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8.3 KiB
Markdown
210 lines
8.3 KiB
Markdown
---
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slug: /en/sql-reference/table-functions/gcs
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sidebar_position: 70
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sidebar_label: gcs
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keywords: [gcs, bucket]
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---
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# gcs Table Function
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Provides a table-like interface to `SELECT` and `INSERT` data from [Google Cloud Storage](https://cloud.google.com/storage/). Requires the [`Storage Object User` IAM role](https://cloud.google.com/storage/docs/access-control/iam-roles).
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This is an alias of the [s3 table function](../../sql-reference/table-functions/s3.md).
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If you have multiple replicas in your cluster, you can use the [s3Cluster function](../../sql-reference/table-functions/s3Cluster.md) (which works with GCS) instead to parallelize inserts.
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**Syntax**
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``` sql
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gcs(url [, NOSIGN | hmac_key, hmac_secret] [,format] [,structure] [,compression_method])
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gcs(named_collection[, option=value [,..]])
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```
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:::tip GCS
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The GCS 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.
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:::
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**Parameters**
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- `url` — Bucket path to file. Supports following wildcards in readonly mode: `*`, `**`, `?`, `{abc,def}` and `{N..M}` where `N`, `M` — numbers, `'abc'`, `'def'` — strings.
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:::note GCS
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The GCS path is in this format as the endpoint for the Google XML API is different than the JSON API:
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```
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https://storage.googleapis.com/<bucket>/<folder>/<filename(s)>
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```
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and not ~~https://storage.cloud.google.com~~.
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:::
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- `NOSIGN` — If this keyword is provided in place of credentials, all the requests will not be signed.
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- `hmac_key` and `hmac_secret` — Keys that specify credentials to use with given endpoint. Optional.
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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_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.
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Arguments can also be passed using [named collections](/docs/en/operations/named-collections.md). In this case `url`, `format`, `structure`, `compression_method` work in the same way, and some extra parameters are supported:
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- `access_key_id` — `hmac_key`, optional.
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- `secret_access_key` — `hmac_secret`, optional.
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- `filename` — appended to the url if specified.
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- `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`.
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- `no_sign_request` — disabled by default.
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- `expiration_window_seconds` — default value is 120.
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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 GCS file `https://storage.googleapis.com/my-test-bucket-768/data.csv`:
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``` sql
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SELECT *
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FROM gcs('https://storage.googleapis.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 method:
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``` sql
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SELECT *
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FROM gcs('https://storage.googleapis.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 GCS:
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- 'https://storage.googleapis.com/my-test-bucket-768/some_prefix/some_file_1.csv'
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- 'https://storage.googleapis.com/my-test-bucket-768/some_prefix/some_file_2.csv'
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- 'https://storage.googleapis.com/my-test-bucket-768/some_prefix/some_file_3.csv'
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- 'https://storage.googleapis.com/my-test-bucket-768/some_prefix/some_file_4.csv'
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- 'https://storage.googleapis.com/my-test-bucket-768/another_prefix/some_file_1.csv'
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- 'https://storage.googleapis.com/my-test-bucket-768/another_prefix/some_file_2.csv'
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- 'https://storage.googleapis.com/my-test-bucket-768/another_prefix/some_file_3.csv'
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- 'https://storage.googleapis.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 gcs('https://storage.googleapis.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 gcs('https://storage.googleapis.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 gcs('https://storage.googleapis.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 gcs('https://storage.googleapis.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 gcs('https://storage.googleapis.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 gcs('https://storage.googleapis.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 gcs('https://storage.googleapis.com/my-test-bucket-768/**/test-data.csv.gz', 'CSV', 'name String, value UInt32', 'gzip');
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```
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For production use cases it is recommended to use [named collections](/docs/en/operations/named-collections.md). Here is the example:
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``` sql
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CREATE NAMED COLLECTION creds AS
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access_key_id = '***',
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secret_access_key = '***';
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SELECT count(*)
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FROM gcs(creds, url='https://s3-object-url.csv')
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```
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## Partitioned Write
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If you specify `PARTITION BY` expression when inserting data into `GCS` 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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gcs('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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gcs('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 table function](s3.md)
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- [S3 engine](../../engines/table-engines/integrations/s3.md)
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