Grouping and Summing Slices of Structs in Golang
Similar to LINQ queries in .NET, it's possible to perform in-memory queries on structs in Golang. This article demonstrates how to group a slice of structs by multiple fields and subsequently sum a specific integer field.
Problem
Given a slice of structs with fields named id1 to id8 and an integer field money, the objective is to group structs by the id fields and sum the money field.
Solution with a Custom Comparison Function and Hash Table
One approach is to define an Equal function to compare structs based on the eight id fields. Subsequently, iterate over the input slice and check if each struct is present in a hash table. If it's already there, the money field is incremented; otherwise, a new entry is added to the hash table.
However, there's a more efficient and elegant solution using Golang's built-in capabilities.
Refactoring Struct Types and Using a Map
The key concept is to restructure the Register struct to contain only the grouping fields in a separate struct called Key. Since structs are comparable in Golang, you can use the Key as the key for a map. The money field can then be accessed via the Value of the map.
Here are the updated struct definitions:
<code class="go">type Key struct { id1 int id2 int id3 int id4 int id5 int id6 int id7 int id8 int } type Register struct { key Key money int }</code>
Grouping and Summing Using a Map
To group and sum the registers, a map of type map[Key]int is used with the Register.key as the key. This approach ensures that registers with the same id fields are grouped together, and the money field is accumulated for each group.
<code class="go">m := map[Key]int{} for _, v := range regs { m[v.key] += v.money }</code>
The map m now contains the grouped and summed values, which can be printed for analysis.
Conclusion
The solution described provides a robust and efficient mechanism for grouping and summing slices of structs in Golang. By leveraging the built-in capabilities of the language, it avoids the need for custom comparison functions or hash tables, resulting in concise and maintainable code.
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