mirror of
https://github.com/ClickHouse/ClickHouse.git
synced 2024-12-14 02:12:21 +00:00
74 lines
2.7 KiB
Markdown
74 lines
2.7 KiB
Markdown
---
|
|
slug: /en/engines/table-engines/integrations/azure-queue
|
|
sidebar_position: 181
|
|
sidebar_label: AzureQueue
|
|
---
|
|
|
|
# AzureQueue Table Engine
|
|
|
|
This engine provides an integration with [Azure Blob Storage](https://azure.microsoft.com/en-us/products/storage/blobs) ecosystem, allowing streaming data import.
|
|
|
|
## Create Table {#creating-a-table}
|
|
|
|
``` sql
|
|
CREATE TABLE test (name String, value UInt32)
|
|
ENGINE = AzureQueue(...)
|
|
[SETTINGS]
|
|
[mode = '',]
|
|
[after_processing = 'keep',]
|
|
[keeper_path = '',]
|
|
...
|
|
```
|
|
|
|
**Engine parameters**
|
|
|
|
`AzureQueue` parameters are the same as `AzureBlobStorage` table engine supports. See parameters section [here](../../../engines/table-engines/integrations/azureBlobStorage.md).
|
|
|
|
**Example**
|
|
|
|
```sql
|
|
CREATE TABLE azure_queue_engine_table (name String, value UInt32)
|
|
ENGINE=AzureQueue('DefaultEndpointsProtocol=http;AccountName=devstoreaccount1;AccountKey=Eby8vdM02xNOcqFlqUwJPLlmEtlCDXJ1OUzFT50uSRZ6IFsuFq2UVErCz4I6tq/K1SZFPTOtr/KBHBeksoGMGw==;BlobEndpoint=http://azurite1:10000/devstoreaccount1/data/')
|
|
SETTINGS
|
|
mode = 'unordered'
|
|
```
|
|
|
|
## Settings {#settings}
|
|
|
|
The set of supported settings is the same as for `S3Queue` table engine, but without `s3queue_` prefix. See [full list of settings settings](../../../engines/table-engines/integrations/s3queue.md#settings).
|
|
To get a list of settings, configured for the table, use `system.s3_queue_settings` table. Available from `24.10`.
|
|
|
|
## Description {#description}
|
|
|
|
`SELECT` is not particularly useful for streaming import (except for debugging), because each file can be imported only once. It is more practical to create real-time threads using [materialized views](../../../sql-reference/statements/create/view.md). To do this:
|
|
|
|
1. Use the engine to create a table for consuming from specified path in S3 and consider it a data stream.
|
|
2. Create a table with the desired structure.
|
|
3. Create a materialized view that converts data from the engine and puts it into a previously created table.
|
|
|
|
When the `MATERIALIZED VIEW` joins the engine, it starts collecting data in the background.
|
|
|
|
Example:
|
|
|
|
``` sql
|
|
CREATE TABLE azure_queue_engine_table (name String, value UInt32)
|
|
ENGINE=AzureQueue('<endpoint>', 'CSV', 'gzip')
|
|
SETTINGS
|
|
mode = 'unordered';
|
|
|
|
CREATE TABLE stats (name String, value UInt32)
|
|
ENGINE = MergeTree() ORDER BY name;
|
|
|
|
CREATE MATERIALIZED VIEW consumer TO stats
|
|
AS SELECT name, value FROM azure_queue_engine_table;
|
|
|
|
SELECT * FROM stats ORDER BY name;
|
|
```
|
|
|
|
## Virtual columns {#virtual-columns}
|
|
|
|
- `_path` — Path to the file.
|
|
- `_file` — Name of the file.
|
|
|
|
For more information about virtual columns see [here](../../../engines/table-engines/index.md#table_engines-virtual_columns).
|