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Volcano engine ByteHouse: how to optimize the materialized view capability of ClickHouse?

2024-04-13 Update From: SLTechnology News&Howtos shulou NAV: SLTechnology News&Howtos > IT Information >

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Recently, the volcano engine ByteHouse has upgraded the materialized view ability based on ClickHouse, which provides an effective means to solve the problems such as query speed and slow response time caused by the explosive growth of data.

Volcano engine ByteHouse is a cloud native data warehouse that provides users with extremely fast analysis experience, supporting real-time data analysis and massive data offline analysis, as well as convenient flexible capacity expansion, extreme analysis performance and rich enterprise-class features. As a practical technology to improve database query performance and response speed, materialized view is also one of the core competencies of ByteHouse.

It is reported that the materialized view is a set of results stored in the form of a table, which calculates the view in the background and stores the results in the table, so that when querying the view, the results can be obtained directly from the table without recalculating. Compared with the ordinary view, the materialized view greatly improves the query speed and response time, especially when dealing with a large amount of data.

On the one hand, ByteHouse materialized views have the ability to update in real time, support manual or automatic updates, and also support management through the interface or SQL; on the other hand, in order to improve ease of use, ByteHouse will automatically create materialized views for high-frequency and complex queries. For enterprise-level requirements, ByteHouse supports RBAC rights management and provides data and suggestions to help users further optimize their materialized views.

As a daily practical technology, materialized view can speed up and increase efficiency in multiple scenarios. For example, in the data analysis scene, in the face of complex query and analysis of a large amount of data, materialized view can reduce the execution time of complex query and improve the efficiency of data analysis. In the advertising scene, in order to monitor and analyze the advertising data in real time, the materialized view can quickly store the calculation results of the advertising data, so as to quickly query and analyze, reduce the error rate of advertising, and improve the efficiency and profitability of the advertising platform. In the recommendation system, the materialized view reduces the computational complexity, stores the user's interest tags in the table, and accelerates the personalized recommendation to the user.

So, how can users quickly get started with the materialized view capabilities of ByteHouse? Just go through the following three steps:

Enter the materialized view: in the ByteHouse database interface, click the "New" button, and then select "New materialized View".

Fill in the materialized view statement according to the SQL sample. Users can fill in the creation statement of materialized view according to their own needs.

After the creation is successful, if you need to materialize the historical data partition, refresh the defined partition manually according to the SQL sample.

In addition, when using materialized view, we need to pay attention to its real-time update, storage space occupation and query complexity, and make tradeoff and optimization according to the actual situation in order to give full play to the role of materialized view.

The materialized view function of ByteHouse provides users with a more efficient and flexible way to query the database. By using materialized views, users can not only reduce computational complexity and improve query performance, but also better protect data security. With the introduction of the materialized view function of ByteHouse, more enterprises and developers will be able to enjoy this efficient and secure database service. It is believed that with the continuous development of technology, ByteHouse materialized view can bring more efficient data processing experience for more fields in the future.

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