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Скачать или смотреть Efficiently Stream Results from ClickHouse with Linq2Db

  • vlogize
  • 2025-03-19
  • 12
Efficiently Stream Results from ClickHouse with Linq2Db
Linq2Db streaming from database (ClickHouse)c#clickhouselinq2db
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Описание к видео Efficiently Stream Results from ClickHouse with Linq2Db

Discover how to implement efficient streaming of query results from ClickHouse using Linq2Db to avoid excessive memory usage. Learn about the `AsAsyncEnumerable()` method for optimal performance.
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This video is based on the question https://stackoverflow.com/q/74082066/ asked by the user 'Jewry' ( https://stackoverflow.com/u/15568313/ ) and on the answer https://stackoverflow.com/a/75277774/ provided by the user 'Jewry' ( https://stackoverflow.com/u/15568313/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

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Efficiently Stream Results from ClickHouse with Linq2Db

Are you struggling with the best way to handle large datasets when querying a ClickHouse database using Linq2Db? You’re not alone! Many developers face the issue of executing database queries that return massive results, leading to memory inefficiencies and application slowdowns. If you want to execute a database query that loads results not entirely into memory, but in manageable chunks, this guide is for you! We will explore an effective solution using the AsAsyncEnumerable() method provided by the Linq2Db framework.

Understanding the Challenge

When querying a database like ClickHouse, you might encounter situations where retrieving all records at once can lead to high memory consumption and performance problems. Instead of loading the entire dataset into memory, the goal is to fetch results in smaller portions, or “chunks,” while keeping the context of the previous chunks intact. This approach not only makes your application more efficient but also enhances the overall user experience.

Key Considerations

When dealing with large amounts of data, consider the following:

Memory Usage: Loading too much data into memory can crash applications or degrade performance.

Query Optimization: Each chunk should leverage the previous results to maintain context.

Streamlined Processing: Chunks should be processed in a way that keeps the database connection alive, avoiding the overhead of repeatedly establishing new connections.

The Solution: Leveraging AsAsyncEnumerable()

For developers using the ClickHouse.Client provider in Linq2Db, achieving efficient data streaming can be done with a straightforward method call: AsAsyncEnumerable(). This method is designed to work seamlessly with asynchronous operations and allows you to efficiently retrieve data in chunks.

How to Use AsAsyncEnumerable()

Here’s a step-by-step guide to implementing this functionality in your application:

Set Up Your Connection: Ensure you have a properly set up connection to your ClickHouse database.

Write Your Query: Prepare the SQL query or Linq expression you intend to execute.

Invoke AsAsyncEnumerable(): Call this method on your query to initiate the streaming process.

Iterate Over the Results: You can now loop through the result set as if it were an IEnumerable collection. This allows you to process each chunk independently without loading everything into memory at once.

Example Code

Here is a simplified example demonstrating how this works in practice:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By utilizing the AsAsyncEnumerable() method in Linq2Db with the ClickHouse client, you can efficiently stream data from your ClickHouse database in chunks. This approach minimizes memory usage while maintaining the context of your queries, thereby enhancing application performance and user experience.

Thus, if you aim to optimize your data retrieval processes with ClickHouse, this method provides a robust and efficient solution. So why compromise on performance? Implement streaming today and unlock the full potential of your applications!

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