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What is the structured query language in SQL? aiming at this problem, this article introduces the corresponding analysis and solution in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible method.

SQL (structured query language) is a standardized programming language for managing relational databases and performing multiple operations on the data in the database. Founded in the 1970s, SQL is used by database administrators and used by developers to build and execute analysis queries when writing data integration scripts and data analysis.

The use of SQL includes modifying database table and index structures; adding, updating, and deleting data records; and restoring a subset of database information for transaction processing and analysis applications. Queries and other SQL operations take the form of writing commands as statements. Commonly used SQL statements are select, add, insert, update, delete, create, modify, and truncate.

SQL became the de facto standard programming language for relational databases in the late 1970s and early 1980s, also known as SQL databases. A relational system consists of a set of tables with rows and columns. Each column in the table corresponds to a data category, such as the customer name and address, while each row contains a data value for the cross column.

American National Standards Institute (ANSI American National Standards Institute) adopted the formal SQL standard in 1986 and was adopted by International Organization for Standardization (ISO International Organization for Standardization) in 1987. Since then, the two standard-setting entities have released more than six joint updates to the standard; the version of SQL 2011 was approved this year.

Proprietary and open source relational database management systems are built around SQL and are available to enterprises. They include Microsoft SQL Server,Oracle Database,IBM DB2,SAP HANA,SAP Adaptive Server,MySQL (now owned by Oracle) and PostgreSQL. However, many of these database products support SQL through proprietary extensions that are specific to standard languages for process programming and other functions. For example, Microsoft provides a set of extensions called Transart-SQL (T-SQL), while Oracle's extended version of the standard is PL/SQL. Therefore, the different variants of SQL provided by the vendor are not fully compatible with each other.

There are several different types of SQL commands, including data manipulation language (DML) and data definition language (DDL) statements, transaction controls, and security measures. The DML vocabulary is used to recover and manipulate data, while DDL statements are used to define and modify the database structure. Transaction control helps manage transactions, ensuring that transactions are either completed or rolled back when errors or problems occur. Security statements are used to control database access and to create user roles and permissions.

SQL syntax is the encoding format when writing a statement. Figure 1 shows an example of a DDL statement written in Microsoft's T-SQL to modify a database table in SQL Server 2016.

T-SQL code example in SQL Server 2016

Figure 1. T-SQL code example in SQL Server 2016. This is the code for the ALTER TABLE WITH (ONLINE = ON | OFF) option.

SQL-on-Hadoop query engine is a relatively new branch of SQL, which allows enterprises with big data architecture built around Hadoop systems to take advantage of it without having to use more complex and unfamiliar languages. In particular, the MapReduce programming environment for developing batch applications. More than a dozen SQL-on-Hadoop tools are available through Hadoop distributors and other vendors. Many of them are open source software or commercial versions of these technologies. In addition, the Apache Spark processing engine is commonly used to connect to Hadoop, including a Spark SQL model, which also supports SQL-based programming.

In general, SQL-on-Hadoop is still an emerging technology, and most of the tools available do not support all the functionality provided in SQL relational implementations. But as companies seek developers and analysts with big data's application programming SQL skills, they are becoming a fixed component of Hadoop deployment.

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