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Using SPL for performance optimization, such as converting data types like string to integer during data dump, can reduce storage space and improve computing performance. This article will present how to implement integerization through a practical example. Problem description The following table is the data structure of a certain space-time collision problem: Field name Field type Field meaning Remarks Sample data no String Object flag Unique flag of object 100000000009 ct Int Timestamp Unix timestamp (seconds) 1690819200 lac String Space flag 1 40000 ci String Space flag 2 66000000 After understanding the business, we know that all the values of

The association calculation in SPL differs significantly from that in SQL. SQL defines join as an operation that first calculates the Cartesian product and then filters. SPL also provides this operation, yet it has better alternatives in most scenarios, so this operation is not recommended. Programming in SPL to implement association calculation needs to subdivide join into different types first, and then select the corresponding function to code. Classification of association calculations The equivalence JOIN in the figure refers to the join whose filter condition is that the field of one table is equal to the corresponding field of associated

Customer information labeling, also known as customer profiling, refers to the process of collecting data from various dimensions such as customers’ social attributes, consumption habits, and preference characteristics, and then characterizing customer or product characteristic attributes. These characteristics are analyzed and statistically analyzed to explore potential value information, thus abstracting the full picture of customer information. From the way customers are labeled, there are generally three types: ① Statistical labels; ② Rule-based labels; ③ Machine learning mining labels (predictive labels). ① Statistical labels This type of label is the most basic and common type of label. For example, for a

As business expands, the database or the data warehouse for an OLAP application stores more and more data, and their workload increases. This results in slower response times. Scaling up or scaling out the database is not enough to solve the problem because both are not only expensive but can hardly push the performance up once the database capacity reaches the limit. Separating cold data from hot data is a better solution. It stores and computes the small amount of frequently accessed hot data and the large amount of seldom accessed cold data separately in order to reduce database workload and