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133 lines
4.5 KiB
Markdown
133 lines
4.5 KiB
Markdown
---
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sidebar_position: 45
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sidebar_label: s3
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---
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# S3 表函数 {#s3-table-function}
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提供类似于表的接口来 select/insert [Amazon S3](https://aws.amazon.com/s3/)中的文件。这个表函数类似于[hdfs](../../sql-reference/table-functions/hdfs.md)，但提供了 S3 特有的功能。
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**语法**
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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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**参数**
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- `path` — 带有文件路径的 Bucket url。在只读模式下支持以下通配符: `*`, `?`, `{abc,def}` 和 `{N..M}` 其中 `N`, `M` 是数字, `'abc'`, `'def'` 是字符串. 更多信息见[下文](#wildcards-in-path).
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- `format` — 文件的[格式](../../interfaces/formats.md#formats).
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- `structure` — 表的结构. 格式像这样 `'column1_name column1_type, column2_name column2_type, ...'`.
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- `compression` — 压缩类型. 支持的值: `none`, `gzip/gz`, `brotli/br`, `xz/LZMA`, `zstd/zst`. 参数是可选的. 默认情况下，通过文件扩展名自动检测压缩类型.
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**返回值**
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一个具有指定结构的表，用于读取或写入指定文件中的数据。
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**示例**
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从 S3 文件`https://storage.yandexcloud.net/my-test-bucket-768/data.csv`中选择表格的前两行:
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``` sql
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SELECT *
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FROM s3('https://storage.yandexcloud.net/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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类似的情况，但来源是`gzip`压缩的文件:
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``` sql
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SELECT *
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FROM s3('https://storage.yandexcloud.net/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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## 用法 {#usage-examples}
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假设我们在S3上有几个文件，URI如下:
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- 'https://storage.yandexcloud.net/my-test-bucket-768/some_prefix/some_file_1.csv'
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- 'https://storage.yandexcloud.net/my-test-bucket-768/some_prefix/some_file_2.csv'
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- 'https://storage.yandexcloud.net/my-test-bucket-768/some_prefix/some_file_3.csv'
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- 'https://storage.yandexcloud.net/my-test-bucket-768/some_prefix/some_file_4.csv'
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- 'https://storage.yandexcloud.net/my-test-bucket-768/another_prefix/some_file_1.csv'
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- 'https://storage.yandexcloud.net/my-test-bucket-768/another_prefix/some_file_2.csv'
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- 'https://storage.yandexcloud.net/my-test-bucket-768/another_prefix/some_file_3.csv'
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- 'https://storage.yandexcloud.net/my-test-bucket-768/another_prefix/some_file_4.csv'
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计算以数字1至3结尾的文件的总行数:
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``` sql
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SELECT count(*)
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FROM s3('https://storage.yandexcloud.net/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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计算这两个目录中所有文件的行的总量:
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``` sql
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SELECT count(*)
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FROM s3('https://storage.yandexcloud.net/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 "Warning"
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如果文件列表中包含有从零开头的数字范围，请对每个数字分别使用带括号的结构，或者使用`?`。
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计算名为 `file-000.csv`, `file-001.csv`, … , `file-999.csv` 文件的总行数:
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``` sql
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SELECT count(*)
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FROM s3('https://storage.yandexcloud.net/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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插入数据到 `test-data.csv.gz` 文件:
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``` sql
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INSERT INTO FUNCTION s3('https://storage.yandexcloud.net/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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从已有的表插入数据到 `test-data.csv.gz` 文件:
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``` sql
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INSERT INTO FUNCTION s3('https://storage.yandexcloud.net/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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**另请参阅**
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- [S3 引擎](../../engines/table-engines/integrations/s3.md)
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[原始文章](https://clickhouse.com/docs/en/sql-reference/table-functions/s3/) <!--hide-->
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