How to efficiently handle timed data crawling: the best strategy for deduplication and data filling?

Timely data grabbing: Cleverly solve the problems of deduplication and data filling

This article discusses how to efficiently process timed data crawling, especially how to ensure data integrity, that is, in every two-hour crawling task, data has been available at every point in time and repetitive data is effectively processed.

Suppose the crawler crawls the data every two hours, for example, starts crawling at 1 o'clock. If the data is obtained at 1:03, then it is classified into the data of 1 point, which is convenient for subsequent chart production. The key is to deal with duplicate data and periods of uncrawled data.

First, a unique classification ID is generated for each crawling task, such as the timestamp "2023-02-21 01:00:00", representing the corresponding time period of the task. This facilitates the distinction between data in different time periods. Of course, you can also consider using database table optimization efficiency. The task record example is as follows:

<code>分类ID: 2023-02-21 01:00:00任务: https://segmentfault.com/q/1010000043447558</code>

<code>分类ID: 2023-02-21 01:00:00任务: https://segmentfault.com/</code>

For duplicate data, database-unique key constraints can be used to avoid duplicate insertions. Another method is to directly store the original response data (response), and subsequent modifications only update the fields of the corresponding record to avoid redundant records.

For data missing, new tasks can be generated using historical data and modified their classification ID while retaining the original response data. In this way, even if certain time points are not successfully crawled, it can ensure that there is data at each time point.

Finally, in terms of database selection, commonly used relational databases (such as MySQL) can meet the needs.

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