Tag: SQL

Sometimes the order of members in an ordered data set is important, such as the scenario where each record is compared with its next neighbor and put them into the same group if they have same value for a specific field or if they satisfy the same condition(s). Examples include finding the maximum frequency of consecutive winning of the champion title and the largest number of days when temperature rises continuously in Beijing. They involve the order-based grouping. This essay explains the solutions and basic principles of SQL and SPL, two commonly used programming languages, for handling the order-based grouping scenarios, and offers the simpler and more efficient way through SQL and SPL sample programs.

The equi-grouping operations divides a data table according to same field values or expression results, while there are certain common grouping operations use special grouping conditions. Grouping customers according to the order of Beijing, Shanghai, Guangzhou and Shenzhen and calculate total sales amount in each group, and divide families into wealthy, well-off, comfortable, and poor according to the yearly income. This essay will explain the solutions and basic principles of SQL and SPL, the two commonly used programming languages, in dealing with this type of grouping scenarios, and find the simpler and more efficient way through sample programs.

A grouping operation puts members of same attribute in a set into same group. In an employee table grouped by department, each group of employees work in the same department. In a grouped sales table by the year, each group contains sales records of the same year. Both SQL and SPL are commonly used programming languages. In this essay, we’ll take a look at their solutions and basic principles in handling the grouping operations through sample programs, aiming to find the faster and more efficient way for you.

 This is the type of common and simple order-based calculations. When records are sorted in a specific order, a neighboring record reference references a neighboring record during a calculation.

Set-oriented operations include a set of basic operations, including concatenation, intersection, union, difference, XOR, and inclusion. As structured data often exists in the form of sets, both SQL and SPL, the two commonly used programming languages, provide a wealth of set-oriented methods and functions to handle the data. This essay explores solutions and basic principles of the two languages for handling set-oriented operations through examples and corresponding sample programs and tries to introduce the more convenient and more efficient way for you.

A post-SQL script is used for performing complex computations that SQL is awkward to handle. Here I select three post scripting tools to compare them in terms of efficiency, syntax expressiveness and function library capability, and to examine their performances in handling various scenarios such as set-based operations and order-based computations. The findings show that esProc SPL is the best.

As the most commonly used data processing language, SQL is widely used in many scenarios, such as query and batch operating. When the amount of data is large, SQL (and stored procedure) often leads to very slow running. In this case, it needs to optimize SQL. To optimize SQL, there are some specific systemized ways. Normally, we need to check the execution plan first to find out the reason, and then recode in a targeted manner to achieve the purpose of optimization. For example, for the judgment of continuous numbers, we can use between to replace in, use the select

During contemporary application development, SQL is generally used to achieve simple data loading and saving while most part of the computing procedure and business logic is implemented in the application. This is because: In view of this, it is important to implement post-SQL computations in the application. Generally, SQL returns structured data. So, a good post-SQL computation technology should have a convenient structured data object for further computing and processing data returned by SQL, offer enough library functions and have computing ability at least as good as SQL, and ideally, support loop and branching statements to achieve procedural control. Additionally,

SQL is arguably the most commonly used programming language in the field of data processing and analysis, and many people who want to enter this field but know little about it all ask this question. However, it’s probably a pointless question. Why? Let me tell you the reason. Those who ask this question should ask themselves first: what’s my purpose of learning SQL? Well, I suppose most of them will answer this way: I hope I can code SQL statement so as to work in the field of data analysis after learning! Obviously, this is a very common purpose, but

One approach is to start MongoDB Connector and execute SQL through MySQL JDBC. The official tool is perfectly compatible, almost without configurations. Yet its computing ability is weak – the official advice is to use it only for BI tools. An alternative is using Calcite class library to execute general SQL on MongoDB. The solution is easily migratable but offers extremely weak computing ability. It does not support even the fuzzy query and date functions, needs to convert all multilevel collections into single-level ones – otherwise, some computations cannot be achieved, and is characterized by complicated configurations. The wise choice