#!/usr/bin/env python3

"""Example: translate Spark SQL to Feldera SQL with the felderize Python API.

Run from `python/felderize/` with the project venv (so `felderize` is importable

and ANTHROPIC_API_KEY is available, e.g. from a .env file):

.venv/bin/python examples/api_usage.py

"""

from felderize import Config, Status, translate_spark_to_feldera

SCHEMA = """

CREATE TABLE orders (

id BIGINT,

region STRING,

status STRING,

amount DECIMAL(10, 2),

created_at TIMESTAMP

) USING parquet;

"""

QUERY = """

CREATE OR REPLACE TEMP VIEW revenue_by_region AS

SELECT

region,

date_trunc('MONTH', created_at) AS month,

SUM(amount) AS total

FROM orders

WHERE status = 'PAID'

GROUP BY region, date_trunc('MONTH', created_at);

"""

def main() -> None:

# Reads ANTHROPIC_API_KEY, FELDERIZE_MODEL, and (for validate=True)

# FELDERA_COMPILER from the environment / a .env file.

cfg = Config.from_env()

# validate=True compiles the output against the Feldera compiler and repairs

# it using the compiler's feedback. Set validate=False to skip the compiler

# (faster, but the output SQL is not verified).

result = translate_spark_to_feldera(SCHEMA, QUERY, cfg, validate=False)

print(f"status: {result.status.value}\n")

print("-- Schema --")

print(result.feldera_schema.strip())

print("\n-- Query --")

print(result.feldera_query.strip())

if result.unsupported:

print("\nunsupported:")

for item in result.unsupported:

print(f" - {item}")

if result.warnings:

print("\nwarnings:")

for warning in result.warnings:

print(f" - {warning}")

# Branch on status the way a real integration would.

if result.status is Status.SUCCESS:

print("\nOK — the translated SQL is ready to deploy to a Feldera pipeline.")

else:

# Status.UNSUPPORTED -> some constructs became CAST(NULL ...) placeholders;

# Status.ERROR -> best-effort SQL that did not compile.

print("\nReview needed — see the unsupported/warnings above.")

if __name__ == "__main__":

main()