Dask-sql as a Fugue SQL Engine#

Pandas and Spark already have solutions that allow users to execute SQL code to describe computation workflows. Dask, on the other hand, does not have a standard SQL interface yet. FugueSQL provides this feature with the DaskExecutionEngine, but users should also be aware that dask-sql is a relatively new project and has a majority of SQL keywords implemented already. Additionally, it is also faster than FugueSql on average. However, there are still some features under development. Most notably, the SQL WINDOW is not yet implemented.

We are collaborating to have our solutions converge to create the de facto SQL interface for Dask. In the meantime, we have unified our solutions by allowing FugueSQL to use dask-sql as an execution engine. The dask_sql project has added a DaskSQLExecutionEngine into their code to let us import it and pass it into FugueSQLWorkflow. Note this is a different engine from Fugue's DaskExecutionEngine

FugueSQLWorkflow usage is nearly identical to the fsql function we saw previously. The main difference is that it takes in a SQL engine as seen in the example below.

Sample Usage#

This example below shows that when the SQL query cannot be executed in dask-sql, it will use the FugueSQL. We are able to use the TAKE and PRINT keywords even if they don’t exist in dask-sql. We can also use the TRANSFORM and PREPARTITION even if these are Fugue keywords.

FugueSQL and dask-sql together can provide a more powerful solution. This allows us to use both solutions to get the best of both worlds in terms of speed and operation completeness. All we need to do is pass the DaskSQLExecutionEngine into FugueSQLWorkflow.

NOTE: In order for the code below to run, dask-sql needs to be installed.

from dask_sql.integrations.fugue import DaskSQLExecutionEngine
from fugue_sql import FugueSQLWorkflow
import pandas as pd

data = [
    ["A", "2020-01-01", 10],
    ["A", "2020-01-02", 20],
    ["A", "2020-01-03", 30],
    ["B", "2020-01-01", 20],
    ["B", "2020-01-02", 30],
    ["B", "2020-01-03", 40]
]
schema = "id:str,date:date,value:int"

# schema: *, cumsum:int
def cumsum(df: pd.DataFrame) -> pd.DataFrame:
    df["cumsum"] = df['value'].cumsum()
    return df

# Run the DAG on the DaskSQLExecutionEngine by dask-sql
with FugueSQLWorkflow(DaskSQLExecutionEngine) as dag:
    df = dag.df(data, schema)
    dag("""
    SELECT *
    FROM df
    TRANSFORM PREPARTITION BY id PRESORT date ASC USING cumsum
    TAKE 5 ROWS
    PRINT
    """)

When a SQL keywords don’t exist in dask-sql, it will default to the Fugue DaskExecutionEngine. However, when the keyword is registered by dask-sql it will use their implementation. OVER PARITION is registered but still being developed, which will cause errors. One workaround is to use Fugue's TRANSFORM and PREPARTITION like above to avoid using OVER PARTITION for now.