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FugueSQL is designed for heavy SQL users to extend the boundaries of traditional SQL workflows. FugueSQL allows the expression of logic for end-to-end distributed computing workflows. It can also be combined with Python code to use custom functions alongside the SQL commands. It provides a unified interface, allowing the same SQL code to run on Pandas, Dask, and Spark.

The SQL code is parsed with ANTLR and mapped to the equivalent functions in the Fugue programming interface.

1. Installation

In order to use FugueSQL, you first need to make sure you have installed the sql extra

pip install fugue[sql]

To run on Spark or Dask execution engines, install the appropriate extras. Alternatively, all can be used as an extra.

pip install fugue[sql, spark] 
pip install fugue[sql, dask]
pip install fugue[all]

2. FugueSQL Syntax

Get started with FugueSQL. This shows input and output of data, enhancements over standard SQL, and how to use SQL to describe computation logic. After this, users will be able to use FugueSQL with the familiar SQL keywords to perform operations on top of Pandas, Spark, and Dask.

3. Additional SQL Operators

Go over the implemented operations that Fugue has on top of the ones provided by standard SQL. FugueSQL is extensible with Python code, but the most common functions are added as built-ins. These include filling NULL values, dropping NULL values, renaming columns, changing schema, etc. This section goes over the most used additional keywords.

4. Integrating Python

Explore Jinja templating for variable passing, and using a Python functions as a Transformer in a %%fsql cell.

5. Using Other Fugue Extensions

The Transformer is just one of many possible Fugue extensions. In this section we’ll explore the syntax of all the other Fugue extensions: Creator, Processor, Outputter, and CoTransformer.

6. FugueSQL with Pandas

%%fsql takes in the NativeExecutionEngine as a default parameter. This engine runs on Pandas. All of the SQL operations have equivalents in Pandas, but the behavior can be inconsistent sometimes. For example, Pandas will drop NULL values by default in a groupby operation. The NativeExecutionEngine was designed to mostly make operations consistent with Spark and SQL.

7. FugueSQL with Dask

Fugue and dask-sql are collaborating to have our solutions converge and bring the SQL interface for Dask. Currently, dask-sql is faster on average, while FugueSQL is more complete in terms of SQL keywords implemented. Conveniently, our solutions can be used together to bring the best of both worlds. This is done by using dask-sql as the underlying execution engine of the FugueSQLWorkflow context manager.

8. FugueSQL with Spark

FugueSQL also works on Spark by passing in the execution engine. This looks like %%fsql spark. The operations are mapped to Spark and Spark SQL operations. The difference is FugueSQL has added functionality for syntax compared to SparkSQL as seen in the syntax tutorial. Additionally with FugueSQL, the same code will execute on Pandas and Dask without modification. This allows for quick testing without having to spin up a cluster. Users prototype with the NativeExecutionEngine, and then move to the Spark cluster by changing the execution engine.