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s3 Table Function

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.

If you have multiple replicas in your cluster, you can use the s3Cluster function instead to parallelize inserts.

When using the s3 table function with INSERT INTO...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

s3(url [, NOSIGN | access_key_id, secret_access_key, [session_token]] [,format] [,structure] [,compression_method])
s3(named_collection[, option=value [,..]])
GCS

The S3 Table Function integrates with Google Cloud Storage by using the GCS XML API and HMAC keys. See the Google interoperability docs 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:

  • 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.
    GCS

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

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

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

  • 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 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/gz, brotli/br, xz/LZMA, zstd/zst. By default, it will autodetect compression method by file extension.

Arguments can also be passed using named collections. 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:

  • 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

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

Examples

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

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

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​

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

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

SELECT count(*)
FROM s3('https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/{some,another}_prefix/some_file_{1..3}.csv', 'CSV', 'name String, value UInt32')
┌─count()─┐
│      18 │
└─────────┘

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

SELECT count(*)
FROM s3('https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/{some,another}_prefix/*', 'CSV', 'name String, value UInt32')
┌─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:

SELECT count(*)
FROM s3('https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/big_prefix/file-{000..999}.csv', 'CSV', 'name String, value UInt32');
┌─count()─┐
│      12 │
└─────────┘

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

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:

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:

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:

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:

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:

<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. Here is the example:


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

Partitioned Write​

If you specify PARTITION BY expression when inserting data into S3 table, a separate file is created for each partition value. Splitting the data into separate files helps to improve reading operations efficiency.

Examples

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

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

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.

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

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: 

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.

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

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

Working with archives​

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

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).

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

Virtual Columns​

  • _path — Path to the file. Type: LowCardinalty(String). In case of archive, shows path in a format: "{path_to_archive}::{path_to_file_inside_archive}"
  • _file — Name of the file. Type: LowCardinalty(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.

Hive-style partitioning​

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

Use virtual column, created with Hive-style partitioning

SET use_hive_partitioning = 1;
SELECT * from s3('s3://data/path/date=*/country=*/code=*/*.parquet') where _date > '2020-01-01' and _country = 'Netherlands' and _code = 42;

Storage Settings​

See Also