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Guides | Guides | Data Viewers | * [How to visualize DuckDB databases with Tableau](#docs:guides:data_viewers:tableau)
* [How to draw command-line plots with DuckDB and YouPlot](#docs:guides:data_viewers:youplot) | 51 | [
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|
Guides | Data Viewers | Tableau – A Data Visualization Tool | [Tableau](https://www.tableau.com/) is a popular commercial data visualization tool.
In addition to a large number of built in connectors,
it also provides generic database connectivity via ODBC and JDBC connectors.
Tableau has two main versions: Desktop and Online (Server).
* For Desktop, connecting to a DuckDB database is similar to working in an embedded environment like Python.
* For Online, since DuckDB is in-process, the data needs to be either on the server itself
or in a remote data bucket that is accessible from the server. | 114 | [
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|
Guides | Data Viewers | Database Creation | When using a DuckDB database file
the data sets do not actually need to be imported into DuckDB tables;
it suffices to create views of the data.
For example, this will create a view of the `h2oai` Parquet test file in the current DuckDB code base:
```sql
CREATE VIEW h2oai AS (
FROM read_parquet('/Users/username/duckdb/data/parquet-testing/h2oai/h2oai_group_small.parquet')
);
```
Note that you should use full path names to local files so that they can be found from inside Tableau.
Also note that you will need to use a version of the driver that is compatible (i.e., from the same release)
as the database format used by the DuckDB tool (e.g., Python module, command line) that was used to create the file. | 180 | [
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|
Guides | Data Viewers | Installing the JDBC Driver | Tableau provides documentation on how to [install a JDBC driver](https://help.tableau.com/current/pro/desktop/en-gb/jdbc_tableau.htm)
for Tableau to use.
> Tableau (both Desktop and Server versions) need to be restarted any time you add or modify drivers.
#### Driver Links {#docs:guides:data_viewers:tableau::driver-links}
The link here is for a recent version of the JDBC driver that is compatible with Tableau.
If you wish to connect to a database file,
you will need to make sure the file was created with a file-compatible version of DuckDB.
Also, check that there is only one version of the driver installed as there are multiple filenames in use.
<!-- markdownlint-disable MD034 -->
Download the [JAR file](https://repo1.maven.org/maven2/org/duckdb/duckdb_jdbc/{{ site.currentjavaversion }}/duckdb_jdbc-{{ site.currentjavaversion }}.jar).
<!-- markdownlint-enable MD034 -->
* macOS: Copy it to `~/Library/Tableau/Drivers/`
* Windows: Copy it to `C:\Program Files\Tableau\Drivers`
* Linux: Copy it to `/opt/tableau/tableau_driver/jdbc`. | 260 | [
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|
Guides | Data Viewers | Using the PostgreSQL Dialect | If you just want to do something simple, you can try connecting directly to the JDBC driver
and using Tableau-provided PostgreSQL dialect.
1. Create a DuckDB file containing your views and/or data.
2. Launch Tableau
3. Under Connect > To a Server > More… click on “Other Databases (JDBC)” This will bring up the connection dialogue box. For the URL, enter `jdbc:duckdb:/User/username/path/to/database.db`. For the Dialect, choose PostgreSQL. The rest of the fields can be ignored:
However, functionality will be missing such as `median` and `percentile` aggregate functions.
To make the data source connection more compatible with the PostgreSQL dialect,
please use the DuckDB taco connector as described below. | 180 | [
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|
Guides | Data Viewers | Installing the Tableau DuckDB Connector | While it is possible to use the Tableau-provided PostgreSQL dialect to communicate with the DuckDB JDBC driver,
we strongly recommend using the [DuckDB "taco" connector](https://github.com/MotherDuck-Open-Source/duckdb-tableau-connector).
This connector has been fully tested against the Tableau dialect generator
and [is more compatible](https://github.com/MotherDuck-Open-Source/duckdb-tableau-connector/blob/main/tableau_connectors/duckdb_jdbc/dialect.tdd)
than the provided PostgreSQL dialect.
The documentation on how to install and use the connector is in its repository,
but essentially you will need the
[`duckdb_jdbc.taco`](https://github.com/MotherDuck-Open-Source/duckdb-tableau-connector/raw/main/packaged-connector/duckdb_jdbc-v1.0.0-signed.taco) file.
(Despite what the Tableau documentation says, the real security risk is in the JDBC driver code,
not the small amount of JavaScript in the Taco.)
#### Server (Online) {#docs:guides:data_viewers:tableau::server-online}
On Linux, copy the Taco file to `/opt/tableau/connectors`.
On Windows, copy the Taco file to `C:\Program Files\Tableau\Connectors`.
Then issue these commands to disable signature validation:
```bash
tsm configuration set -k native_api.disable_verify_connector_plugin_signature -v true
```
```bash
tsm pending-changes apply
```
The last command will restart the server with the new settings.
#### macOS {#docs:guides:data_viewers:tableau::macos}
Copy the Taco file to the `/Users/[User]/Documents/My Tableau Repository/Connectors` folder.
Then launch Tableau Desktop from the Terminal with the command line argument to disable signature validation:
```bash
/Applications/Tableau\ Desktop\ ⟨year⟩.⟨quarter⟩.app/Contents/MacOS/Tableau -DDisableVerifyConnectorPluginSignature=true
```
You can also package this up with AppleScript by using the following script:
```tableau
do shell script "\"/Applications/Tableau Desktop 2023.2.app/Contents/MacOS/Tableau\" -DDisableVerifyConnectorPluginSignature=true"
quit
```
Create this file with [the Script Editor](https://support.apple.com/guide/script-editor/welcome/mac)
(located in `/Applications/Utilities`)
and [save it as a packaged application](https://support.apple.com/guide/script-editor/save-a-script-as-an-app-scpedt1072/mac):
You can then double-click it to launch Tableau.
You will need to change the application name in the script when you get upgrades.
#### Windows Desktop {#docs:guides:data_viewers:tableau::windows-desktop}
Copy the Taco file to the `C:\Users\[Windows User]\Documents\My Tableau Repository\Connectors` directory.
Then launch Tableau Desktop from a shell with the `-DDisableVerifyConnectorPluginSignature=true` argument
to disable signature validation. | 672 | [
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|
Guides | Data Viewers | Output | Once loaded, you can run queries against your data!
Here is the result of the first H2O.ai benchmark query from the Parquet test file:
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|
Guides | Data Viewers | CLI Charting with YouPlot | DuckDB can be used with CLI graphing tools to quickly pipe input to stdout to graph your data in one line.
[YouPlot](https://github.com/red-data-tools/YouPlot) is a Ruby-based CLI tool for drawing visually pleasing plots on the terminal. It can accept input from other programs by piping data from `stdin`. It takes tab-separated (or delimiter of your choice) data and can easily generate various types of plots including bar, line, histogram and scatter.
With DuckDB, you can write to the console (` stdout`) by using the `TO '/dev/stdout'` command. And you can also write comma-separated values by using `WITH (FORMAT 'csv', HEADER)`. | 146 | [
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|
Guides | Data Viewers | Installing YouPlot | Installation instructions for YouPlot can be found on the main [YouPlot repository](https://github.com/red-data-tools/YouPlot#installation). If you're on a Mac, you can use:
```bash
brew install youplot
```
Run `uplot --help` to ensure you've installed it successfully! | 66 | [
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|
Guides | Data Viewers | Piping DuckDB Queries to stdout | By combining the [`COPY...TO`](#docs:sql:statements:copy::copy-to) function with a CSV output file, data can be read from any format supported by DuckDB and piped to YouPlot. There are three important steps to doing this.
1. As an example, this is how to read all data from `input.json`:
```bash
duckdb -s "SELECT * FROM read_json_auto('input.json')"
```
2. To prepare the data for YouPlot, write a simple aggregate:
```bash
duckdb -s "SELECT date, sum(purchases) AS total_purchases FROM read_json_auto('input.json') GROUP BY 1 ORDER BY 2 DESC LIMIT 10"
```
3. Finally, wrap the `SELECT` in the `COPY ... TO` function with an output location of `/dev/stdout`.
The syntax looks like this:
```sql
COPY (⟨query⟩) TO '/dev/stdout' WITH (FORMAT 'csv', HEADER);
```
The full DuckDB command below outputs the query in CSV format with a header:
```bash
duckdb -s "COPY (SELECT date, sum(purchases) AS total_purchases FROM read_json_auto('input.json') GROUP BY 1 ORDER BY 2 DESC LIMIT 10) TO '/dev/stdout' WITH (FORMAT 'csv', HEADER)"
``` | 287 | [
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|
Guides | Data Viewers | Connecting DuckDB to YouPlot | Finally, the data can now be piped to YouPlot! Let's assume we have an `input.json` file with dates and number of purchases made by somebody on that date. Using the query above, we'll pipe the data to the `uplot` command to draw a plot of the Top 10 Purchase Dates
```bash
duckdb -s "COPY (SELECT date, sum(purchases) AS total_purchases FROM read_json_auto('input.json') GROUP BY 1 ORDER BY 2 DESC LIMIT 10) TO '/dev/stdout' WITH (FORMAT 'csv', HEADER)" \
| uplot bar -d, -H -t "Top 10 Purchase Dates"
```
This tells `uplot` to draw a bar plot, use a comma-seperated delimiter (` -d,`), that the data has a header (` -H`), and give the plot a title (` -t`).
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|
Guides | Data Viewers | Bonus Round! stdin + stdout | Maybe you're piping some data through `jq`. Maybe you're downloading a JSON file from somewhere. You can also tell DuckDB to read the data from another process by changing the filename to `/dev/stdin`.
Let's combine this with a quick `curl` from GitHub to see what a certain user has been up to lately.
```bash
curl -sL "https://api.github.com/users/dacort/events?per_page=100" \
| duckdb -s "COPY (SELECT type, count(*) AS event_count FROM read_json_auto('/dev/stdin') GROUP BY 1 ORDER BY 2 DESC LIMIT 10) TO '/dev/stdout' WITH (FORMAT 'csv', HEADER)" \
| uplot bar -d, -H -t "GitHub Events for @dacort"
```
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|
Guides | Database Integration | MySQL Import | To run a query directly on a running MySQL database, the [`mysql` extension](#docs:extensions:mysql) is required. | 26 | [
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|
Guides | Database Integration | Installation and Loading | The extension can be installed using the `INSTALL` SQL command. This only needs to be run once.
```sql
INSTALL mysql;
```
To load the `mysql` extension for usage, use the `LOAD` SQL command:
```sql
LOAD mysql;
``` | 56 | [
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|
Guides | Database Integration | Usage | After the `mysql` extension is installed, you can attach to a MySQL database using the following command:
```sql
ATTACH 'host=localhost user=root port=0 database=mysqlscanner' AS mysql_db (TYPE mysql_scanner, READ_ONLY);
USE mysql_db;
```
The string used by `ATTACH` is a PostgreSQL-style connection string (_not_ a MySQL connection string!). It is a list of connection arguments provided in `{key}={value}` format. Below is a list of valid arguments. Any options not provided are replaced by their default values.
<div class="narrow_table"></div>
| Setting | Default |
|------------|--------------|
| `database` | `NULL` |
| `host` | `localhost` |
| `password` | |
| `port` | `0` |
| `socket` | `NULL` |
| `user` | current user |
You can directly read and write the MySQL database:
```sql
CREATE TABLE tbl (id INTEGER, name VARCHAR);
INSERT INTO tbl VALUES (42, 'DuckDB');
```
For a list of supported operations, see the [MySQL extension documentation](#docs:extensions:mysql::supported-operations). | 262 | [
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|
Guides | Database Integration | PostgreSQL Import | To run a query directly on a running PostgreSQL database, the [`postgres` extension](#docs:extensions:postgres) is required. | 27 | [
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|
Guides | Database Integration | Installation and Loading | The extension can be installed using the `INSTALL` SQL command. This only needs to be run once.
```sql
INSTALL postgres;
```
To load the `postgres` extension for usage, use the `LOAD` SQL command:
```sql
LOAD postgres;
``` | 56 | [
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|
Guides | Database Integration | Usage | After the `postgres` extension is installed, tables can be queried from PostgreSQL using the `postgres_scan` function:
```sql
-- Scan the table "mytable" from the schema "public" in the database "mydb"
SELECT * FROM postgres_scan('host=localhost port=5432 dbname=mydb', 'public', 'mytable');
```
The first parameter to the `postgres_scan` function is the [PostgreSQL connection string](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING), a list of connection arguments provided in `{key}={value}` format. Below is a list of valid arguments.
<div class="narrow_table"></div>
| Name | Description | Default |
|------------|--------------------------------------|----------------|
| `host` | Name of host to connect to | `localhost` |
| `hostaddr` | Host IP address | `localhost` |
| `port` | Port number | `5432` |
| `user` | PostgreSQL user name | [OS user name] |
| `password` | PostgreSQL password | |
| `dbname` | Database name | [user] |
| `passfile` | Name of file passwords are stored in | `~/.pgpass` |
Alternatively, the entire database can be attached using the `ATTACH` command. This allows you to query all tables stored within the PostgreSQL database as if it was a regular database.
```sql
-- Attach the PostgreSQL database using the given connection string
ATTACH 'host=localhost port=5432 dbname=mydb' AS test (TYPE postgres);
-- The table "tbl_name" can now be queried as if it is a regular table
SELECT * FROM test.tbl_name;
-- Switch the active database to "test"
USE test;
-- List all tables in the file
SHOW TABLES;
```
For more information see the [PostgreSQL extension documentation](#docs:extensions:postgres). | 423 | [
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Guides | Database Integration | SQLite Import | To run a query directly on a SQLite file, the `sqlite` extension is required. | 18 | [
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|
Guides | Database Integration | Installation and Loading | The extension can be installed using the `INSTALL` SQL command. This only needs to be run once.
```sql
INSTALL sqlite;
```
To load the `sqlite` extension for usage, use the `LOAD` SQL command:
```sql
LOAD sqlite;
``` | 56 | [
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|
Guides | Database Integration | Usage | After the SQLite extension is installed, tables can be queried from SQLite using the `sqlite_scan` function:
```sql
-- Scan the table "tbl_name" from the SQLite file "test.db"
SELECT * FROM sqlite_scan('test.db', 'tbl_name');
```
Alternatively, the entire file can be attached using the `ATTACH` command. This allows you to query all tables stored within a SQLite database file as if they were a regular database.
```sql
-- Attach the SQLite file "test.db"
ATTACH 'test.db' AS test (TYPE sqlite);
-- The table "tbl_name" can now be queried as if it is a regular table
SELECT * FROM test.tbl_name;
-- Switch the active database to "test"
USE test;
-- List all tables in the file
SHOW TABLES;
```
For more information see the [SQLite extension documentation](#docs:extensions:sqlite). | 187 | [
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|
Guides | File Formats | CSV Import | To read data from a CSV file, use the `read_csv` function in the `FROM` clause of a query:
```sql
SELECT * FROM read_csv('input.csv');
```
Alternatively, you can omit the `read_csv` function and let DuckDB infer it from the extension:
```sql
SELECT * FROM 'input.csv';
```
To create a new table using the result from a query, use [`CREATE TABLE ... AS SELECT` statement](#docs:sql:statements:create_table::create-table--as-select-ctas):
```sql
CREATE TABLE new_tbl AS
SELECT * FROM read_csv('input.csv');
```
We can use DuckDB's [optional `FROM`-first syntax](#docs:sql:query_syntax:from) to omit `SELECT *`:
```sql
CREATE TABLE new_tbl AS
FROM read_csv('input.csv');
```
To load data into an existing table from a query, use `INSERT INTO` from a `SELECT` statement:
```sql
INSERT INTO tbl
SELECT * FROM read_csv('input.csv');
```
Alternatively, the `COPY` statement can also be used to load data from a CSV file into an existing table:
```sql
COPY tbl FROM 'input.csv';
```
For additional options, see the [CSV import reference](#docs:data:csv:overview) and the [`COPY` statement documentation](#docs:sql:statements:copy). | 299 | [
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|
Guides | File Formats | CSV Export | To export the data from a table to a CSV file, use the `COPY` statement:
```sql
COPY tbl TO 'output.csv' (HEADER, DELIMITER ',');
```
The result of queries can also be directly exported to a CSV file:
```sql
COPY (SELECT * FROM tbl) TO 'output.csv' (HEADER, DELIMITER ',');
```
For additional options, see the [`COPY` statement documentation](#docs:sql:statements:copy::csv-options). | 102 | [
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|
Guides | File Formats | Directly Reading Files | DuckDB allows directly reading files via the [`read_text`](#::read_text) and [`read_blob`](#::read_blob) functions.
These functions accept a filename, a list of filenames or a glob pattern, and output the content of each file as a `VARCHAR` or `BLOB`, respectively, as well as additional metadata such as the file size and last modified time. | 79 | [
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|
Guides | File Formats | `read_text` | The `read_text` table function reads from the selected source(s) to a `VARCHAR`. Each file results in a single row with the `content` field holding the entire content of the respective file.
```sql
SELECT size, parse_path(filename), content
FROM read_text('test/sql/table_function/files/*.txt');
```
| size | parse_path(filename) | content |
|-----:|-----------------------------------------------|------------------|
| 12 | [test, sql, table_function, files, one.txt] | Hello World! |
| 2 | [test, sql, table_function, files, three.txt] | 42 |
| 10 | [test, sql, table_function, files, two.txt] | Foo Bar\nFöö Bär |
The file content is first validated to be valid UTF-8. If `read_text` attempts to read a file with invalid UTF-8, an error is thrown suggesting to use [`read_blob`](#::read_blob) instead. | 217 | [
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|
Guides | File Formats | `read_blob` | The `read_blob` table function reads from the selected source(s) to a `BLOB`:
```sql
SELECT size, content, filename
FROM read_blob('test/sql/table_function/files/*');
```
| size | content | filename |
|-----:|--------------------------------------------------------------|-----------------------------------------|
| 178 | PK\x03\x04\x0A\x00\x00\x00\x00\x00\xACi=X\x14t\xCE\xC7\x0A… | test/sql/table_function/files/four.blob |
| 12 | Hello World! | test/sql/table_function/files/one.txt |
| 2 | 42 | test/sql/table_function/files/three.txt |
| 10 | F\xC3\xB6\xC3\xB6 B\xC3\xA4r | test/sql/table_function/files/two.txt | | 191 | [
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|
Guides | File Formats | Schema | The schemas of the tables returned by `read_text` and `read_blob` are identical:
```sql
DESCRIBE FROM read_text('README.md');
```
| column_name | column_type | null | key | default | extra |
|---------------|-------------|------|------|---------|-------|
| filename | VARCHAR | YES | NULL | NULL | NULL |
| content | VARCHAR | YES | NULL | NULL | NULL |
| size | BIGINT | YES | NULL | NULL | NULL |
| last_modified | TIMESTAMP | YES | NULL | NULL | NULL | | 139 | [
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|
Guides | File Formats | Support for Projection Pushdown | The table functions also utilize projection pushdown to avoid computing properties unnecessarily. So you could e.g., use this to glob a directory full of huge files to get the file size in the size column, as long as you omit the content column the data won't be read into DuckDB. | 58 | [
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|
Guides | File Formats | Installing the Extension | To read data from an Excel file, install and load the [spatial extension](#docs:extensions:spatial:overview).
This is only needed once per DuckDB connection.
```sql
INSTALL spatial;
LOAD spatial;
``` | 48 | [
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|
Guides | File Formats | Importing Excel Sheets | Use the `st_read` function in the `FROM` clause of a query:
```sql
SELECT * FROM st_read('test_excel.xlsx');
```
The `layer` parameter allows specifying the name of the Excel worksheet:
```sql
SELECT * FROM st_read('test_excel.xlsx', layer = 'Sheet1');
```
#### Creating a New Table {#docs:guides:file_formats:excel_import::creating-a-new-table}
To create a new table using the result from a query, use `CREATE TABLE ... AS` from a `SELECT` statement:
```sql
CREATE TABLE new_tbl AS
SELECT * FROM st_read('test_excel.xlsx', layer = 'Sheet1');
```
#### Loading to an Existing Table {#docs:guides:file_formats:excel_import::loading-to-an-existing-table}
To load data into an existing table from a query, use `INSERT INTO` from a `SELECT` statement:
```sql
INSERT INTO tbl
SELECT * FROM st_read('test_excel.xlsx', layer = 'Sheet1');
```
#### Options {#docs:guides:file_formats:excel_import::options}
Several configuration options are also available for the underlying GDAL library that is doing the XLSX parsing.
You can pass them via the `open_options` parameter of the `st_read` function as a list of `'KEY=VALUE'` strings. | 291 | [
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|
Guides | File Formats | Importing Excel Sheets | Importing a Sheet with/without a Header | The option `HEADERS` has three possible values:
* `FORCE`: treat the first row as a header
* `DISABLE` treat the first row as a row of data
* `AUTO` attempt auto-detection (default)
For example, to treat the first row as a header, run:
```sql
SELECT *
FROM st_read(
'test_excel.xlsx',
layer = 'Sheet1',
open_options = ['HEADERS=FORCE']
);
``` | 96 | [
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Guides | File Formats | Importing Excel Sheets | Detecting Types | The option `FIELD_TYPE` defines how field types should be treated:
* `STRING`: all fields should be loaded as strings (` VARCHAR` type)
* `AUTO`: field types should be auto-detected (default)
For example, to treat the first row as a header and use auto-detection for types, run:
```sql
SELECT *
FROM st_read(
'test_excel.xlsx',
layer = 'Sheet1',
open_options = ['HEADERS=FORCE', 'FIELD_TYPES=AUTO']
);
```
To treat the fields as strings:
```sql
SELECT *
FROM st_read(
'test_excel.xlsx',
layer = 'Sheet1',
open_options = ['FIELD_TYPES=STRING']
);
``` | 145 | [
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Guides | File Formats | See Also | DuckDB can also [export Excel files](#docs:guides:file_formats:excel_export).
For additional details on Excel support, see the [spatial extension page](#docs:extensions:spatial:overview), the [GDAL XLSX driver page](https://gdal.org/drivers/vector/xlsx.html), and the [GDAL configuration options page](https://gdal.org/user/configoptions.html#configoptions). | 90 | [
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|
Guides | File Formats | Installing the Extension | To export the data from a table to an Excel file, install and load the [spatial extension](#docs:extensions:spatial:overview).
This is only needed once per DuckDB connection.
```sql
INSTALL spatial;
LOAD spatial;
``` | 52 | [
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|
Guides | File Formats | Exporting Excel Sheets | Then use the `COPY` statement. The file will contain one worksheet with the same name as the file, but without the `.xlsx` extension:
```sql
COPY tbl TO 'output.xlsx' WITH (FORMAT GDAL, DRIVER 'xlsx');
```
The result of a query can also be directly exported to an Excel file:
```sql
COPY (SELECT * FROM tbl) TO 'output.xlsx' WITH (FORMAT GDAL, DRIVER 'xlsx');
```
> Dates and timestamps are currently not supported by the `xlsx` writer.
> Cast columns of those types to `VARCHAR` prior to creating the `xlsx` file. | 130 | [
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|
Guides | File Formats | See Also | DuckDB can also [import Excel files](#docs:guides:file_formats:excel_import).
For additional details, see the [spatial extension page](#docs:extensions:spatial:overview) and the [GDAL XLSX driver page](https://gdal.org/drivers/vector/xlsx.html). | 66 | [
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|
Guides | File Formats | JSON Import | To read data from a JSON file, use the `read_json_auto` function in the `FROM` clause of a query:
```sql
SELECT *
FROM read_json_auto('input.json');
```
To create a new table using the result from a query, use `CREATE TABLE AS` from a `SELECT` statement:
```sql
CREATE TABLE new_tbl AS
SELECT *
FROM read_json_auto('input.json');
```
To load data into an existing table from a query, use `INSERT INTO` from a `SELECT` statement:
```sql
INSERT INTO tbl
SELECT *
FROM read_json_auto('input.json');
```
Alternatively, the `COPY` statement can also be used to load data from a JSON file into an existing table:
```sql
COPY tbl FROM 'input.json';
```
For additional options, see the [JSON Loading reference](#docs:data:json:overview) and the [`COPY` statement documentation](#docs:sql:statements:copy). | 204 | [
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|
Guides | File Formats | JSON Export | To export the data from a table to a JSON file, use the `COPY` statement:
```sql
COPY tbl TO 'output.json';
```
The result of queries can also be directly exported to a JSON file:
```sql
COPY (SELECT * FROM tbl) TO 'output.json';
```
The JSON export writes JSON lines by default. The `ARRAY` option can be used to write a JSON array instead.
```sql
COPY tbl TO 'output.json' (ARRAY);
```
For additional options, see the [`COPY` statement documentation](#docs:sql:statements:copy). | 125 | [
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|
Guides | File Formats | Parquet Import | To read data from a Parquet file, use the `read_parquet` function in the `FROM` clause of a query:
```sql
SELECT * FROM read_parquet('input.parquet');
```
Alternatively, you can omit the `read_parquet` function and let DuckDB infer it from the extension:
```sql
SELECT * FROM 'input.parquet';
```
To create a new table using the result from a query, use [`CREATE TABLE ... AS SELECT` statement](#docs:sql:statements:create_table::create-table--as-select-ctas):
```sql
CREATE TABLE new_tbl AS
SELECT * FROM read_parquet('input.parquet');
```
To load data into an existing table from a query, use `INSERT INTO` from a `SELECT` statement:
```sql
INSERT INTO tbl
SELECT * FROM read_parquet('input.parquet');
```
Alternatively, the `COPY` statement can also be used to load data from a Parquet file into an existing table:
```sql
COPY tbl FROM 'input.parquet' (FORMAT PARQUET);
```
For additional options, see the [Parquet loading reference](#docs:data:parquet:overview). | 252 | [
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|
Guides | File Formats | Parquet Export | To export the data from a table to a Parquet file, use the `COPY` statement:
```sql
COPY tbl TO 'output.parquet' (FORMAT PARQUET);
```
The result of queries can also be directly exported to a Parquet file:
```sql
COPY (SELECT * FROM tbl) TO 'output.parquet' (FORMAT PARQUET);
```
The flags for setting compression, row group size, etc. are listed in the [Reading and Writing Parquet files](#docs:data:parquet:overview) page. | 115 | [
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|
Guides | File Formats | Querying Parquet Files | To run a query directly on a Parquet file, use the `read_parquet` function in the `FROM` clause of a query.
```sql
SELECT * FROM read_parquet('input.parquet');
```
The Parquet file will be processed in parallel. Filters will be automatically pushed down into the Parquet scan, and only the relevant columns will be read automatically.
For more information see the blog post [“Querying Parquet with Precision using DuckDB”](https://duckdb.org/2021/06/25/querying-parquet). | 116 | [
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|
Guides | Network and Cloud Storage | HTTP Parquet Import | To load a Parquet file over HTTP(S), the [`httpfs` extension](#docs:extensions:httpfs:overview) is required. This can be installed using the `INSTALL` SQL command. This only needs to be run once.
```sql
INSTALL httpfs;
```
To load the `httpfs` extension for usage, use the `LOAD` SQL command:
```sql
LOAD httpfs;
```
After the `httpfs` extension is set up, Parquet files can be read over `http(s)`:
```sql
SELECT * FROM read_parquet('https://⟨domain⟩/path/to/file.parquet');
```
For example:
```sql
SELECT * FROM read_parquet('https://duckdb.org/data/prices.parquet');
```
The function `read_parquet` can be omitted if the URL ends with `.parquet`:
```sql
SELECT * FROM read_parquet('https://duckdb.org/data/holdings.parquet');
```
Moreover, the `read_parquet` function itself can also be omitted thanks to DuckDB's [replacement scan mechanism](#docs:api:c:replacement_scans):
```sql
SELECT * FROM 'https://duckdb.org/data/holdings.parquet';
``` | 263 | [
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|
Guides | Network and Cloud Storage | Prerequisites | To load a Parquet file from S3, the [`httpfs` extension](#docs:extensions:httpfs:overview) is required. This can be installed using the `INSTALL` SQL command. This only needs to be run once.
```sql
INSTALL httpfs;
```
To load the `httpfs` extension for usage, use the `LOAD` SQL command:
```sql
LOAD httpfs;
``` | 87 | [
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|
Guides | Network and Cloud Storage | Credentials and Configuration | After loading the `httpfs` extension, set up the credentials and S3 region to read data:
```sql
CREATE SECRET (
TYPE S3,
KEY_ID 'AKIAIOSFODNN7EXAMPLE',
SECRET 'wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY',
REGION 'us-east-1'
);
```
> **Tip. ** If you get an IO Error (` Connection error for HTTP HEAD`), configure the endpoint explicitly via `ENDPOINT 's3.⟨your-region⟩.amazonaws.com'`.
Alternatively, use the [`aws` extension](#docs:extensions:aws) to retrieve the credentials automatically:
```sql
CREATE SECRET (
TYPE S3,
PROVIDER CREDENTIAL_CHAIN
);
``` | 164 | [
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|
Guides | Network and Cloud Storage | Querying | After the `httpfs` extension is set up and the S3 configuration is set correctly, Parquet files can be read from S3 using the following command:
```sql
SELECT * FROM read_parquet('s3://⟨bucket⟩/⟨file⟩');
``` | 58 | [
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|
Guides | Network and Cloud Storage | Google Cloud Storage (GCS) and Cloudflare R2 | DuckDB can also handle [Google Cloud Storage (GCS)](#docs:guides:network_cloud_storage:gcs_import) and [Cloudflare R2](#docs:guides:network_cloud_storage:cloudflare_r2_import) via the S3 API.
See the relevant guides for details. | 63 | [
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|
Guides | Network and Cloud Storage | S3 Parquet Export | To write a Parquet file to S3, the [`httpfs` extension](#docs:extensions:httpfs:overview) is required. This can be installed using the `INSTALL` SQL command. This only needs to be run once.
```sql
INSTALL httpfs;
```
To load the `httpfs` extension for usage, use the `LOAD` SQL command:
```sql
LOAD httpfs;
```
After loading the `httpfs` extension, set up the credentials to write data. Note that the `region` parameter should match the region of the bucket you want to access.
```sql
CREATE SECRET (
TYPE S3,
KEY_ID 'AKIAIOSFODNN7EXAMPLE',
SECRET 'wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY',
REGION 'us-east-1'
);
```
> **Tip. ** If you get an IO Error (` Connection error for HTTP HEAD`), configure the endpoint explicitly via `ENDPOINT 's3.⟨your-region⟩.amazonaws.com'`.
Alternatively, use the [`aws` extension](#docs:extensions:aws) to retrieve the credentials automatically:
```sql
CREATE SECRET (
TYPE S3,
PROVIDER CREDENTIAL_CHAIN
);
```
After the `httpfs` extension is set up and the S3 credentials are correctly configured, Parquet files can be written to S3 using the following command:
```sql
COPY ⟨table_name⟩ TO 's3://bucket/file.parquet';
```
Similarly, Google Cloud Storage (GCS) is supported through the Interoperability API. You need to create [HMAC keys](https://console.cloud.google.com/storage/settings;tab=interoperability) and provide the credentials as follows:
```sql
CREATE SECRET (
TYPE GCS,
KEY_ID 'AKIAIOSFODNN7EXAMPLE',
SECRET 'wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY'
);
```
After setting up the GCS credentials, you can export using:
```sql
COPY ⟨table_name⟩ TO 'gs://gcs_bucket/file.parquet';
``` | 455 | [
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|
Guides | Network and Cloud Storage | Prerequisites | To load an Iceberg file from S3, both the [`httpfs`](#docs:extensions:httpfs:overview) and [`iceberg`](#docs:extensions:iceberg) extensions are required. They can be installed using the `INSTALL` SQL command. The extensions only need to be installed once.
```sql
INSTALL httpfs;
INSTALL iceberg;
```
To load the extensions for usage, use the `LOAD` command:
```sql
LOAD httpfs;
LOAD iceberg;
``` | 102 | [
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|
Guides | Network and Cloud Storage | Credentials | After loading the extensions, set up the credentials and S3 region to read data. You may either use an access key and secret, or a token.
```sql
CREATE SECRET (
TYPE S3,
KEY_ID 'AKIAIOSFODNN7EXAMPLE',
SECRET 'wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY',
REGION 'us-east-1'
);
```
Alternatively, use the [`aws` extension](#docs:extensions:aws) to retrieve the credentials automatically:
```sql
CREATE SECRET (
TYPE S3,
PROVIDER CREDENTIAL_CHAIN
);
``` | 130 | [
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|
Guides | Network and Cloud Storage | Loading Iceberg Tables from S3 | After the extensions are set up and the S3 credentials are correctly configured, Iceberg table can be read from S3 using the following command:
```sql
SELECT *
FROM iceberg_scan('s3://⟨bucket⟩/⟨iceberg-table-folder⟩/metadata/⟨id⟩.metadata.json');
```
Note that you need to link directly to the manifest file. Otherwise you'll get an error like this:
```console
Error: IO Error: Cannot open file "s3://⟨bucket⟩/⟨iceberg-table-folder⟩/metadata/version-hint.text": No such file or directory
``` | 130 | [
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|
Guides | Network and Cloud Storage | S3 Express One | In late 2023, AWS [announced](https://aws.amazon.com/about-aws/whats-new/2023/11/amazon-s3-express-one-zone-storage-class/) the [S3 Express One Zone](https://docs.aws.amazon.com/AmazonS3/latest/userguide/s3-express-one-zone.html), a high-speed variant of traditional S3 buckets.
DuckDB can read S3 Express One buckets using the [`httpfs` extension](#docs:extensions:httpfs:overview). | 105 | [
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|
Guides | Network and Cloud Storage | Credentials and Configuration | The configuration of S3 Express One buckets is similar to [regular S3 buckets](#docs:guides:network_cloud_storage:s3_import) with one exception:
we have to specify the endpoint according to the following pattern:
```text
s3express-⟨availability zone⟩.⟨region⟩.amazonaws.com
```
where the `⟨availability zone⟩` (e.g., `use-az5`) can be obtained from the S3 Express One bucket's configuration page and the `⟨region⟩` is the AWS region (e.g., `us-east-1`).
For example, to allow DuckDB to use an S3 Express One bucket, configure the [Secrets manager](#docs:sql:statements:create_secret) as follows:
```sql
CREATE SECRET (
TYPE S3,
REGION 'us-east-1',
KEY_ID 'AKIAIOSFODNN7EXAMPLE',
SECRET 'wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY',
ENDPOINT 's3express-use1-az5.us-east-1.amazonaws.com'
);
``` | 235 | [
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|
Guides | Network and Cloud Storage | Instance Location | For best performance, make sure that the EC2 instance is in the same availability zone as the S3 Express One bucket you are querying. To determine the mapping between zone names and zone IDs, use the `aws ec2 describe-availability-zones` command.
* Zone name to zone ID mapping:
```batch
aws ec2 describe-availability-zones --output json \
| jq -r '.AvailabilityZones[] | select(.ZoneName == "us-east-1f") | .ZoneId'
```
```text
use1-az5
```
* Zone ID to zone name mapping:
```batch
aws ec2 describe-availability-zones --output json \
| jq -r '.AvailabilityZones[] | select(.ZoneId == "use1-az5") | .ZoneName'
```
```text
us-east-1f
``` | 181 | [
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|
Guides | Network and Cloud Storage | Querying | You can query the S3 Express One bucket as any other S3 bucket:
```sql
SELECT *
FROM 's3://express-bucket-name--use1-az5--x-s3/my-file.parquet';
``` | 47 | [
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|
Guides | Network and Cloud Storage | Performance | We ran two experiments on a `c7gd.12xlarge` instance using the [LDBC SF300 Comments `creationDate` Parquet file](https://blobs.duckdb.org/data/ldbc-sf300-comments-creationDate.parquet) file (also used in the [microbenchmarks of the performance guide](#docs:guides:performance:benchmarks::data-sets)).
<div class="narrow_table"></div>
| Experiment | File size | Runtime |
|:-----|--:|--:|
| Loading only from Parquet | 4.1 GB | 3.5s |
| Creating local table from Parquet | 4.1 GB | 5.1s |
The “loading only” variant is running the load as part of an [`EXPLAIN ANALYZE`](#docs:guides:meta:explain_analyze) statement to measure the runtime without account creating a local table, while the “creating local table” variant uses [`CREATE TABLE ... AS SELECT`](#docs:sql:statements:create_table::create-table--as-select-ctas) to create a persistent table on the local disk. | 240 | [
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|
Guides | Network and Cloud Storage | Prerequisites | The Google Cloud Storage (GCS) can be used via the [`httpfs` extension](#docs:extensions:httpfs:overview).
This can be installed with the `INSTALL httpfs` SQL command. This only needs to be run once. | 50 | [
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|
Guides | Network and Cloud Storage | Credentials and Configuration | You need to create [HMAC keys](https://console.cloud.google.com/storage/settings;tab=interoperability) and declare them:
```sql
CREATE SECRET (
TYPE GCS,
KEY_ID 'AKIAIOSFODNN7EXAMPLE',
SECRET 'wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY'
);
``` | 77 | [
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|
Guides | Network and Cloud Storage | Querying | After setting up the GCS credentials, you can query the GCS data using:
```sql
SELECT *
FROM read_parquet('gs://⟨gcs_bucket⟩/⟨file.parquet⟩');
``` | 45 | [
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|
Guides | Network and Cloud Storage | Attaching to a Database | You can [attach to a database file](#docs:guides:network_cloud_storage:duckdb_over_https_or_s3) in read-only mode:
```sql
LOAD httpfs;
ATTACH 'gs://⟨gcs_bucket⟩/⟨file.duckdb⟩' AS ⟨duckdb_database⟩ (READ_ONLY);
```
> Databases in Google Cloud Storage can only be attached in read-only mode. | 88 | [
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|
Guides | Network and Cloud Storage | Prerequisites | For Cloudflare R2, the [S3 Compatibility API](https://developers.cloudflare.com/r2/api/s3/api/) allows you to use DuckDB's S3 support to read and write from R2 buckets.
This requires the [`httpfs` extension](#docs:extensions:httpfs:overview), which can be installed using the `INSTALL` SQL command. This only needs to be run once. | 83 | [
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|
Guides | Network and Cloud Storage | Credentials and Configuration | You will need to [generate an S3 auth token](https://developers.cloudflare.com/r2/api/s3/tokens/) and create an `R2` secret in DuckDB:
```sql
CREATE SECRET (
TYPE R2,
KEY_ID 'AKIAIOSFODNN7EXAMPLE',
SECRET 'wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY',
ACCOUNT_ID 'your-account-id-here' -- your 33 character hexadecimal account ID
);
``` | 104 | [
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|
Guides | Network and Cloud Storage | Querying | After setting up the R2 credentials, you can query the R2 data using DuckDB's built-in methods, such as `read_csv` or `read_parquet`:
```sql
SELECT * FROM read_parquet('r2://⟨r2_bucket_name⟩/⟨file⟩');
``` | 63 | [
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Guides | Network and Cloud Storage | Attach to a DuckDB Database over HTTPS or S3 | You can establish a read-only connection to a DuckDB instance via HTTPS or the S3 API. | 20 | [
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|
Guides | Network and Cloud Storage | Prerequisites | This guide requires the [`httpfs` extension](#docs:extensions:httpfs:overview), which can be installed using the `INSTALL httpfs` SQL command. This only needs to be run once. | 41 | [
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|
Guides | Network and Cloud Storage | Attaching to a Database over HTTPS | To connect to a DuckDB database via HTTPS, use the [`ATTACH` statement](#docs:sql:statements:attach) as follows:
```sql
ATTACH 'https://blobs.duckdb.org/databases/stations.duckdb' AS stations_db;
```
> Since DuckDB version 1.1, the `ATTACH` statement creates a read-only connection to HTTP endpoints.
> In prior versions, it is necessary to use the `READ_ONLY` flag.
Then, the database can be queried using:
```sql
SELECT count(*) AS num_stations
FROM stations_db.stations;
```
| num_stations |
|-------------:|
| 578 | | 145 | [
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|
Guides | Network and Cloud Storage | Attaching to a Database over the S3 API | To connect to a DuckDB database via the S3 API, [configure the authentication](#docs:guides:network_cloud_storage:s3_import::credentials-and-configuration) for your bucket (if required).
Then, use the [`ATTACH` statement](#docs:sql:statements:attach) as follows:
```sql
ATTACH 's3://duckdb-blobs/databases/stations.duckdb' AS stations_db;
```
> Since DuckDB version 1.1, the `ATTACH` statement creates a read-only connection to HTTP endpoints.
> In prior versions, it is necessary to use the `READ_ONLY` flag.
The database can be queried using:
```sql
SELECT count(*) AS num_stations
FROM stations_db.stations;
```
| num_stations |
|-------------:|
| 578 |
> Connecting to S3-compatible APIs such as the [Google Cloud Storage (` gs://`)](#docs:guides:network_cloud_storage:gcs_import::attaching-to-a-database) is also supported. | 221 | [
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|
Guides | Meta Queries | Describing a Table | In order to view the schema of a table, use `DESCRIBE` or `SHOW` followed by the table name.
```sql
CREATE TABLE tbl (i INTEGER PRIMARY KEY, j VARCHAR);
DESCRIBE tbl;
SHOW tbl; -- equivalent to DESCRIBE tbl;
```
<div class="narrow_table"></div>
| column_name | column_type | null | key | default | extra |
|-------------|-------------|------|------|---------|-------|
| i | INTEGER | NO | PRI | NULL | NULL |
| j | VARCHAR | YES | NULL | NULL | NULL | | 136 | [
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|
Guides | Meta Queries | Describing a Query | In order to view the schema of the result of a query, prepend `DESCRIBE` to a query.
```sql
DESCRIBE SELECT * FROM tbl;
```
<div class="narrow_table"></div>
| column_name | column_type | null | key | default | extra |
|-------------|-------------|------|------|---------|-------|
| i | INTEGER | YES | NULL | NULL | NULL |
| j | VARCHAR | YES | NULL | NULL | NULL |
Note that there are subtle differences: compared to the result when [describing a table](#::describing-a-table), nullability (` null`) and key information (` key`) are lost. | 152 | [
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|
Guides | Meta Queries | Using `DESCRIBE` in a Subquery | `DESCRIBE` can be used a subquery. This allows creating a table from the description, for example:
```sql
CREATE TABLE tbl_description AS SELECT * FROM (DESCRIBE tbl);
``` | 43 | [
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|
Guides | Meta Queries | Describing Remote Tables | It is possible to describe remote tables via the [`httpfs` extension](#docs:extensions:httpfs:overview) using the `DESCRIBE TABLE` statement. For example:
```sql
DESCRIBE TABLE 'https://blobs.duckdb.org/data/Star_Trek-Season_1.csv';
```
| column_name | column_type | null | key | default | extra |
|-----------------------------------------|-------------|------|------|---------|-------|
| season_num | BIGINT | YES | NULL | NULL | NULL |
| episode_num | BIGINT | YES | NULL | NULL | NULL |
| aired_date | DATE | YES | NULL | NULL | NULL |
| cnt_kirk_hookups | BIGINT | YES | NULL | NULL | NULL |
| cnt_downed_redshirts | BIGINT | YES | NULL | NULL | NULL |
| bool_aliens_almost_took_over_planet | BIGINT | YES | NULL | NULL | NULL |
| bool_aliens_almost_took_over_enterprise | BIGINT | YES | NULL | NULL | NULL |
| cnt_vulcan_nerve_pinch | BIGINT | YES | NULL | NULL | NULL |
| cnt_warp_speed_orders | BIGINT | YES | NULL | NULL | NULL |
| highest_warp_speed_issued | BIGINT | YES | NULL | NULL | NULL |
| bool_hand_phasers_fired | BIGINT | YES | NULL | NULL | NULL |
| bool_ship_phasers_fired | BIGINT | YES | NULL | NULL | NULL |
| bool_ship_photon_torpedos_fired | BIGINT | YES | NULL | NULL | NULL |
| cnt_transporter_pax | BIGINT | YES | NULL | NULL | NULL |
| cnt_damn_it_jim_quote | BIGINT | YES | NULL | NULL | NULL |
| cnt_im_givin_her_all_shes_got_quote | BIGINT | YES | NULL | NULL | NULL |
| cnt_highly_illogical_quote | BIGINT | YES | NULL | NULL | NULL |
| bool_enterprise_saved_the_day | BIGINT | YES | NULL | NULL | NULL | | 539 | [
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|
Guides | Meta Queries | EXPLAIN: Inspect Query Plans | ```sql
EXPLAIN SELECT * FROM tbl;
```
The `EXPLAIN` statement displays the physical plan, i.e., the query plan that will get executed,
and is enabled by prepending the query with `EXPLAIN`.
The physical plan is a tree of operators that are executed in a specific order to produce the result of the query.
To generate an efficient physical plan, the query optimizer transforms the existing physical plan into a better physical plan.
To demonstrate, see the below example:
```sql
CREATE TABLE students (name VARCHAR, sid INTEGER);
CREATE TABLE exams (eid INTEGER, subject VARCHAR, sid INTEGER);
INSERT INTO students VALUES ('Mark', 1), ('Joe', 2), ('Matthew', 3);
INSERT INTO exams VALUES (10, 'Physics', 1), (20, 'Chemistry', 2), (30, 'Literature', 3);
EXPLAIN ANALYZE
SELECT name
FROM students
JOIN exams USING (sid)
WHERE name LIKE 'Ma%';
```
```text
┌─────────────────────────────┐
│┌───────────────────────────┐│
││ Physical Plan ││
│└───────────────────────────┘│
└─────────────────────────────┘
┌───────────────────────────┐
│ PROJECTION │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ name │
└─────────────┬─────────────┘
┌─────────────┴─────────────┐
│ HASH_JOIN │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ INNER │
│ sid = sid ├──────────────┐
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │ │
│ EC: 1 │ │
└─────────────┬─────────────┘ │
┌─────────────┴─────────────┐┌─────────────┴─────────────┐
│ SEQ_SCAN ││ FILTER │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ││ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ exams ││ prefix(name, 'Ma') │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ││ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ sid ││ EC: 1 │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ││ │
│ EC: 3 ││ │
└───────────────────────────┘└─────────────┬─────────────┘
┌─────────────┴─────────────┐
│ SEQ_SCAN │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ students │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ sid │
│ name │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ Filters: name>=Ma AND name│
│ <Mb AND name IS NOT NULL │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ EC: 1 │
└───────────────────────────┘
```
Note that the query is not actually executed – therefore, we can only see the estimated cardinality (` EC`) for each operator, which is calculated by using the statistics of the base tables and applying heuristics for each operator. | 920 | [
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|
Guides | Meta Queries | Additional Explain Settings | The `EXPLAIN` statement supports additional settings that can be used to control the output. The following settings are available:
The default setting. Only shows the physical plan.
```sql
PRAGMA explain_output = 'physical_only';
```
Shows only the optimized plan.
```sql
PRAGMA explain_output = 'optimized_only';
```
Shows both the physical and optimized plans.
```sql
PRAGMA explain_output = 'all';
``` | 92 | [
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|
Guides | Meta Queries | See Also | For more information, see the [”Profiling” page](#docs:dev:profiling). | 21 | [
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|
Guides | Meta Queries | EXPLAIN ANALYZE: Profile Queries | Prepending a query with `EXPLAIN ANALYZE` both pretty-prints the query plan,
and executes it, providing run-time performance numbers for every operator, as well as the estimated cardinality (` EC`) and the actual cardinality.
```sql
EXPLAIN ANALYZE SELECT * FROM tbl;
```
Note that the **cumulative** wall-clock time that is spent on every operator is shown. When multiple threads are processing the query in parallel, the total processing time of the query may be lower than the sum of all the times spent on the individual operators.
Below is an example of running `EXPLAIN ANALYZE` on a query:
```sql
CREATE TABLE students (name VARCHAR, sid INTEGER);
CREATE TABLE exams (eid INTEGER, subject VARCHAR, sid INTEGER);
INSERT INTO students VALUES ('Mark', 1), ('Joe', 2), ('Matthew', 3);
INSERT INTO exams VALUES (10, 'Physics', 1), (20, 'Chemistry', 2), (30, 'Literature', 3);
EXPLAIN ANALYZE
SELECT name
FROM students
JOIN exams USING (sid)
WHERE name LIKE 'Ma%';
```
```text
┌─────────────────────────────────────┐
│┌───────────────────────────────────┐│
││ Total Time: 0.0008s ││
│└───────────────────────────────────┘│
└─────────────────────────────────────┘
┌───────────────────────────┐
│ EXPLAIN_ANALYZE │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ 0 │
│ (0.00s) │
└─────────────┬─────────────┘
┌─────────────┴─────────────┐
│ PROJECTION │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ name │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ 2 │
│ (0.00s) │
└─────────────┬─────────────┘
┌─────────────┴─────────────┐
│ HASH_JOIN │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ INNER │
│ sid = sid │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ├──────────────┐
│ EC: 1 │ │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │ │
│ 2 │ │
│ (0.00s) │ │
└─────────────┬─────────────┘ │
┌─────────────┴─────────────┐┌─────────────┴─────────────┐
│ SEQ_SCAN ││ FILTER │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ││ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ exams ││ prefix(name, 'Ma') │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ││ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ sid ││ EC: 1 │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ││ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ EC: 3 ││ 2 │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ││ (0.00s) │
│ 3 ││ │
│ (0.00s) ││ │
└───────────────────────────┘└─────────────┬─────────────┘
┌─────────────┴─────────────┐
│ SEQ_SCAN │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ students │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ sid │
│ name │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ Filters: name>=Ma AND name│
│ <Mb AND name IS NOT NULL │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ EC: 1 │
│ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ │
│ 2 │
│ (0.00s) │
└───────────────────────────┘
``` | 1,226 | [
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|
Guides | Meta Queries | See Also | For more information, see the [”Profiling” page](#docs:dev:profiling). | 21 | [
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|
Guides | Meta Queries | List Tables | The `SHOW TABLES` command can be used to obtain a list of all tables within the selected schema.
```sql
CREATE TABLE tbl (i INTEGER);
SHOW TABLES;
```
<div class="narrow_table"></div>
| name |
|------|
| tbl |
`SHOW` or `SHOW ALL TABLES` can be used to obtain a list of all tables within **all** attached databases and schemas.
```sql
CREATE TABLE tbl (i INTEGER);
CREATE SCHEMA s1;
CREATE TABLE s1.tbl (v VARCHAR);
SHOW ALL TABLES;
```
<div class="narrow_table"></div>
| database | schema | table_name | column_names | column_types | temporary |
|----------|--------|------------|--------------|--------------|-----------|
| memory | main | tbl | [i] | [INTEGER] | false |
| memory | s1 | tbl | [v] | [VARCHAR] | false |
To view the schema of an individual table, use the [`DESCRIBE` command](#docs:guides:meta:describe). | 237 | [
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|
Guides | Meta Queries | See Also | The SQL-standard [`information_schema`](#docs:sql:meta:information_schema) views are also defined. Moreover, DuckDB defines `sqlite_master` and many [PostgreSQL system catalog tables](https://www.postgresql.org/docs/16/catalogs.html) for compatibility with SQLite and PostgreSQL respectively. | 61 | [
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|
Guides | Meta Queries | Summarize | The `SUMMARIZE` command can be used to easily compute a number of aggregates over a table or a query.
The `SUMMARIZE` command launches a query that computes a number of aggregates over all columns (` min`, `max`, `approx_unique`, `avg`, `std`, `q25`, `q50`, `q75`, `count`), and return these along the column name, column type, and the percentage of `NULL` values in the column. | 98 | [
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|
Guides | Meta Queries | Usage | In order to summarize the contents of a table, use `SUMMARIZE` followed by the table name.
```sql
SUMMARIZE tbl;
```
In order to summarize a query, prepend `SUMMARIZE` to a query.
```sql
SUMMARIZE SELECT * FROM tbl;
``` | 63 | [
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|
Guides | Meta Queries | Example | Below is an example of `SUMMARIZE` on the `lineitem` table of TPC-H `SF1` table, generated using the [`tpch` extension](#docs:extensions:tpch).
```sql
INSTALL tpch;
LOAD tpch;
CALL dbgen(sf = 1);
```
```sql
SUMMARIZE lineitem;
```
| column_name | column_type | min | max | approx_unique | avg | std | q25 | q50 | q75 | count | null_percentage |
|-----------------|---------------|-------------|---------------------|---------------|---------------------|----------------------|---------|---------|---------|---------|-----------------|
| l_orderkey | INTEGER | 1 | 6000000 | 1508227 | 3000279.604204982 | 1732187.8734803519 | 1509447 | 2989869 | 4485232 | 6001215 | 0.0% |
| l_partkey | INTEGER | 1 | 200000 | 202598 | 100017.98932999402 | 57735.69082650496 | 49913 | 99992 | 150039 | 6001215 | 0.0% |
| l_suppkey | INTEGER | 1 | 10000 | 10061 | 5000.602606138924 | 2886.9619987306114 | 2501 | 4999 | 7500 | 6001215 | 0.0% |
| l_linenumber | INTEGER | 1 | 7 | 7 | 3.0005757167506912 | 1.7324314036519328 | 2 | 3 | 4 | 6001215 | 0.0% |
| l_quantity | DECIMAL(15,2) | 1.00 | 50.00 | 50 | 25.507967136654827 | 14.426262537016918 | 13 | 26 | 38 | 6001215 | 0.0% |
| l_extendedprice | DECIMAL(15,2) | 901.00 | 104949.50 | 923139 | 38255.138484656854 | 23300.43871096221 | 18756 | 36724 | 55159 | 6001215 | 0.0% |
| l_discount | DECIMAL(15,2) | 0.00 | 0.10 | 11 | 0.04999943011540163 | 0.03161985510812596 | 0 | 0 | 0 | 6001215 | 0.0% |
| l_tax | DECIMAL(15,2) | 0.00 | 0.08 | 9 | 0.04001350893110812 | 0.025816551798842728 | 0 | 0 | 0 | 6001215 | 0.0% |
| l_returnflag | VARCHAR | A | R | 3 | NULL | NULL | NULL | NULL | NULL | 6001215 | 0.0% |
| l_linestatus | VARCHAR | F | O | 2 | NULL | NULL | NULL | NULL | NULL | 6001215 | 0.0% |
| l_shipdate | DATE | 1992-01-02 | 1998-12-01 | 2516 | NULL | NULL | NULL | NULL | NULL | 6001215 | 0.0% |
| l_commitdate | DATE | 1992-01-31 | 1998-10-31 | 2460 | NULL | NULL | NULL | NULL | NULL | 6001215 | 0.0% |
| l_receiptdate | DATE | 1992-01-04 | 1998-12-31 | 2549 | NULL | NULL | NULL | NULL | NULL | 6001215 | 0.0% |
| l_shipinstruct | VARCHAR | COLLECT COD | TAKE BACK RETURN | 4 | NULL | NULL | NULL | NULL | NULL | 6001215 | 0.0% |
| l_shipmode | VARCHAR | AIR | TRUCK | 7 | NULL | NULL | NULL | NULL | NULL | 6001215 | 0.0% |
| l_comment | VARCHAR | Tiresias | zzle? furiously iro | 3558599 | NULL | NULL | NULL | NULL | NULL | 6001215 | 0.0% | | 1,160 | [
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|
Guides | Meta Queries | Using `SUMMARIZE` in a Subquery | `SUMMARIZE` can be used a subquery. This allows creating a table from the summary, for example:
```sql
CREATE TABLE tbl_summary AS SELECT * FROM (SUMMARIZE tbl);
``` | 43 | [
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|
Guides | Meta Queries | Summarizing Remote Tables | It is possible to summarize remote tables via the [`httpfs` extension](#docs:extensions:httpfs:overview) using the `SUMMARIZE TABLE` statement. For example:
```sql
SUMMARIZE TABLE 'https://blobs.duckdb.org/data/Star_Trek-Season_1.csv';
``` | 66 | [
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Guides | Meta Queries | DuckDB Environment | DuckDB provides a number of functions and `PRAGMA` options to retrieve information on the running DuckDB instance and its environment. | 27 | [
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|
Guides | Meta Queries | Version | The `version()` function returns the version number of DuckDB.
```sql
SELECT version() AS version;
```
<div class="narrow_table monospace_table"></div>
| version |
|-----------|
| v{{ site.currentduckdbversion }} |
Using a `PRAGMA`:
```sql
PRAGMA version;
```
<div class="narrow_table monospace_table"></div>
| library_version | source_id |
|-----------------|------------|
| v{{ site.currentduckdbversion }} | {{ site.currentduckdbhash }} | | 116 | [
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|
Guides | Meta Queries | Platform | The platform information consists of the operating system, system architecture, and, optionally, the compiler.
The platform is used when [installing extensions](#docs:extensions:working_with_extensions::platforms).
To retrieve the platform, use the following `PRAGMA`:
```sql
PRAGMA platform;
```
On macOS, running on Apple Silicon architecture, the result is:
| platform |
|-----------|
| osx_arm64 |
On Windows, running on an AMD64 architecture, the platform is `windows_amd64`.
On CentOS 7, running on the AMD64 architecture, the platform is `linux_amd64_gcc4`.
On Ubuntu 22.04, running on the ARM64 architecture, the platform is `linux_arm64`. | 156 | [
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|
Guides | Meta Queries | Extensions | To get a list of DuckDB extension and their status (e.g., `loaded`, `installed`), use the [`duckdb_extensions()` function](#docs:extensions:overview::listing-extensions):
```sql
SELECT *
FROM duckdb_extensions();
``` | 54 | [
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|
Guides | Meta Queries | Meta Table Functions | DuckDB has the following built-in table functions to obtain metadata about available catalog objects:
* [`duckdb_columns()`](#docs:sql:meta:duckdb_table_functions::duckdb_columns): columns
* [`duckdb_constraints()`](#docs:sql:meta:duckdb_table_functions::duckdb_constraints): constraints
* [`duckdb_databases()`](#docs:sql:meta:duckdb_table_functions::duckdb_databases): lists the databases that are accessible from within the current DuckDB process
* [`duckdb_dependencies()`](#docs:sql:meta:duckdb_table_functions::duckdb_dependencies): dependencies between objects
* [`duckdb_extensions()`](#docs:sql:meta:duckdb_table_functions::duckdb_extensions): extensions
* [`duckdb_functions()`](#docs:sql:meta:duckdb_table_functions::duckdb_functions): functions
* [`duckdb_indexes()`](#docs:sql:meta:duckdb_table_functions::duckdb_indexes): secondary indexes
* [`duckdb_keywords()`](#docs:sql:meta:duckdb_table_functions::duckdb_keywords): DuckDB's keywords and reserved words
* [`duckdb_optimizers()`](#docs:sql:meta:duckdb_table_functions::duckdb_optimizers): the available optimization rules in the DuckDB instance
* [`duckdb_schemas()`](#docs:sql:meta:duckdb_table_functions::duckdb_schemas): schemas
* [`duckdb_sequences()`](#docs:sql:meta:duckdb_table_functions::duckdb_sequences): sequences
* [`duckdb_settings()`](#docs:sql:meta:duckdb_table_functions::duckdb_settings): settings
* [`duckdb_tables()`](#docs:sql:meta:duckdb_table_functions::duckdb_tables): base tables
* [`duckdb_types()`](#docs:sql:meta:duckdb_table_functions::duckdb_types): data types
* [`duckdb_views()`](#docs:sql:meta:duckdb_table_functions::duckdb_views): views
* [`duckdb_temporary_files()`](#docs:sql:meta:duckdb_table_functions::duckdb_temporary_files): the temporary files DuckDB has written to disk, to offload data from memory | 476 | [
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|
Guides | ODBC | What is ODBC? | [ODBC](https://learn.microsoft.com/en-us/sql/odbc/microsoft-open-database-connectivity-odbc?view=sql-server-ver16) which stands for Open Database Connectivity, is a standard that allows different programs to talk to different databases including, of course, **DuckDB** 🦆. This makes it easier to build programs that work with many different databases, which saves time as developers don't have to write custom code to connect to each database. Instead, they can use the standardized ODBC interface, which reduces development time and costs, and programs are easier to maintain. However, ODBC can be slower than other methods of connecting to a database, such as using a native driver, as it adds an extra layer of abstraction between the application and the database. Furthermore, because DuckDB is column-based and ODBC is row-based, there can be some inefficiencies when using ODBC with DuckDB.
> There are links throughout this page to the official [Microsoft ODBC documentation](https://learn.microsoft.com/en-us/sql/odbc/reference/odbc-programmer-s-reference?view=sql-server-ver16), which is a great resource for learning more about ODBC. | 249 | [
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] |
|
Guides | ODBC | General Concepts | * [Handles](#::handles)
* [Connecting](#::connecting)
* [Error Handling and Diagnostics](#::error-handling-and-diagnostics)
* [Buffers and Binding](#::buffers-and-binding)
#### Handles {#docs:guides:odbc:general::handles}
A [handle](https://learn.microsoft.com/en-us/sql/odbc/reference/develop-app/handles?view=sql-server-ver16) is a pointer to a specific ODBC object which is used to interact with the database. There are several different types of handles, each with a different purpose, these are the environment handle, the connection handle, the statement handle, and the descriptor handle. Handles are allocated using the [`SQLAllocHandle`](https://learn.microsoft.com/en-us/sql/odbc/reference/syntax/sqlallochandle-function?view=sql-server-ver16) which takes as input the type of handle to allocate, and a pointer to the handle, the driver then creates a new handle of the specified type which it returns to the application.
The DuckDB ODBC driver has the following handle types. | 229 | [
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|
Guides | ODBC | General Concepts | Environment | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Handle name** |[Environment](https://learn.microsoft.com/en-us/sql/odbc/reference/develop-app/environment-handles?view=sql-server-ver16) |
| **Type name** | `SQL_HANDLE_ENV` |
| | |
|:--|:--------|
| **Description** |Manages the environment settings for ODBC operations, and provides a global context in which to access data. |
| **Use case** | Initializing ODBC, managing driver behavior, resource allocation |
| **Additional information** | Must be [allocated](https://learn.microsoft.com/en-us/sql/odbc/reference/develop-app/allocating-the-environment-handle?view=sql-server-ver16) once per application upon starting, and freed at the end. | | 180 | [
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Guides | ODBC | General Concepts | Connection | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Handle name** |[Connection](https://learn.microsoft.com/en-us/sql/odbc/reference/develop-app/connection-handles?view=sql-server-ver16) |
| **Type name** | `SQL_HANDLE_DBC` |
| | |
|:--|:--------|
| **Description** |Represents a connection to a data source. Used to establish, manage, and terminate connections. Defines both the driver and the data source to use within the driver. |
| **Use case** | Establishing a connection to a database, managing the connection state |
| **Additional information** | Multiple connection handles can be [created](https://learn.microsoft.com/en-us/sql/odbc/reference/develop-app/allocating-a-connection-handle-odbc?view=sql-server-ver16) as needed, allowing simultaneous connections to multiple data sources. *Note:* Allocating a connection handle does not establish a connection, but must be allocated first, and then used once the connection has been established. | | 231 | [
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Guides | ODBC | General Concepts | Statement | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Handle name** |[Statement](https://learn.microsoft.com/en-us/sql/odbc/reference/develop-app/statement-handles?view=sql-server-ver16)
| **Type name** | `SQL_HANDLE_STMT`
| | |
|:--|:--------|
| **Description** |Handles the execution of SQL statements, as well as the returned result sets.
| **Use case** | Executing SQL queries, fetching result sets, managing statement options.
| **Additional information** | To facilitate the execution of concurrent queries, multiple handles can be [allocated](https://learn.microsoft.com/en-us/sql/odbc/reference/develop-app/allocating-a-statement-handle-odbc?view=sql-server-ver16) per connection. | 177 | [
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Guides | ODBC | General Concepts | Descriptor | <div class="nostroke_table"></div>
| | |
|:--|:--------|
| **Handle name** |[Descriptor](https://learn.microsoft.com/en-us/sql/odbc/reference/develop-app/descriptor-handles?view=sql-server-ver16)
| **Type name** | `SQL_HANDLE_DESC`
| | |
|:--|:--------|
| **Description** |Describes the attributes of a data structure or parameter, and allows the application to specify the structure of data to be bound/retrieved.
| **Use case** | Describing table structures, result sets, binding columns to application buffers
| **Additional information** | Used in situations where data structures need to be explicitly defined, for example during parameter binding or result set fetching. They are automatically allocated when a statement is allocated, but can also be allocated explicitly.
#### Connecting {#docs:guides:odbc:general::connecting}
The first step is to connect to the data source so that the application can perform database operations. First the application must allocate an environment handle, and then a connection handle. The connection handle is then used to connect to the data source. There are two functions which can be used to connect to a data source, [`SQLDriverConnect`](https://learn.microsoft.com/en-us/sql/odbc/reference/syntax/sqldriverconnect-function?view=sql-server-ver16) and [`SQLConnect`](https://learn.microsoft.com/en-us/sql/odbc/reference/syntax/sqlconnect-function?view=sql-server-ver16). The former is used to connect to a data source using a connection string, while the latter is used to connect to a data source using a DSN. | 358 | [
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Guides | ODBC | General Concepts | Connection String | A [connection string](https://learn.microsoft.com/en-us/sql/odbc/reference/develop-app/connection-strings?view=sql-server-ver16) is a string which contains the information needed to connect to a data source. It is formatted as a semicolon separated list of key-value pairs, however DuckDB currently only utilizes the DSN and ignores the rest of the parameters. | 79 | [
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Guides | ODBC | General Concepts | DSN | A DSN (_Data Source Name_) is a string that identifies a database. It can be a file path, URL, or a database name. For example: `C:\Users\me\duckdb.db` and `DuckDB` are both valid DSNs. More information on DSNs can be found on the [“Choosing a Data Source or Driver” page of the SQL Server documentation](https://learn.microsoft.com/en-us/sql/odbc/reference/develop-app/choosing-a-data-source-or-driver?view=sql-server-ver16).
#### Error Handling and Diagnostics {#docs:guides:odbc:general::error-handling-and-diagnostics}
All functions in ODBC return a code which represents the success or failure of the function. This allows for easy error handling, as the application can simply check the return code of each function call to determine if it was successful. When unsuccessful, the application can then use the [`SQLGetDiagRec`](https://learn.microsoft.com/en-us/sql/odbc/reference/syntax/sqlgetdiagrec-function?view=sql-server-ver16) function to retrieve the error information. The following table defines the [return codes](https://learn.microsoft.com/en-us/sql/odbc/reference/develop-app/return-codes-odbc?view=sql-server-ver16):
| Return code | Description |
|----------|---------------------|
| `SQL_SUCCESS` | The function completed successfully. |
| `SQL_SUCCESS_WITH_INFO` | The function completed successfully, but additional information is available, including a warning |
| `SQL_ERROR` | The function failed. |
| `SQL_INVALID_HANDLE` | The handle provided was invalid, indicating a programming error, i.e., when a handle is not allocated before it is used, or is the wrong type |
| `SQL_NO_DATA` | The function completed successfully, but no more data is available |
| `SQL_NEED_DATA` | More data is needed, such as when a parameter data is sent at execution time, or additional connection information is required. |
| `SQL_STILL_EXECUTING` | A function that was asynchronously executed is still executing. |
#### Buffers and Binding {#docs:guides:odbc:general::buffers-and-binding}
A buffer is a block of memory used to store data. Buffers are used to store data retrieved from the database, or to send data to the database. Buffers are allocated by the application, and then bound to a column in a result set, or a parameter in a query, using the [`SQLBindCol`](https://learn.microsoft.com/en-us/sql/odbc/reference/syntax/sqlbindcol-function?view=sql-server-ver16) and [`SQLBindParameter`](https://learn.microsoft.com/en-us/sql/odbc/reference/syntax/sqlbindparameter-function?view=sql-server-ver16) functions. When the application fetches a row from the result set, or executes a query, the data is stored in the buffer. When the application sends a query to the database, the data in the buffer is sent to the database. | 662 | [
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Guides | ODBC | Setting up an Application | The following is a step-by-step guide to setting up an application that uses ODBC to connect to a database, execute a query, and fetch the results in `C++`.
> To install the driver as well as anything else you will need follow these [instructions](#docs:api:odbc:overview).
#### 1. Include the SQL Header Files {#docs:guides:odbc:general::1-include-the-sql-header-files}
The first step is to include the SQL header files:
```cpp
#include <sql.h>
#include <sqlext.h>
```
These files contain the definitions of the ODBC functions, as well as the data types used by ODBC. In order to be able to use these header files you have to have the `unixodbc` package installed:
On macOS:
```bash
brew install unixodbc
```
On Ubuntu and Debian:
```bash
sudo apt-get install -y unixodbc-dev
```
On Fedora, CentOS, and Red Hat:
```bash
sudo yum install -y unixODBC-devel
```
Remember to include the header file location in your `CFLAGS`.
For `MAKEFILE`:
```make
CFLAGS=-I/usr/local/include
# or
CFLAGS=-/opt/homebrew/Cellar/unixodbc/2.3.11/include
```
For `CMAKE`:
```cmake
include_directories(/usr/local/include)
# or
include_directories(/opt/homebrew/Cellar/unixodbc/2.3.11/include)
```
You also have to link the library in your `CMAKE` or `MAKEFILE`.
For `CMAKE`:
```cmake
target_link_libraries(ODBC_application /path/to/duckdb_odbc/libduckdb_odbc.dylib)
```
For `MAKEFILE`:
```make
LDLIBS=-L/path/to/duckdb_odbc/libduckdb_odbc.dylib
```
#### 2. Define the ODBC Handles and Connect to the Database {#docs:guides:odbc:general::2-define-the-odbc-handles-and-connect-to-the-database} | 462 | [
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|
Guides | ODBC | Setting up an Application | 2.a. Connecting with SQLConnect | Then set up the ODBC handles, allocate them, and connect to the database. First the environment handle is allocated, then the environment is set to ODBC version 3, then the connection handle is allocated, and finally the connection is made to the database. The following code snippet shows how to do this:
```cpp
SQLHANDLE env;
SQLHANDLE dbc;
SQLAllocHandle(SQL_HANDLE_ENV, SQL_NULL_HANDLE, &env);
SQLSetEnvAttr(env, SQL_ATTR_ODBC_VERSION, (void*)SQL_OV_ODBC3, 0);
SQLAllocHandle(SQL_HANDLE_DBC, env, &dbc);
std::string dsn = "DSN=duckdbmemory";
SQLConnect(dbc, (SQLCHAR*)dsn.c_str(), SQL_NTS, NULL, 0, NULL, 0);
std::cout << "Connected!" << std::endl;
``` | 183 | [
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Guides | ODBC | Setting up an Application | 2.b. Connecting with SQLDriverConnect | Alternatively, you can connect to the ODBC driver using [`SQLDriverConnect`](https://learn.microsoft.com/en-us/sql/odbc/reference/syntax/sqldriverconnect-function?view=sql-server-ver16).
`SQLDriverConnect` accepts a connection string in which you can configure the database using any of the available [DuckDB configuration options](#docs:configuration:overview).
```cpp
SQLHANDLE env;
SQLHANDLE dbc;
SQLAllocHandle(SQL_HANDLE_ENV, SQL_NULL_HANDLE, &env);
SQLSetEnvAttr(env, SQL_ATTR_ODBC_VERSION, (void*)SQL_OV_ODBC3, 0);
SQLAllocHandle(SQL_HANDLE_DBC, env, &dbc);
SQLCHAR str[1024];
SQLSMALLINT strl;
std::string dsn = "DSN=DuckDB;allow_unsigned_extensions=true;access_mode=READ_ONLY"
SQLDriverConnect(dbc, nullptr, (SQLCHAR*)dsn.c_str(), SQL_NTS, str, sizeof(str), &strl, SQL_DRIVER_COMPLETE)
std::cout << "Connected!" << std::endl;
```
#### 3. Adding a Query {#docs:guides:odbc:general::3-adding-a-query}
Now that the application is set up, we can add a query to it. First, we need to allocate a statement handle:
```cpp
SQLHANDLE stmt;
SQLAllocHandle(SQL_HANDLE_STMT, dbc, &stmt);
```
Then we can execute a query:
```cpp
SQLExecDirect(stmt, (SQLCHAR*)"SELECT * FROM integers", SQL_NTS);
```
#### 4. Fetching Results {#docs:guides:odbc:general::4-fetching-results}
Now that we have executed a query, we can fetch the results. First, we need to bind the columns in the result set to buffers:
```cpp
SQLLEN int_val;
SQLLEN null_val;
SQLBindCol(stmt, 1, SQL_C_SLONG, &int_val, 0, &null_val);
```
Then we can fetch the results:
```cpp
SQLFetch(stmt);
```
#### 5. Go Wild {#docs:guides:odbc:general::5-go-wild}
Now that we have the results, we can do whatever we want with them. For example, we can print them:
```cpp
std::cout << "Value: " << int_val << std::endl;
```
or do any other processing we want. As well as executing more queries and doing any thing else we want to do with the database such as inserting, updating, or deleting data.
#### 6. Free the Handles and Disconnecting {#docs:guides:odbc:general::6-free-the-handles-and-disconnecting}
Finally, we need to free the handles and disconnect from the database. First, we need to free the statement handle:
```cpp
SQLFreeHandle(SQL_HANDLE_STMT, stmt);
```
Then we need to disconnect from the database:
```cpp
SQLDisconnect(dbc);
```
And finally, we need to free the connection handle and the environment handle:
```cpp
SQLFreeHandle(SQL_HANDLE_DBC, dbc);
SQLFreeHandle(SQL_HANDLE_ENV, env);
```
Freeing the connection and environment handles can only be done after the connection to the database has been closed. Trying to free them before disconnecting from the database will result in an error. | 726 | [
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