Can Kotlin replace SQL?

Kotlin is based on Stream and has improved its shortcomings, simplifying Lamda syntax, adding set calculations, and supplementing many set functions. As a result, the code is shorter and easier to understand. But Kotlin still cannot replace SQL.

Like Stream, once the data object becomes a record, not a simple data type, it is not so convenient. Kotlin still does not provide professional structured data objects, nor does it support dynamic data structures. The data structure of the result must be defined before calculation. It is difficult for SQL programmers to adapt to this rigid usage. In contrast, SQL is an interpreted language, and there is no need to define the data structure in advance.

In fact, for most programmers accustom to using SQL to manipulate data, if you use the Open-esProc calculation package, you can also get the benefits of SQL without SQL. Its usage is similar to using SQL to calculate the data set and calling its encapsulated SPL calculation script in the program. For example, “find out classes with an average English score of less than 70”, most databases operate like this, “select CLASS, avg(English) as avg_En from students_scores group by CLASS having avg(English)<70”, which is implemented with SPL code.







It is not complicated to use Stream directly to accomplish the above tasks, but SPL is simpler and easier to learn. Store the script file (such as condition.dfx) and Java together and call it in JAVA through the JDBC interface. The usage is similar to a stored procedure. In this way, the SPL calculation process is independent, and it is very convenient to change when the demand changes.

 ResultSet result = statement.executeQuery("call condition.dfx");

SPL also supports SQL-like usage without script files, directly embedding it in Java.

 ResultSet result = statement.executeQuery("

SPL also provides a method of querying data with SQL, convenient for programs familiar to us directly. For example, state, department, and employee information are stored in three text files (the same for replacing the three files with three SPL table sequence objects) and query employees in New York state whose manager is in California.



$select   e.NAME as ENAME
from   E:/txt/EMPLOYEE.txt  as e
     join E:/txt/DEPARTMENT.txt as d on   e.DEPT=d.NAME
     join E:/txt/EMPLOYEE.txt  as emp on d.MANAGER=emp.EID
where   e.STATE='New York' and emp.STATE='California'

When the data is large, the memory size will be a processing bottleneck. SPL can read through cursors, similar to the cursors in the database, reads in small batches, and then binds calculations on the cursors to achieve sorting, association, and grouping calculation to use small memory to process the big data.







=joinx(A2:O,SellerId; A1:E,EId)



Big data processing often needs to add parallel computing to improve computing efficiency. Each thread processes a piece of data and finally summarizes each thread’s processing results.

1 =file(“E:/txt/user_info_reg.csv”).cursor@tcm(;4)
2 =A1.groups(id_province;count(~):cnt)

It is very easy to use parallel speedup in SPL. @m means parallel computing, and parameter 4 means 4-way parallel. Compared with single-threaded code, there is only one more cursor option and parameter, making it very convenient for users to use parallelism.

Using SPL can greatly simplify the calculation of structured data in Java programs. Examples are summarized as follows:

Loop operations

Accessing members of data set by sequence numbers

Locate operations on ordered sets

Alignment operations between ordered sets

TopN operations

Existence checking

Membership test

Unconventional aggregation

Alignment grouping

Select operation

More calculation examples: Use SPL in applications