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The content of this article mainly focuses on what is the method to realize the aggregate requirements of big data report. The content of the article is clear and clear. It is very suitable for beginners to learn and is worth reading. Interested friends can follow the editor to read together. I hope you can get something through this article!

I. Common aggregate schemes

Before professional reporting tools were available, common implementations were as follows:

1) SQL

"select sum (field) from table group by field order by field" can realize simple grouping data aggregate and conditional aggregate. This method has an obvious disadvantage, that is, with the increase of aggregate demand complexity, the complexity of sql is exponential.

2) Excel

The Excel tool makes up for the shortcomings of sql to some extent, and the aggregate requirements can be realized relatively easily through built-in functions. For example, for condition summarization, you can directly call the formula = SUMIF (A1:A5, "> 3"); paging summarization can also be implemented using the built-in "categorization summary" function.

However, for complex and changeable aggregate requirements, it is still willing but insufficient, because excel tends to be more fixed in the location of the cells where the data is located when summarizing.

These two solutions can not meet all the combined needs of customers, so the emergence of professional reporting tools is to solve this problem perfectly, let's take a look at how it is implemented by drying the reporting tool.

II. Professional reporting tool scheme

The moisturizing report provides the sum function to quickly realize the aggregate requirements. Let's take a look at the following function help:

Through the introduction of function help, we know that data aggregation requirements can be easily achieved through the sum function. Let's take a look at it through a specific example.

2.1 Total grouping

Without saying much, let's take a look at the demand effect map:

This report needs to calculate a summary value of all data and the summary value under each category. When you use the sum function in the drying report, you can use the following configuration:

Among them

The G4 cell expression = sum (E3G3Tech B3 {}) means to calculate the "unit price of inventory" for all the row data extended by B3, and finally to sum the calculated values.

G5 cell expression: = sum (G4 {}) means to sum all the extended data from G4.

2.2 Total conditions

Add a requirement to the 2.1 report and summarize only part of the supplier's inventory when making the total, so how can it be realized in the drying report?

The answer is: add filter criteria to {}.

Sum (G4 {A3 > 4}) indicates the sum of the inventory amount that satisfies the supplier's ID greater than 4.

2.3 pages total

Now upgrade the requirements again and make a subtotal for each page of data. are there any built-in functions that can be used? The answer is yes, let's take a look at the use of the pcsum () function:

We can write directly into the expression in the report cell:

Configure the C5 cell expression as: = pcsum (E3 {})

The G5 cell expression is: = pcsum (G4 {})

The effect of the report after adding the page subtotal is as follows:

Similarly, we can add filter conditions to the pcsum function to implement page subtotals of some data.

Thank you for your reading. I believe you have a certain understanding of "what is the realization method of big data report aggregate requirements". Go and practice quickly. If you want to know more related knowledge points, you can pay attention to the website! The editor will continue to bring you better articles!

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