SQL Engine Definition

SQL engine is defined as software that recognizes and interprets SQL commands to access a relational database and interrogate data. SQL engine is also commonly referred to as a SQL database engine or a SQL query engine.

OmniSci SQL Engine diagram shows the integration of big data through the OmniSciDB open source SQL Engine.

 

FAQs

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, OmniSci supports a visualization API called OpenGL as part of the OmniSci 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.

 

What is a SQL Server Storage Engine?

A SQL server storage engine is software used to create, read and update data between the disk and memory. The SQL server maps the database with files that store database objects, tables and indexes. Those files can then be stored on either a FAT or NTFS file system. There are three main types of SQL server database files - a primary data file, a secondary data file and a transaction log file.

 

What is a Distributed SQL Query Engine?

A distributed SQL query engine is a software tool with an architecture that uses cluster computing (MPP), allowing users to query a variety of data sources, or data from multiple data sources within a single query. Distributed SQL queries are important because they can more effectively deal with the complexity of various frameworks and technologies. This allows data analysts to combine data residing on multiple and independent engines to perform complex analytics queries.

 

Does OmniSci Offer a SQL Engine Solution?

OmniSciDB natively supports standard SQL queries as well as offering a visualization API that maps OpenGL primitives onto SQL result sets. OmniSciDB harnesses the power of GPUs to return query results hundreds of times faster than CPU-only analytical database platforms.

OmniSciDB uses a JIT (Just-In-Time) compilation framework built on LLVM (Low-Level Virtual Machine) to avoid many of the memory bandwidth and cache-space inefficiencies of traditional virtual machine or transpiler approaches, making it one of the fastest SQL database engines. Compilation times are much quicker with LLVM - generally under 30 milliseconds for new SQL queries - allowing users leveraging OmniSci Immerse to cross-filter billions of rows over multiple correlated visualizations.

Learn more about interactive visual analytics for big data with OmniSci Immerse.