---
description: 'Provides a table-like interface to select/insert files in Amazon S3
  and Google Cloud Storage. This table function is similar to the hdfs function, but
  provides S3-specific features.'
keywords: ['s3', 'gcs', 'bucket']
sidebar_label: 's3'
sidebar_position: 180
slug: /sql-reference/table-functions/s3
title: 's3 Table Function'
---

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import CloudNotSupportedBadge from '@theme/badges/CloudNotSupportedBadge';

# s3 Table Function

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

If you have multiple replicas in your cluster, you can use the [s3Cluster function](../../sql-reference/table-functions/s3Cluster.md) instead to parallelize inserts.

When using the `s3 table function` with [`INSERT INTO...SELECT`](../../sql-reference/statements/insert-into#inserting-the-results-of-select), data is read and inserted in a streaming fashion. Only a few blocks of data reside in memory while the blocks are continuously read from S3 and pushed into the destination table.

## Syntax {#syntax}

```sql
s3(url [, NOSIGN | access_key_id, secret_access_key, [session_token]] [,format] [,structure] [,compression_method],[,headers], [,partition_strategy], [,partition_columns_in_data_file])
s3(named_collection[, option=value [,..]])
```

:::tip GCS
The S3 Table Function integrates with Google Cloud Storage by using the GCS XML API and HMAC keys.  See the [Google interoperability docs]( https://cloud.google.com/storage/docs/interoperability) for more details about the endpoint and HMAC.

For GCS, substitute your HMAC key and HMAC secret where you see `access_key_id` and `secret_access_key`.
:::

**Parameters**

`s3` table function supports the following plain parameters:

| Parameter                               | Description                                                                                                                                                                                                                                                                                                                                                                      |
|-----------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `url`                                   | 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 [here](../../engines/table-engines/integrations/s3.md#wildcards-in-path).                                                                                                   |
| `NOSIGN`                                | If this keyword is provided in place of credentials, all the requests will not be signed.                                                                                                                                                                                                                                                                                        |
| `access_key_id` and `secret_access_key` | Keys that specify credentials to use with given endpoint. Optional.                                                                                                                                                                                                                                                                                                              |
| `session_token`                         | Session token to use with the given keys. Optional when passing keys.                                                                                                                                                                                                                                                                                                            |
| `format`                                | The [format](/sql-reference/formats) of the file.                                                                                                                                                                                                                                                                                                                                |
| `structure`                             | Structure of the table. Format `'column1_name column1_type, column2_name column2_type, ...'`.                                                                                                                                                                                                                                                                                    |
| `compression_method`                    | Parameter is optional. Supported values: `none`, `gzip` or `gz`, `brotli` or `br`, `xz` or `LZMA`, `zstd` or `zst`. By default, it will autodetect compression method by file extension.                                                                                                                                                                                         |
| `headers`                               | Parameter is optional. Allows headers to be passed in the S3 request. Pass in the format `headers(key=value)` e.g. `headers('x-amz-request-payer' = 'requester')`.                                                                                                                                                                                                               |
| `partition_strategy`                    | Parameter is optional. Supported values: `WILDCARD` or `HIVE`. `WILDCARD` requires a `{_partition_id}` in the path, which is replaced with the partition key. `HIVE` does not allow wildcards, assumes the path is the table root, and generates Hive-style partitioned directories with Snowflake IDs as filenames and the file format as the extension. Defaults to `WILDCARD` |
| `partition_columns_in_data_file`        | Parameter is optional. Only used with `HIVE` partition strategy. Tells ClickHouse whether to expect partition columns to be written in the data file. Defaults `false`.                                                                                                                                                                                                          |

:::note GCS
The GCS url is in this format as the endpoint for the Google XML API is different than the JSON API:

```text
  https://storage.googleapis.com/<bucket>/<folder>/<filename(s)>
```

and not ~~https://storage.cloud.google.com~~.
:::

Arguments can also be passed using [named collections](operations/named-collections.md). In this case `url`, `access_key_id`, `secret_access_key`, `format`, `structure`, `compression_method` work in the same way, and some extra parameters are supported:

| Argument                      | Description                                                                                                                                                                       |
|-------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `filename`                    | appended to the url if specified.                                                                                                                                                 |
| `use_environment_credentials` | enabled by default, allows passing extra parameters using environment variables `AWS_CONTAINER_CREDENTIALS_RELATIVE_URI`, `AWS_CONTAINER_CREDENTIALS_FULL_URI`, `AWS_CONTAINER_AUTHORIZATION_TOKEN`, `AWS_EC2_METADATA_DISABLED`. |
| `no_sign_request`             | disabled by default.                                                                                                                                                              |
| `expiration_window_seconds`   | default value is 120.                                                                                                                                                             |

## Returned value {#returned_value}

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

## Examples {#examples}

Selecting the first 5 rows from the table from S3 file `https://datasets-documentation.s3.eu-west-3.amazonaws.com/aapl_stock.csv`:

```sql
SELECT *
FROM s3(
   'https://datasets-documentation.s3.eu-west-3.amazonaws.com/aapl_stock.csv',
   'CSVWithNames'
)
LIMIT 5;
```

```response
┌───────Date─┬────Open─┬────High─┬─────Low─┬───Close─┬───Volume─┬─OpenInt─┐
│ 1984-09-07 │ 0.42388 │ 0.42902 │ 0.41874 │ 0.42388 │ 23220030 │       0 │
│ 1984-09-10 │ 0.42388 │ 0.42516 │ 0.41366 │ 0.42134 │ 18022532 │       0 │
│ 1984-09-11 │ 0.42516 │ 0.43668 │ 0.42516 │ 0.42902 │ 42498199 │       0 │
│ 1984-09-12 │ 0.42902 │ 0.43157 │ 0.41618 │ 0.41618 │ 37125801 │       0 │
│ 1984-09-13 │ 0.43927 │ 0.44052 │ 0.43927 │ 0.43927 │ 57822062 │       0 │
└────────────┴─────────┴─────────┴─────────┴─────────┴──────────┴─────────┘
```

:::note
ClickHouse uses filename extensions to determine the format of the data. For example, we could have run the previous command without the `CSVWithNames`:

```sql
SELECT *
FROM s3(
   'https://datasets-documentation.s3.eu-west-3.amazonaws.com/aapl_stock.csv'
)
LIMIT 5;
```

ClickHouse also can determine the compression method of the file. For example, if the file was zipped up with a `.csv.gz` extension, ClickHouse would decompress the file automatically.
:::

## Usage {#usage}

Suppose that we have several files with following URIs on S3:

- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/some_prefix/some_file_1.csv'
- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/some_prefix/some_file_2.csv'
- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/some_prefix/some_file_3.csv'
- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/some_prefix/some_file_4.csv'
- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/another_prefix/some_file_1.csv'
- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/another_prefix/some_file_2.csv'
- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/another_prefix/some_file_3.csv'
- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/another_prefix/some_file_4.csv'

Count the number of rows in files ending with numbers from 1 to 3:

```sql
SELECT count(*)
FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/my-test-bucket-768/{some,another}_prefix/some_file_{1..3}.csv', 'CSV', 'column1 UInt32, column2 UInt32, column3 UInt32')
```

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

Count the total amount of rows in all files in these two directories:

```sql
SELECT count(*)
FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/my-test-bucket-768/{some,another}_prefix/*', 'CSV', 'column1 UInt32, column2 UInt32, column3 UInt32')
```

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

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

Count the total amount of rows in files named `file-000.csv`, `file-001.csv`, ... , `file-999.csv`:

```sql
SELECT count(*)
FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/my-test-bucket-768/big_prefix/file-{000..999}.csv', 'CSV', 'column1 UInt32, column2 UInt32, column3 UInt32');
```

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

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

```sql
INSERT INTO FUNCTION s3('https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/test-data.csv.gz', 'CSV', 'name String, value UInt32', 'gzip')
VALUES ('test-data', 1), ('test-data-2', 2);
```

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

```sql
INSERT INTO FUNCTION s3('https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/test-data.csv.gz', 'CSV', 'name String, value UInt32', 'gzip')
SELECT name, value FROM existing_table;
```

Glob ** can be used for recursive directory traversal. Consider the below example, it will fetch all files from `my-test-bucket-768` directory recursively:

```sql
SELECT * FROM s3('https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/**', 'CSV', 'name String, value UInt32', 'gzip');
```

The below get data from all `test-data.csv.gz` files from any folder inside `my-test-bucket` directory recursively:

```sql
SELECT * FROM s3('https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/**/test-data.csv.gz', 'CSV', 'name String, value UInt32', 'gzip');
```

Note. It is possible to specify custom URL mappers in the server configuration file. Example:
```sql
SELECT * FROM s3('s3://clickhouse-public-datasets/my-test-bucket-768/**/test-data.csv.gz', 'CSV', 'name String, value UInt32', 'gzip');
```
The URL `'s3://clickhouse-public-datasets/my-test-bucket-768/**/test-data.csv.gz'` would be replaced to `'http://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/**/test-data.csv.gz'`

Custom mapper can be added into `config.xml`:
```xml
<url_scheme_mappers>
   <s3>
      <to>https://{bucket}.s3.amazonaws.com</to>
   </s3>
   <gs>
      <to>https://{bucket}.storage.googleapis.com</to>
   </gs>
   <oss>
      <to>https://{bucket}.oss.aliyuncs.com</to>
   </oss>
</url_scheme_mappers>
```

For production use cases it is recommended to use [named collections](operations/named-collections.md). Here is the example:
```sql

CREATE NAMED COLLECTION creds AS
        access_key_id = '***',
        secret_access_key = '***';
SELECT count(*)
FROM s3(creds, url='https://s3-object-url.csv')
```

## Partitioned Write {#partitioned-write}

### Partition Strategy {#partition-strategy}

Supported for INSERT queries only.

`WILDCARD` (default): Replaces the `{_partition_id}` wildcard in the file path with the actual partition key.

`HIVE` implements hive style partitioning for reads & writes. It generates files using the following format: `<prefix>/<key1=val1/key2=val2...>/<snowflakeid>.<toLower(file_format)>`.

**Example of `HIVE` partition strategy**

```sql
INSERT INTO FUNCTION s3(s3_conn, filename='t_03363_function', format=Parquet, partition_strategy='hive') PARTITION BY (year, country) SELECT 2020 as year, 'Russia' as country, 1 as id;
```

```result
SELECT _path, * FROM s3(s3_conn, filename='t_03363_function/**.parquet');

   ┌─_path──────────────────────────────────────────────────────────────────────┬─id─┬─country─┬─year─┐
1. │ test/t_03363_function/year=2020/country=Russia/7351295896279887872.parquet │  1 │ Russia  │ 2020 │
   └────────────────────────────────────────────────────────────────────────────┴────┴─────────┴──────┘
```

**Examples of `WILDCARD` partition strategy**

1. Using partition ID in a key creates separate files:

```sql
INSERT INTO TABLE FUNCTION
    s3('http://bucket.amazonaws.com/my_bucket/file_{_partition_id}.csv', 'CSV', 'a String, b UInt32, c UInt32')
    PARTITION BY a VALUES ('x', 2, 3), ('x', 4, 5), ('y', 11, 12), ('y', 13, 14), ('z', 21, 22), ('z', 23, 24);
```
As a result, the data is written into three files: `file_x.csv`, `file_y.csv`, and `file_z.csv`.

2. Using partition ID in a bucket name creates files in different buckets:

```sql
INSERT INTO TABLE FUNCTION
    s3('http://bucket.amazonaws.com/my_bucket_{_partition_id}/file.csv', 'CSV', 'a UInt32, b UInt32, c UInt32')
    PARTITION BY a VALUES (1, 2, 3), (1, 4, 5), (10, 11, 12), (10, 13, 14), (20, 21, 22), (20, 23, 24);
```
As a result, the data is written into three files in different buckets: `my_bucket_1/file.csv`, `my_bucket_10/file.csv`, and `my_bucket_20/file.csv`.

## Accessing public buckets {#accessing-public-buckets}

ClickHouse tries to fetch credentials from many different types of sources.
Sometimes, it can produce problems when accessing some buckets that are public causing the client to return `403` error code.
This issue can be avoided by using `NOSIGN` keyword, forcing the client to ignore all the credentials, and not sign the requests.

```sql
SELECT *
FROM s3(
   'https://datasets-documentation.s3.eu-west-3.amazonaws.com/aapl_stock.csv',
   NOSIGN,
   'CSVWithNames'
)
LIMIT 5;
```

## Using S3 credentials (ClickHouse Cloud) {#using-s3-credentials-clickhouse-cloud}

For non-public buckets, users can pass an `aws_access_key_id` and `aws_secret_access_key` to the function. For example:

```sql
SELECT count() FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/mta/*.tsv', '<KEY>', '<SECRET>','TSVWithNames')
```

This is appropriate for one-off accesses or in cases where credentials can easily be rotated. However, this is not recommended as a long-term solution for repeated access or where credentials are sensitive. In this case, we recommend users rely on role-based access.

Role-based access for S3 in ClickHouse Cloud is documented [here](/cloud/security/secure-s3#access-your-s3-bucket-with-the-clickhouseaccess-role).

Once configured, a `roleARN` can be passed to the s3 function via an `extra_credentials` parameter. For example:

```sql
SELECT count() FROM s3('https://datasets-documentation.s3.eu-west-3.amazonaws.com/mta/*.tsv','CSVWithNames',extra_credentials(role_arn = 'arn:aws:iam::111111111111:role/ClickHouseAccessRole-001'))
```

Further examples can be found [here](/cloud/security/secure-s3#access-your-s3-bucket-with-the-clickhouseaccess-role)

## Working with archives {#working-with-archives}

Suppose that we have several archive files with following URIs on S3:

- 'https://s3-us-west-1.amazonaws.com/umbrella-static/top-1m-2018-01-10.csv.zip'
- 'https://s3-us-west-1.amazonaws.com/umbrella-static/top-1m-2018-01-11.csv.zip'
- 'https://s3-us-west-1.amazonaws.com/umbrella-static/top-1m-2018-01-12.csv.zip'

Extracting data from these archives is possible using ::. Globs can be used both in the url part as well as in the part after :: (responsible for the name of a file inside the archive).

```sql
SELECT *
FROM s3(
   'https://s3-us-west-1.amazonaws.com/umbrella-static/top-1m-2018-01-1{0..2}.csv.zip :: *.csv'
);
```

:::note
ClickHouse supports three archive formats:
ZIP
TAR
7Z
While ZIP and TAR archives can be accessed from any supported storage location, 7Z archives can only be read from the local filesystem where ClickHouse is installed.
:::

## Inserting Data {#inserting-data}

Note that rows can only be inserted into new files. There are no merge cycles or file split operations. Once a file is written, subsequent inserts will fail. See more details [here](/integrations/s3#inserting-data).

## Virtual Columns {#virtual-columns}

- `_path` — Path to the file. Type: `LowCardinality(String)`. In case of archive, shows path in a format: `"{path_to_archive}::{path_to_file_inside_archive}"`
- `_file` — Name of the file. Type: `LowCardinality(String)`. In case of archive shows name of the file inside the archive.
- `_size` — Size of the file in bytes. Type: `Nullable(UInt64)`. If the file size is unknown, the value is `NULL`. In case of archive shows uncompressed file size of the file inside the archive.
- `_time` — Last modified time of the file. Type: `Nullable(DateTime)`. If the time is unknown, the value is `NULL`.

## use_hive_partitioning setting {#hive-style-partitioning}

This is a hint for ClickHouse to parse hive style partitioned files upon reading time. It has no effect on writing. For symmetrical reads and writes, use the `partition_strategy` argument.

When setting `use_hive_partitioning` is set to 1, ClickHouse will detect Hive-style partitioning in the path (`/name=value/`) and will allow to use partition columns as virtual columns in the query. These virtual columns will have the same names as in the partitioned path, but starting with `_`.

**Example**

```sql
SELECT * FROM s3('s3://data/path/date=*/country=*/code=*/*.parquet') WHERE date > '2020-01-01' AND country = 'Netherlands' AND code = 42;
```

## Accessing requester-pays buckets {#accessing-requester-pays-buckets}

To access a requester-pays bucket, a header `x-amz-request-payer = requester` must be passed in any requests. This is achieved by passing the parameter `headers('x-amz-request-payer' = 'requester')` to the s3 function. For example:

```sql
SELECT
    count() AS num_rows,
    uniqExact(_file) AS num_files
FROM s3('https://coiled-datasets-rp.s3.us-east-1.amazonaws.com/1trc/measurements-100*.parquet', 'AWS_ACCESS_KEY_ID', 'AWS_SECRET_ACCESS_KEY', headers('x-amz-request-payer' = 'requester'))

┌───num_rows─┬─num_files─┐
│ 1110000000 │       111 │
└────────────┴───────────┘

1 row in set. Elapsed: 3.089 sec. Processed 1.09 billion rows, 0.00 B (353.55 million rows/s., 0.00 B/s.)
Peak memory usage: 192.27 KiB.
```

## Storage Settings {#storage-settings}

- [s3_truncate_on_insert](operations/settings/settings.md#s3_truncate_on_insert) - allows to truncate file before insert into it. Disabled by default.
- [s3_create_new_file_on_insert](operations/settings/settings.md#s3_create_new_file_on_insert) - allows to create a new file on each insert if format has suffix. Disabled by default.
- [s3_skip_empty_files](operations/settings/settings.md#s3_skip_empty_files) - allows to skip empty files while reading. Enabled by default.

## Related {#related}

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