mirror of
https://github.com/ClickHouse/ClickHouse.git
synced 2024-11-11 01:54:55 +00:00
185 lines
6.6 KiB
Markdown
185 lines
6.6 KiB
Markdown
---
|
|
slug: /en/sql-reference/table-functions/gcs
|
|
sidebar_position: 70
|
|
sidebar_label: gcs
|
|
keywords: [gcs, bucket]
|
|
---
|
|
|
|
# gcs Table Function
|
|
|
|
Provides a table-like interface to select/insert files in [Google Cloud Storage](https://cloud.google.com/storage/).
|
|
|
|
**Syntax**
|
|
|
|
``` sql
|
|
gcs(path [,hmac_key, hmac_secret] [,format] [,structure] [,compression])
|
|
```
|
|
|
|
:::tip GCS
|
|
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.
|
|
|
|
:::
|
|
|
|
**Arguments**
|
|
|
|
- `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.
|
|
|
|
:::note GCS
|
|
The GCS path 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~~.
|
|
:::
|
|
|
|
- `format` — The [format](../../interfaces/formats.md#formats) of the file.
|
|
- `structure` — Structure of the table. Format `'column1_name column1_type, column2_name column2_type, ...'`.
|
|
- `compression` — Parameter is optional. Supported values: `none`, `gzip/gz`, `brotli/br`, `xz/LZMA`, `zstd/zst`. By default, it will autodetect compression by file extension.
|
|
|
|
**Returned value**
|
|
|
|
A table with the specified structure for reading or writing data in the specified file.
|
|
|
|
**Examples**
|
|
|
|
Selecting the first two rows from the table from GCS file `https://storage.googleapis.com/my-test-bucket-768/data.csv`:
|
|
|
|
``` sql
|
|
SELECT *
|
|
FROM gcs('https://storage.googleapis.com/my-test-bucket-768/data.csv', 'CSV', 'column1 UInt32, column2 UInt32, column3 UInt32')
|
|
LIMIT 2;
|
|
```
|
|
|
|
``` text
|
|
┌─column1─┬─column2─┬─column3─┐
|
|
│ 1 │ 2 │ 3 │
|
|
│ 3 │ 2 │ 1 │
|
|
└─────────┴─────────┴─────────┘
|
|
```
|
|
|
|
The similar but from file with `gzip` compression:
|
|
|
|
``` sql
|
|
SELECT *
|
|
FROM gcs('https://storage.googleapis.com/my-test-bucket-768/data.csv.gz', 'CSV', 'column1 UInt32, column2 UInt32, column3 UInt32', 'gzip')
|
|
LIMIT 2;
|
|
```
|
|
|
|
``` text
|
|
┌─column1─┬─column2─┬─column3─┐
|
|
│ 1 │ 2 │ 3 │
|
|
│ 3 │ 2 │ 1 │
|
|
└─────────┴─────────┴─────────┘
|
|
```
|
|
|
|
## Usage
|
|
|
|
Suppose that we have several files with following URIs on GCS:
|
|
|
|
- 'https://storage.googleapis.com/my-test-bucket-768/some_prefix/some_file_1.csv'
|
|
- 'https://storage.googleapis.com/my-test-bucket-768/some_prefix/some_file_2.csv'
|
|
- 'https://storage.googleapis.com/my-test-bucket-768/some_prefix/some_file_3.csv'
|
|
- 'https://storage.googleapis.com/my-test-bucket-768/some_prefix/some_file_4.csv'
|
|
- 'https://storage.googleapis.com/my-test-bucket-768/another_prefix/some_file_1.csv'
|
|
- 'https://storage.googleapis.com/my-test-bucket-768/another_prefix/some_file_2.csv'
|
|
- 'https://storage.googleapis.com/my-test-bucket-768/another_prefix/some_file_3.csv'
|
|
- 'https://storage.googleapis.com/my-test-bucket-768/another_prefix/some_file_4.csv'
|
|
|
|
Count the amount of rows in files ending with numbers from 1 to 3:
|
|
|
|
``` sql
|
|
SELECT count(*)
|
|
FROM gcs('https://storage.googleapis.com/my-test-bucket-768/{some,another}_prefix/some_file_{1..3}.csv', 'CSV', 'name String, value UInt32')
|
|
```
|
|
|
|
``` text
|
|
┌─count()─┐
|
|
│ 18 │
|
|
└─────────┘
|
|
```
|
|
|
|
Count the total amount of rows in all files in these two directories:
|
|
|
|
``` sql
|
|
SELECT count(*)
|
|
FROM gcs('https://storage.googleapis.com/my-test-bucket-768/{some,another}_prefix/*', 'CSV', 'name String, value UInt32')
|
|
```
|
|
|
|
``` text
|
|
┌─count()─┐
|
|
│ 24 │
|
|
└─────────┘
|
|
```
|
|
|
|
:::warning
|
|
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`:
|
|
|
|
``` sql
|
|
SELECT count(*)
|
|
FROM gcs('https://storage.googleapis.com/my-test-bucket-768/big_prefix/file-{000..999}.csv', 'CSV', 'name String, value UInt32');
|
|
```
|
|
|
|
``` text
|
|
┌─count()─┐
|
|
│ 12 │
|
|
└─────────┘
|
|
```
|
|
|
|
Insert data into file `test-data.csv.gz`:
|
|
|
|
``` sql
|
|
INSERT INTO FUNCTION gcs('https://storage.googleapis.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:
|
|
|
|
``` sql
|
|
INSERT INTO FUNCTION gcs('https://storage.googleapis.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:
|
|
|
|
``` sql
|
|
SELECT * FROM gcs('https://storage.googleapis.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:
|
|
|
|
``` sql
|
|
SELECT * FROM gcs('https://storage.googleapis.com/my-test-bucket-768/**/test-data.csv.gz', 'CSV', 'name String, value UInt32', 'gzip');
|
|
```
|
|
|
|
## Partitioned Write
|
|
|
|
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.
|
|
|
|
**Examples**
|
|
|
|
1. Using partition ID in a key creates separate files:
|
|
|
|
```sql
|
|
INSERT INTO TABLE FUNCTION
|
|
gcs('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`.
|
|
|
|
2. Using partition ID in a bucket name creates files in different buckets:
|
|
|
|
```sql
|
|
INSERT INTO TABLE FUNCTION
|
|
gcs('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`.
|
|
|
|
**See Also**
|
|
|
|
- [S3 table function](s3.md)
|
|
- [S3 engine](../../engines/table-engines/integrations/s3.md)
|