How Do SQL Database Engines Work?

What is a SQL Engine?

    A typical SQL server database engine configuration includes a storage engine and the query processor. Different SQL engine types support different SQL server database engine architectures, but in general the SQL engine is a component of the system that is used to create, read, update and delete (CRUD) data from a database. Enterprises use SQL server database engines to create relational databases for online transaction processing (OLTP) and online analytical processing (OLAP).

    Most SQL engines support standard SQL, and many database management systems (DBMS) also offer application programming interfaces (APIs) to access database tools beyond the actual database user interface. For example, HEAVY.AI supports a visualization API called OpenGL as part of the HEAVY.AI Immerse platform to process visual analytics from big data.

SQL engines are often proprietary architecture designs that offer unique capabilities for storing and querying data within a relational database system. Each database instance supports various APIs, programming languages, partitioning methods, capabilities and more.

How SQL Database Engines Work

    In order for users to interact with a relational DBMS, their request in a valid query/database language must be translated into a SQL request before the SQL engine can process it. The SQL storage engine writes to and retrieves data from a data warehouse server, often done by converting the data to a compatible format such as a JSON file.

    To retrieve the data, the query processor accepts, parses, and executes SQL commands for the data warehouse to forward to an application server. The application server processes the SQL request and sends it to a web server where the client can access the information via SQL data tables.

    The SQL engine processes data in stages. The stages of processing vary based on the client but in general, the first stage of SQL processing begins with the RDBMS parsing a SQL statement via a parse call, to get ready for execution. The statement is separated into a data structure that other routines can process, then there are three checks completed - syntax check, semantic check, and shared pool check.

    The second step is query optimization. The RDBMS optimizes the query and chooses the best algorithms for searching and sifting through data. Finally, the RDBMS executes the SQL statement by running the query plan.