SQLFluff is designed to be flexible in supporting a variety of dialects. Not all potential dialects are supported so far, but several have been implemented by the community. Below are a list of the currently available dialects. Each inherits from another, up to the root ansi dialect.
For a canonical list of supported dialects, run the sqlfluff dialects command, which will output a list of the current dialects available on your installation of SQLFluff.
For technical users looking to add new dialects or add new features to existing ones, the dependent nature of how dialects have been implemented is to try and reduce the amount of repetition in how different elements are defined. As an example, when we say that the Redshift dialect inherits from the PostgreSQL dialect this is not because there is an agreement between those projects which means that features in one must end up in the other, but that the design of the Redshift dialect was heavily inspired by the postgres dialect and therefore when defining the dialect within sqlfuff it makes sense to use PostgreSQL as a starting point rather than starting from scratch.
Consider when adding new features to a dialect:
Should I be adding it just to this dialect, or adding it to a parent dialect?
If I’m creating a new dialect, which dialect would be best to inherit from?
Will the feature I’m adding break any downstream dependencies within dialects which inherit from this one?
This is the base dialect which holds most of the definitions of common SQL commands and structures. If the dialect which you’re actually using isn’t specifically implemented by SQLFluff, using this dialect is a good place to start.
This dialect doesn’t intend to be brutal in adhering to (and only to) the ANSI SQL spec (mostly because ANSI charges for access to that spec). It aims to be a representation of vanilla SQL before any other project adds their spin to it, and so may contain a slightly wider set of functions than actually available in true ANSI SQL.
The dialect for Amazon Athena.
The dialect for Google BigQuery.
The dialect for ClickHouse.
Since Databricks builds on top of Apache Spark, the Spark SQL dialect holds most of the definitions of common commands and structures.
Specifics to Databricks, such as Delta Live Table syntax, are added to the Spark SQL dialect to simplify implementation and prevent code duplication for minor syntax updates. This follows SQLFluff’s philosophy of not being strict in adhering to dialect specifications to permit slightly wider set of functions than actually available in a given dialect.
The dialect for Db2.
The dialect for Exasol.
The dialect for Hive.
The dialect for Materialize.
The dialect for MySQL.
The dialect for Oracle SQL. Note: this does not include PL/SQL.
This is based around the PostgreSQL spec. Many other SQL instances are often based on PostreSQL syntax. If you’re running an unsupported dialect, then this is often the dialect to use (until someone makes a specific dialect).
The dialect for Amazon Redshift.
The dialect for SOQL (Salesforce Object Query Language).
The dialect for Apache Spark SQL. It inherits from ANSI and includes relevant syntax from Hive for commands that permit Hive Format. Spark SQL extensions provided by the Delta Lake project are also implemented in this dialect.
This implementation focuses on the Ansi Compliant Mode introduced in Spark3, instead of being Hive Compliant. The introduction of ANSI Compliance provides better data quality and easier migration from traditional DBMS.
Versions of Spark prior to 3.x will only support the Hive dialect.
The dialect for SQLite.
The dialect for T-SQL (aka Transact-SQL).
The dialect for Teradata.