ClickHouse/docs/en/engines/table-engines/integrations/s3.md

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7	S3

S3 Table Engine

This engine provides integration with Amazon S3 ecosystem. This engine is similar to the HDFS engine, but provides S3-specific features.

Create Table

CREATE TABLE s3_engine_table (name String, value UInt32) 
ENGINE = S3(path, [aws_access_key_id, aws_secret_access_key,] format, [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.
• format — The format of the file.
• aws_access_key_id, aws_secret_access_key - Long-term credentials for the AWS account user. You can use these to authenticate your requests. Parameter is optional. If credentials are not specified, they are used from the configuration file. For more information see Using S3 for Data Storage.
• 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:

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

2. Fill file:

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

3. Query the data:

SELECT * FROM s3_engine_table LIMIT 2;

┌─name─┬─value─┐
│ one  │     1 │
│ two  │     2 │
└──────┴───────┘

Virtual columns

• _path — Path to the file.
• _file — Name of the file.

For more information about virtual columns see here.

Implementation Details

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

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 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:

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:

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):

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:

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

• _path — Path to the file.
• _file — Name of the file.

See Also

• Virtual columns

S3-related 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. 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 attacks; or alternatively, remote_host_filter must be specified in server configuration.

Endpoint-based 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:

<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

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:

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:

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):

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:

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