refers http://c.raqsoft.com/article/1687916213139
In addition to common static code, sometimes dynamic code is also needed to solve problems, such as generating code (or part of code) based on parameters and dynamically executing it. For programming languages that lack dynamic coding mechanisms, it is usually necessary to write the variable parts of the code in string form. For example, when referencing dataset field names in Python, it is necessary to write them as strings to achieve the effect of dynamic code. However, this will make it inconvenient to read and write more common static code. SQL, on the other hand, can directly write field names (as well as filter conditions, grouping expressions, etc.) in the code without having to write them into strings, making it easier to read and write static code, but it is difficult to handle dynamic code.
SPL inherits the SQL style of static code, allowing for direct writing of code parts, such as field names, without the need to be written as strings. In addition, SPL also provides macros to achieve dynamic code effects.
Example 1: Dynamically sort the order table based on the parameter pSortList, which contains an indefinite number of sorting fields separated by commas.
This dynamic code can be implemented using SPL macros: T("Orders.txt").sort(${pSortList})
Integrate SPL and SQL.
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.
Refer to http://c.raqsoft.com/article/1672969702567
"Hadoop/Spark is too heavy, esProc SPL is light", refer to
refer to http://c.raqsoft.com/article/1665212186752
With the advent of the era of big data, the amount of data continues to grow. In this case, it is difficult and costly to expand the capacity of database running on a traditional small computer, making it hard to support business development. In order to cope with this problem, many users begin to turn to the distributed computing route, that is, use multiple inexpensive PC servers to form a cluster to perform big data computing tasks. Hadoop/Spark is one of the important software technologies in this route, which is popular because it is open source and free. After years of application and development, Hadoop has been widely accepted, and not only can it be applied to data computing directly, but many new databases are developed based on it, such as Hive and Impala.
Usually, the streaming data sources are dynamic and unbounded, and appear quite different from the static and bounded batch data source. For framework reasons, it is difficult for traditional database technologies to directly process streaming data source, so programmers have to resort to later technologies. The computing frameworks such as heron\samza\storm\spark\flink were the first to make breakthroughs and gained first-mover advantage in stream computing technology. These frameworks are so successful that as soon as a stream computing is involved, the application programmers will naturally turn to one of them. On the contrary, for those computing technologies that do not claim to be a certain framework, they are generally considered unsuitable for implementing stream computing.
Refer to http://c.raqsoft.com/article/1693970501878
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