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Extensions | AWS Extension | Installing and Loading | The `aws` extension will be transparently [autoloaded](#docs:extensions:overview::autoloading-extensions) on first use from the official extension repository.
If you would like to install and load it manually, run:
```sql
INSTALL aws;
LOAD aws;
``` | 59 | [
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|
Extensions | AWS Extension | Related Extensions | `aws` depends on `httpfs` extension capabilities, and both will be autoloaded on the first call to `load_aws_credentials`.
If autoinstall or autoload are disabled, you can always explicitly install and load `httpfs` as follows:
```sql
INSTALL httpfs;
LOAD httpfs;
``` | 64 | [
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|
Extensions | AWS Extension | Legacy Features | > **Deprecated. ** The `load_aws_credentials` function is deprecated.
Prior to version 0.10.0, DuckDB did not have a [Secrets manager](#docs:sql:statements:create_secret), to load the credentials automatically, the AWS extension provided
a special function to load the AWS credentials in the [legacy authentication method](#docs:extensions:httpfs:s3api_legacy_authentication).
| Function | Type | Description |
|---|---|-------|
| `load_aws_credentials` | `PRAGMA` function | Loads the AWS credentials through the [AWS Default Credentials Provider Chain](https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/credentials-chain.html). |
#### Load AWS Credentials (Legacy) {#docs:extensions:aws::load-aws-credentials-legacy}
To load the AWS credentials, run:
```sql
CALL load_aws_credentials();
```
<div class="narrow_table monospace_table"></div>
| loaded_access_key_id | loaded_secret_access_key | loaded_session_token | loaded_region |
|----------------------|--------------------------|----------------------|---------------|
| AKIAIOSFODNN7EXAMPLE | `<redacted>` | NULL | us-east-2 |
The function takes a string parameter to specify a specific profile:
```sql
CALL load_aws_credentials('minio-testing-2');
```
<div class="narrow_table monospace_table"></div>
| loaded_access_key_id | loaded_secret_access_key | loaded_session_token | loaded_region |
|----------------------|--------------------------|----------------------|---------------|
| minio_duckdb_user_2 | `<redacted>` | NULL | NULL |
There are several parameters to tweak the behavior of the call:
```sql
CALL load_aws_credentials('minio-testing-2', set_region = false, redact_secret = false);
```
<div class="narrow_table monospace_table"></div>
| loaded_access_key_id | loaded_secret_access_key | loaded_session_token | loaded_region |
|----------------------|------------------------------|----------------------|---------------|
| minio_duckdb_user_2 | minio_duckdb_user_password_2 | NULL | NULL | | 469 | [
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|
Extensions | Azure Extension | The `azure` extension is a loadable extension that adds a filesystem abstraction for the [Azure Blob storage](https://azure.microsoft.com/en-us/products/storage/blobs) to DuckDB. | 38 | [
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||
Extensions | Azure Extension | Installing and Loading | The `azure` extension will be transparently [autoloaded](#docs:extensions:overview::autoloading-extensions) on first use from the official extension repository.
If you would like to install and load it manually, run:
```sql
INSTALL azure;
LOAD azure;
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|
Extensions | Azure Extension | Usage | Once the [authentication](#::authentication) is set up, you can query Azure storage as follows:
#### Azure Blob Storage {#docs:extensions:azure::azure-blob-storage}
Allowed URI schemes: `az` or `azure`
```sql
SELECT count(*)
FROM 'az://⟨my_container⟩/⟨path⟩/⟨my_file⟩.⟨parquet_or_csv⟩';
```
Globs are also supported:
```sql
SELECT *
FROM 'az://⟨my_container⟩/⟨path⟩/*.csv';
```
```sql
SELECT *
FROM 'az://⟨my_container⟩/⟨path⟩/**';
```
Or with a fully qualified path syntax:
```sql
SELECT count(*)
FROM 'az://⟨my_storage_account⟩.blob.core.windows.net/⟨my_container⟩/⟨path⟩/⟨my_file⟩.⟨parquet_or_csv⟩';
```
```sql
SELECT *
FROM 'az://⟨my_storage_account⟩.blob.core.windows.net/⟨my_container⟩/⟨path⟩/*.csv';
```
#### Azure Data Lake Storage (ADLS) {#docs:extensions:azure::azure-data-lake-storage-adls}
Allowed URI schemes: `abfss`
```sql
SELECT count(*)
FROM 'abfss://⟨my_filesystem⟩/⟨path⟩/⟨my_file⟩.⟨parquet_or_csv⟩';
```
Globs are also supported:
```sql
SELECT *
FROM 'abfss://⟨my_filesystem⟩/⟨path⟩/*.csv';
```
```sql
SELECT *
FROM 'abfss://⟨my_filesystem⟩/⟨path⟩/**';
```
Or with a fully qualified path syntax:
```sql
SELECT count(*)
FROM 'abfss://⟨my_storage_account⟩.dfs.core.windows.net/⟨my_filesystem⟩/⟨path⟩/⟨my_file⟩.⟨parquet_or_csv⟩';
```
```sql
SELECT *
FROM 'abfss://⟨my_storage_account⟩.dfs.core.windows.net/⟨my_filesystem⟩/⟨path⟩/*.csv';
``` | 476 | [
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|
Extensions | Azure Extension | Configuration | Use the following [configuration options](#docs:configuration:overview) how the extension reads remote files:
| Name | Description | Type | Default |
|:---|:---|:---|:---|
| `azure_http_stats` | Include http info from Azure Storage in the [`EXPLAIN ANALYZE` statement](#docs:dev:profiling). | `BOOLEAN` | `false` |
| `azure_read_transfer_concurrency` | Maximum number of threads the Azure client can use for a single parallel read. If `azure_read_transfer_chunk_size` is less than `azure_read_buffer_size` then setting this > 1 will allow the Azure client to do concurrent requests to fill the buffer. | `BIGINT` | `5` |
| `azure_read_transfer_chunk_size` | Maximum size in bytes that the Azure client will read in a single request. It is recommended that this is a factor of `azure_read_buffer_size`. | `BIGINT` | `1024*1024` |
| `azure_read_buffer_size` | Size of the read buffer. It is recommended that this is evenly divisible by `azure_read_transfer_chunk_size`. | `UBIGINT` | `1024*1024` |
| `azure_transport_option_type` | Underlying [adapter](https://github.com/Azure/azure-sdk-for-cpp/blob/main/doc/HttpTransportAdapter.md) to use in the Azure SDK. Valid values are: `default` or `curl`. | `VARCHAR` | `default` |
| `azure_context_caching` | Enable/disable the caching of the underlying Azure SDK HTTP connection in the DuckDB connection context when performing queries. If you suspect that this is causing some side effect, you can try to disable it by setting it to false (not recommended). | `BOOLEAN` | `true` |
> Setting `azure_transport_option_type` explicitly to `curl` with have the following effect:
> * On Linux, this may solve certificates issue (` Error: Invalid Error: Fail to get a new connection for: https://⟨storage account name⟩.blob.core.windows.net/. Problem with the SSL CA cert (path? access rights?)`) because when specifying the extension will try to find the bundle certificate in various paths (that is not done by *curl* by default and might be wrong due to static linking).
> * On Windows, this replaces the default adapter (*WinHTTP*) allowing you to use all *curl* capabilities (for example using a socks proxies).
> * On all operating systems, it will honor the following environment variables:
> * `CURL_CA_INFO`: Path to a PEM encoded file containing the certificate authorities sent to libcurl. Note that this option is known to only work on Linux and might throw if set on other platforms.
> * `CURL_CA_PATH`: Path to a directory which holds PEM encoded file, containing the certificate authorities sent to libcurl.
Example:
```sql
SET azure_http_stats = false;
SET azure_read_transfer_concurrency = 5;
SET azure_read_transfer_chunk_size = 1_048_576;
SET azure_read_buffer_size = 1_048_576;
``` | 655 | [
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|
Extensions | Azure Extension | Authentication | The Azure extension has two ways to configure the authentication. The preferred way is to use Secrets.
#### Authentication with Secret {#docs:extensions:azure::authentication-with-secret}
Multiple [Secret Providers](#docs:configuration:secrets_manager::secret-providers) are available for the Azure extension:
* If you need to define different secrets for different storage accounts, use [the `SCOPE` configuration](#docs:configuration:secrets_manager::creating-multiple-secrets-for-the-same-service-type). Note that the `SCOPE` requires a trailing slash (` SCOPE 'azure://some_container/'`).
* If you use fully qualified path then the `ACCOUNT_NAME` attribute is optional. | 145 | [
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|
Extensions | Azure Extension | Authentication | `CONFIG` Provider | The default provider, `CONFIG` (i.e., user-configured), allows access to the storage account using a connection string or anonymously. For example:
```sql
CREATE SECRET secret1 (
TYPE AZURE,
CONNECTION_STRING '⟨value⟩'
);
```
If you do not use authentication, you still need to specify the storage account name. For example:
```sql
CREATE SECRET secret2 (
TYPE AZURE,
PROVIDER CONFIG,
ACCOUNT_NAME '⟨storage account name⟩'
);
```
The default `PROVIDER` is `CONFIG`. | 118 | [
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Extensions | Azure Extension | Authentication | `CREDENTIAL_CHAIN` Provider | The `CREDENTIAL_CHAIN` provider allows connecting using credentials automatically fetched by the Azure SDK via the Azure credential chain.
By default, the `DefaultAzureCredential` chain used, which tries credentials according to the order specified by the [Azure documentation](https://learn.microsoft.com/en-us/javascript/api/@azure/identity/defaultazurecredential?view=azure-node-latest#@azure-identity-defaultazurecredential-constructor).
For example:
```sql
CREATE SECRET secret3 (
TYPE AZURE,
PROVIDER CREDENTIAL_CHAIN,
ACCOUNT_NAME '⟨storage account name⟩'
);
```
DuckDB also allows specifying a specific chain using the `CHAIN` keyword. This takes a semicolon-separated list (` a;b;c`) of providers that will be tried in order. For example:
```sql
CREATE SECRET secret4 (
TYPE AZURE,
PROVIDER CREDENTIAL_CHAIN,
CHAIN 'cli;env',
ACCOUNT_NAME '⟨storage account name⟩'
);
```
The possible values are the following:
[`cli`](https://learn.microsoft.com/en-us/cli/azure/authenticate-azure-cli);
[`managed_identity`](https://learn.microsoft.com/en-us/entra/identity/managed-identities-azure-resources/overview);
[`env`](https://github.com/Azure/azure-sdk-for-cpp/blob/azure-identity_1.6.0/sdk/identity/azure-identity/README.md#environment-variables);
[`default`](https://github.com/Azure/azure-sdk-for-cpp/blob/azure-identity_1.6.0/sdk/identity/azure-identity/README.md#defaultazurecredential);
If no explicit `CHAIN` is provided, the default one will be [`default`](https://github.com/Azure/azure-sdk-for-cpp/blob/azure-identity_1.6.0/sdk/identity/azure-identity/README.md#defaultazurecredential) | 388 | [
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Extensions | Azure Extension | Authentication | `SERVICE_PRINCIPAL` Provider | The `SERVICE_PRINCIPAL` provider allows connecting using a [Azure Service Principal (SPN)](https://learn.microsoft.com/en-us/entra/architecture/service-accounts-principal).
Either with a secret:
```sql
CREATE SECRET azure_spn (
TYPE AZURE,
PROVIDER SERVICE_PRINCIPAL,
TENANT_ID '⟨tenant id⟩',
CLIENT_ID '⟨client id⟩',
CLIENT_SECRET '⟨client secret⟩',
ACCOUNT_NAME '⟨storage account name⟩'
);
```
Or with a certificate:
```sql
CREATE SECRET azure_spn_cert (
TYPE AZURE,
PROVIDER SERVICE_PRINCIPAL,
TENANT_ID '⟨tenant id⟩',
CLIENT_ID '⟨client id⟩',
CLIENT_CERTIFICATE_PATH '⟨client cert path⟩',
ACCOUNT_NAME '⟨storage account name⟩'
);
``` | 184 | [
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Extensions | Azure Extension | Authentication | Configuring a Proxy | To configure proxy information when using secrets, you can add `HTTP_PROXY`, `PROXY_USER_NAME`, and `PROXY_PASSWORD` in the secret definition. For example:
```sql
CREATE SECRET secret5 (
TYPE AZURE,
CONNECTION_STRING '⟨value⟩',
HTTP_PROXY 'http://localhost:3128',
PROXY_USER_NAME 'john',
PROXY_PASSWORD 'doe'
);
```
> * When using secrets, the `HTTP_PROXY` environment variable will still be honored except if you provide an explicit value for it.
> * When using secrets, the `SET` variable of the *Authentication with variables* session will be ignored.
> * The Azure `CREDENTIAL_CHAIN` provider, the actual token is fetched at query time, not at the time of creating the secret.
#### Authentication with Variables (Deprecated) {#docs:extensions:azure::authentication-with-variables-deprecated}
```sql
SET variable_name = variable_value;
```
Where `variable_name` can be one of the following:
| Name | Description | Type | Default |
|:---|:---|:---|:---|
| `azure_storage_connection_string` | Azure connection string, used for authenticating and configuring Azure requests. | `STRING` | - |
| `azure_account_name` | Azure account name, when set, the extension will attempt to automatically detect credentials (not used if you pass the connection string). | `STRING` | - |
| `azure_endpoint` | Override the Azure endpoint for when the Azure credential providers are used. | `STRING` | `blob.core.windows.net` |
| `azure_credential_chain`| Ordered list of Azure credential providers, in string format separated by `;`. For example: `'cli;managed_identity;env'`. See the list of possible values in the [`CREDENTIAL_CHAIN` provider section](#::credential_chain-provider). Not used if you pass the connection string. | `STRING` | - |
| `azure_http_proxy`| Proxy to use when login & performing request to Azure. | `STRING` | `HTTP_PROXY` environment variable (if set). |
| `azure_proxy_user_name`| Http proxy username if needed. | `STRING` | - |
| `azure_proxy_password`| Http proxy password if needed. | `STRING` | - | | 480 | [
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Extensions | Azure Extension | Additional Information | #### Logging {#docs:extensions:azure::logging}
The Azure extension relies on the Azure SDK to connect to Azure Blob storage and supports printing the SDK logs to the console.
To control the log level, set the [`AZURE_LOG_LEVEL`](https://github.com/Azure/azure-sdk-for-cpp/blob/main/sdk/core/azure-core/README.md#sdk-log-messages) environment variable.
For instance, verbose logs can be enabled as follows in Python:
```python
import os
import duckdb
os.environ["AZURE_LOG_LEVEL"] = "verbose"
duckdb.sql("CREATE SECRET myaccount (TYPE AZURE, PROVIDER CREDENTIAL_CHAIN, SCOPE 'az://myaccount.blob.core.windows.net/')")
duckdb.sql("SELECT count(*) FROM 'az://myaccount.blob.core.windows.net/path/to/blob.parquet'")
```
#### Difference between ADLS and Blob Storage {#docs:extensions:azure::difference-between-adls-and-blob-storage}
Even though ADLS implements similar functionality as the Blob storage, there are some important performance benefits to using the ADLS endpoints for globbing, especially when using (complex) glob patterns.
To demonstrate, lets look at an example of how the a glob is performed internally using respectively the Glob and ADLS endpoints.
Using the following filesystem:
```text
root
├── l_receipmonth=1997-10
│ ├── l_shipmode=AIR
│ │ └── data_0.csv
│ ├── l_shipmode=SHIP
│ │ └── data_0.csv
│ └── l_shipmode=TRUCK
│ └── data_0.csv
├── l_receipmonth=1997-11
│ ├── l_shipmode=AIR
│ │ └── data_0.csv
│ ├── l_shipmode=SHIP
│ │ └── data_0.csv
│ └── l_shipmode=TRUCK
│ └── data_0.csv
└── l_receipmonth=1997-12
├── l_shipmode=AIR
│ └── data_0.csv
├── l_shipmode=SHIP
│ └── data_0.csv
└── l_shipmode=TRUCK
└── data_0.csv
```
The following query performed through the blob endpoint
```sql
SELECT count(*)
FROM 'az://root/l_receipmonth=1997-*/l_shipmode=SHIP/*.csv';
```
will perform the following steps:
* List all the files with the prefix `root/l_receipmonth=1997-`
* `root/l_receipmonth=1997-10/l_shipmode=SHIP/data_0.csv`
* `root/l_receipmonth=1997-10/l_shipmode=AIR/data_0.csv`
* `root/l_receipmonth=1997-10/l_shipmode=TRUCK/data_0.csv`
* `root/l_receipmonth=1997-11/l_shipmode=SHIP/data_0.csv`
* `root/l_receipmonth=1997-11/l_shipmode=AIR/data_0.csv`
* `root/l_receipmonth=1997-11/l_shipmode=TRUCK/data_0.csv`
* `root/l_receipmonth=1997-12/l_shipmode=SHIP/data_0.csv`
* `root/l_receipmonth=1997-12/l_shipmode=AIR/data_0.csv`
* `root/l_receipmonth=1997-12/l_shipmode=TRUCK/data_0.csv`
* Filter the result with the requested pattern `root/l_receipmonth=1997-*/l_shipmode=SHIP/*.csv`
* `root/l_receipmonth=1997-10/l_shipmode=SHIP/data_0.csv`
* `root/l_receipmonth=1997-11/l_shipmode=SHIP/data_0.csv`
* `root/l_receipmonth=1997-12/l_shipmode=SHIP/data_0.csv`
Meanwhile, the same query performed through the datalake endpoint,
```sql
SELECT count(*)
FROM 'abfss://root/l_receipmonth=1997-*/l_shipmode=SHIP/*.csv';
```
will perform the following steps:
* List all directories in `root/`
* `root/l_receipmonth=1997-10`
* `root/l_receipmonth=1997-11`
* `root/l_receipmonth=1997-12`
* Filter and list subdirectories: `root/l_receipmonth=1997-10`, `root/l_receipmonth=1997-11`, `root/l_receipmonth=1997-12`
* `root/l_receipmonth=1997-10/l_shipmode=SHIP`
* `root/l_receipmonth=1997-10/l_shipmode=AIR`
* `root/l_receipmonth=1997-10/l_shipmode=TRUCK`
* `root/l_receipmonth=1997-11/l_shipmode=SHIP`
* `root/l_receipmonth=1997-11/l_shipmode=AIR`
* `root/l_receipmonth=1997-11/l_shipmode=TRUCK`
* `root/l_receipmonth=1997-12/l_shipmode=SHIP`
* `root/l_receipmonth=1997-12/l_shipmode=AIR`
* `root/l_receipmonth=1997-12/l_shipmode=TRUCK`
* Filter and list subdirectories: `root/l_receipmonth=1997-10/l_shipmode=SHIP`, `root/l_receipmonth=1997-11/l_shipmode=SHIP`, `root/l_receipmonth=1997-12/l_shipmode=SHIP`
* `root/l_receipmonth=1997-10/l_shipmode=SHIP/data_0.csv`
* `root/l_receipmonth=1997-11/l_shipmode=SHIP/data_0.csv`
* `root/l_receipmonth=1997-12/l_shipmode=SHIP/data_0.csv`
As you can see because the Blob endpoint does not support the notion of directories, the filter can only be performed after the listing, whereas the ADLS endpoint will list files recursively. Especially with higher partition/directory counts, the performance difference can be very significant. | 1,389 | [
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|
Extensions | Delta Extension | The `delta` extension adds support for the [Delta Lake open-source storage format](https://delta.io/). It is built using the [Delta Kernel](https://github.com/delta-incubator/delta-kernel-rs). The extension offers **read support** for Delta tables, both local and remote.
For implementation details, see the [announcement blog post](https://duckdb.org/2024/06/10/delta).
> **Warning. ** The `delta` extension is currently experimental and is [only supported on given platforms](#::supported-duckdb-versions-and-platforms). | 127 | [
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||
Extensions | Delta Extension | Installing and Loading | The `delta` extension will be transparently [autoloaded](#docs:extensions:overview::autoloading-extensions) on first use from the official extension repository.
If you would like to install and load it manually, run:
```sql
INSTALL delta;
LOAD delta;
``` | 59 | [
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|
Extensions | Delta Extension | Usage | To scan a local Delta table, run:
```sql
SELECT *
FROM delta_scan('file:///some/path/on/local/machine');
```
#### Reading from an S3 Bucket {#docs:extensions:delta::reading-from-an-s3-bucket}
To scan a Delta table in an [S3 bucket](#docs:extensions:httpfs:s3api), run:
```sql
SELECT *
FROM delta_scan('s3://some/delta/table');
```
For authenticating to S3 buckets, DuckDB [Secrets](#docs:configuration:secrets_manager) are supported:
```sql
CREATE SECRET (
TYPE S3,
PROVIDER CREDENTIAL_CHAIN
);
SELECT *
FROM delta_scan('s3://some/delta/table/with/auth');
```
To scan public buckets on S3, you may need to pass the correct region by creating a secret containing the region of your public S3 bucket:
```sql
CREATE SECRET (
TYPE S3,
REGION 'my-region'
);
SELECT *
FROM delta_scan('s3://some/public/table/in/my-region');
```
#### Reading from Azure Blob Storage {#docs:extensions:delta::reading-from-azure-blob-storage}
To scan a Delta table in an [Azure Blob Storage bucket](#docs:extensions:azure::azure-blob-storage), run:
```sql
SELECT *
FROM delta_scan('az://my-container/my-table');
```
For authenticating to Azure Blob Storage, DuckDB [Secrets](#docs:configuration:secrets_manager) are supported:
```sql
CREATE SECRET (
TYPE AZURE,
PROVIDER CREDENTIAL_CHAIN
);
SELECT *
FROM delta_scan('az://my-container/my-table-with-auth');
``` | 357 | [
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|
Extensions | Delta Extension | Features | While the `delta` extension is still experimental, many (scanning) features and optimizations are already supported:
* multithreaded scans and Parquet metadata reading
* data skipping/filter pushdown
* skipping row-groups in file (based on Parquet metadata)
* skipping complete files (based on Delta partition information)
* projection pushdown
* scanning tables with deletion vectors
* all primitive types
* structs
* S3 support with secrets
More optimizations are going to be released in the future. | 104 | [
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|
Extensions | Delta Extension | Supported DuckDB Versions and Platforms | The `delta` extension requires DuckDB version 0.10.3 or newer.
The `delta` extension currently only supports the following platforms:
* Linux AMD64 (x86_64 and ARM64): `linux_amd64`, `linux_amd64_gcc4`, and `linux_arm64`
* macOS Intel and Apple Silicon: `osx_amd64` and `osx_arm64`
* Windows AMD64: `windows_amd64`
Support for the [other DuckDB platforms](#docs:extensions:working_with_extensions::platforms) is work-in-progress. | 125 | [
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|
Extensions | Excel Extension | The `excel` extension, unlike what its name may suggest, does not provide support for reading Excel files.
Instead, provides a function that wraps the number formatting functionality of the [i18npool library](https://www.openoffice.org/l10n/i18n_framework/index.html), which formats numbers per Excel's formatting rules.
Excel files can be currently handled through the [`spatial` extension](#docs:extensions:spatial:overview): see the [Excel Import](#docs:guides:file_formats:excel_import) and [Excel Export](#docs:guides:file_formats:excel_export) pages for instructions. | 131 | [
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||
Extensions | Excel Extension | Installing and Loading | The `excel` extension will be transparently [autoloaded](#docs:extensions:overview::autoloading-extensions) on first use from the official extension repository.
If you would like to install and load it manually, run:
```sql
INSTALL excel;
LOAD excel;
``` | 59 | [
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|
Extensions | Excel Extension | Functions | | Function | Description |
|:--|:---|
| `excel_text(number, format_string)`| Format the given `number` per the rules given in the `format_string`. |
| `text(number, format_string)` | Alias for `excel_text`. | | 55 | [
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|
Extensions | Excel Extension | Examples | ```sql
SELECT excel_text(1_234_567.897, 'h:mm AM/PM') AS timestamp;
```
| timestamp |
|-----------|
| 9:31 PM |
```sql
SELECT excel_text(1_234_567.897, 'h AM/PM') AS timestamp;
```
| timestamp |
|-----------|
| 9 PM | | 80 | [
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||
Extensions | Full-Text Search Extension | Installing and Loading | The `fts` extension will be transparently [autoloaded](#docs:extensions:overview::autoloading-extensions) on first use from the official extension repository.
If you would like to install and load it manually, run:
```sql
INSTALL fts;
LOAD fts;
``` | 61 | [
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|
Extensions | Full-Text Search Extension | Usage | The extension adds two `PRAGMA` statements to DuckDB: one to create, and one to drop an index. Additionally, a scalar macro `stem` is added, which is used internally by the extension.
#### `PRAGMA create_fts_index` {#docs:extensions:full_text_search::pragma-create_fts_index}
```python
create_fts_index(input_table, input_id, *input_values, stemmer = 'porter',
stopwords = 'english', ignore = '(\\.|[^a-z])+',
strip_accents = 1, lower = 1, overwrite = 0)
```
`PRAGMA` that creates a FTS index for the specified table.
<!-- markdownlint-disable MD056 -->
| Name | Type | Description |
|:--|:--|:----------|
| `input_table` | `VARCHAR` | Qualified name of specified table, e.g., `'table_name'` or `'main.table_name'` |
| `input_id` | `VARCHAR` | Column name of document identifier, e.g., `'document_identifier'` |
| `input_values…` | `VARCHAR` | Column names of the text fields to be indexed (vararg), e.g., `'text_field_1'`, `'text_field_2'`, ..., `'text_field_N'`, or `'\*'` for all columns in input_table of type `VARCHAR` |
| `stemmer` | `VARCHAR` | The type of stemmer to be used. One of `'arabic'`, `'basque'`, `'catalan'`, `'danish'`, `'dutch'`, `'english'`, `'finnish'`, `'french'`, `'german'`, `'greek'`, `'hindi'`, `'hungarian'`, `'indonesian'`, `'irish'`, `'italian'`, `'lithuanian'`, `'nepali'`, `'norwegian'`, `'porter'`, `'portuguese'`, `'romanian'`, `'russian'`, `'serbian'`, `'spanish'`, `'swedish'`, `'tamil'`, `'turkish'`, or `'none'` if no stemming is to be used. Defaults to `'porter'` |
| `stopwords` | `VARCHAR` | Qualified name of table containing a single `VARCHAR` column containing the desired stopwords, or `'none'` if no stopwords are to be used. Defaults to `'english'` for a pre-defined list of 571 English stopwords |
| `ignore` | `VARCHAR` | Regular expression of patterns to be ignored. Defaults to `'(\\.|[^a-z])+'`, ignoring all escaped and non-alphabetic lowercase characters |
| `strip_accents` | `BOOLEAN` | Whether to remove accents (e.g., convert `á` to `a`). Defaults to `1` |
| `lower` | `BOOLEAN` | Whether to convert all text to lowercase. Defaults to `1` |
| `overwrite` | `BOOLEAN` | Whether to overwrite an existing index on a table. Defaults to `0` |
<!-- markdownlint-enable MD056 -->
This `PRAGMA` builds the index under a newly created schema. The schema will be named after the input table: if an index is created on table `'main.table_name'`, then the schema will be named `'fts_main_table_name'`.
#### `PRAGMA drop_fts_index` {#docs:extensions:full_text_search::pragma-drop_fts_index}
```python
drop_fts_index(input_table)
```
Drops a FTS index for the specified table.
<div class="narrow_table"></div>
| Name | Type | Description |
|:--|:--|:-----------|
| `input_table` | `VARCHAR` | Qualified name of input table, e.g., `'table_name'` or `'main.table_name'` |
#### `match_bm25` Function {#docs:extensions:full_text_search::match_bm25-function}
```python
match_bm25(input_id, query_string, fields := NULL, k := 1.2, b := 0.75, conjunctive := 0)
```
When an index is built, this retrieval macro is created that can be used to search the index.
| Name | Type | Description |
|:--|:--|:----------|
| `input_id` | `VARCHAR` | Column name of document identifier, e.g., `'document_identifier'` |
| `query_string` | `VARCHAR` | The string to search the index for |
| `fields` | `VARCHAR` | Comma-separarated list of fields to search in, e.g., `'text_field_2, text_field_N'`. Defaults to `NULL` to search all indexed fields |
| `k` | `DOUBLE` | Parameter _k<sub>1</sub>_ in the Okapi BM25 retrieval model. Defaults to `1.2` |
| `b` | `DOUBLE` | Parameter _b_ in the Okapi BM25 retrieval model. Defaults to `0.75` |
| `conjunctive` | `BOOLEAN` | Whether to make the query conjunctive i.e., all terms in the query string must be present in order for a document to be retrieved |
#### `stem` Function {#docs:extensions:full_text_search::stem-function}
```python
stem(input_string, stemmer)
```
Reduces words to their base. Used internally by the extension.
| Name | Type | Description |
|:--|:--|:----------|
| `input_string` | `VARCHAR` | The column or constant to be stemmed. |
| `stemmer` | `VARCHAR` | The type of stemmer to be used. One of `'arabic'`, `'basque'`, `'catalan'`, `'danish'`, `'dutch'`, `'english'`, `'finnish'`, `'french'`, `'german'`, `'greek'`, `'hindi'`, `'hungarian'`, `'indonesian'`, `'irish'`, `'italian'`, `'lithuanian'`, `'nepali'`, `'norwegian'`, `'porter'`, `'portuguese'`, `'romanian'`, `'russian'`, `'serbian'`, `'spanish'`, `'swedish'`, `'tamil'`, `'turkish'`, or `'none'` if no stemming is to be used. | | 1,370 | [
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|
Extensions | Full-Text Search Extension | Example Usage | Create a table and fill it with text data:
```sql
CREATE TABLE documents (
document_identifier VARCHAR,
text_content VARCHAR,
author VARCHAR,
doc_version INTEGER
);
INSERT INTO documents
VALUES ('doc1',
'The mallard is a dabbling duck that breeds throughout the temperate.',
'Hannes Mühleisen',
3),
('doc2',
'The cat is a domestic species of small carnivorous mammal.',
'Laurens Kuiper',
2
);
```
Build the index, and make both the `text_content` and `author` columns searchable.
```sql
PRAGMA create_fts_index(
'documents', 'document_identifier', 'text_content', 'author'
);
```
Search the `author` field index for documents that are authored by `Muhleisen`. This retrieves `doc1`:
```sql
SELECT document_identifier, text_content, score
FROM (
SELECT *, fts_main_documents.match_bm25(
document_identifier,
'Muhleisen',
fields := 'author'
) AS score
FROM documents
) sq
WHERE score IS NOT NULL
AND doc_version > 2
ORDER BY score DESC;
```
| document_identifier | text_content | score |
|---------------------|----------------------------------------------------------------------|------:|
| doc1 | The mallard is a dabbling duck that breeds throughout the temperate. | 0.0 |
Search for documents about `small cats`. This retrieves `doc2`:
```sql
SELECT document_identifier, text_content, score
FROM (
SELECT *, fts_main_documents.match_bm25(
document_identifier,
'small cats'
) AS score
FROM documents
) sq
WHERE score IS NOT NULL
ORDER BY score DESC;
```
| document_identifier | text_content | score |
|---------------------|------------------------------------------------------------|------:|
| doc2 | The cat is a domestic species of small carnivorous mammal. | 0.0 |
> **Warning. ** The FTS index will not update automatically when input table changes.
> A workaround of this limitation can be recreating the index to refresh. | 437 | [
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|
Extensions | httpfs (HTTP and S3) | httpfs Extension for HTTP and S3 Support | The `httpfs` extension is an autoloadable extension implementing a file system that allows reading remote/writing remote files.
For plain HTTP(S), only file reading is supported. For object storage using the S3 API, the `httpfs` extension supports reading/writing/[globbing](#docs:sql:functions:pattern_matching::globbing) files. | 73 | [
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|
Extensions | httpfs (HTTP and S3) | Installation and Loading | The `httpfs` extension will be, by default, autoloaded on first use of any functionality exposed by this extension.
To manually install and load the `httpfs` extension, run:
```sql
INSTALL httpfs;
LOAD httpfs;
``` | 53 | [
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|
Extensions | httpfs (HTTP and S3) | HTTP(S) | The `httpfs` extension supports connecting to [HTTP(S) endpoints](#docs:extensions:httpfs:https). | 24 | [
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|
Extensions | httpfs (HTTP and S3) | S3 API | The `httpfs` extension supports connecting to [S3 API endpoints](#docs:extensions:httpfs:s3api). | 25 | [
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|
Extensions | httpfs (HTTP and S3) | HTTP(S) Support | With the `httpfs` extension, it is possible to directly query files over the HTTP(S) protocol. This works for all files supported by DuckDB or its various extensions, and provides read-only access.
```sql
SELECT *
FROM 'https://domain.tld/file.extension';
``` | 59 | [
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|
Extensions | httpfs (HTTP and S3) | Partial Reading | For CSV files, files will be downloaded entirely in most cases, due to the row-based nature of the format.
For Parquet files, DuckDB supports [partial reading](#docs:data:parquet:overview::partial-reading), i.e., it can use a combination of the Parquet metadata and [HTTP range requests](https://developer.mozilla.org/en-US/docs/Web/HTTP/Range_requests) to only download the parts of the file that are actually required by the query. For example, the following query will only read the Parquet metadata and the data for the `column_a` column:
```sql
SELECT column_a
FROM 'https://domain.tld/file.parquet';
```
In some cases, no actual data needs to be read at all as they only require reading the metadata:
```sql
SELECT count(*)
FROM 'https://domain.tld/file.parquet';
``` | 184 | [
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|
Extensions | httpfs (HTTP and S3) | Scanning Multiple Files | Scanning multiple files over HTTP(S) is also supported:
```sql
SELECT *
FROM read_parquet([
'https://domain.tld/file1.parquet',
'https://domain.tld/file2.parquet'
]);
``` | 45 | [
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|
Extensions | httpfs (HTTP and S3) | Using a Custom Certificate File | > This feature is currently only available in the nightly build. It will be [released](#docs:dev:release_calendar) in version 0.10.1.
To use the `httpfs` extension with a custom certificate file, set the following [configuration options](#docs:configuration:pragmas) prior to loading the extension:
```sql
LOAD httpfs;
SET ca_cert_file = '⟨certificate_file⟩';
SET enable_server_cert_verification = true;
``` | 101 | [
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|
Extensions | httpfs (HTTP and S3) | Hugging Face Support | The `httpfs` extension introduces support for the `hf://` protocol to access data sets hosted in [Hugging Face](https://huggingface.co/) repositories.
See the [announcement blog post](https://duckdb.org/2024/05/29/access-150k-plus-datasets-from-hugging-face-with-duckdb) for details. | 73 | [
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|
Extensions | httpfs (HTTP and S3) | Usage | Hugging Face repositories can be queried using the following URL pattern:
```text
hf://datasets/⟨my_username⟩/⟨my_dataset⟩/⟨path_to_file⟩
```
For example, to read a CSV file, you can use the following query:
```sql
SELECT *
FROM 'hf://datasets/datasets-examples/doc-formats-csv-1/data.csv';
```
Where:
* `datasets-examples` is the name of the user/organization
* `doc-formats-csv-1` is the name of the dataset repository
* `data.csv` is the file path in the repository
The result of the query is:
| kind | sound |
|---------|-------|
| dog | woof |
| cat | meow |
| pokemon | pika |
| human | hello |
To read a JSONL file, you can run:
```sql
SELECT *
FROM 'hf://datasets/datasets-examples/doc-formats-jsonl-1/data.jsonl';
```
Finally, for reading a Parquet file, use the following query:
```sql
SELECT *
FROM 'hf://datasets/datasets-examples/doc-formats-parquet-1/data/train-00000-of-00001.parquet';
```
Each of these commands reads the data from the specified file format and displays it in a structured tabular format. Choose the appropriate command based on the file format you are working with. | 309 | [
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|
Extensions | httpfs (HTTP and S3) | Creating a Local Table | To avoid accessing the remote endpoint for every query, you can save the data in a DuckDB table by running a [`CREATE TABLE ... AS` command](#docs:sql:statements:create_table::create-table--as-select-ctas). For example:
```sql
CREATE TABLE data AS
SELECT *
FROM 'hf://datasets/datasets-examples/doc-formats-csv-1/data.csv';
```
Then, simply query the `data` table as follows:
```sql
SELECT *
FROM data;
``` | 106 | [
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|
Extensions | httpfs (HTTP and S3) | Multiple Files | To query all files under a specific directory, you can use a [glob pattern](#docs:data:multiple_files:overview::multi-file-reads-and-globs). For example:
```sql
SELECT count(*) AS count
FROM 'hf://datasets/cais/mmlu/astronomy/*.parquet';
```
| count |
|------:|
| 173 |
By using glob patterns, you can efficiently handle large datasets and perform comprehensive queries across multiple files, simplifying your data inspections and processing tasks.
Here, you can see how you can look for questions that contain the word “planet” in astronomy:
```sql
SELECT count(*) AS count
FROM 'hf://datasets/cais/mmlu/astronomy/*.parquet'
WHERE question LIKE '%planet%';
```
| count |
|------:|
| 21 | | 176 | [
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|
Extensions | httpfs (HTTP and S3) | Versioning and Revisions | In Hugging Face repositories, dataset versions or revisions are different dataset updates. Each version is a snapshot at a specific time, allowing you to track changes and improvements. In git terms, it can be understood as a branch or specific commit.
You can query different dataset versions/revisions by using the following URL:
```text
hf://datasets/⟨my-username⟩/⟨my-dataset⟩@⟨my_branch⟩/⟨path_to_file⟩
```
For example:
```sql
SELECT *
FROM 'hf://datasets/datasets-examples/doc-formats-csv-1@~parquet/**/*.parquet';
```
| kind | sound |
|---------|-------|
| dog | woof |
| cat | meow |
| pokemon | pika |
| human | hello |
The previous query will read all parquet files under the `~parquet` revision. This is a special branch where Hugging Face automatically generates the Parquet files of every dataset to enable efficient scanning. | 218 | [
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] |
|
Extensions | httpfs (HTTP and S3) | Authentication | Configure your Hugging Face Token in the DuckDB Secrets Manager to access private or gated datasets.
First, visit [Hugging Face Settings – Tokens](https://huggingface.co/settings/tokens) to obtain your access token.
Second, set it in your DuckDB session using DuckDB’s [Secrets Manager](#docs:configuration:secrets_manager). DuckDB supports two providers for managing secrets:
#### `CONFIG` {#docs:extensions:httpfs:hugging_face::config}
The user must pass all configuration information into the `CREATE SECRET` statement. To create a secret using the `CONFIG` provider, use the following command:
```sql
CREATE SECRET hf_token (
TYPE HUGGINGFACE,
TOKEN 'your_hf_token'
);
```
#### `CREDENTIAL_CHAIN` {#docs:extensions:httpfs:hugging_face::credential_chain}
Automatically tries to fetch credentials. For the Hugging Face token, it will try to get it from `~/.cache/huggingface/token`. To create a secret using the `CREDENTIAL_CHAIN` provider, use the following command:
```sql
CREATE SECRET hf_token (
TYPE HUGGINGFACE,
PROVIDER CREDENTIAL_CHAIN
);
``` | 250 | [
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|
Extensions | httpfs (HTTP and S3) | S3 API Support | The `httpfs` extension supports reading/writing/[globbing](#::globbing) files on object storage servers using the S3 API. S3 offers a standard API to read and write to remote files (while regular http servers, predating S3, do not offer a common write API). DuckDB conforms to the S3 API, that is now common among industry storage providers. | 80 | [
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|
Extensions | httpfs (HTTP and S3) | Platforms | The `httpfs` filesystem is tested with [AWS S3](https://aws.amazon.com/s3/), [Minio](https://min.io/), [Google Cloud](https://cloud.google.com/storage/docs/interoperability), and [lakeFS](https://docs.lakefs.io/integrations/duckdb.html). Other services that implement the S3 API (such as [Cloudflare R2](https://www.cloudflare.com/en-gb/developer-platform/r2/)) should also work, but not all features may be supported.
The following table shows which parts of the S3 API are required for each `httpfs` feature.
<div class="narrow_table"></div>
| Feature | Required S3 API features |
|:---|:---|
| Public file reads | HTTP Range requests |
| Private file reads | Secret key or session token authentication |
| File glob | [ListObjectV2](https://docs.aws.amazon.com/AmazonS3/latest/API/API_ListObjectsV2.html) |
| File writes | [Multipart upload](https://docs.aws.amazon.com/AmazonS3/latest/userguide/mpuoverview.html) | | 241 | [
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|
Extensions | httpfs (HTTP and S3) | Configuration and Authentication | The preferred way to configure and authenticate to S3 endpoints is to use [secrets](#docs:sql:statements:create_secret). Multiple secret providers are available.
> **Deprecated. ** Prior to version 0.10.0, DuckDB did not have a [Secrets manager](#docs:sql:statements:create_secret). Hence, the configuration of and authentication to S3 endpoints was handled via variables. See the [legacy authentication scheme for the S3 API](#docs:extensions:httpfs:s3api_legacy_authentication).
#### `CONFIG` Provider {#docs:extensions:httpfs:s3api::config-provider}
The default provider, `CONFIG` (i.e., user-configured), allows access to the S3 bucket by manually providing a key. For example:
```sql
CREATE SECRET secret1 (
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'`.
Now, to query using the above secret, simply query any `s3://` prefixed file:
```sql
SELECT *
FROM 's3://my-bucket/file.parquet';
```
#### `CREDENTIAL_CHAIN` Provider {#docs:extensions:httpfs:s3api::credential_chain-provider}
The `CREDENTIAL_CHAIN` provider allows automatically fetching credentials using mechanisms provided by the AWS SDK. For example, to use the AWS SDK default provider:
```sql
CREATE SECRET secret2 (
TYPE S3,
PROVIDER CREDENTIAL_CHAIN
);
```
Again, to query a file using the above secret, simply query any `s3://` prefixed file.
DuckDB also allows specifying a specific chain using the `CHAIN` keyword. This takes a semicolon-separated list (` a;b;c`) of providers that will be tried in order. For example:
```sql
CREATE SECRET secret3 (
TYPE S3,
PROVIDER CREDENTIAL_CHAIN,
CHAIN 'env;config'
);
```
The possible values for `CHAIN` are the following:
* [`config`](https://sdk.amazonaws.com/cpp/api/LATEST/aws-cpp-sdk-core/html/class_aws_1_1_auth_1_1_profile_config_file_a_w_s_credentials_provider.html)
* [`sts`](https://sdk.amazonaws.com/cpp/api/LATEST/aws-cpp-sdk-core/html/class_aws_1_1_auth_1_1_s_t_s_assume_role_web_identity_credentials_provider.html)
* [`sso`](https://sdk.amazonaws.com/cpp/api/LATEST/aws-cpp-sdk-core/html/class_aws_1_1_auth_1_1_s_s_o_credentials_provider.html)
* [`env`](https://sdk.amazonaws.com/cpp/api/LATEST/aws-cpp-sdk-core/html/class_aws_1_1_auth_1_1_environment_a_w_s_credentials_provider.html)
* [`instance`](https://sdk.amazonaws.com/cpp/api/LATEST/aws-cpp-sdk-core/html/class_aws_1_1_auth_1_1_instance_profile_credentials_provider.html)
* [`process`](https://sdk.amazonaws.com/cpp/api/LATEST/aws-cpp-sdk-core/html/class_aws_1_1_auth_1_1_process_credentials_provider.html)
The `CREDENTIAL_CHAIN` provider also allows overriding the automatically fetched config. For example, to automatically load credentials, and then override the region, run:
```sql
CREATE SECRET secret4 (
TYPE S3,
PROVIDER CREDENTIAL_CHAIN,
CHAIN 'config',
REGION 'eu-west-1'
);
```
#### Overview of S3 Secret Parameters {#docs:extensions:httpfs:s3api::overview-of-s3-secret-parameters}
Below is a complete list of the supported parameters that can be used for both the `CONFIG` and `CREDENTIAL_CHAIN` providers:
| Name | Description | Secret | Type | Default |
|:------------------------------|:--------------------------------------------------------------------------------------|:------------------|:----------|:--------------------------------------------|
| `KEY_ID` | The ID of the key to use | `S3`, `GCS`, `R2` | `STRING` | - |
| `SECRET` | The secret of the key to use | `S3`, `GCS`, `R2` | `STRING` | - |
| `REGION` | The region for which to authenticate (should match the region of the bucket to query) | `S3`, `GCS`, `R2` | `STRING` | `us-east-1` |
| `SESSION_TOKEN` | Optionally, a session token can be passed to use temporary credentials | `S3`, `GCS`, `R2` | `STRING` | - |
| `ENDPOINT` | Specify a custom S3 endpoint | `S3`, `GCS`, `R2` | `STRING` | `s3.amazonaws.com` for `S3`, |
| `URL_STYLE` | Either `vhost` or `path` | `S3`, `GCS`, `R2` | `STRING` | `vhost` for `S3`, `path` for `R2` and `GCS` |
| `USE_SSL` | Whether to use HTTPS or HTTP | `S3`, `GCS`, `R2` | `BOOLEAN` | `true` |
| `URL_COMPATIBILITY_MODE` | Can help when urls contain problematic characters. | `S3`, `GCS`, `R2` | `BOOLEAN` | `true` |
| `ACCOUNT_ID` | The R2 account ID to use for generating the endpoint url | `R2` | `STRING` | - |
#### Platform-Specific Secret Types {#docs:extensions:httpfs:s3api::platform-specific-secret-types} | 1,282 | [
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0.01533113420009613,
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] |
|
Extensions | httpfs (HTTP and S3) | Configuration and Authentication | R2 Secrets | While [Cloudflare R2](https://www.cloudflare.com/developer-platform/r2) uses the regular S3 API, DuckDB has a special Secret type, `R2`, to make configuring it a bit simpler:
```sql
CREATE SECRET secret5 (
TYPE R2,
KEY_ID 'AKIAIOSFODNN7EXAMPLE',
SECRET 'wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY',
ACCOUNT_ID 'my_account_id'
);
```
Note the addition of the `ACCOUNT_ID` which is used to generate to correct endpoint url for you. Also note that for `R2` Secrets can also use both the `CONFIG` and `CREDENTIAL_CHAIN` providers. Finally, `R2` secrets are only available when using urls starting with `r2://`, for example:
```sql
SELECT *
FROM read_parquet('r2://some/file/that/uses/r2/secret/file.parquet');
``` | 202 | [
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Extensions | httpfs (HTTP and S3) | Configuration and Authentication | GCS Secrets | While [Google Cloud Storage](https://cloud.google.com/storage) is accessed by DuckDB using the S3 API, DuckDB has a special Secret type, `GCS`, to make configuring it a bit simpler:
```sql
CREATE SECRET secret6 (
TYPE GCS,
KEY_ID 'my_key',
SECRET 'my_secret'
);
```
Note that the above secret, will automatically have the correct Google Cloud Storage endpoint configured. Also note that for `GCS` Secrets can also use both the `CONFIG` and `CREDENTIAL_CHAIN` providers. Finally, `GCS` secrets are only available when using urls starting with `gcs://` or `gs://`, for example:
```sql
SELECT *
FROM read_parquet('gcs://some/file/that/uses/gcs/secret/file.parquet');
``` | 170 | [
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Extensions | httpfs (HTTP and S3) | Reading | Reading files from S3 is now as simple as:
```sql
SELECT *
FROM 's3://bucket/file.extension';
```
#### Partial Reading {#docs:extensions:httpfs:s3api::partial-reading}
The `httpfs` extension supports [partial reading](#docs:extensions:httpfs:https::partial-reading) from S3 buckets.
#### Reading Multiple Files {#docs:extensions:httpfs:s3api::reading-multiple-files}
Multiple files are also possible, for example:
```sql
SELECT *
FROM read_parquet([
's3://bucket/file1.parquet',
's3://bucket/file2.parquet'
]);
```
#### Globbing {#docs:extensions:httpfs:s3api::globbing}
File [globbing](#docs:sql:functions:pattern_matching::globbing) is implemented using the ListObjectV2 API call and allows to use filesystem-like glob patterns to match multiple files, for example:
```sql
SELECT *
FROM read_parquet('s3://bucket/*.parquet');
```
This query matches all files in the root of the bucket with the [Parquet extension](#docs:data:parquet:overview).
Several features for matching are supported, such as `*` to match any number of any character, `?` for any single character or `[0-9]` for a single character in a range of characters:
```sql
SELECT count(*) FROM read_parquet('s3://bucket/folder*/100?/t[0-9].parquet');
```
A useful feature when using globs is the `filename` option, which adds a column named `filename` that encodes the file that a particular row originated from:
```sql
SELECT *
FROM read_parquet('s3://bucket/*.parquet', filename = true);
```
could for example result in:
<div class="narrow_table"></div>
| column_a | column_b | filename |
|:---|:---|:---|
| 1 | examplevalue1 | s3://bucket/file1.parquet |
| 2 | examplevalue1 | s3://bucket/file2.parquet |
#### Hive Partitioning {#docs:extensions:httpfs:s3api::hive-partitioning}
DuckDB also offers support for the [Hive partitioning scheme](#docs:data:partitioning:hive_partitioning), which is available when using HTTP(S) and S3 endpoints. | 517 | [
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|
Extensions | httpfs (HTTP and S3) | Writing | Writing to S3 uses the multipart upload API. This allows DuckDB to robustly upload files at high speed. Writing to S3 works for both CSV and Parquet:
```sql
COPY table_name TO 's3://bucket/file.extension';
```
Partitioned copy to S3 also works:
```sql
COPY table TO 's3://my-bucket/partitioned' (
FORMAT PARQUET,
PARTITION_BY (part_col_a, part_col_b)
);
```
An automatic check is performed for existing files/directories, which is currently quite conservative (and on S3 will add a bit of latency). To disable this check and force writing, an `OVERWRITE_OR_IGNORE` flag is added:
```sql
COPY table TO 's3://my-bucket/partitioned' (
FORMAT PARQUET,
PARTITION_BY (part_col_a, part_col_b),
OVERWRITE_OR_IGNORE true
);
```
The naming scheme of the written files looks like this:
```text
s3://my-bucket/partitioned/part_col_a=⟨val⟩/part_col_b=⟨val⟩/data_⟨thread_number⟩.parquet
```
#### Configuration {#docs:extensions:httpfs:s3api::configuration}
Some additional configuration options exist for the S3 upload, though the default values should suffice for most use cases.
<div class="narrow_table"></div>
| Name | Description |
|:---|:---|
| `s3_uploader_max_parts_per_file` | used for part size calculation, see [AWS docs](https://docs.aws.amazon.com/AmazonS3/latest/userguide/qfacts.html) |
| `s3_uploader_max_filesize` | used for part size calculation, see [AWS docs](https://docs.aws.amazon.com/AmazonS3/latest/userguide/qfacts.html) |
| `s3_uploader_thread_limit` | maximum number of uploader threads | | 407 | [
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|
Extensions | httpfs (HTTP and S3) | Legacy Authentication Scheme for S3 API | Prior to version 0.10.0, DuckDB did not have a [Secrets manager](#docs:sql:statements:create_secret). Hence, the configuration of and authentication to S3 endpoints was handled via variables. This page documents the legacy authentication scheme for the S3 API.
> The recommended way to configuration and authentication of S3 endpoints is to use [secrets](#docs:extensions:httpfs:s3api::configuration-and-authentication). | 94 | [
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|
Extensions | httpfs (HTTP and S3) | Legacy Authentication Scheme | To be able to read or write from S3, the correct region should be set:
```sql
SET s3_region = 'us-east-1';
```
Optionally, the endpoint can be configured in case a non-AWS object storage server is used:
```sql
SET s3_endpoint = '⟨domain⟩.⟨tld⟩:⟨port⟩';
```
If the endpoint is not SSL-enabled then run:
```sql
SET s3_use_ssl = false;
```
Switching between [path-style](https://docs.aws.amazon.com/AmazonS3/latest/userguide/VirtualHosting.html#path-style-access) and [vhost-style](https://docs.aws.amazon.com/AmazonS3/latest/userguide/VirtualHosting.html#virtual-hosted-style-access) URLs is possible using:
```sql
SET s3_url_style = 'path';
```
However, note that this may also require updating the endpoint. For example for AWS S3 it is required to change the endpoint to `s3.⟨region⟩.amazonaws.com`.
After configuring the correct endpoint and region, public files can be read. To also read private files, authentication credentials can be added:
```sql
SET s3_access_key_id = '⟨AWS access key id⟩';
SET s3_secret_access_key = '⟨AWS secret access key⟩';
```
Alternatively, session tokens are also supported and can be used instead:
```sql
SET s3_session_token = '⟨AWS session token⟩';
```
The [`aws` extension](#docs:extensions:aws) allows for loading AWS credentials. | 342 | [
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|
Extensions | httpfs (HTTP and S3) | Per-Request Configuration | Aside from the global S3 configuration described above, specific configuration values can be used on a per-request basis. This allows for use of multiple sets of credentials, regions, etc. These are used by including them on the S3 URI as query parameters. All the individual configuration values listed above can be set as query parameters. For instance:
```sql
SELECT *
FROM 's3://bucket/file.parquet?s3_access_key_id=accessKey&s3_secret_access_key=secretKey';
```
Multiple configurations per query are also allowed:
```sql
SELECT *
FROM 's3://bucket/file.parquet?s3_region=region&s3_session_token=session_token' t1
INNER JOIN 's3://bucket/file.csv?s3_access_key_id=accessKey&s3_secret_access_key=secretKey' t2;
``` | 170 | [
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|
Extensions | httpfs (HTTP and S3) | Configuration | Some additional configuration options exist for the S3 upload, though the default values should suffice for most use cases.
Additionally, most of the configuration options can be set via environment variables:
<div class="narrow_table"></div>
| DuckDB setting | Environment variable | Note |
|:-----------------------|:------------------------|:-----------------------------------------|
| `s3_region` | `AWS_REGION` | Takes priority over `AWS_DEFAULT_REGION` |
| `s3_region` | `AWS_DEFAULT_REGION` | |
| `s3_access_key_id` | `AWS_ACCESS_KEY_ID` | |
| `s3_secret_access_key` | `AWS_SECRET_ACCESS_KEY` | |
| `s3_session_token` | `AWS_SESSION_TOKEN` | |
| `s3_endpoint` | `DUCKDB_S3_ENDPOINT` | |
| `s3_use_ssl` | `DUCKDB_S3_USE_SSL` | | | 212 | [
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|
Extensions | Iceberg Extension | The `iceberg` extension is a loadable extension that implements support for the [Apache Iceberg format](https://iceberg.apache.org/). | 30 | [
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||
Extensions | Iceberg Extension | Installing and Loading | To install and load the `iceberg` extension, run:
```sql
INSTALL iceberg;
LOAD iceberg;
``` | 24 | [
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|
Extensions | Iceberg Extension | Usage | To test the examples, download the [`iceberg_data.zip`](https://duckdb.org/data/iceberg_data.zip) file and unzip it.
#### Querying Individual Tables {#docs:extensions:iceberg::querying-individual-tables}
```sql
SELECT count(*)
FROM iceberg_scan('data/iceberg/lineitem_iceberg', allow_moved_paths = true);
```
| count_star() |
|--------------|
| 51793 |
> The `allow_moved_paths` option ensures that some path resolution is performed, which allows scanning Iceberg tables that are moved.
You can also address specify the current manifest directly in the query, this may be resolved from the catalog prior to the query, in this example the manifest version is a UUID.
```sql
SELECT count(*)
FROM iceberg_scan('data/iceberg/lineitem_iceberg/metadata/02701-1e474dc7-4723-4f8d-a8b3-b5f0454eb7ce.metadata.json');
```
This extension can be paired with the [`httpfs` extension](#docs:extensions:httpfs:overview) to access Iceberg tables in object stores such as S3.
```sql
SELECT count(*)
FROM iceberg_scan('s3://bucketname/lineitem_iceberg/metadata/02701-1e474dc7-4723-4f8d-a8b3-b5f0454eb7ce.metadata.json', allow_moved_paths = true);
```
#### Access Iceberg Metadata {#docs:extensions:iceberg::access-iceberg-metadata}
```sql
SELECT *
FROM iceberg_metadata('data/iceberg/lineitem_iceberg', allow_moved_paths = true);
```
<div class="narrow_table monospace_table"></div>
| manifest_path | manifest_sequence_number | manifest_content | status | content | file_path | file_format | record_count |
|------------------------------------------------------------------------|--------------------------|------------------|---------|----------|------------------------------------------------------------------------------------|-------------|--------------|
| lineitem_iceberg/metadata/10eaca8a-1e1c-421e-ad6d-b232e5ee23d3-m1.avro | 2 | DATA | ADDED | EXISTING | lineitem_iceberg/data/00041-414-f3c73457-bbd6-4b92-9c15-17b241171b16-00001.parquet | PARQUET | 51793 |
| lineitem_iceberg/metadata/10eaca8a-1e1c-421e-ad6d-b232e5ee23d3-m0.avro | 2 | DATA | DELETED | EXISTING | lineitem_iceberg/data/00000-411-0792dcfe-4e25-4ca3-8ada-175286069a47-00001.parquet | PARQUET | 60175 |
#### Visualizing Snapshots {#docs:extensions:iceberg::visualizing-snapshots}
```sql
SELECT *
FROM iceberg_snapshots('data/iceberg/lineitem_iceberg');
```
<div class="narrow_table monospace_table"></div>
| sequence_number | snapshot_id | timestamp_ms | manifest_list |
|-----------------|---------------------|-------------------------|------------------------------------------------------------------------------------------------|
| 1 | 3776207205136740581 | 2023-02-15 15:07:54.504 | lineitem_iceberg/metadata/snap-3776207205136740581-1-cf3d0be5-cf70-453d-ad8f-48fdc412e608.avro |
| 2 | 7635660646343998149 | 2023-02-15 15:08:14.73 | lineitem_iceberg/metadata/snap-7635660646343998149-1-10eaca8a-1e1c-421e-ad6d-b232e5ee23d3.avro | | 880 | [
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|
Extensions | Iceberg Extension | Limitations | Writing (i.e., exporting to) Iceberg files is currently not supported. | 16 | [
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|
Extensions | ICU Extension | The `icu` extension contains an easy-to-use version of the collation/timezone part of the [ICU library](https://github.com/unicode-org/icu). | 36 | [
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||
Extensions | ICU Extension | Installing and Loading | The `icu` extension will be transparently [autoloaded](#docs:extensions:overview::autoloading-extensions) on first use from the official extension repository.
If you would like to install and load it manually, run:
```sql
INSTALL icu;
LOAD icu;
``` | 62 | [
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|
Extensions | ICU Extension | Features | The `icu` extension introduces the following features:
* [region-dependent collations](#docs:sql:expressions:collations)
* [time zones](#docs:sql:data_types:timezones), used for [timestamp data types](#docs:sql:data_types:timestamp) and [timestamp functions](#docs:sql:functions:timestamptz) | 78 | [
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|
Extensions | inet Extension | The `inet` extension defines the `INET` data type for storing [IPv4](https://en.wikipedia.org/wiki/Internet_Protocol_version_4) and [IPv6](https://en.wikipedia.org/wiki/IPv6) Internet addresses. It supports the [CIDR notation](https://en.wikipedia.org/wiki/Classless_Inter-Domain_Routing#CIDR_notation) for subnet masks (e.g., `198.51.100.0/22`, `2001:db8:3c4d::/48`). | 112 | [
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||
Extensions | inet Extension | Installing and Loading | The `inet` extension will be transparently [autoloaded](#docs:extensions:overview::autoloading-extensions) on first use from the official extension repository.
If you would like to install and load it manually, run:
```sql
INSTALL inet;
LOAD inet;
``` | 59 | [
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|
Extensions | inet Extension | Examples | ```sql
SELECT '127.0.0.1'::INET AS ipv4, '2001:db8:3c4d::/48'::INET AS ipv6;
```
| ipv4 | ipv6 |
|-----------|--------------------|
| 127.0.0.1 | 2001:db8:3c4d::/48 |
```sql
CREATE TABLE tbl (id INTEGER, ip INET);
INSERT INTO tbl VALUES
(1, '192.168.0.0/16'),
(2, '127.0.0.1'),
(3, '8.8.8.8'),
(4, 'fe80::/10'),
(5, '2001:db8:3c4d:15::1a2f:1a2b');
SELECT * FROM tbl;
```
| id | ip |
|---:|-----------------------------|
| 1 | 192.168.0.0/16 |
| 2 | 127.0.0.1 |
| 3 | 8.8.8.8 |
| 4 | fe80::/10 |
| 5 | 2001:db8:3c4d:15::1a2f:1a2b | | 287 | [
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|
Extensions | inet Extension | Operations on `INET` Values | `INET` values can be compared naturally, and IPv4 will sort before IPv6. Additionally, IP addresses can be modified by adding or subtracting integers.
```sql
CREATE TABLE tbl (cidr INET);
INSERT INTO tbl VALUES
('127.0.0.1'::INET + 10),
('fe80::10'::INET - 9),
('127.0.0.1'),
('2001:db8:3c4d:15::1a2f:1a2b');
SELECT cidr FROM tbl ORDER BY cidr ASC;
```
| cidr |
|-----------------------------|
| 127.0.0.1 |
| 127.0.0.11 |
| 2001:db8:3c4d:15::1a2f:1a2b |
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|
Extensions | inet Extension | `host` Function | The host component of an `INET` value can be extracted using the `HOST()` function.
```sql
CREATE TABLE tbl (cidr INET);
INSERT INTO tbl VALUES
('192.168.0.0/16'),
('127.0.0.1'),
('2001:db8:3c4d:15::1a2f:1a2b/96');
SELECT cidr, host(cidr) FROM tbl;
```
| cidr | host(cidr) |
|--------------------------------|-----------------------------|
| 192.168.0.0/16 | 192.168.0.0 |
| 127.0.0.1 | 127.0.0.1 |
| 2001:db8:3c4d:15::1a2f:1a2b/96 | 2001:db8:3c4d:15::1a2f:1a2b | | 210 | [
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|
Extensions | inet Extension | HTML Escape and Unescape Functions | ```sql
SELECT html_escape('&');
```
```text
┌──────────────────┐
│ html_escape('&') │
│ varchar │
├──────────────────┤
│ & │
└──────────────────┘
```
```sql
SELECT html_unescape('&');
```
```text
┌────────────────────────┐
│ html_unescape('&') │
│ varchar │
├────────────────────────┤
│ & │
└────────────────────────┘
``` | 120 | [
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|
Extensions | jemalloc Extension | The `jemalloc` extension replaces the system's memory allocator with [jemalloc](https://jemalloc.net/).
Unlike other DuckDB extensions, the `jemalloc` extension is statically linked and cannot be installed or loaded during runtime. | 48 | [
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||
Extensions | jemalloc Extension | Operating System Support | The availability of the `jemalloc` extension depends on the operating system.
#### Linux {#docs:extensions:jemalloc::linux}
On Linux, the AMD64 (x86_64) distribution of DuckDB ships with the `jemalloc` extension.
To disable the `jemalloc` extension, [build DuckDB from source](#docs:dev:building:build_instructions) and set the `SKIP_EXTENSIONS` flag as follows:
```bash
GEN=ninja SKIP_EXTENSIONS="jemalloc" make
```
The ARM64 (AArch64) DuckDB distribution on Linux does not ship with the `jemalloc` extension.
To include it, build it as follows;
```bash
GEN=ninja BUILD_JEMALLOC=1 make
```
#### macOS {#docs:extensions:jemalloc::macos}
The macOS version of DuckDB does not ship with the `jemalloc` extension but can be [built from source](#docs:dev:building:build_instructions) to include it:
```bash
GEN=ninja BUILD_JEMALLOC=1 make
```
#### Windows {#docs:extensions:jemalloc::windows}
On Windows, this extension is not available. | 252 | [
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|
Extensions | jemalloc Extension | Configuration Flags | By default, jemalloc's background threads are disabled. To enable them, use the following configuration option:
```sql
SET allocator_background_threads = true;
``` | 34 | [
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|
Extensions | MySQL Extension | The [`mysql` extension](https://github.com/duckdb/duckdb_mysql) allows DuckDB to directly read and write data from/to a running MySQL instance. The data can be queried directly from the underlying MySQL database. Data can be loaded from MySQL tables into DuckDB tables, or vice versa. | 62 | [
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||
Extensions | MySQL Extension | Installing and Loading | To install the `mysql` extension, run:
```sql
INSTALL mysql;
```
The extension is loaded automatically upon first use. If you prefer to load it manually, run:
```sql
LOAD mysql;
``` | 46 | [
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|
Extensions | MySQL Extension | Reading Data from MySQL | To make a MySQL database accessible to DuckDB use the `ATTACH` command with the `MYSQL` or the `MYSQL_SCANNER` type:
```sql
ATTACH 'host=localhost user=root port=0 database=mysql' AS mysqldb (TYPE MYSQL);
USE mysqldb;
```
#### Configuration {#docs:extensions:mysql::configuration}
The connection string determines the parameters for how to connect to MySQL as a set of `key=value` pairs. Any options not provided are replaced by their default values, as per the table below. Connection information can also be specified with [environment variables](https://dev.mysql.com/doc/refman/8.3/en/environment-variables.html). If no option is provided explicitly, the MySQL extension tries to read it from an environment variable.
<div class="narrow_table monospace_table"></div>
| Setting | Default | Environment variable |
|-------------|----------------|----------------------|
| database | NULL | MYSQL_DATABASE |
| host | localhost | MYSQL_HOST |
| password | | MYSQL_PWD |
| port | 0 | MYSQL_TCP_PORT |
| socket | NULL | MYSQL_UNIX_PORT |
| user | ⟨current user⟩ | MYSQL_USER |
| ssl_mode | preferred | |
| ssl_ca | | |
| ssl_capath | | |
| ssl_cert | | |
| ssl_cipher | | |
| ssl_crl | | |
| ssl_crlpath | | |
| ssl_key | | |
#### Configuring via Secrets {#docs:extensions:mysql::configuring-via-secrets}
MySQL connection information can also be specified with [secrets](https://duckdb.org/docs/configuration/secrets_manager). The following syntax can be used to create a secret.
```sql
CREATE SECRET (
TYPE MYSQL,
HOST '127.0.0.1',
PORT 0,
DATABASE mysql,
USER 'mysql',
PASSWORD ''
);
```
The information from the secret will be used when `ATTACH` is called. We can leave the connection string empty to use all of the information stored in the secret.
```sql
ATTACH '' AS mysql_db (TYPE MYSQL);
```
We can use the connection string to override individual options. For example, to connect to a different database while still using the same credentials, we can override only the database name in the following manner.
```sql
ATTACH 'database=my_other_db' AS mysql_db (TYPE MYSQL);
```
By default, created secrets are temporary. Secrets can be persisted using the [`CREATE PERSISTENT SECRET` command](#docs:configuration:secrets_manager::persistent-secrets). Persistent secrets can be used across sessions. | 585 | [
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|
Extensions | MySQL Extension | Reading Data from MySQL | Managing Multiple Secrets | Named secrets can be used to manage connections to multiple MySQL database instances. Secrets can be given a name upon creation.
```sql
CREATE SECRET mysql_secret_one (
TYPE MYSQL,
HOST '127.0.0.1',
PORT 0,
DATABASE mysql,
USER 'mysql',
PASSWORD ''
);
```
The secret can then be explicitly referenced using the `SECRET` parameter in the `ATTACH`.
```sql
ATTACH '' AS mysql_db_one (TYPE MYSQL, SECRET mysql_secret_one);
```
#### SSL Connections {#docs:extensions:mysql::ssl-connections}
The [`ssl` connection parameters](https://dev.mysql.com/doc/refman/8.4/en/using-encrypted-connections.html) can be used to make SSL connections. Below is a description of the supported parameters.
| Setting | Description |
|-------------|--------------------------------------------------------------------------------------------------------------------------------------------------|
| ssl_mode | The security state to use for the connection to the server: `disabled, required, verify_ca, verify_identity or preferred` (default: `preferred`) |
| ssl_ca | The path name of the Certificate Authority (CA) certificate file. |
| ssl_capath | The path name of the directory that contains trusted SSL CA certificate files. |
| ssl_cert | The path name of the client public key certificate file. |
| ssl_cipher | The list of permissible ciphers for SSL encryption. |
| ssl_crl | The path name of the file containing certificate revocation lists. |
| ssl_crlpath | The path name of the directory that contains files containing certificate revocation lists. |
| ssl_key | The path name of the client private key file. |
#### Reading MySQL Tables {#docs:extensions:mysql::reading-mysql-tables}
The tables in the MySQL database can be read as if they were normal DuckDB tables, but the underlying data is read directly from MySQL at query time.
```sql
SHOW ALL TABLES;
```
<div class="narrow_table monospace_table"></div>
| name |
|-----------------|
| signed_integers |
```sql
SELECT * FROM signed_integers;
```
<div class="narrow_table monospace_table"></div>
| t | s | m | i | b |
|-----:|-------:|---------:|------------:|---------------------:|
| -128 | -32768 | -8388608 | -2147483648 | -9223372036854775808 |
| 127 | 32767 | 8388607 | 2147483647 | 9223372036854775807 |
| NULL | NULL | NULL | NULL | NULL |
It might be desirable to create a copy of the MySQL databases in DuckDB to prevent the system from re-reading the tables from MySQL continuously, particularly for large tables.
Data can be copied over from MySQL to DuckDB using standard SQL, for example:
```sql
CREATE TABLE duckdb_table AS FROM mysqlscanner.mysql_table;
``` | 654 | [
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Extensions | MySQL Extension | Writing Data to MySQL | In addition to reading data from MySQL, create tables, ingest data into MySQL and make other modifications to a MySQL database using standard SQL queries.
This allows you to use DuckDB to, for example, export data that is stored in a MySQL database to Parquet, or read data from a Parquet file into MySQL.
Below is a brief example of how to create a new table in MySQL and load data into it.
```sql
ATTACH 'host=localhost user=root port=0 database=mysqlscanner' AS mysql_db (TYPE MYSQL);
CREATE TABLE mysql_db.tbl (id INTEGER, name VARCHAR);
INSERT INTO mysql_db.tbl VALUES (42, 'DuckDB');
```
Many operations on MySQL tables are supported. All these operations directly modify the MySQL database, and the result of subsequent operations can then be read using MySQL.
Note that if modifications are not desired, `ATTACH` can be run with the `READ_ONLY` property which prevents making modifications to the underlying database. For example:
```sql
ATTACH 'host=localhost user=root port=0 database=mysqlscanner' AS mysql_db (TYPE MYSQL, READ_ONLY);
``` | 232 | [
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|
Extensions | MySQL Extension | Supported Operations | Below is a list of supported operations.
#### `CREATE TABLE` {#docs:extensions:mysql::create-table}
```sql
CREATE TABLE mysql_db.tbl (id INTEGER, name VARCHAR);
```
#### `INSERT INTO` {#docs:extensions:mysql::insert-into}
```sql
INSERT INTO mysql_db.tbl VALUES (42, 'DuckDB');
```
#### `SELECT` {#docs:extensions:mysql::select}
```sql
SELECT * FROM mysql_db.tbl;
```
| id | name |
|---:|--------|
| 42 | DuckDB |
#### `COPY` {#docs:extensions:mysql::copy}
```sql
COPY mysql_db.tbl TO 'data.parquet';
COPY mysql_db.tbl FROM 'data.parquet';
```
You may also create a full copy of the database using the [`COPY FROM DATABASE` statement](#docs:sql:statements:copy::copy-from-database--to):
```sql
COPY FROM DATABASE mysql_db TO my_duckdb_db;
```
#### `UPDATE` {#docs:extensions:mysql::update}
```sql
UPDATE mysql_db.tbl
SET name = 'Woohoo'
WHERE id = 42;
```
#### `DELETE` {#docs:extensions:mysql::delete}
```sql
DELETE FROM mysql_db.tbl
WHERE id = 42;
```
#### `ALTER TABLE` {#docs:extensions:mysql::alter-table}
```sql
ALTER TABLE mysql_db.tbl
ADD COLUMN k INTEGER;
```
#### `DROP TABLE` {#docs:extensions:mysql::drop-table}
```sql
DROP TABLE mysql_db.tbl;
```
#### `CREATE VIEW` {#docs:extensions:mysql::create-view}
```sql
CREATE VIEW mysql_db.v1 AS SELECT 42;
```
#### `CREATE SCHEMA` and `DROP SCHEMA` {#docs:extensions:mysql::create-schema-and-drop-schema}
```sql
CREATE SCHEMA mysql_db.s1;
CREATE TABLE mysql_db.s1.integers (i INTEGER);
INSERT INTO mysql_db.s1.integers VALUES (42);
SELECT * FROM mysql_db.s1.integers;
```
| i |
|---:|
| 42 |
```sql
DROP SCHEMA mysql_db.s1;
```
#### Transactions {#docs:extensions:mysql::transactions}
```sql
CREATE TABLE mysql_db.tmp (i INTEGER);
BEGIN;
INSERT INTO mysql_db.tmp VALUES (42);
SELECT * FROM mysql_db.tmp;
```
This returns:
| i |
|---:|
| 42 |
```sql
ROLLBACK;
SELECT * FROM mysql_db.tmp;
```
This returns an empty table.
> The DDL statements are not transactional in MySQL. | 571 | [
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|
Extensions | MySQL Extension | Running SQL Queries in MySQL | #### The `mysql_query` Table Function {#docs:extensions:mysql::the-mysql_query-table-function}
The `mysql_query` table function allows you to run arbitrary read queries within an attached database. `mysql_query` takes the name of the attached MySQL database to execute the query in, as well as the SQL query to execute. The result of the query is returned. Single-quote strings are escaped by repeating the single quote twice.
```sql
mysql_query(attached_database::VARCHAR, query::VARCHAR)
```
For example:
```sql
ATTACH 'host=localhost database=mysql' AS mysqldb (TYPE MYSQL);
SELECT * FROM mysql_query('mysqldb', 'SELECT * FROM cars LIMIT 3');
```
#### The `mysql_execute` Function {#docs:extensions:mysql::the-mysql_execute-function}
The `mysql_execute` function allows running arbitrary queries within MySQL, including statements that update the schema and content of the database.
```sql
ATTACH 'host=localhost database=mysql' AS mysqldb (TYPE MYSQL);
CALL mysql_execute('mysqldb', 'CREATE TABLE my_table (i INTEGER)');
``` | 235 | [
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|
Extensions | MySQL Extension | Settings | | Name | Description | Default |
|--------------------------------------|----------------------------------------------------------------|-----------|
| `mysql_bit1_as_boolean` | Whether or not to convert `BIT(1)` columns to `BOOLEAN` | `true` |
| `mysql_debug_show_queries` | DEBUG SETTING: print all queries sent to MySQL to stdout | `false` |
| `mysql_experimental_filter_pushdown` | Whether or not to use filter pushdown (currently experimental) | `false` |
| `mysql_tinyint1_as_boolean` | Whether or not to convert `TINYINT(1)` columns to `BOOLEAN` | `true` | | 145 | [
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|
Extensions | MySQL Extension | Schema Cache | To avoid having to continuously fetch schema data from MySQL, DuckDB keeps schema information – such as the names of tables, their columns, etc. – cached. If changes are made to the schema through a different connection to the MySQL instance, such as new columns being added to a table, the cached schema information might be outdated. In this case, the function `mysql_clear_cache` can be executed to clear the internal caches.
```sql
CALL mysql_clear_cache();
``` | 96 | [
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|
Extensions | PostgreSQL Extension | The `postgres` extension allows DuckDB to directly read and write data from a running PostgreSQL database instance. The data can be queried directly from the underlying PostgreSQL database. Data can be loaded from PostgreSQL tables into DuckDB tables, or vice versa. See the [official announcement](https://duckdb.org/2022/09/30/postgres-scanner) for implementation details and background. | 78 | [
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||
Extensions | PostgreSQL Extension | Installing and Loading | The `postgres` extension will be transparently [autoloaded](#docs:extensions:overview::autoloading-extensions) on first use from the official extension repository.
If you would like to install and load it manually, run:
```sql
INSTALL postgres;
LOAD postgres;
``` | 59 | [
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|
Extensions | PostgreSQL Extension | Connecting | To make a PostgreSQL database accessible to DuckDB, use the `ATTACH` command with the `POSTGRES` or `POSTGRES_SCANNER` type.
To connect to the `public` schema of the PostgreSQL instance running on localhost in read-write mode, run:
```sql
ATTACH '' AS postgres_db (TYPE POSTGRES);
```
To connect to the PostgreSQL instance with the given parameters in read-only mode, run:
```sql
ATTACH 'dbname=postgres user=postgres host=127.0.0.1' AS db (TYPE POSTGRES, READ_ONLY);
```
By default, all schemas are attached. When working with large instances, it can be useful to only attach a specific schema. This can be accomplished using the `SCHEMA` command.
```sql
ATTACH 'dbname=postgres user=postgres host=127.0.0.1' AS db (TYPE POSTGRES, SCHEMA 'public');
```
#### Configuration {#docs:extensions:postgres::configuration}
The `ATTACH` command takes as input either a [`libpq` connection string](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING)
or a [PostgreSQL URI](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING-URIS).
Below are some example connection strings and commonly used parameters. A full list of available parameters can be found in the [PostgreSQL documentation](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-PARAMKEYWORDS).
```text
dbname=postgresscanner
host=localhost port=5432 dbname=mydb connect_timeout=10
```
| Name | Description | Default |
| ---------- | ------------------------------------ | ------------ |
| `dbname` | Database name | [user] |
| `host` | Name of host to connect to | `localhost` |
| `hostaddr` | Host IP address | `localhost` |
| `passfile` | Name of file passwords are stored in | `~/.pgpass` |
| `password` | PostgreSQL password | (empty) |
| `port` | Port number | `5432` |
| `user` | PostgreSQL user name | current user |
An example URI is `postgresql://username@hostname/dbname`.
#### Configuring via Secrets {#docs:extensions:postgres::configuring-via-secrets}
PostgreSQL connection information can also be specified with [secrets](https://duckdb.org/docs/configuration/secrets_manager). The following syntax can be used to create a secret.
```sql
CREATE SECRET (
TYPE POSTGRES,
HOST '127.0.0.1',
PORT 5432,
DATABASE postgres,
USER 'postgres',
PASSWORD ''
);
```
The information from the secret will be used when `ATTACH` is called. We can leave the Postgres connection string empty to use all of the information stored in the secret.
```sql
ATTACH '' AS postgres_db (TYPE POSTGRES);
```
We can use the Postgres connection string to override individual options. For example, to connect to a different database while still using the same credentials, we can override only the database name in the following manner.
```sql
ATTACH 'dbname=my_other_db' AS postgres_db (TYPE POSTGRES);
```
By default, created secrets are temporary. Secrets can be persisted using the [`CREATE PERSISTENT SECRET` command](#docs:configuration:secrets_manager::persistent-secrets). Persistent secrets can be used across sessions. | 757 | [
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|
Extensions | PostgreSQL Extension | Connecting | Managing Multiple Secrets | Named secrets can be used to manage connections to multiple Postgres database instances. Secrets can be given a name upon creation.
```sql
CREATE SECRET postgres_secret_one (
TYPE POSTGRES,
HOST '127.0.0.1',
PORT 5432,
DATABASE postgres,
USER 'postgres',
PASSWORD ''
);
```
The secret can then be explicitly referenced using the `SECRET` parameter in the `ATTACH`.
```sql
ATTACH '' AS postgres_db_one (TYPE POSTGRES, SECRET postgres_secret_one);
```
#### Configuring via Environment Variables {#docs:extensions:postgres::configuring-via-environment-variables}
PostgreSQL connection information can also be specified with [environment variables](https://www.postgresql.org/docs/current/libpq-envars.html).
This can be useful in a production environment where the connection information is managed externally
and passed in to the environment.
```bash
export PGPASSWORD="secret"
export PGHOST=localhost
export PGUSER=owner
export PGDATABASE=mydatabase
```
Then, to connect, start the `duckdb` process and run:
```sql
ATTACH '' AS p (TYPE POSTGRES);
``` | 241 | [
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Extensions | PostgreSQL Extension | Usage | The tables in the PostgreSQL database can be read as if they were normal DuckDB tables, but the underlying data is read directly from PostgreSQL at query time.
```sql
SHOW ALL TABLES;
```
<div class="narrow_table monospace_table"></div>
| name |
| ----- |
| uuids |
```sql
SELECT * FROM uuids;
```
<div class="narrow_table monospace_table"></div>
| u |
| ------------------------------------ |
| 6d3d2541-710b-4bde-b3af-4711738636bf |
| NULL |
| 00000000-0000-0000-0000-000000000001 |
| ffffffff-ffff-ffff-ffff-ffffffffffff |
It might be desirable to create a copy of the PostgreSQL databases in DuckDB to prevent the system from re-reading the tables from PostgreSQL continuously, particularly for large tables.
Data can be copied over from PostgreSQL to DuckDB using standard SQL, for example:
```sql
CREATE TABLE duckdb_table AS FROM postgres_db.postgres_tbl;
``` | 232 | [
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|
Extensions | PostgreSQL Extension | Writing Data to PostgreSQL | In addition to reading data from PostgreSQL, the extension allows you to create tables, ingest data into PostgreSQL and make other modifications to a PostgreSQL database using standard SQL queries.
This allows you to use DuckDB to, for example, export data that is stored in a PostgreSQL database to Parquet, or read data from a Parquet file into PostgreSQL.
Below is a brief example of how to create a new table in PostgreSQL and load data into it.
```sql
ATTACH 'dbname=postgresscanner' AS postgres_db (TYPE POSTGRES);
CREATE TABLE postgres_db.tbl (id INTEGER, name VARCHAR);
INSERT INTO postgres_db.tbl VALUES (42, 'DuckDB');
```
Many operations on PostgreSQL tables are supported. All these operations directly modify the PostgreSQL database, and the result of subsequent operations can then be read using PostgreSQL.
Note that if modifications are not desired, `ATTACH` can be run with the `READ_ONLY` property which prevents making modifications to the underlying database. For example:
```sql
ATTACH 'dbname=postgresscanner' AS postgres_db (TYPE POSTGRES, READ_ONLY);
```
Below is a list of supported operations.
#### `CREATE TABLE` {#docs:extensions:postgres::create-table}
```sql
CREATE TABLE postgres_db.tbl (id INTEGER, name VARCHAR);
```
#### `INSERT INTO` {#docs:extensions:postgres::insert-into}
```sql
INSERT INTO postgres_db.tbl VALUES (42, 'DuckDB');
```
#### `SELECT` {#docs:extensions:postgres::select}
```sql
SELECT * FROM postgres_db.tbl;
```
| id | name |
| ---: | ------ |
| 42 | DuckDB |
#### `COPY` {#docs:extensions:postgres::copy}
You can copy tables back and forth between PostgreSQL and DuckDB:
```sql
COPY postgres_db.tbl TO 'data.parquet';
COPY postgres_db.tbl FROM 'data.parquet';
```
These copies use [PostgreSQL binary wire encoding](https://www.postgresql.org/docs/current/sql-copy.html).
DuckDB can also write data using this encoding to a file which you can then load into PostgreSQL using a client of your choosing if you would like to do your own connection management:
```sql
COPY 'data.parquet' TO 'pg.bin' WITH (FORMAT POSTGRES_BINARY);
```
The file produced will be the equivalent of copying the file to PostgreSQL using DuckDB and then dumping it from PostgreSQL using `psql` or another client:
DuckDB:
```sql
COPY postgres_db.tbl FROM 'data.parquet';
```
PostgreSQL:
```sql
\copy tbl TO 'data.bin' WITH (FORMAT BINARY);
```
You may also create a full copy of the database using the [`COPY FROM DATABASE` statement](#docs:sql:statements:copy::copy-from-database--to):
```sql
COPY FROM DATABASE postgres_db TO my_duckdb_db;
```
#### `UPDATE` {#docs:extensions:postgres::update}
```sql
UPDATE postgres_db.tbl
SET name = 'Woohoo'
WHERE id = 42;
```
#### `DELETE` {#docs:extensions:postgres::delete}
```sql
DELETE FROM postgres_db.tbl
WHERE id = 42;
```
#### `ALTER TABLE` {#docs:extensions:postgres::alter-table}
```sql
ALTER TABLE postgres_db.tbl
ADD COLUMN k INTEGER;
```
#### `DROP TABLE` {#docs:extensions:postgres::drop-table}
```sql
DROP TABLE postgres_db.tbl;
```
#### `CREATE VIEW` {#docs:extensions:postgres::create-view}
```sql
CREATE VIEW postgres_db.v1 AS SELECT 42;
```
#### `CREATE SCHEMA` / `DROP SCHEMA` {#docs:extensions:postgres::create-schema--drop-schema}
```sql
CREATE SCHEMA postgres_db.s1;
CREATE TABLE postgres_db.s1.integers (i INTEGER);
INSERT INTO postgres_db.s1.integers VALUES (42);
SELECT * FROM postgres_db.s1.integers;
```
| i |
| ---: |
| 42 |
```sql
DROP SCHEMA postgres_db.s1;
``` | 889 | [
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|
Extensions | PostgreSQL Extension | `DETACH` | ```sql
DETACH postgres_db;
```
#### Transactions {#docs:extensions:postgres::transactions}
```sql
CREATE TABLE postgres_db.tmp (i INTEGER);
BEGIN;
INSERT INTO postgres_db.tmp VALUES (42);
SELECT * FROM postgres_db.tmp;
```
This returns:
| i |
| ---: |
| 42 |
```sql
ROLLBACK;
SELECT * FROM postgres_db.tmp;
```
This returns an empty table. | 94 | [
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|
Extensions | PostgreSQL Extension | Running SQL Queries in PostgreSQL | #### The `postgres_query` Table Function {#docs:extensions:postgres::the-postgres_query-table-function}
The `postgres_query` table function allows you to run arbitrary read queries within an attached database. `postgres_query` takes the name of the attached PostgreSQL database to execute the query in, as well as the SQL query to execute. The result of the query is returned. Single-quote strings are escaped by repeating the single quote twice.
```sql
postgres_query(attached_database::VARCHAR, query::VARCHAR)
```
For example:
```sql
ATTACH 'dbname=postgresscanner' AS postgres_db (TYPE POSTGRES);
SELECT * FROM postgres_query('postgres_db', 'SELECT * FROM cars LIMIT 3');
```
<!--
CREATE TABLE cars (brand VARCHAR, model VARCHAR, color VARCHAR);
INSERT INTO cars VALUES
('Ferrari', 'Testarossa', 'red'),
('Aston Martin', 'DB2', 'blue'),
('Bentley', 'Mulsanne', 'gray')
;
-->
| brand | model | color |
| ------------ | ---------- | ----- |
| Ferrari | Testarossa | red |
| Aston Martin | DB2 | blue |
| Bentley | Mulsanne | gray |
#### The `postgres_execute` Function {#docs:extensions:postgres::the-postgres_execute-function}
The `postgres_execute` function allows running arbitrary queries within PostgreSQL, including statements that update the schema and content of the database.
```sql
ATTACH 'dbname=postgresscanner' AS postgres_db (TYPE POSTGRES);
CALL postgres_execute('postgres_db', 'CREATE TABLE my_table (i INTEGER)');
``` | 348 | [
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|
Extensions | PostgreSQL Extension | Settings | The extension exposes the following configuration parameters.
| Name | Description | Default |
| --------------------------------- | ---------------------------------------------------------------------------- | ------- |
| `pg_array_as_varchar` | Read PostgreSQL arrays as varchar - enables reading mixed dimensional arrays | `false` |
| `pg_connection_cache` | Whether or not to use the connection cache | `true` |
| `pg_connection_limit` | The maximum amount of concurrent PostgreSQL connections | `64` |
| `pg_debug_show_queries` | DEBUG SETTING: print all queries sent to PostgreSQL to stdout | `false` |
| `pg_experimental_filter_pushdown` | Whether or not to use filter pushdown (currently experimental) | `false` |
| `pg_pages_per_task` | The amount of pages per task | `1000` |
| `pg_use_binary_copy` | Whether or not to use BINARY copy to read data | `true` |
| `pg_null_byte_replacement` | When writing NULL bytes to Postgres, replace them with the given character | `NULL` |
| `pg_use_ctid_scan` | Whether or not to parallelize scanning using table ctids | `true` | | 261 | [
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|
Extensions | PostgreSQL Extension | Schema Cache | To avoid having to continuously fetch schema data from PostgreSQL, DuckDB keeps schema information – such as the names of tables, their columns, etc. – cached. If changes are made to the schema through a different connection to the PostgreSQL instance, such as new columns being added to a table, the cached schema information might be outdated. In this case, the function `pg_clear_cache` can be executed to clear the internal caches.
```sql
CALL pg_clear_cache();
```
> **Deprecated. ** The old `postgres_attach` function is deprecated. It is recommended to switch over to the new `ATTACH` syntax. | 128 | [
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|
Extensions | Spatial | Spatial Extension | The `spatial` extension provides support for geospatial data processing in DuckDB.
For an overview of the extension, see our [blog post](https://duckdb.org/2023/04/28/spatial). | 46 | [
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|
Extensions | Spatial | Installing and Loading | To install and load the `spatial` extension, run:
```sql
INSTALL spatial;
LOAD spatial;
``` | 24 | [
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|
Extensions | Spatial | The `GEOMETRY` Type | The core of the spatial extension is the `GEOMETRY` type. If you're unfamiliar with geospatial data and GIS tooling, this type probably works very different from what you'd expect.
On the surface, the `GEOMETRY` type is a binary representation of “geometry” data made up out of sets of vertices (pairs of X and Y `double` precision floats). But what makes it somewhat special is that its actually used to store one of several different geometry subtypes. These are `POINT`, `LINESTRING`, `POLYGON`, as well as their “collection” equivalents, `MULTIPOINT`, `MULTILINESTRING` and `MULTIPOLYGON`. Lastly there is `GEOMETRYCOLLECTION`, which can contain any of the other subtypes, as well as other `GEOMETRYCOLLECTION`s recursively.
This may seem strange at first, since DuckDB already have types like `LIST`, `STRUCT` and `UNION` which could be used in a similar way, but the design and behavior of the `GEOMETRY` type is actually based on the [Simple Features](https://en.wikipedia.org/wiki/Simple_Features) geometry model, which is a standard used by many other databases and GIS software.
The spatial extension also includes a couple of experimental non-standard explicit geometry types, such as `POINT_2D`, `LINESTRING_2D`, `POLYGON_2D` and `BOX_2D` that are based on DuckDBs native nested types, such as `STRUCT` and `LIST`. Since these have a fixed and predictable internal memory layout, it is theoretically possible to optimize a lot of geospatial algorithms to be much faster when operating on these types than on the `GEOMETRY` type. However, only a couple of functions in the spatial extension have been explicitly specialized for these types so far. All of these new types are implicitly castable to `GEOMETRY`, but with a small conversion cost, so the `GEOMETRY` type is still the recommended type to use for now if you are planning to work with a lot of different spatial functions.
`GEOMETRY` is not currently capable of storing additional geometry types such as curved geometries or triangle networks. Additionally, the `GEOMETRY` type does not store SRID information on a per value basis. These limitations may be addressed in the future. | 484 | [
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|
Extensions | Spatial | Function Index | **[Scalar Functions](#::scalar-functions)**
| Function | Summary |
| ------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
| [`ST_Area`](#::st_area) | Compute the area of a geometry. |
| [`ST_Area_Spheroid`](#::st_area_spheroid) | Returns the area of a geometry in meters, using an ellipsoidal model of the earth |
| [`ST_AsGeoJSON`](#::st_asgeojson) | Returns the geometry as a GeoJSON fragment |
| [`ST_AsHEXWKB`](#::st_ashexwkb) | Returns the geometry as a HEXWKB string |
| [`ST_AsSVG`](#::st_assvg) | Convert the geometry into a SVG fragment or path |
| [`ST_AsText`](#::st_astext) | Returns the geometry as a WKT string |
| [`ST_AsWKB`](#::st_aswkb) | Returns the geometry as a WKB blob |
| [`ST_Boundary`](#::st_boundary) | Returns the "boundary" of a geometry |
| [`ST_Buffer`](#::st_buffer) | Returns a buffer around the input geometry at the target distance |
| [`ST_Centroid`](#::st_centroid) | Calculates the centroid of a geometry |
| [`ST_Collect`](#::st_collect) | Collects a list of geometries into a collection geometry. |
| [`ST_CollectionExtract`](#::st_collectionextract) | Extracts geometries from a GeometryCollection into a typed multi geometry. |
| [`ST_Contains`](#::st_contains) | Returns true if geom1 contains geom2. |
| [`ST_ContainsProperly`](#::st_containsproperly) | Returns true if geom1 "properly contains" geom2 |
| [`ST_ConvexHull`](#::st_convexhull) | Returns the convex hull enclosing the geometry |
| [`ST_CoveredBy`](#::st_coveredby) | Returns true if geom1 is "covered" by geom2 |
| [`ST_Covers`](#::st_covers) | Returns if geom1 "covers" geom2 |
| [`ST_Crosses`](#::st_crosses) | Returns true if geom1 "crosses" geom2 |
| [`ST_DWithin`](#::st_dwithin) | Returns if two geometries are within a target distance of each-other |
| [`ST_DWithin_Spheroid`](#::st_dwithin_spheroid) | Returns if two POINT_2D's are within a target distance in meters, using an ellipsoidal model of the earths surface |
| [`ST_Difference`](#::st_difference) | Returns the "difference" between two geometries |
| [`ST_Dimension`](#::st_dimension) | Returns the dimension of a geometry. |
| [`ST_Disjoint`](#::st_disjoint) | Returns if two geometries are disjoint |
| [`ST_Distance`](#::st_distance) | Returns the distance between two geometries. |
| [`ST_Distance_Sphere`](#::st_distance_sphere) | Returns the haversine distance between two geometries. |
| [`ST_Distance_Spheroid`](#::st_distance_spheroid) | Returns the distance between two geometries in meters using a ellipsoidal model of the earths surface |
| [`ST_Dump`](#::st_dump) | Dumps a geometry into a list of sub-geometries and their "path" in the original geometry. |
| [`ST_EndPoint`](#::st_endpoint) | Returns the last point of a line. |
| [`ST_Envelope`](#::st_envelope) | Returns the minimum bounding box for the input geometry as a polygon geometry. |
| [`ST_Equals`](#::st_equals) | Compares two geometries for equality |
| [`ST_Extent`](#::st_extent) | Returns the minimal bounding box enclosing the input geometry |
| [`ST_ExteriorRing`](#::st_exteriorring) | Returns the exterior ring (shell) of a polygon geometry. |
| [`ST_FlipCoordinates`](#::st_flipcoordinates) | Returns a new geometry with the coordinates of the input geometry "flipped" so that x = y and y = x. |
| [`ST_Force2D`](#::st_force2d) | Forces the vertices of a geometry to have X and Y components |
| [`ST_Force3DM`](#::st_force3dm) | Forces the vertices of a geometry to have X, Y and M components |
| [`ST_Force3DZ`](#::st_force3dz) | Forces the vertices of a geometry to have X, Y and Z components |
| [`ST_Force4D`](#::st_force4d) | Forces the vertices of a geometry to have X, Y, Z and M components |
| [`ST_GeomFromGeoJSON`](#::st_geomfromgeojson) | Deserializes a GEOMETRY from a GeoJSON fragment. |
| [`ST_GeomFromHEXEWKB`](#::st_geomfromhexewkb) | Deserialize a GEOMETRY from a HEXEWKB encoded string |
| [`ST_GeomFromHEXWKB`](#::st_geomfromhexwkb) | Creates a GEOMETRY from a HEXWKB string |
| [`ST_GeomFromText`](#::st_geomfromtext) | Deserializes a GEOMETRY from a WKT string, optionally ignoring invalid geometries |
| [`ST_GeomFromWKB`](#::st_geomfromwkb) | Deserializes a GEOMETRY from a WKB encoded blob |
| [`ST_GeometryType`](#::st_geometrytype) | Returns a 'GEOMETRY_TYPE' enum identifying the input geometry type. |
| [`ST_HasM`](#::st_hasm) | Check if the input geometry has M values. |
| [`ST_HasZ`](#::st_hasz) | Check if the input geometry has Z values. |
| [`ST_Hilbert`](#::st_hilbert) | Encodes the X and Y values as the hilbert curve index for a curve covering the given bounding box. |
| [`ST_Intersection`](#::st_intersection) | Returns the "intersection" of geom1 and geom2 |
| [`ST_Intersects`](#::st_intersects) | Returns true if two geometries intersects |
| [`ST_Intersects_Extent`](#::st_intersects_extent) | Returns true if the extent of two geometries intersects |
| [`ST_IsClosed`](#::st_isclosed) | Returns true if a geometry is "closed" |
| [`ST_IsEmpty`](#::st_isempty) | Returns true if the geometry is "empty" |
| [`ST_IsRing`](#::st_isring) | Returns true if the input line geometry is a ring (both ST_IsClosed and ST_IsSimple). |
| [`ST_IsSimple`](#::st_issimple) | Returns true if the input geometry is "simple" |
| [`ST_IsValid`](#::st_isvalid) | Returns true if the geometry is topologically "valid" |
| [`ST_Length`](#::st_length) | Returns the length of the input line geometry |
| [`ST_Length_Spheroid`](#::st_length_spheroid) | Returns the length of the input geometry in meters, using a ellipsoidal model of the earth |
| [`ST_LineMerge`](#::st_linemerge) | "Merges" the input line geometry, optionally taking direction into account. |
| [`ST_M`](#::st_m) | Returns the M value of a point geometry, or NULL if not a point or empty |
| [`ST_MMax`](#::st_mmax) | Returns the maximum M value of a geometry |
| [`ST_MMin`](#::st_mmin) | Returns the minimum M value of a geometry |
| [`ST_MakeEnvelope`](#::st_makeenvelope) | Returns a minimal bounding box polygon enclosing the input geometry |
| [`ST_MakeLine`](#::st_makeline) | Creates a LINESTRING geometry from a pair or list of input points |
| [`ST_MakePolygon`](#::st_makepolygon) | Creates a polygon from a shell geometry and an optional set of holes |
| [`ST_MakeValid`](#::st_makevalid) | Attempts to make an invalid geometry valid without removing any vertices |
| [`ST_Multi`](#::st_multi) | Turns a single geometry into a multi geometry. |
| [`ST_NGeometries`](#::st_ngeometries) | Returns the number of component geometries in a collection geometry. |
| [`ST_NInteriorRings`](#::st_ninteriorrings) | Returns the number if interior rings of a polygon |
| [`ST_NPoints`](#::st_npoints) | Returns the number of vertices within a geometry |
| [`ST_Normalize`](#::st_normalize) | Returns a "normalized" version of the input geometry. |
| [`ST_NumGeometries`](#::st_numgeometries) | Returns the number of component geometries in a collection geometry. |
| [`ST_NumInteriorRings`](#::st_numinteriorrings) | Returns the number if interior rings of a polygon |
| [`ST_NumPoints`](#::st_numpoints) | Returns the number of vertices within a geometry |
| [`ST_Overlaps`](#::st_overlaps) | Returns true if geom1 "overlaps" geom2 |
| [`ST_Perimeter`](#::st_perimeter) | Returns the length of the perimeter of the geometry |
| [`ST_Perimeter_Spheroid`](#::st_perimeter_spheroid) | Returns the length of the perimeter in meters using an ellipsoidal model of the earths surface |
| [`ST_Point`](#::st_point) | Creates a GEOMETRY point |
| [`ST_Point2D`](#::st_point2d) | Creates a POINT_2D |
| [`ST_Point3D`](#::st_point3d) | Creates a POINT_3D |
| [`ST_Point4D`](#::st_point4d) | Creates a POINT_4D |
| [`ST_PointN`](#::st_pointn) | Returns the n'th vertex from the input geometry as a point geometry |
| [`ST_PointOnSurface`](#::st_pointonsurface) | Returns a point that is guaranteed to be on the surface of the input geometry. Sometimes a useful alternative to ST_Centroid. |
| [`ST_Points`](#::st_points) | Collects all the vertices in the geometry into a multipoint |
| [`ST_QuadKey`](#::st_quadkey) | Compute the [quadkey](https://learn.microsoft.com/en-us/bingmaps/articles/bing-maps-tile-system) for a given lon/lat point at a given level. |
| [`ST_ReducePrecision`](#::st_reduceprecision) | Returns the geometry with all vertices reduced to the target precision |
| [`ST_RemoveRepeatedPoints`](#::st_removerepeatedpoints) | Returns a new geometry with repeated points removed, optionally within a target distance of eachother. |
| [`ST_Reverse`](#::st_reverse) | Returns a new version of the input geometry with the order of its vertices reversed |
| [`ST_ShortestLine`](#::st_shortestline) | Returns the line between the two closest points between geom1 and geom2 |
| [`ST_Simplify`](#::st_simplify) | Simplifies the input geometry by collapsing edges smaller than 'distance' |
| [`ST_SimplifyPreserveTopology`](#::st_simplifypreservetopology) | Returns a simplified geometry but avoids creating invalid topologies |
| [`ST_StartPoint`](#::st_startpoint) | Returns the first point of a line geometry |
| [`ST_Touches`](#::st_touches) | Returns true if geom1 "touches" geom2 |
| [`ST_Transform`](#::st_transform) | Transforms a geometry between two coordinate systems |
| [`ST_Union`](#::st_union) | Returns the union of two geometries. |
| [`ST_Within`](#::st_within) | Returns true if geom1 is "within" geom2 |
| [`ST_X`](#::st_x) | Returns the X value of a point geometry, or NULL if not a point or empty |
| [`ST_XMax`](#::st_xmax) | Returns the maximum X value of a geometry |
| [`ST_XMin`](#::st_xmin) | Returns the minimum X value of a geometry |
| [`ST_Y`](#::st_y) | Returns the Y value of a point geometry, or NULL if not a point or empty |
| [`ST_YMax`](#::st_ymax) | Returns the maximum Y value of a geometry |
| [`ST_YMin`](#::st_ymin) | Returns the minimum Y value of a geometry |
| [`ST_Z`](#::st_z) | Returns the Z value of a point geometry, or NULL if not a point or empty |
| [`ST_ZMFlag`](#::st_zmflag) | Returns a flag indicating the presence of Z and M values in the input geometry. |
| [`ST_ZMax`](#::st_zmax) | Returns the maximum Z value of a geometry |
| [`ST_ZMin`](#::st_zmin) | Returns the minimum Z value of a geometry |
**[Aggregate Functions](#::aggregate-functions)**
| Function | Summary |
| --------------------------------------------- | -------------------------------------------------------------------------------- |
| [`ST_Envelope_Agg`](#::st_envelope_agg) | Alias for [ST_Extent_Agg](#::st_extent_agg). |
| [`ST_Extent_Agg`](#::st_extent_agg) | Computes the minimal-bounding-box polygon containing the set of input geometries |
| [`ST_Intersection_Agg`](#::st_intersection_agg) | Computes the intersection of a set of geometries |
| [`ST_Union_Agg`](#::st_union_agg) | Computes the union of a set of input geometries |
**[Table Functions](#::table-functions)**
| Function | Summary |
| ------------------------------- | ---------------------------------------------------------------------------------------------------------------- |
| [`ST_Drivers`](#::st_drivers) | Returns the list of supported GDAL drivers and file formats |
| [`ST_Read`](#::st_read) | Read and import a variety of geospatial file formats using the GDAL library. |
| [`ST_ReadOSM`](#::st_readosm) | The `ST_ReadOsm()` table function enables reading compressed OpenStreetMap data directly from a `.osm.pbf file.` |
| [`ST_Read_Meta`](#::st_read_meta) | Read the metadata from a variety of geospatial file formats using the GDAL library. |
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|
Extensions | Spatial | Scalar Functions | #### ST_Area {#docs:extensions:spatial:functions::st_area} | 18 | [
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|
Extensions | Spatial | Scalar Functions | Signatures | ```sql
DOUBLE ST_Area (col0 POINT_2D)
DOUBLE ST_Area (col0 LINESTRING_2D)
DOUBLE ST_Area (col0 POLYGON_2D)
DOUBLE ST_Area (col0 GEOMETRY)
DOUBLE ST_Area (col0 BOX_2D)
``` | 64 | [
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] |
Extensions | Spatial | Scalar Functions | Description | Compute the area of a geometry.
Returns `0.0` for any geometry that is not a `POLYGON`, `MULTIPOLYGON` or `GEOMETRYCOLLECTION` containing polygon geometries.
The area is in the same units as the spatial reference system of the geometry.
The `POINT_2D` and `LINESTRING_2D` overloads of this function always return `0.0` but are included for completeness. | 93 | [
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Extensions | Spatial | Scalar Functions | Example | ```sql
SELECT ST_Area('POLYGON((0 0, 0 1, 1 1, 1 0, 0 0))'::GEOMETRY);
-- 1.0
```
----
#### ST_Area_Spheroid {#docs:extensions:spatial:functions::st_area_spheroid} | 74 | [
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Extensions | Spatial | Scalar Functions | Signatures | ```sql
DOUBLE ST_Area_Spheroid (col0 POLYGON_2D)
DOUBLE ST_Area_Spheroid (col0 GEOMETRY)
``` | 33 | [
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Extensions | Spatial | Scalar Functions | Description | Returns the area of a geometry in meters, using an ellipsoidal model of the earth
The input geometry is assumed to be in the [EPSG:4326](https://en.wikipedia.org/wiki/World_Geodetic_System) coordinate system (WGS84), with [latitude, longitude] axis order and the area is returned in square meters. This function uses the [GeographicLib](https://geographiclib.sourceforge.io/) library, calculating the area using an ellipsoidal model of the earth. This is a highly accurate method for calculating the area of a polygon taking the curvature of the earth into account, but is also the slowest.
Returns `0.0` for any geometry that is not a `POLYGON`, `MULTIPOLYGON` or `GEOMETRYCOLLECTION` containing polygon geometries.
----
#### ST_AsGeoJSON {#docs:extensions:spatial:functions::st_asgeojson} | 194 | [
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Extensions | Spatial | Scalar Functions | Signature | ```sql
JSON ST_AsGeoJSON (col0 GEOMETRY)
``` | 15 | [
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Extensions | Spatial | Scalar Functions | Description | Returns the geometry as a GeoJSON fragment
This does not return a complete GeoJSON document, only the geometry fragment. To construct a complete GeoJSON document or feature, look into using the DuckDB JSON extension in conjunction with this function.
This function supports geometries with Z values, but not M values. | 62 | [
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Extensions | Spatial | Scalar Functions | Example | ```sql
SELECT ST_AsGeoJSON('POLYGON((0 0, 0 1, 1 1, 1 0, 0 0))'::GEOMETRY);
----
{"type":"Polygon","coordinates":[[[0.0,0.0],[0.0,1.0],[1.0,1.0],[1.0,0.0],[0.0,0.0]]]}
-- Convert a geometry into a full GeoJSON feature (requires the JSON extension to be loaded)
SELECT CAST({
type: 'Feature',
geometry: ST_AsGeoJSON(ST_Point(1,2)),
properties: {
name: 'my_point'
}
} AS JSON);
----
{"type":"Feature","geometry":{"type":"Point","coordinates":[1.0,2.0]},"properties":{"name":"my_point"}}
```
----
#### ST_AsHEXWKB {#docs:extensions:spatial:functions::st_ashexwkb} | 205 | [
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Extensions | Spatial | Scalar Functions | Signature | ```sql
VARCHAR ST_AsHEXWKB (col0 GEOMETRY)
``` | 17 | [
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Subsets and Splits