ClickHouse/docs/en/sql-reference/table-functions/s3.md

---
toc_priority: 45
toc_title: s3
---

# S3 Table Function {#s3-table-function}

Provides table-like interface to select/insert files in [Amazon S3](https://aws.amazon.com/s3/). This table function is similar to [hdfs](../../sql-reference/table-functions/hdfs.md), but provides S3-specific features.

**Syntax**

``` sql
s3(path, [aws_access_key_id, aws_secret_access_key,] format, structure, [compression])
```

**Arguments**

-   `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.
-   `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` — Parameter is optional. Supported values: none, gzip/gz, brotli/br, xz/LZMA, zstd/zst. By default, it will autodetect compression by file extension.

**Returned value**

A table with the specified structure for reading or writing data in the specified file.

**Examples**

Table from S3 file `https://storage.yandexcloud.net/my-test-bucket-768/data.csv` and selection of the first two rows from it:

``` sql
SELECT *
FROM s3('https://storage.yandexcloud.net/my-test-bucket-768/data.csv', 'CSV', 'column1 UInt32, column2 UInt32, column3 UInt32')
LIMIT 2;
```

``` text
┌─column1─┬─column2─┬─column3─┐
│       1 │       2 │       3 │
│       3 │       2 │       1 │
└─────────┴─────────┴─────────┘
```

The similar but from file with `gzip` compression:

``` sql
SELECT *
FROM s3('https://storage.yandexcloud.net/my-test-bucket-768/data.csv.gz', 'CSV', 'column1 UInt32, column2 UInt32, column3 UInt32', 'gzip')
LIMIT 2;
```

``` text
┌─column1─┬─column2─┬─column3─┐
│       1 │       2 │       3 │
│       3 │       2 │       1 │
└─────────┴─────────┴─────────┘
```

## Usage {#usage-examples}

Suppose that we have several files with 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/some_prefix/some_file_4.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'
-   'https://storage.yandexcloud.net/my-test-bucket-768/another_prefix/some_file_4.csv'

1.  Query the amount of rows in files end with number from 1 to 3:

``` sql
SELECT count(*)
FROM s3('https://storage.yandexcloud.net/my-test-bucket-768/{some,another}_prefix/some_file_{1..3}.csv', 'CSV', 'name String, value UInt32')
```

``` text
┌─count()─┐
│      18 │
└─────────┘
```

2.  Query the amount of rows in all files of these two directories:

``` sql
SELECT count(*)
FROM s3('https://storage.yandexcloud.net/my-test-bucket-768/{some,another}_prefix/*', 'CSV', 'name String, value UInt32')
```

``` text
┌─count()─┐
│      24 │
└─────────┘
```

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

3. Query the data from files named `file-000.csv`, `file-001.csv`, … , `file-999.csv`:

``` sql
SELECT count(*)
FROM s3('https://storage.yandexcloud.net/my-test-bucket-768/big_prefix/file-{000..999}.csv', 'CSV', 'name String, value UInt32');
```

``` text
┌─count()─┐
│      12 │
└─────────┘
```

4. Insert a data into file `test-data.csv.gz`:

``` sql
INSERT INTO s3('https://storage.yandexcloud.net/my-test-bucket-768/test-data.csv.gz', 'CSV', 'name String, value UInt32', 'gzip')
VALUES ('test-data', 1), ('test-data-2', 2);
```

5. Insert a data into file `test-data.csv.gz` from existing table:

``` sql
INSERT INTO s3('https://storage.yandexcloud.net/my-test-bucket-768/test-data.csv.gz', 'CSV', 'name String, value UInt32', 'gzip')
SELECT name, value FROM existing_table;
```

**See Also**

-   [S3 engine](../../engines/table-engines/integrations/s3.md)

[Original article](https://clickhouse.tech/docs/en/sql-reference/table-functions/s3/) <!--hide-->