SQLFrame implements the PySpark DataFrame API in order to enable running transformation pipelines directly on database engines - no Spark clusters or dependencies required.

SQLFrame currently supports the following engines (many more in development):

• BigQuery
• DuckDB
• Postgres
• Snowflake
• Spark

SQLFrame also has a "Standalone" session that be used to generate SQL without any connection to a database engine.

• Standalone

SQLFrame is great for:

• Users who want to run PySpark DataFrame code without having to use a Spark cluster
• Users who want a SQL representation of their DataFrame code for debugging or sharing with others
  • See Spark Engine for more details
• Users who want a DataFrame API that leverages the full power of their engine to do the processing

Installation

# BigQuery
pip install "sqlframe[bigquery]"
# DuckDB
pip install "sqlframe[duckdb]"
# Postgres
pip install "sqlframe[postgres]"
# Snowflake
pip install "sqlframe[snowflake]"
# Spark
pip install "sqlframe[spark]"
# Standalone
pip install sqlframe

See specific engine documentation for additional setup instructions.

Configuration

SQLFrame generates consistently accurate yet complex SQL for engine execution. However, when using df.sql(), it produces more human-readable SQL. For details on how to configure this output and leverage OpenAI to enhance the SQL, see Generated SQL Configuration.

Example Usage

from sqlframe.bigquery import BigQuerySession
from sqlframe.bigquery import functions as F
from sqlframe.bigquery import Window

session = BigQuerySession()
table_path = "bigquery-public-data.samples.natality"
# Top 5 years with the greatest year-over-year % change in new families with single child
df = (
    session.table(table_path)
    .where(F.col("ever_born") == 1)
    .groupBy("year")
    .agg(F.count("*").alias("num_single_child_families"))
    .withColumn(
        "last_year_num_single_child_families", 
        F.lag(F.col("num_single_child_families"), 1).over(Window.orderBy("year"))
    )
    .withColumn(
        "percent_change", 
        (F.col("num_single_child_families") - F.col("last_year_num_single_child_families")) 
        / F.col("last_year_num_single_child_families")
    )
    .orderBy(F.abs(F.col("percent_change")).desc())
    .select(
        F.col("year").alias("year"),
        F.format_number("num_single_child_families", 0).alias("new families single child"),
        F.format_number(F.col("percent_change") * 100, 2).alias("percent change"),
    )
    .limit(5)
)

>>> df.sql()
WITH `t94228` AS (
  SELECT
    `natality`.`year` AS `year`,
    COUNT(*) AS `num_single_child_families`
  FROM `bigquery-public-data`.`samples`.`natality` AS `natality`
  WHERE
    `natality`.`ever_born` = 1
  GROUP BY
    `natality`.`year`
), `t39093` AS (
  SELECT
    `t94228`.`year` AS `year`,
    `t94228`.`num_single_child_families` AS `num_single_child_families`,
    LAG(`t94228`.`num_single_child_families`, 1) OVER (ORDER BY `t94228`.`year`) AS `last_year_num_single_child_families`
  FROM `t94228` AS `t94228`
)
SELECT
  `t39093`.`year` AS `year`,
  FORMAT('%\'.0f', ROUND(CAST(`t39093`.`num_single_child_families` AS FLOAT64), 0)) AS `new families single child`,
  FORMAT('%\'.2f', ROUND(CAST((((`t39093`.`num_single_child_families` - `t39093`.`last_year_num_single_child_families`) / `t39093`.`last_year_num_single_child_families`) * 100) AS FLOAT64), 2)) AS `percent change`
FROM `t39093` AS `t39093`
ORDER BY
  ABS(`percent_change`) DESC
LIMIT 5

>>> df.show()
+------+---------------------------+----------------+
| year | new families single child | percent change |
+------+---------------------------+----------------+
| 1989 |         1,650,246         |     25.02      |
| 1974 |          783,448          |     14.49      |
| 1977 |         1,057,379         |     11.38      |
| 1985 |         1,308,476         |     11.15      |
| 1975 |          868,985          |     10.92      |
+------+---------------------------+----------------+