Common Labeling Errors and How to Correct Them

The article provides practical methods for troubleshooting common problems such as duplicate tags and lost data.By analyzing the reasons for the mislabeling of data, it helps website operators to quickly locate and fix problems, improving the efficiency of data management.The book is intended to serve as a guide for those who need to optimize their data labeling systems.

Why do your data labels always have problems?

Have you ever encountered a situation where a label is repeated, causing confusion in the statistics, or where a key piece of data has disappeared for no reason?Rather than rush to change the code, first take a deep breath and follow the steps below. You'll save yourself a lot of time.

Step 1: Check the data source and the logic behind the points.

Determine the triggering conditions.

The most common reason for multiple tags is that the same action is triggered multiple times.For example, if a page has both a "click event" and a "scroll event" bound to a button, then when a user clicks on that button, duplicate data may be generated.Open the "network requests" panel in your developer tools and manually operate the page to observe if the tracking requests are being sent as expected.

Check the scope of the data collected.

Check to see whether the same tracking code has been introduced onto multiple pages, particularly SPA (single-page app) pages, which can be easily loaded repeatedly because of routing switching.Search the entire database using an ID code to make sure that there are no duplicate initialization situations.

Step Two: Deal with the duplicate labels that have already been generated.

Data de-duplication techniques.

If the problem has already occurred, first use SQL's DISTINCT or GROUP BY statements to clean up the historical data.For real-time data streams, unique identifiers (such as the combination of a time stamp and user ID) can be added to each item, and the program can automatically filter out any duplicates.

A mechanism to prevent duplication.

On the client side, the most recent timestamp is recorded in localStorage, and the same action is not reported again within one second.The server can also use Redis to cache the characteristics of recent requests and intercept high-frequency repeated data.

Step 3: Retrieve the lost data.

Check the stability of network transmission.

The data may have been lost because of network fluctuations that prevented the reports from getting through.Add a retransmission mechanism to the request to bury data: after the first failure, retransmit after a delay of two seconds, up to a maximum of 3 retries.At the same time, the system monitors interface error logs to detect abnormal status codes (such as 404 or 500).

Verify the data storage link.

From the time the data is reported to the time it is stored in the database, it passes through several stages.In the test environment, simulated tools are used to send test data, and the receiving server, message queue, ETL process, and database are checked one by one to see if any data is being cut off or filtered out.

Set up a data backup strategy.

For critical business data, it is recommended that a backup be made on both the client and server ends.For example, after a user makes a successful payment, it is reported through the front-end, and the back-end order system is updated synchronously, with the two systems checking each other and filling in any gaps.

A few tips for daily maintenance.

We use automated scripts to scan for unusual data (for example, the same user performing the same operation several times within one second), and establish a dashboard to monitor data health.Before any important function goes live, it is necessary to complete a full set of tests in a staging environment. That's a lot easier than trying to fix things after the fact!