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169 lines
6.6 KiB
Markdown
169 lines
6.6 KiB
Markdown
---
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slug: /en/sql-reference/table-functions/s3
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sidebar_position: 45
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sidebar_label: s3
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---
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# s3 Table Function
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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.
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**Syntax**
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``` sql
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s3(path [,aws_access_key_id, aws_secret_access_key] [,format] [,structure] [,compression])
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```
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**Arguments**
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- `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 [here](../../engines/table-engines/integrations/s3.md#wildcards-in-path).
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- `format` — The [format](../../interfaces/formats.md#formats) of the file.
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- `structure` — Structure of the table. Format `'column1_name column1_type, column2_name column2_type, ...'`.
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- `compression` — Parameter is optional. Supported values: `none`, `gzip/gz`, `brotli/br`, `xz/LZMA`, `zstd/zst`. By default, it will autodetect compression by file extension.
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**Returned value**
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A table with the specified structure for reading or writing data in the specified file.
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**Examples**
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Selecting the first two rows from the table from S3 file `https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/data.csv`:
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``` sql
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SELECT *
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FROM s3('https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/data.csv', 'CSV', 'column1 UInt32, column2 UInt32, column3 UInt32')
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LIMIT 2;
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```
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``` text
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┌─column1─┬─column2─┬─column3─┐
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│ 1 │ 2 │ 3 │
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│ 3 │ 2 │ 1 │
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└─────────┴─────────┴─────────┘
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```
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The similar but from file with `gzip` compression:
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``` sql
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SELECT *
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FROM s3('https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/data.csv.gz', 'CSV', 'column1 UInt32, column2 UInt32, column3 UInt32', 'gzip')
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LIMIT 2;
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```
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``` text
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┌─column1─┬─column2─┬─column3─┐
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│ 1 │ 2 │ 3 │
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│ 3 │ 2 │ 1 │
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└─────────┴─────────┴─────────┘
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```
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## Usage
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Suppose that we have several files with following URIs on S3:
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- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/some_prefix/some_file_1.csv'
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- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/some_prefix/some_file_2.csv'
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- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/some_prefix/some_file_3.csv'
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- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/some_prefix/some_file_4.csv'
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- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/another_prefix/some_file_1.csv'
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- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/another_prefix/some_file_2.csv'
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- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/another_prefix/some_file_3.csv'
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- 'https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/another_prefix/some_file_4.csv'
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Count the amount of rows in files ending with numbers from 1 to 3:
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``` sql
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SELECT count(*)
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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')
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```
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``` text
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┌─count()─┐
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│ 18 │
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└─────────┘
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```
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Count the total amount of rows in all files in these two directories:
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``` sql
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SELECT count(*)
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FROM s3('https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/{some,another}_prefix/*', 'CSV', 'name String, value UInt32')
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```
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``` text
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┌─count()─┐
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│ 24 │
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└─────────┘
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```
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:::warning
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If your listing of files contains number ranges with leading zeros, use the construction with braces for each digit separately or use `?`.
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:::
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Count the total amount of rows in files named `file-000.csv`, `file-001.csv`, … , `file-999.csv`:
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``` sql
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SELECT count(*)
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FROM s3('https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/big_prefix/file-{000..999}.csv', 'CSV', 'name String, value UInt32');
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```
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``` text
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┌─count()─┐
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│ 12 │
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└─────────┘
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```
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Insert data into file `test-data.csv.gz`:
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``` sql
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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')
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VALUES ('test-data', 1), ('test-data-2', 2);
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```
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Insert data into file `test-data.csv.gz` from existing table:
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``` sql
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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')
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SELECT name, value FROM existing_table;
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```
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Glob ** can be used for recursive directory traversal. Consider the below example, it will fetch all files from `my-test-bucket-768` directory recursively:
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``` sql
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SELECT * FROM s3('https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/**', 'CSV', 'name String, value UInt32', 'gzip');
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```
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The below get data from all `test-data.csv.gz` files from any folder inside `my-test-bucket` directory recursively:
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``` sql
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SELECT * FROM s3('https://clickhouse-public-datasets.s3.amazonaws.com/my-test-bucket-768/**/test-data.csv.gz', 'CSV', 'name String, value UInt32', 'gzip');
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```
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## Partitioned Write
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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.
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**Examples**
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1. Using partition ID in a key creates separate files:
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```sql
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INSERT INTO TABLE FUNCTION
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s3('http://bucket.amazonaws.com/my_bucket/file_{_partition_id}.csv', 'CSV', 'a String, b UInt32, c UInt32')
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PARTITION BY a VALUES ('x', 2, 3), ('x', 4, 5), ('y', 11, 12), ('y', 13, 14), ('z', 21, 22), ('z', 23, 24);
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```
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As a result, the data is written into three files: `file_x.csv`, `file_y.csv`, and `file_z.csv`.
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2. Using partition ID in a bucket name creates files in different buckets:
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```sql
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INSERT INTO TABLE FUNCTION
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s3('http://bucket.amazonaws.com/my_bucket_{_partition_id}/file.csv', 'CSV', 'a UInt32, b UInt32, c UInt32')
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PARTITION BY a VALUES (1, 2, 3), (1, 4, 5), (10, 11, 12), (10, 13, 14), (20, 21, 22), (20, 23, 24);
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```
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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`.
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**See Also**
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- [S3 engine](../../engines/table-engines/integrations/s3.md)
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