---
toc_priority: 7
toc_title: S3
---

# S3 Table Engine {#table-engine-s3}

This engine provides integration with [Amazon S3](https://aws.amazon.com/s3/) ecosystem. This engine is similar to the [HDFS](../../../engines/table-engines/special/file.md#table_engines-hdfs) engine, but provides S3-specific features.

## Create Table {#creating-a-table}

``` sql
CREATE TABLE s3_engine_table (name String, value UInt32) 
ENGINE = S3(path, [aws_access_key_id, aws_secret_access_key,] format, structure, [compression])
```

**Engine parameters**

-   `path` — Bucket url with path to file. Supports following wildcards in readonly mode: `*`, `?`, `{abc,def}` and `{N..M}` where `N`, `M` — numbers, `'abc'`, `'def'` — strings. For more information see [below](#wildcards-in-path).
-   `format` — The [format](../../../interfaces/formats.md#formats) of the file.
-   `structure` — Structure of the table. Format `'column1_name column1_type, column2_name column2_type, ...'`.
-   `compression` — Compression type. Supported values: `none`, `gzip/gz`, `brotli/br`, `xz/LZMA`, `zstd/zst`. Parameter is optional. By default, it will autodetect compression by file extension.

**Example**

1. Set up the `s3_engine_table` table:

``` sql
CREATE TABLE s3_engine_table (name String, value UInt32) ENGINE=S3('https://storage.yandexcloud.net/my-test-bucket-768/test-data.csv.gz', 'CSV', 'name String, value UInt32', 'gzip');
```

2. Fill file:

``` sql
INSERT INTO s3_engine_table VALUES ('one', 1), ('two', 2), ('three', 3);
```

3. Query the data:

``` sql
SELECT * FROM s3_engine_table LIMIT 2;
```

```text
┌─name─┬─value─┐
│ one  │     1 │
│ two  │     2 │
└──────┴───────┘
```
## Virtual columns {#virtual-columns}

-   `_path` — Path to the file.
-   `_file` — Name of the file.

For more information about virtual columns see [here](../../../engines/table-engines/index.md#table_engines-virtual_columns).

## Implementation Details {#implementation-details}

-   Reads and writes can be parallel
-   Not supported:
    -   `ALTER` and `SELECT...SAMPLE` operations.
    -   Indexes.
    -   Replication.

## Wildcards In Path {#wildcards-in-path}

`path` argument can specify multiple files using bash-like wildcards. For being processed file should exist and match to the whole path pattern. Listing of files is determined during `SELECT` (not at `CREATE` moment).

-   `*` — Substitutes any number of any characters except `/` including empty string.
-   `?` — Substitutes any single character.
-   `{some_string,another_string,yet_another_one}` — Substitutes any of strings `'some_string', 'another_string', 'yet_another_one'`.
-   `{N..M}` — Substitutes any number in range from N to M including both borders. N and M can have leading zeroes e.g. `000..078`.

Constructions with `{}` are similar to the [remote](../../../sql-reference/table-functions/remote.md) table function.

**Example**

1. Suppose we have several files in CSV format with the following URIs on S3:

-   ‘https://storage.yandexcloud.net/my-test-bucket-768/some_prefix/some_file_1.csv’
-   ‘https://storage.yandexcloud.net/my-test-bucket-768/some_prefix/some_file_2.csv’
-   ‘https://storage.yandexcloud.net/my-test-bucket-768/some_prefix/some_file_3.csv’
-   ‘https://storage.yandexcloud.net/my-test-bucket-768/another_prefix/some_file_1.csv’
-   ‘https://storage.yandexcloud.net/my-test-bucket-768/another_prefix/some_file_2.csv’
-   ‘https://storage.yandexcloud.net/my-test-bucket-768/another_prefix/some_file_3.csv’

There are several ways to make a table consisting of all six files:

The first way:

``` sql
CREATE TABLE table_with_range (name String, value UInt32) ENGINE = S3('https://storage.yandexcloud.net/my-test-bucket-768/{some,another}_prefix/some_file_{1..3}', 'CSV');
```

Another way:

``` sql
CREATE TABLE table_with_question_mark (name String, value UInt32) ENGINE = S3('https://storage.yandexcloud.net/my-test-bucket-768/{some,another}_prefix/some_file_?', 'CSV');
```

Table consists of all the files in both directories (all files should satisfy format and schema described in query):

``` sql
CREATE TABLE table_with_asterisk (name String, value UInt32) ENGINE = S3('https://storage.yandexcloud.net/my-test-bucket-768/{some,another}_prefix/*', 'CSV');
```

If the listing of files contains number ranges with leading zeros, use the construction with braces for each digit separately or use `?`.

**Example**

Create table with files named `file-000.csv`, `file-001.csv`, … , `file-999.csv`:

``` sql
CREATE TABLE big_table (name String, value UInt32) ENGINE = S3('https://storage.yandexcloud.net/my-test-bucket-768/big_prefix/file-{000..999}.csv', 'CSV');
```

## Virtual Columns {#virtual-columns}

-   `_path` — Path to the file.
-   `_file` — Name of the file.

**See Also**

-   [Virtual columns](../../../engines/table-engines/index.md#table_engines-virtual_columns)

## S3-related settings {#settings}

The following settings can be set before query execution or placed into configuration file.

-   `s3_max_single_part_upload_size` — The maximum size of object to upload using singlepart upload to S3. Default value is `64Mb`.
-   `s3_min_upload_part_size` — The minimum size of part to upload during multipart upload to [S3 Multipart upload](https://docs.aws.amazon.com/AmazonS3/latest/dev/uploadobjusingmpu.html). Default value is `512Mb`.
-   `s3_max_redirects` — Max number of S3 redirects hops allowed. Default value is `10`.

Security consideration: if malicious user can specify arbitrary S3 URLs, `s3_max_redirects` must be set to zero to avoid [SSRF](https://en.wikipedia.org/wiki/Server-side_request_forgery) attacks; or alternatively, `remote_host_filter` must be specified in server configuration.

## Endpoint-based Settings {#endpoint-settings}

The following settings can be specified in configuration file for given endpoint (which will be matched by exact prefix of a URL):

-   `endpoint` — Specifies prefix of an endpoint. Mandatory.
-   `access_key_id` and `secret_access_key` — Specifies credentials to use with given endpoint. Optional.
-   `use_environment_credentials` — If set to `true`, S3 client will try to obtain credentials from environment variables and Amazon EC2 metadata for given endpoint. Optional, default value is `false`.
-   `use_insecure_imds_request` — If set to `true`, S3 client will use insecure IMDS request while obtaining credentials from Amazon EC2 metadata. Optional, default value is `false`.
-   `header` —  Adds specified HTTP header to a request to given endpoint. Optional, can be speficied multiple times.
-   `server_side_encryption_customer_key_base64` — If specified, required headers for accessing S3 objects with SSE-C encryption will be set. Optional.

**Example:**

``` xml
<s3>
    <endpoint-name>
        <endpoint>https://storage.yandexcloud.net/my-test-bucket-768/</endpoint>
        <!-- <access_key_id>ACCESS_KEY_ID</access_key_id> -->
        <!-- <secret_access_key>SECRET_ACCESS_KEY</secret_access_key> -->
        <!-- <use_environment_credentials>false</use_environment_credentials> -->
        <!-- <use_insecure_imds_request>false</use_insecure_imds_request> -->
        <!-- <header>Authorization: Bearer SOME-TOKEN</header> -->
        <!-- <server_side_encryption_customer_key_base64>BASE64-ENCODED-KEY</server_side_encryption_customer_key_base64> -->
    </endpoint-name>
</s3>
```

## Usage {#usage-examples}

Suppose we have several files in TSV format with the following URIs on HDFS:

-   'https://storage.yandexcloud.net/my-test-bucket-768/some_prefix/some_file_1.csv'
-   'https://storage.yandexcloud.net/my-test-bucket-768/some_prefix/some_file_2.csv'
-   'https://storage.yandexcloud.net/my-test-bucket-768/some_prefix/some_file_3.csv'
-   'https://storage.yandexcloud.net/my-test-bucket-768/another_prefix/some_file_1.csv'
-   'https://storage.yandexcloud.net/my-test-bucket-768/another_prefix/some_file_2.csv'
-   'https://storage.yandexcloud.net/my-test-bucket-768/another_prefix/some_file_3.csv'


1. There are several ways to make a table consisting of all six files:

``` sql
CREATE TABLE table_with_range (name String, value UInt32) 
ENGINE = S3('https://storage.yandexcloud.net/my-test-bucket-768/{some,another}_prefix/some_file_{1..3}', 'CSV');
```

2. Another way:

``` sql
CREATE TABLE table_with_question_mark (name String, value UInt32) 
ENGINE = S3('https://storage.yandexcloud.net/my-test-bucket-768/{some,another}_prefix/some_file_?', 'CSV');
```

3. Table consists of all the files in both directories (all files should satisfy format and schema described in query):

``` sql
CREATE TABLE table_with_asterisk (name String, value UInt32) 
ENGINE = S3('https://storage.yandexcloud.net/my-test-bucket-768/{some,another}_prefix/*', 'CSV');
```

!!! warning "Warning"
    If the listing of files contains number ranges with leading zeros, use the construction with braces for each digit separately or use `?`.

4. Create table with files named `file-000.csv`, `file-001.csv`, … , `file-999.csv`:

``` sql
CREATE TABLE big_table (name String, value UInt32) 
ENGINE = S3('https://storage.yandexcloud.net/my-test-bucket-768/big_prefix/file-{000..999}.csv', 'CSV');
```

## See also

-  [S3 table function](../../../sql-reference/table-functions/s3.md)