Edit on GitHub

# PySpark DataFrame SQL Generator

This is a drop-in replacement for the PySpark DataFrame API that will generate SQL instead of executing DataFrame operations directly. This, when combined with the transpiling support in SQLGlot, allows one to write PySpark DataFrame code and execute it on other engines like DuckDB, Presto, Spark, Snowflake, and BigQuery.

Currently many of the common operations are covered and more functionality will be added over time. Please open an issue or PR with your feedback or contribution to help influence what should be prioritized next and make sure your use case is properly supported.

# How to use

## Instructions

- Install SQLGlot and that is all that is required to just generate SQL. The examples show generating SQL and then executing that SQL on a specific engine and that will require that engine's client library.
- Find/replace all `from pyspark.sql` with `from sqlglot.dataframe`.
- Prior to any `spark.read.table` or `spark.table` run `sqlglot.schema.add_table('<table_name>', <column_structure>)`.
  - The column structure can be defined the following ways:
    - Dictionary where the keys are column names and values are string of the Spark SQL type name.
      - Ex: `{'cola': 'string', 'colb': 'int'}`
    - PySpark DataFrame `StructType` similar to when using `createDataFrame`.
      - Ex: `StructType([StructField('cola', StringType()), StructField('colb', IntegerType())])`
    - A string of names and types similar to what is supported in `createDataFrame`.
      - Ex: `cola: STRING, colb: INT`
    - [Not Recommended] A list of string column names without type.
      - Ex: `['cola', 'colb']`
      - The lack of types may limit functionality in future releases.
  - See Registering Custom Schema for information on how to skip this step if the information is stored externally.
- Add `.sql(pretty=True)` to your final DataFrame command to return a list of sql statements to run that command.
  - In most cases a single SQL statement is returned. Currently the only exception is when caching DataFrames which isn't supported in other dialects.
  - Spark is the default output dialect. See dialects for a full list of dialects.
  - Ex: `.sql(pretty=True, dialect='bigquery')`

## Examples

    import sqlglot
    from sqlglot.dataframe.sql.session import SparkSession
    from sqlglot.dataframe.sql import functions as F

    sqlglot.schema.add_table('employee', {
      'employee_id': 'INT',
      'fname': 'STRING',
      'lname': 'STRING',
      'age': 'INT',
    })  # Register the table structure prior to reading from the table

    spark = SparkSession()

    df = (
        spark
        .table('employee')
        .groupBy(F.col("age"))
        .agg(F.countDistinct(F.col("employee_id")).alias("num_employees"))
    )

    print(df.sql(pretty=True))  # Spark will be the dialect used by default

    SELECT
      `employee`.`age` AS `age`,
      COUNT(DISTINCT `employee`.`employee_id`) AS `num_employees`
    FROM `employee` AS `employee`
    GROUP BY
      `employee`.`age`

## Registering Custom Schema Class

The step of adding `sqlglot.schema.add_table` can be skipped if you have the column structure stored externally like in a file or from an external metadata table. This can be done by writing a class that implements the `sqlglot.schema.Schema` abstract class and then assigning that class to `sqlglot.schema`.

    import sqlglot
    from sqlglot.dataframe.sql.session import SparkSession
    from sqlglot.dataframe.sql import functions as F
    from sqlglot.schema import Schema

    class ExternalSchema(Schema):
      ...

    sqlglot.schema = ExternalSchema()

    spark = SparkSession()

    df = (
        spark
        .table('employee')
        .groupBy(F.col("age"))
        .agg(F.countDistinct(F.col("employee_id")).alias("num_employees"))
    )

    print(df.sql(pretty=True))

## Example Implementations

### Bigquery

    from google.cloud import bigquery
    from sqlglot.dataframe.sql.session import SparkSession
    from sqlglot.dataframe.sql import types
    from sqlglot.dataframe.sql import functions as F

    client = bigquery.Client()

    data = [
        (1, "Jack", "Shephard", 34),
        (2, "John", "Locke", 48),
        (3, "Kate", "Austen", 34),
        (4, "Claire", "Littleton", 22),
        (5, "Hugo", "Reyes", 26),
    ]
    schema = types.StructType([
        types.StructField('employee_id', types.IntegerType(), False),
        types.StructField('fname', types.StringType(), False),
        types.StructField('lname', types.StringType(), False),
        types.StructField('age', types.IntegerType(), False),
    ])

    sql_statements = (
        SparkSession()
        .createDataFrame(data, schema)
        .groupBy(F.col("age"))
        .agg(F.countDistinct(F.col("employee_id")).alias("num_employees"))
        .sql(dialect="bigquery")
    )

    result = None
    for sql in sql_statements:
      result = client.query(sql)

    assert result is not None
    for row in client.query(result):
        print(f"Age: {row['age']}, Num Employees: {row['num_employees']}")

### Snowflake

    import os

    import snowflake.connector
    from sqlglot.dataframe.session import SparkSession
    from sqlglot.dataframe import types
    from sqlglot.dataframe import functions as F

    ctx = snowflake.connector.connect(
        user=os.environ["SNOWFLAKE_USER"],
        password=os.environ["SNOWFLAKE_PASS"],
        account=os.environ["SNOWFLAKE_ACCOUNT"]
    )
    cs = ctx.cursor()

    data = [
        (1, "Jack", "Shephard", 34),
        (2, "John", "Locke", 48),
        (3, "Kate", "Austen", 34),
        (4, "Claire", "Littleton", 22),
        (5, "Hugo", "Reyes", 26),
    ]
    schema = types.StructType([
        types.StructField('employee_id', types.IntegerType(), False),
        types.StructField('fname', types.StringType(), False),
        types.StructField('lname', types.StringType(), False),
        types.StructField('age', types.IntegerType(), False),
    ])

    sql_statements = (
        SparkSession()
        .createDataFrame(data, schema)
        .groupBy(F.col("age"))
        .agg(F.countDistinct(F.col("lname")).alias("num_employees"))
        .sql(dialect="snowflake")
    )

    try:
        for sql in sql_statements:
            cs.execute(sql)
        results = cs.fetchall()
        for row in results:
            print(f"Age: {row[0]}, Num Employees: {row[1]}")
    finally:
        cs.close()
    ctx.close()

### Spark

    from pyspark.sql.session import SparkSession as PySparkSession
    from sqlglot.dataframe.sql.session import SparkSession
    from sqlglot.dataframe.sql import types
    from sqlglot.dataframe.sql import functions as F

    data = [
        (1, "Jack", "Shephard", 34),
        (2, "John", "Locke", 48),
        (3, "Kate", "Austen", 34),
        (4, "Claire", "Littleton", 22),
        (5, "Hugo", "Reyes", 26),
    ]
    schema = types.StructType([
        types.StructField('employee_id', types.IntegerType(), False),
        types.StructField('fname', types.StringType(), False),
        types.StructField('lname', types.StringType(), False),
        types.StructField('age', types.IntegerType(), False),
    ])

    sql_statements = (
        SparkSession()
        .createDataFrame(data, schema)
        .groupBy(F.col("age"))
        .agg(F.countDistinct(F.col("employee_id")).alias("num_employees"))
        .sql(dialect="spark")
    )

    pyspark = PySparkSession.builder.master("local[*]").getOrCreate()

    df = None
    for sql in sql_statements:
        df = pyspark.sql(sql)

    assert df is not None
    df.show()

# Unsupportable Operations

Any operation that lacks a way to represent it in SQL cannot be supported by this tool. An example of this would be rdd operations. Since the DataFrame API though is mostly modeled around SQL concepts most operations can be supported.

View Source

    1"""
    2.. include:: ./README.md
    3"""