Tag: SPL

Yes! You read that right. SPL (abbreviation of Structured Process Language) is an open-source programming language that codes in a grid and that specifically designed to process structured data.As almost all programming languages code in the form of text, what does the SPL code written in the grid like? And what differences does it make? Let’s first take a look at SPL’s programming environment. Features of SPL Code written in the grid In the above screenshot, the middle part is SPL’s grid code. But what are the advantages of writing code in the grid?Coding always involves intermediate variables, to which

SQL is wasting lives of data scientists SQL is difficult to write Nearly all data scientists use SQL for data exploration and analysis. SQL appears deceptively simple and offers a certain degree of interactivity, making it a seemingly ideal choice for these purposes. To perform filtering and grouping operations, for example, SQL just needs one line of code: But this is only limited to simple cases. When the computation becomes complex, SQL code becomes complicated, too. For example, to count the longest consecutive rising days of each stock, the SQL statement is as follows: And to perform the commonly seen

Computing dilemma in the application Development and Framework, which should be given the higher priority? Java is the most commonly used programming language in application development. But writing code to process data in Java isn’t simple. For example, below is the Java code for performing grouping & aggregation on two fields: By contrast, the SQL counterpart is much simpler. One GROUP BY clause is enough to close the computation. SELECT year(orderdate),sellerid,sum(amount) FROM orders GROUP BY year(orderDate),sellerid Indeed, early applications worked with the collaboration of Java and SQL. The business process was implemented in Java at the application side, and data

Most of the time, businesses store their data in RDBs. It is convenient and fast to read and write data in the database in SPL (Structured Process Language). Take the following employees table in MySQL MyCompany database as an example, and we’ll look at how SPL performs database data reading and writing.

SQL and SPL are both general-purpose processing technologies for structured data, and each has its own characteristics. Specifically, SQL is highly popularized and widely used, many users have a natural ability to query data with SQL, and it is easy for them to get started once the data engine supports SQL; it is relatively easy to migrate historical programs. SPL is concise and efficient, providing more agile syntax that can simplify complex calculations, while supporting the procedural computing and naturally supporting step-wise coding; the computing system of SPL is more open, making it possible to perform mixed computing for multiple data sources at the same time, and easily obtain higher computing performance with built-in high-performance storage and high-performance algorithms; it is more flexible to utilize, enabling it to be used independently or integrated into applications.

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.

ClickHouse, as an open-source analysis database, is known for being fast. Is that true? Let’s test and verify it through comparative test. ClickHouse vs Oracle First of all, we conduct a comparative test on ClickHouse (CH for short) and Oracle database (ORA for short) under the same hardware and software environment, and use the internationally recognized TPC-H as the test benchmark. The test is to execute the calculation requirements (Q1 to Q22) defined with 22 SQL statements for 8 tables. The test is performed on one machine within 12 threads, and the total data amount is around 100G. Since the

Though a lot of embedded data engines for Java are available, the competent and effective ones are rare. Redis has poor computing ability and can only handle simple query scenarios. Spark has complex and heavy framework and is inconvenient to deploy and maintain. Embedded databases like H2\HSQLDB\Derby have lightweight frameworks but they lack sufficient computing abilities – they even do not support the basic window functions. SQLite achieves the best balance between a good framework and satisfactory computing ability. It is now a widely-used Java embedded data engine. SQLite suits regular basic computing scenarios SQLite has lightweight framework. The library

In terms of structured data calculation outside the database, Stream has taken a step from scratch; Kotlin has slightly enhanced this capability, but the nature of the compiled language makes it impossible to go further; To really solve the problem of structured data computing outside the database, SPL, a professional structured data computing language, is needed. In java development, we often encounter the situation that it is inconvenient to use database but structured data calculation is needed. For a long time, Java has not provided a class library to deal with this situation. Even basic calculations such as sorting and

In some report development scenarios, the original data will undergo complicated computations through a number of steps before it is presented as expected in the desired layout. Finding a simple and efficient way or tool to get the multistep, complicated preparations done is crucial to increasing report development performance. This essay will examine several such methods to try to give you the best choice. Complicated SQL/Stored procedure RDB data sources have strong computing power. Using SQL enables us to handle most of the data processing scenarios. Yet some SQL queries are too hard to write for ordinary report development staff,