_views() WHERE view_name = 'v1';
``` | 137 | [
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|
SQL | Statements | Syntax | <div id="rrdiagram"></div>
`CREATE VIEW` defines a view of a query. The view is not physically materialized. Instead, the query is run every time the view is referenced in a query.
`CREATE OR REPLACE VIEW` is similar, but if a view of the same name already exists, it is replaced.
If a schema name is given then the view is created in the specified schema. Otherwise, it is created in the current schema. Temporary views exist in a special schema, so a schema name cannot be given when creating a temporary view. The name of the view must be distinct from the name of any other view or table in the same schema. | 140 | [
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|
SQL | Statements | Examples | Create a simple `ENUM` type:
```sql
CREATE TYPE mood AS ENUM ('happy', 'sad', 'curious');
```
Create a simple `STRUCT` type:
```sql
CREATE TYPE many_things AS STRUCT(k INTEGER, l VARCHAR);
```
Create a simple `UNION` type:
```sql
CREATE TYPE one_thing AS UNION(number INTEGER, string VARCHAR);
```
Create a type alias:
```sql
CREATE TYPE x_index AS INTEGER;
``` | 101 | [
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|
SQL | Statements | Syntax | <div id="rrdiagram"></div>
The `CREATE TYPE` clause defines a new data type available to this DuckDB instance.
These new types can then be inspected in the [`duckdb_types` table](#docs:sql:meta:duckdb_table_functions::duckdb_types). | 60 | [
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|
SQL | Statements | Limitations | Extending types to support custom operators (such as the PostgreSQL `&&` operator) is not possible via plain SQL.
Instead, it requires adding additional C++ code. To do this, create an [extension](#docs:extensions:overview). | 50 | [
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SQL | Statements | DELETE Statement | The `DELETE` statement removes rows from the table identified by the table-name. | 16 | [
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|
SQL | Statements | Examples | Remove the rows matching the condition `i = 2` from the database:
```sql
DELETE FROM tbl WHERE i = 2;
```
Delete all rows in the table `tbl`:
```sql
DELETE FROM tbl;
```
The `TRUNCATE` statement removes all rows from a table, acting as an alias for `DELETE FROM` without a `WHERE` clause:
```sql
TRUNCATE tbl;
``` | 90 | [
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|
SQL | Statements | Syntax | <div id="rrdiagram"></div>
The `DELETE` statement removes rows from the table identified by the table-name.
If the `WHERE` clause is not present, all records in the table are deleted. If a `WHERE` clause is supplied, then only those rows for which the `WHERE` clause results in true are deleted. Rows for which the expression is false or NULL are retained.
The `USING` clause allows deleting based on the content of other tables or subqueries. | 103 | [
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|
SQL | Statements | Limitations on Reclaiming Memory and Disk Space | Running `DELETE` does not mean space is reclaimed. In general, rows are only marked as deleted. DuckDB reclaims space upon [performing a `CHECKPOINT`](#docs:sql:statements:checkpoint). [`VACUUM`](#docs:sql:statements:vacuum) currently does not reclaim space. | 66 | [
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|
SQL | Statements | Usage | ```sql
DESCRIBE tbl;
```
In order to summarize a query, prepend `DESCRIBE` to a query.
```sql
DESCRIBE SELECT * FROM tbl;
``` | 40 | [
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|
SQL | Statements | Alias | The `SHOW` statement is an alias for `DESCRIBE`. | 14 | [
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|
SQL | Statements | See Also | For more examples, see the [guide on `DESCRIBE`](#docs:guides:meta:describe). | 24 | [
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|
SQL | Statements | DROP Statement | The `DROP` statement removes a catalog entry added previously with the `CREATE` command. | 18 | [
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|
SQL | Statements | Examples | Delete the table with the name `tbl`:
```sql
DROP TABLE tbl;
```
Drop the view with the name `v1`; do not throw an error if the view does not exist:
```sql
DROP VIEW IF EXISTS v1;
```
Drop function `fn`:
```sql
DROP FUNCTION fn;
```
Drop index `idx`:
```sql
DROP INDEX idx;
```
Drop schema `sch`:
```sql
DROP SCHEMA sch;
```
Drop sequence `seq`:
```sql
DROP SEQUENCE seq;
```
Drop macro `mcr`:
```sql
DROP MACRO mcr;
```
Drop macro table `mt`:
```sql
DROP MACRO TABLE mt;
```
Drop type `typ`:
```sql
DROP TYPE typ;
``` | 166 | [
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|
SQL | Statements | Dependencies of Dropped Objects | DuckDB performs limited dependency tracking for some object types.
By default or if the `RESTRICT` clause is provided, the entry will not be dropped if there are any other objects that depend on it.
If the `CASCADE` clause is provided then all the objects that are dependent on the object will be dropped as well.
```sql
CREATE SCHEMA myschema;
CREATE TABLE myschema.t1 (i INTEGER);
DROP SCHEMA myschema;
```
```console
Dependency Error: Cannot drop entry `myschema` because there are entries that depend on it.
Use DROP...CASCADE to drop all dependents.
```
The `CASCADE` modifier drops both myschema and `myschema.t1`:
```sql
CREATE SCHEMA myschema;
CREATE TABLE myschema.t1 (i INTEGER);
DROP SCHEMA myschema CASCADE;
```
The following dependencies are tracked and thus will raise an error if the user tries to drop the depending object without the `CASCADE` modifier.
| Depending object type | Dependant object type |
|--|--|
| `SCHEMA` | `FUNCTION` |
| `SCHEMA` | `INDEX` |
| `SCHEMA` | `MACRO TABLE` |
| `SCHEMA` | `MACRO` |
| `SCHEMA` | `SCHEMA` |
| `SCHEMA` | `SEQUENCE` |
| `SCHEMA` | `TABLE` |
| `SCHEMA` | `TYPE` |
| `SCHEMA` | `VIEW` |
| `TABLE` | `INDEX` | | 319 | [
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|
SQL | Statements | Limitations | #### Dependencies on Views {#docs:sql:statements:drop::dependencies-on-views}
Currently, dependencies are not tracked for views. For example, if a view is created that references a table and the table is dropped, then the view will be in an invalid state:
```sql
CREATE TABLE tbl (i INTEGER);
CREATE VIEW v AS
SELECT i FROM tbl;
DROP TABLE tbl RESTRICT;
SELECT * FROM v;
```
```console
Catalog Error: Table with name tbl does not exist!
``` | 106 | [
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|
SQL | Statements | Limitations on Reclaiming Disk Space | Running `DROP TABLE` should free the memory used by the table, but not always disk space.
Even if disk space does not decrease, the free blocks will be marked as `free`.
For example, if we have a 2 GB file and we drop a 1 GB table, the file might still be 2 GB, but it should have 1 GB of free blocks in it.
To check this, use the following `PRAGMA` and check the number of `free_blocks` in the output:
```sql
PRAGMA database_size;
```
For instruction on reclaiming space after dropping a table, refer to the [“Reclaiming space” page](#docs:operations_manual:footprint_of_duckdb:reclaiming_space). | 156 | [
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|
SQL | Statements | EXPORT and IMPORT DATABASE Statements | The `EXPORT DATABASE` command allows you to export the contents of the database to a specific directory. The `IMPORT DATABASE` command allows you to then read the contents again. | 35 | [
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|
SQL | Statements | Examples | Export the database to the target directory 'target_directory' as CSV files:
```sql
EXPORT DATABASE 'target_directory';
```
Export to directory 'target_directory', using the given options for the CSV serialization:
```sql
EXPORT DATABASE 'target_directory' (FORMAT CSV, DELIMITER '|');
```
Export to directory 'target_directory', tables serialized as Parquet:
```sql
EXPORT DATABASE 'target_directory' (FORMAT PARQUET);
```
Export to directory 'target_directory', tables serialized as Parquet, compressed with ZSTD, with a row_group_size of 100,000:
```sql
EXPORT DATABASE 'target_directory' (
FORMAT PARQUET,
COMPRESSION ZSTD,
ROW_GROUP_SIZE 100_000
);
```
Reload the database again:
```sql
IMPORT DATABASE 'source_directory';
```
Alternatively, use a `PRAGMA`:
```sql
PRAGMA import_database('source_directory');
```
For details regarding the writing of Parquet files, see the [Parquet Files page in the Data Import section](#docs:data:parquet:overview::writing-to-parquet-files) and the [`COPY` Statement page](#docs:sql:statements:copy). | 249 | [
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|
SQL | Statements | `EXPORT DATABASE` | The `EXPORT DATABASE` command exports the full contents of the database – including schema information, tables, views and sequences – to a specific directory that can then be loaded again. The created directory will be structured as follows:
```text
target_directory/schema.sql
target_directory/load.sql
target_directory/t_1.csv
...
target_directory/t_n.csv
```
The `schema.sql` file contains the schema statements that are found in the database. It contains any `CREATE SCHEMA`, `CREATE TABLE`, `CREATE VIEW` and `CREATE SEQUENCE` commands that are necessary to re-construct the database.
The `load.sql` file contains a set of `COPY` statements that can be used to read the data from the CSV files again. The file contains a single `COPY` statement for every table found in the schema.
#### Syntax {#docs:sql:statements:export::syntax}
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|
SQL | Statements | `IMPORT DATABASE` | The database can be reloaded by using the `IMPORT DATABASE` command again, or manually by running `schema.sql` followed by `load.sql` to re-load the data.
#### Syntax {#docs:sql:statements:export::syntax}
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|
SQL | Statements | INSERT Statement | The `INSERT` statement inserts new data into a table.
#### Examples {#docs:sql:statements:insert::examples}
Insert the values 1, 2, 3 into `tbl`:
```sql
INSERT INTO tbl
VALUES (1), (2), (3);
```
Insert the result of a query into a table:
```sql
INSERT INTO tbl
SELECT * FROM other_tbl;
```
Insert values into the `i` column, inserting the default value into other columns:
```sql
INSERT INTO tbl (i)
VALUES (1), (2), (3);
```
Explicitly insert the default value into a column:
```sql
INSERT INTO tbl (i)
VALUES (1), (DEFAULT), (3);
```
Assuming `tbl` has a primary key/unique constraint, do nothing on conflict:
```sql
INSERT OR IGNORE INTO tbl (i)
VALUES (1);
```
Or update the table with the new values instead:
```sql
INSERT OR REPLACE INTO tbl (i)
VALUES (1);
```
#### Syntax {#docs:sql:statements:insert::syntax}
<div id="rrdiagram"></div>
`INSERT INTO` inserts new rows into a table. One can insert one or more rows specified by value expressions, or zero or more rows resulting from a query. | 282 | [
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|
SQL | Statements | Insert Column Order | It's possible to provide an optional insert column order, this can either be `BY POSITION` (the default) or `BY NAME`.
Each column not present in the explicit or implicit column list will be filled with a default value, either its declared default value or `NULL` if there is none.
If the expression for any column is not of the correct data type, automatic type conversion will be attempted.
#### `INSERT INTO ... [BY POSITION]` {#docs:sql:statements:insert::insert-into--by-position}
The order that values are inserted into the columns of the table is determined by the order that the columns were declared in.
That is, the values supplied by the `VALUES` clause or query are associated with the column list left-to-right.
This is the default option, that can be explicitly specified using the `BY POSITION` option.
For example:
```sql
CREATE TABLE tbl (a INTEGER, b INTEGER);
INSERT INTO tbl
VALUES (5, 42);
```
Specifying `BY POSITION` is optional and is equivalent to the default behavior:
```sql
INSERT INTO tbl
BY POSITION
VALUES (5, 42);
```
To use a different order, column names can be provided as part of the target, for example:
```sql
CREATE TABLE tbl (a INTEGER, b INTEGER);
INSERT INTO tbl (b, a)
VALUES (5, 42);
```
Adding `BY POSITION` results in the same behavior:
```sql
INSERT INTO tbl
BY POSITION (b, a)
VALUES (5, 42);
```
This will insert `5` into `b` and `42` into `a`.
#### `INSERT INTO ... BY NAME` {#docs:sql:statements:insert::insert-into--by-name}
Using the `BY NAME` modifier, the names of the column list of the `SELECT` statement are matched against the column names of the table to determine the order that values should be inserted into the table. This allows inserting even in cases when the order of the columns in the table differs from the order of the values in the `SELECT` statement or certain columns are missing.
For example:
```sql
CREATE TABLE tbl (a INTEGER, b INTEGER);
INSERT INTO tbl BY NAME (SELECT 42 AS b, 32 AS a);
INSERT INTO tbl BY NAME (SELECT 22 AS b);
SELECT * FROM tbl;
```
| a | b |
|-----:|---:|
| 32 | 42 |
| NULL | 22 |
It's important to note that when using `INSERT INTO ... BY NAME`, the column names specified in the `SELECT` statement must match the column names in the table. If a column name is misspelled or does not exist in the table, an error will occur. Columns that are missing from the `SELECT` statement will be filled with the default value. | 609 | [
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|
SQL | Statements | `ON CONFLICT` Clause | An `ON CONFLICT` clause can be used to perform a certain action on conflicts that arise from `UNIQUE` or `PRIMARY KEY` constraints.
An example for such a conflict is shown in the following example:
```sql
CREATE TABLE tbl (i INTEGER PRIMARY KEY, j INTEGER);
INSERT INTO tbl
VALUES (1, 42);
INSERT INTO tbl
VALUES (1, 84);
```
This raises as an error:
```console
Constraint Error: Duplicate key "i: 1" violates primary key constraint.
```
The table will contain the row that was first inserted:
```sql
SELECT * FROM tbl;
```
| i | j |
|--:|---:|
| 1 | 42 |
These error messages can be avoided by explicitly handling conflicts.
DuckDB supports two such clauses: [`ON CONFLICT DO NOTHING`](#::do-nothing-clause) and [`ON CONFLICT DO UPDATE SET ...`](#::do-update-clause-upsert).
#### `DO NOTHING` Clause {#docs:sql:statements:insert::do-nothing-clause}
The `DO NOTHING` clause causes the error(s) to be ignored, and the values are not inserted or updated.
For example:
```sql
CREATE TABLE tbl (i INTEGER PRIMARY KEY, j INTEGER);
INSERT INTO tbl
VALUES (1, 42);
INSERT INTO tbl
VALUES (1, 84)
ON CONFLICT DO NOTHING;
```
These statements finish successfully and leaves the table with the row `<i: 1, j: 42>`. | 327 | [
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|
SQL | Statements | `ON CONFLICT` Clause | `INSERT OR IGNORE INTO` | The `INSERT OR IGNORE INTO ...` statement is a shorter syntax alternative to `INSERT INTO ... ON CONFLICT DO NOTHING`.
For example, the following statements are equivalent:
```sql
INSERT OR IGNORE INTO tbl
VALUES (1, 84);
INSERT INTO tbl
VALUES (1, 84) ON CONFLICT DO NOTHING;
```
#### `DO UPDATE` Clause (Upsert) {#docs:sql:statements:insert::do-update-clause-upsert}
The `DO UPDATE` clause causes the `INSERT` to turn into an `UPDATE` on the conflicting row(s) instead.
The `SET` expressions that follow determine how these rows are updated. The expressions can use the special virtual table `EXCLUDED`, which contains the conflicting values for the row.
Optionally you can provide an additional `WHERE` clause that can exclude certain rows from the update.
The conflicts that don't meet this condition are ignored instead.
Because we need a way to refer to both the **to-be-inserted** tuple and the **existing** tuple, we introduce the special `EXCLUDED` qualifier.
When the `EXCLUDED` qualifier is provided, the reference refers to the **to-be-inserted** tuple, otherwise, it refers to the **existing** tuple.
This special qualifier can be used within the `WHERE` clauses and `SET` expressions of the `ON CONFLICT` clause.
```sql
CREATE TABLE tbl (i INTEGER PRIMARY KEY, j INTEGER);
INSERT INTO tbl VALUES (1, 42);
INSERT INTO tbl VALUES (1, 52), (1, 62) ON CONFLICT DO UPDATE SET j = EXCLUDED.j;
``` | 341 | [
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SQL | Statements | `ON CONFLICT` Clause | Examples | An example using `DO UPDATE` is the following:
```sql
CREATE TABLE tbl (i INTEGER PRIMARY KEY, j INTEGER);
INSERT INTO tbl
VALUES (1, 42);
INSERT INTO tbl
VALUES (1, 84)
ON CONFLICT DO UPDATE SET j = EXCLUDED.j;
SELECT * FROM tbl;
```
| i | j |
|--:|---:|
| 1 | 84 |
Rearranging columns and using `BY NAME` is also possible:
```sql
CREATE TABLE tbl (i INTEGER PRIMARY KEY, j INTEGER);
INSERT INTO tbl
VALUES (1, 42);
INSERT INTO tbl (j, i)
VALUES (168, 1)
ON CONFLICT DO UPDATE SET j = EXCLUDED.j;
INSERT INTO tbl
BY NAME (SELECT 1 AS i, 336 AS j)
ON CONFLICT DO UPDATE SET j = EXCLUDED.j;
SELECT * FROM tbl;
```
| i | j |
|--:|----:|
| 1 | 336 | | 214 | [
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SQL | Statements | `ON CONFLICT` Clause | `INSERT OR REPLACE INTO` | The `INSERT OR REPLACE INTO ...` statement is a shorter syntax alternative to `INSERT INTO ... DO UPDATE SET c1 = EXCLUDED.c1, c2 = EXCLUDED.c2, ...`.
That is, it updates every column of the **existing** row to the new values of the **to-be-inserted** row.
For example, given the following input table:
```sql
CREATE TABLE tbl (i INTEGER PRIMARY KEY, j INTEGER);
INSERT INTO tbl
VALUES (1, 42);
```
These statements are equivalent:
```sql
INSERT OR REPLACE INTO tbl
VALUES (1, 84);
INSERT INTO tbl
VALUES (1, 84)
ON CONFLICT DO UPDATE SET j = EXCLUDED.j;
INSERT INTO tbl (j, i)
VALUES (84, 1)
ON CONFLICT DO UPDATE SET j = EXCLUDED.j;
INSERT INTO tbl BY NAME
(SELECT 84 AS j, 1 AS i)
ON CONFLICT DO UPDATE SET j = EXCLUDED.j;
``` | 207 | [
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SQL | Statements | `ON CONFLICT` Clause | Limitations | When the `ON CONFLICT ... DO UPDATE` clause is used and a conflict occurs, DuckDB internally assigns `NULL` values to the row's columns that are unaffected by the conflict, then re-assigns their values. If the affected columns use a `NOT NULL` constraint, this will trigger a `NOT NULL constraint failed` error. For example:
```sql
CREATE TABLE t1 (id INTEGER PRIMARY KEY, val1 DOUBLE, val2 DOUBLE NOT NULL);
CREATE TABLE t2 (id INTEGER PRIMARY KEY, val1 DOUBLE);
INSERT INTO t1
VALUES (1, 2, 3);
INSERT INTO t2
VALUES (1, 5);
INSERT INTO t1 BY NAME (SELECT id, val1 FROM t2)
ON CONFLICT DO UPDATE
SET val1 = EXCLUDED.val1;
```
This fails with the following error:
```console
Constraint Error: NOT NULL constraint failed: t1.val2
```
#### Defining a Conflict Target {#docs:sql:statements:insert::defining-a-conflict-target}
A conflict target may be provided as `ON CONFLICT (conflict_target)`. This is a group of columns that an index or uniqueness/key constraint is defined on. If the conflict target is omitted, or `PRIMARY KEY` constraint(s) on the table are targeted.
Specifying a conflict target is optional unless using a [`DO UPDATE`](#::do-update-clause-upsert) and there are multiple unique/primary key constraints on the table.
```sql
CREATE TABLE tbl (i INTEGER PRIMARY KEY, j INTEGER UNIQUE, k INTEGER);
INSERT INTO tbl
VALUES (1, 20, 300);
SELECT * FROM tbl;
```
| i | j | k |
|--:|---:|----:|
| 1 | 20 | 300 |
```sql
INSERT INTO tbl
VALUES (1, 40, 700)
ON CONFLICT (i) DO UPDATE SET k = 2 * EXCLUDED.k;
```
| i | j | k |
|--:|---:|-----:|
| 1 | 20 | 1400 |
```sql
INSERT INTO tbl
VALUES (1, 20, 900)
ON CONFLICT (j) DO UPDATE SET k = 5 * EXCLUDED.k;
```
| i | j | k |
|--:|---:|-----:|
| 1 | 20 | 4500 |
When a conflict target is provided, you can further filter this with a `WHERE` clause, that should be met by all conflicts.
```sql
INSERT INTO tbl
VALUES (1, 40, 700)
ON CONFLICT (i) DO UPDATE SET k = 2 * EXCLUDED.k WHERE k < 100;
```
#### Multiple Tuples Conflicting on the Same Key {#docs:sql:statements:insert::multiple-tuples-conflicting-on-the-same-key}
Currently, DuckDB’s `ON CONFLICT DO UPDATE` feature is limited to enforce constraints between committed and newly inserted (transaction-local) data.
In other words, having multiple tuples conflicting on the same key is not supported.
If the newly inserted data has duplicate rows, an error message will be thrown, or unexpected behavior can occur.
This also includes conflicts **only** within the newly inserted data.
```sql
CREATE TABLE tbl (i INTEGER PRIMARY KEY, j INTEGER);
INSERT INTO tbl
VALUES (1, 42);
INSERT INTO tbl
VALUES (1, 84), (1, 168)
ON CONFLICT DO UPDATE SET j = j + EXCLUDED.j;
```
This returns the following message.
```console
Error: Invalid Input Error: ON CONFLICT DO UPDATE can not update the same row twice in the same command.
Ensure that no rows proposed for insertion within the same command have duplicate constrained values
```
To work around this, enforce uniqueness using [`DISTINCT ON`](#docs:sql:query_syntax:select::distinct-on-clause). For example:
```sql
CREATE TABLE tbl (i INTEGER PRIMARY KEY, j INTEGER);
INSERT INTO tbl
VALUES (1, 42);
INSERT INTO tbl
SELECT DISTINCT ON(i) i, j FROM VALUES (1, 84), (1, 168) AS t (i, j)
ON CONFLICT DO UPDATE SET j = j + EXCLUDED.j;
SELECT * FROM tbl;
```
| i | j |
|--:|----:|
| 1 | 126 | | 945 | [
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SQL | Statements | `RETURNING` Clause | The `RETURNING` clause may be used to return the contents of the rows that were inserted. This can be useful if some columns are calculated upon insert. For example, if the table contains an automatically incrementing primary key, then the `RETURNING` clause will include the automatically created primary key. This is also useful in the case of generated columns.
Some or all columns can be explicitly chosen to be returned and they may optionally be renamed using aliases. Arbitrary non-aggregating expressions may also be returned instead of simply returning a column. All columns can be returned using the `*` expression, and columns or expressions can be returned in addition to all columns returned by the `*`.
For example:
```sql
CREATE TABLE t1 (i INTEGER);
INSERT INTO t1
SELECT 42
RETURNING *;
```
<div class="narrow_table"></div>
| i |
|---:|
| 42 |
A more complex example that includes an expression in the `RETURNING` clause:
```sql
CREATE TABLE t2 (i INTEGER, j INTEGER);
INSERT INTO t2
SELECT 2 AS i, 3 AS j
RETURNING *, i * j AS i_times_j;
```
<div class="narrow_table"></div>
| i | j | i_times_j |
|--:|--:|----------:|
| 2 | 3 | 6 |
The next example shows a situation where the `RETURNING` clause is more helpful. First, a table is created with a primary key column. Then a sequence is created to allow for that primary key to be incremented as new rows are inserted. When we insert into the table, we do not already know the values generated by the sequence, so it is valuable to return them. For additional information, see the [`CREATE SEQUENCE` page](#docs:sql:statements:create_sequence).
```sql
CREATE TABLE t3 (i INTEGER PRIMARY KEY, j INTEGER);
CREATE SEQUENCE 't3_key';
INSERT INTO t3
SELECT nextval('t3_key') AS i, 42 AS j
UNION ALL
SELECT nextval('t3_key') AS i, 43 AS j
RETURNING *;
```
<div class="narrow_table"></div>
| i | j |
|--:|---:|
| 1 | 42 |
| 2 | 43 | | 499 | [
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|
SQL | Statements | PIVOT Statement | The `PIVOT` statement allows distinct values within a column to be separated into their own columns.
The values within those new columns are calculated using an aggregate function on the subset of rows that match each distinct value.
DuckDB implements both the SQL Standard `PIVOT` syntax and a simplified `PIVOT` syntax that automatically detects the columns to create while pivoting.
`PIVOT_WIDER` may also be used in place of the `PIVOT` keyword.
For details on how the `PIVOT` statement is implemented, see the [Pivot Internals site](#docs:internals:pivot::pivot).
> The [`UNPIVOT` statement](#docs:sql:statements:unpivot) is the inverse of the `PIVOT` statement. | 167 | [
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|
SQL | Statements | Simplified `PIVOT` Syntax | The full syntax diagram is below, but the simplified `PIVOT` syntax can be summarized using spreadsheet pivot table naming conventions as:
```sql
PIVOT ⟨dataset⟩
ON ⟨columns⟩
USING ⟨values⟩
GROUP BY ⟨rows⟩
ORDER BY ⟨columns_with_order_directions⟩
LIMIT ⟨number_of_rows⟩;
```
The `ON`, `USING`, and `GROUP BY` clauses are each optional, but they may not all be omitted.
#### Example Data {#docs:sql:statements:pivot::example-data}
All examples use the dataset produced by the queries below:
```sql
CREATE TABLE cities (
country VARCHAR, name VARCHAR, year INTEGER, population INTEGER
);
INSERT INTO cities VALUES
('NL', 'Amsterdam', 2000, 1005),
('NL', 'Amsterdam', 2010, 1065),
('NL', 'Amsterdam', 2020, 1158),
('US', 'Seattle', 2000, 564),
('US', 'Seattle', 2010, 608),
('US', 'Seattle', 2020, 738),
('US', 'New York City', 2000, 8015),
('US', 'New York City', 2010, 8175),
('US', 'New York City', 2020, 8772);
```
```sql
SELECT *
FROM cities;
```
<div class="narrow_table"></div>
| country | name | year | population |
|---------|---------------|-----:|-----------:|
| NL | Amsterdam | 2000 | 1005 |
| NL | Amsterdam | 2010 | 1065 |
| NL | Amsterdam | 2020 | 1158 |
| US | Seattle | 2000 | 564 |
| US | Seattle | 2010 | 608 |
| US | Seattle | 2020 | 738 |
| US | New York City | 2000 | 8015 |
| US | New York City | 2010 | 8175 |
| US | New York City | 2020 | 8772 |
#### `PIVOT ON` and `USING` {#docs:sql:statements:pivot::pivot-on-and-using}
Use the `PIVOT` statement below to create a separate column for each year and calculate the total population in each.
The `ON` clause specifies which column(s) to split into separate columns.
It is equivalent to the columns parameter in a spreadsheet pivot table.
The `USING` clause determines how to aggregate the values that are split into separate columns.
This is equivalent to the values parameter in a spreadsheet pivot table.
If the `USING` clause is not included, it defaults to `count(*)`.
```sql
PIVOT cities
ON year
USING sum(population);
```
<div class="narrow_table"></div>
| country | name | 2000 | 2010 | 2020 |
|---------|---------------|-----:|-----:|-----:|
| NL | Amsterdam | 1005 | 1065 | 1158 |
| US | Seattle | 564 | 608 | 738 |
| US | New York City | 8015 | 8175 | 8772 |
In the above example, the `sum` aggregate is always operating on a single value.
If we only want to change the orientation of how the data is displayed without aggregating, use the `first` aggregate function.
In this example, we are pivoting numeric values, but the `first` function works very well for pivoting out a text column.
(This is something that is difficult to do in a spreadsheet pivot table, but easy in DuckDB!)
This query produces a result that is identical to the one above:
```sql
PIVOT cities
ON year
USING first(population);
```
> **Note. ** The SQL syntax permits [`FILTER` clauses](#docs:sql:query_syntax:filter) with aggregate functions in the `USING` clause.
> In DuckDB, the `PIVOT` statement currently does not support these and they are silently ignored.
#### `PIVOT ON`, `USING`, and `GROUP BY` {#docs:sql:statements:pivot::pivot-on-using-and-group-by}
By default, the `PIVOT` statement retains all columns not specified in the `ON` or `USING` clauses.
To include only certain columns and further aggregate, specify columns in the `GROUP BY` clause.
This is equivalent to the rows parameter of a spreadsheet pivot table.
In the below example, the `name` column is no longer included in the output, and the data is aggregated up to the `country` level.
```sql
PIVOT cities
ON year
USING sum(population)
GROUP BY country;
```
<div class="narrow_table"></div>
| country | 2000 | 2010 | 2020 |
|---------|-----:|-----:|-----:|
| NL | 1005 | 1065 | 1158 |
| US | 8579 | 8783 | 9510 |
#### `IN` Filter for `ON` Clause {#docs:sql:statements:pivot::in-filter-for-on-clause}
To only create a separate column for specific values within a column in the `ON` clause, use an optional `IN` expression.
Let's say for example that we wanted to forget about the year 2020 for no particular reason...
```sql
PIVOT cities
ON year IN (2000, 2010)
USING sum(population)
GROUP BY country;
```
<div class="narrow_table"></div>
| country | 2000 | 2010 |
|---------|-----:|-----:|
| NL | 1005 | 1065 |
| US | 8579 | 8783 |
#### Multiple Expressions per Clause {#docs:sql:statements:pivot::multiple-expressions-per-clause}
Multiple columns can be specified in the `ON` and `GROUP BY` clauses, and multiple aggregate expressions can be included in the `USING` clause. | 1,355 | [
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|
SQL | Statements | Simplified `PIVOT` Syntax | Multiple `ON` Columns and `ON` Expressions | Multiple columns can be pivoted out into their own columns.
DuckDB will find the distinct values in each `ON` clause column and create one new column for all combinations of those values (a Cartesian product).
In the below example, all combinations of unique countries and unique cities receive their own column.
Some combinations may not be present in the underlying data, so those columns are populated with `NULL` values.
```sql
PIVOT cities
ON country, name
USING sum(population);
```
<div class="narrow_table"></div>
| year | NL_Amsterdam | NL_New York City | NL_Seattle | US_Amsterdam | US_New York City | US_Seattle |
|-----:|-------------:|------------------|------------|--------------|-----------------:|-----------:|
| 2000 | 1005 | NULL | NULL | NULL | 8015 | 564 |
| 2010 | 1065 | NULL | NULL | NULL | 8175 | 608 |
| 2020 | 1158 | NULL | NULL | NULL | 8772 | 738 |
To pivot only the combinations of values that are present in the underlying data, use an expression in the `ON` clause.
Multiple expressions and/or columns may be provided.
Here, `country` and `name` are concatenated together and the resulting concatenations each receive their own column.
Any arbitrary non-aggregating expression may be used.
In this case, concatenating with an underscore is used to imitate the naming convention the `PIVOT` clause uses when multiple `ON` columns are provided (like in the prior example).
```sql
PIVOT cities
ON country || '_' || name
USING sum(population);
```
<div class="narrow_table"></div>
| year | NL_Amsterdam | US_New York City | US_Seattle |
|-----:|-------------:|-----------------:|-----------:|
| 2000 | 1005 | 8015 | 564 |
| 2010 | 1065 | 8175 | 608 |
| 2020 | 1158 | 8772 | 738 | | 484 | [
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SQL | Statements | Simplified `PIVOT` Syntax | Multiple `USING` Expressions | An alias may also be included for each expression in the `USING` clause.
It will be appended to the generated column names after an underscore (` _`).
This makes the column naming convention much cleaner when multiple expressions are included in the `USING` clause.
In this example, both the `sum` and `max` of the population column are calculated for each year and are split into separate columns.
```sql
PIVOT cities
ON year
USING sum(population) AS total, max(population) AS max
GROUP BY country;
```
<div class="narrow_table"></div>
| country | 2000_total | 2000_max | 2010_total | 2010_max | 2020_total | 2020_max |
|---------|-----------:|---------:|-----------:|---------:|-----------:|---------:|
| US | 8579 | 8015 | 8783 | 8175 | 9510 | 8772 |
| NL | 1005 | 1005 | 1065 | 1065 | 1158 | 1158 | | 250 | [
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SQL | Statements | Simplified `PIVOT` Syntax | Multiple `GROUP BY` Columns | Multiple `GROUP BY` columns may also be provided.
Note that column names must be used rather than column positions (1, 2, etc.), and that expressions are not supported in the `GROUP BY` clause.
```sql
PIVOT cities
ON year
USING sum(population)
GROUP BY country, name;
```
<div class="narrow_table"></div>
| country | name | 2000 | 2010 | 2020 |
|---------|---------------|-----:|-----:|-----:|
| NL | Amsterdam | 1005 | 1065 | 1158 |
| US | Seattle | 564 | 608 | 738 |
| US | New York City | 8015 | 8175 | 8772 |
#### Using `PIVOT` within a `SELECT` Statement {#docs:sql:statements:pivot::using-pivot-within-a-select-statement}
The `PIVOT` statement may be included within a `SELECT` statement as a CTE ([a Common Table Expression, or `WITH` clause](#docs:sql:query_syntax:with)), or a subquery.
This allows for a `PIVOT` to be used alongside other SQL logic, as well as for multiple `PIVOT`s to be used in one query.
No `SELECT` is needed within the CTE, the `PIVOT` keyword can be thought of as taking its place.
```sql
WITH pivot_alias AS (
PIVOT cities
ON year
USING sum(population)
GROUP BY country
)
SELECT * FROM pivot_alias;
```
A `PIVOT` may be used in a subquery and must be wrapped in parentheses.
Note that this behavior is different than the SQL Standard Pivot, as illustrated in subsequent examples.
```sql
SELECT *
FROM (
PIVOT cities
ON year
USING sum(population)
GROUP BY country
) pivot_alias;
```
#### Multiple `PIVOT` Statements {#docs:sql:statements:pivot::multiple-pivot-statements}
Each `PIVOT` can be treated as if it were a `SELECT` node, so they can be joined together or manipulated in other ways.
For example, if two `PIVOT` statements share the same `GROUP BY` expression, they can be joined together using the columns in the `GROUP BY` clause into a wider pivot.
```sql
SELECT *
FROM (PIVOT cities ON year USING sum(population) GROUP BY country) year_pivot
JOIN (PIVOT cities ON name USING sum(population) GROUP BY country) name_pivot
USING (country);
```
<div class="narrow_table"></div>
| country | 2000 | 2010 | 2020 | Amsterdam | New York City | Seattle |
|---------|-----:|-----:|-----:|----------:|--------------:|--------:|
| NL | 1005 | 1065 | 1158 | 3228 | NULL | NULL |
| US | 8579 | 8783 | 9510 | NULL | 24962 | 1910 | | 683 | [
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SQL | Statements | Simplified `PIVOT` Full Syntax Diagram | Below is the full syntax diagram of the `PIVOT` statement.
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|
SQL | Statements | SQL Standard `PIVOT` Syntax | The full syntax diagram is below, but the SQL Standard `PIVOT` syntax can be summarized as:
```sql
SELECT *
FROM ⟨dataset⟩
PIVOT (
⟨values⟩
FOR
⟨column_1⟩ IN (⟨in_list⟩)
⟨column_2⟩ IN (⟨in_list⟩)
...
GROUP BY ⟨rows⟩
);
```
Unlike the simplified syntax, the `IN` clause must be specified for each column to be pivoted.
If you are interested in dynamic pivoting, the simplified syntax is recommended.
Note that no commas separate the expressions in the `FOR` clause, but that `value` and `GROUP BY` expressions must be comma-separated! | 149 | [
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|
SQL | Statements | Examples | This example uses a single value expression, a single column expression, and a single row expression:
```sql
SELECT *
FROM cities
PIVOT (
sum(population)
FOR
year IN (2000, 2010, 2020)
GROUP BY country
);
```
<div class="narrow_table"></div>
| country | 2000 | 2010 | 2020 |
|---------|-----:|-----:|-----:|
| NL | 1005 | 1065 | 1158 |
| US | 8579 | 8783 | 9510 |
This example is somewhat contrived, but serves as an example of using multiple value expressions and multiple columns in the `FOR` clause.
```sql
SELECT *
FROM cities
PIVOT (
sum(population) AS total,
count(population) AS count
FOR
year IN (2000, 2010)
country IN ('NL', 'US')
);
```
| name | 2000_NL_total | 2000_NL_count | 2000_US_total | 2000_US_count | 2010_NL_total | 2010_NL_count | 2010_US_total | 2010_US_count |
|--|-:|-:|-:|-:|-:|-:|-:|-:|
| Amsterdam | 1005 | 1 | NULL | 0 | 1065 | 1 | NULL | 0 |
| Seattle | NULL | 0 | 564 | 1 | NULL | 0 | 608 | 1 |
| New York City | NULL | 0 | 8015 | 1 | NULL | 0 | 8175 | 1 |
#### SQL Standard `PIVOT` Full Syntax Diagram {#docs:sql:statements:pivot::sql-standard-pivot-full-syntax-diagram}
Below is the full syntax diagram of the SQL Standard version of the `PIVOT` statement.
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|
SQL | Statements | Limitations | `PIVOT` currently only accepts an aggregate function, expressions are not allowed.
For example, the following query attempts to get the population as the number of people instead of thousands of people (i.e., instead of 564, get 564000):
```sql
PIVOT cities
ON year
USING sum(population) * 1000;
```
However, it fails with the following error:
```console
Catalog Error: * is not an aggregate function
```
To work around this limitation, perform the `PIVOT` with the aggregation only, then use the [`COLUMNS` expression](#docs:sql:expressions:star::columns-expression):
```sql
SELECT country, name, 1000 * COLUMNS(* EXCLUDE (country, name))
FROM (
PIVOT cities
ON year
USING sum(population)
);
``` | 183 | [
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|
SQL | Statements | `EXPLAIN` | To see the query plan of a query without executing it, run:
```sql
EXPLAIN ⟨query⟩;
```
The output of `EXPLAIN` contains the estimated cardinalities for each operator. | 42 | [
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|
SQL | Statements | `EXPLAIN ANALYZE` | To profile a query, run:
```sql
EXPLAIN ANALYZE ⟨query⟩;
```
The `EXPLAIN ANALYZE` statement runs the query, and shows the actual cardinalities for each operator,
as well as the cumulative wall-clock time spent in each operator. | 58 | [
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|
SQL | Statements | SELECT Statement | The `SELECT` statement retrieves rows from the database.
#### Examples {#docs:sql:statements:select::examples}
Select all columns from the table `tbl`:
```sql
SELECT * FROM tbl;
```
Select the rows from `tbl`:
```sql
SELECT j FROM tbl WHERE i = 3;
```
Perform an aggregate grouped by the column `i`:
```sql
SELECT i, sum(j) FROM tbl GROUP BY i;
```
Select only the top 3 rows from the `tbl`:
```sql
SELECT * FROM tbl ORDER BY i DESC LIMIT 3;
```
Join two tables together using the `USING` clause:
```sql
SELECT * FROM t1 JOIN t2 USING (a, b);
```
Use column indexes to select the first and third column from the table `tbl`:
```sql
SELECT #1, #3 FROM tbl;
```
Select all unique cities from the addresses table:
```sql
SELECT DISTINCT city FROM addresses;
```
Return a `STRUCT` by using a row variable:
```sql
SELECT d
FROM (SELECT 1 AS a, 2 AS b) d;
```
#### Syntax {#docs:sql:statements:select::syntax}
The `SELECT` statement retrieves rows from the database. The canonical order of a `SELECT` statement is as follows, with less common clauses being indented:
```sql
SELECT ⟨select_list⟩
FROM ⟨tables⟩
USING SAMPLE ⟨sample_expression⟩
WHERE ⟨condition⟩
GROUP BY ⟨groups⟩
HAVING ⟨group_filter⟩
WINDOW ⟨window_expression⟩
QUALIFY ⟨qualify_filter⟩
ORDER BY ⟨order_expression⟩
LIMIT ⟨n⟩;
```
Optionally, the `SELECT` statement can be prefixed with a [`WITH` clause](#docs:sql:query_syntax:with).
As the `SELECT` statement is so complex, we have split up the syntax diagrams into several parts. The full syntax diagram can be found at the bottom of the page. | 427 | [
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|
SQL | Statements | `SELECT` Clause | <div id="rrdiagram3"></div>
The [`SELECT` clause](#docs:sql:query_syntax:select) specifies the list of columns that will be returned by the query. While it appears first in the clause, *logically* the expressions here are executed only at the end. The `SELECT` clause can contain arbitrary expressions that transform the output, as well as aggregates and window functions. The `DISTINCT` keyword ensures that only unique tuples are returned.
> Column names are case-insensitive. See the [Rules for Case Sensitivity](#docs:sql:dialect:keywords_and_identifiers::rules-for-case-sensitivity) for more details. | 139 | [
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|
SQL | Statements | `FROM` Clause | <div id="rrdiagram4"></div>
The [`FROM` clause](#docs:sql:query_syntax:from) specifies the *source* of the data on which the remainder of the query should operate. Logically, the `FROM` clause is where the query starts execution. The `FROM` clause can contain a single table, a combination of multiple tables that are joined together, or another `SELECT` query inside a subquery node. | 93 | [
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|
SQL | Statements | `SAMPLE` Clause | <div id="rrdiagram10"></div>
[The `SAMPLE` clause](#docs:sql:query_syntax:sample) allows you to run the query on a sample from the base table. This can significantly speed up processing of queries, at the expense of accuracy in the result. Samples can also be used to quickly see a snapshot of the data when exploring a data set. The `SAMPLE` clause is applied right after anything in the `FROM` clause (i.e., after any joins, but before the where clause or any aggregates). See the [Samples](#docs:sql:samples) page for more information. | 129 | [
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|
SQL | Statements | `WHERE` Clause | <div id="rrdiagram5"></div>
[The `WHERE` clause](#docs:sql:query_syntax:where) specifies any filters to apply to the data. This allows you to select only a subset of the data in which you are interested. Logically the `WHERE` clause is applied immediately after the `FROM` clause. | 71 | [
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|
SQL | Statements | `GROUP BY` and `HAVING` Clauses | <div id="rrdiagram6"></div>
[The `GROUP BY` clause](#docs:sql:query_syntax:groupby) specifies which grouping columns should be used to perform any aggregations in the `SELECT` clause. If the `GROUP BY` clause is specified, the query is always an aggregate query, even if no aggregations are present in the `SELECT` clause. | 81 | [
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|
SQL | Statements | `WINDOW` Clause | <div id="rrdiagram7"></div>
[The `WINDOW` clause](#docs:sql:query_syntax:window) allows you to specify named windows that can be used within window functions. These are useful when you have multiple window functions, as they allow you to avoid repeating the same window clause. | 64 | [
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|
SQL | Statements | `QUALIFY` Clause | <div id="rrdiagram11"></div>
[The `QUALIFY` clause](#docs:sql:query_syntax:qualify) is used to filter the result of [`WINDOW` functions](#docs:sql:functions:window_functions). | 52 | [
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|
SQL | Statements | `ORDER BY`, `LIMIT` and `OFFSET` Clauses | <div id="rrdiagram8"></div>
[`ORDER BY`](#docs:sql:query_syntax:orderby), [`LIMIT` and `OFFSET`](#docs:sql:query_syntax:limit) are output modifiers.
Logically they are applied at the very end of the query.
The `ORDER BY` clause sorts the rows on the sorting criteria in either ascending or descending order.
The `LIMIT` clause restricts the amount of rows fetched, while the `OFFSET` clause indicates at which position to start reading the values. | 108 | [
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|
SQL | Statements | `VALUES` List | <div id="rrdiagram9"></div>
[A `VALUES` list](#docs:sql:query_syntax:values) is a set of values that is supplied instead of a `SELECT` statement. | 43 | [
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|
SQL | Statements | Row IDs | For each table, the [`rowid` pseudocolumn](https://docs.oracle.com/cd/B19306_01/server.102/b14200/pseudocolumns008.htm) returns the row identifiers based on the physical storage.
```sql
CREATE TABLE t (id INTEGER, content VARCHAR);
INSERT INTO t VALUES (42, 'hello'), (43, 'world');
SELECT rowid, id, content FROM t;
```
| rowid | id | content |
|------:|---:|---------|
| 0 | 42 | hello |
| 1 | 43 | world |
In the current storage, these identifiers are contiguous unsigned integers (0, 1, ...) if no rows were deleted. Deletions introduce gaps in the rowids which may be reclaimed later. Therefore, it is strongly recommended *not to use rowids as identifiers*.
> **Tip. ** The `rowid` values are stable within a transaction.
> If there is a user-defined column named `rowid`, it shadows the `rowid` pseudocolumn. | 229 | [
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Subsets and Splits