SQL on Modin Dataframes

Modin provides a SQL API that allows you to intermix SQL and pandas operations without copying the entire dataset into a new structure between the two. This is possible due to the architecture of Modin. Currently, Modin has a query compiler that acts as an intermediate layer between the query language (e.g. SQL, pandas) and the execution (See architecture documentation for details).

To execute SQL queries, Modin uses HDK engine (See Using HDK documentation for details) Thus, to execute SQL queries, pyhdk module must be installed.

A Short Example Using the Google Play Store

import modin.pandas as pd
import modin.experimental.sql as sql

# read google play app store list from csv
gstore_apps_df = pd.read_csv("https://tinyurl.com/googleplaystorecsv")

Imagine that you want to quickly select from ‘gstore_apps_df’ the columns App, Category, and Rating, where Price is ‘0’.

# You can then define the query that you want to perform
query_str = "SELECT App, Category, Rating FROM gstore_apps WHERE Price = '0'"

# And simply apply that query to a dataframe
result_df = sql.query(query_str, gstore_apps=gstore_apps_df)

# Or, in this case, where the query only requires one table,
# you can also ignore the FROM part in the query string:
sql_str = "SELECT App, Category, Rating WHERE Price = '0' "

Writing Complex Queries

For complex queries, it’s recommended to use the HDK engine because it’s much more powerful, comparing to dfsql. Especially, if multiple data frames are involved.

Let’s explore a more complicated example.

gstore_reviews_df = pd.read_csv("https://tinyurl.com/googleplaystoreurcsv")

Say we want to retrieve the top 10 app categories ranked by best average ‘sentiment_polarity’ where the average ‘sentiment_subjectivity’ is less than 0.5.

Since ‘Category’ is on the gstore_apps_df and sentiment_polarity is on gstore_reviews_df, we need to join the two tables, and operate averages on that join.

# Single query with join and group by
sql_str = """
SELECT
category,
AVG(sentiment_polarity) AS avg_sentiment_polarity,
AVG(sentiment_subjectivity) AS avg_sentiment_subjectivity
FROM (
SELECT
    category,
    CAST(sentiment as float) AS sentiment,
    CAST(sentiment_polarity AS float) AS sentiment_polarity,
    CAST(sentiment_subjectivity AS float) AS sentiment_subjectivity
FROM gstore_apps_df
    INNER JOIN gstore_reviews_df
    ON gstore_apps_df.app = gstore_reviews_df.app
) sub
GROUP BY category
HAVING avg_sentiment_subjectivity < 0.5
ORDER BY avg_sentiment_polarity DESC
LIMIT 10
"""

# Run query using apps and reviews dataframes,
# NOTE: that you simply pass the names of the tables in the query as arguments

result_df = sql.query( sql_str,
                        gstore_apps_df = gstore_apps_df,
                        gstore_reviews_df = gstore_reviews_df)

Or, you can bring the best of doing this in python and run the query in multiple parts (it’s up to you).

# join the items and reviews

result_df = sql.query("""
SELECT
    category,
    sentiment,
    sentiment_polarity,
    sentiment_subjectivity
FROM gstore_apps_df INNER JOIN gstore_reviews_df
ON gstore_apps_df.app = gstore_reviews_df.app""",
                      gstore_apps_df=gstore_apps_df,
                      gstore_reviews_df=gstore_reviews_df)

# group by category and calculate averages

result_df = sql.query("""
SELECT
    category,
    AVG(sentiment_polarity) AS avg_sentiment_polarity,
    AVG(sentiment_subjectivity) AS avg_sentiment_subjectivity
FROM result_df
GROUP BY category
HAVING CAST(avg_sentiment_subjectivity AS float) < 0.5
ORDER BY avg_sentiment_polarity DESC
LIMIT 10""",
result_df=result_df)

If you have a cluster or even a computer with more than one CPU core, you can write SQL and Modin will run those queries in a distributed and optimized way.

Further Examples and Full Documentation

In the meantime, you can check out our Example Notebook that contains more examples and ideas, as well as this blog explaining Modin SQL usage.