import argparse
import json
import os

from pyspark.sql import SparkSession
from pyspark.sql import functions as F


def main() -> None:
    p = argparse.ArgumentParser(description="Query assistant actions")
    p.add_argument("--table", default=os.getenv("ACTION_TABLE", "lake.db1.assistant_actions"))
    p.add_argument("--status", default="")
    p.add_argument("--task-type", default="")
    p.add_argument("--release-name", default="")
    p.add_argument("--step-id", default="")
    p.add_argument("--action-type", default="")
    p.add_argument("--limit", type=int, default=50)
    args = p.parse_args()

    spark = SparkSession.builder.appName("query-assistant-actions").getOrCreate()
    df = spark.table(args.table)

    if args.status:
        df = df.where(F.col("status") == args.status)
    if args.task_type:
        df = df.where(F.col("task_type") == args.task_type)
    if args.release_name:
        df = df.where(F.col("release_name") == args.release_name)
    if args.step_id:
        df = df.where(F.col("step_id") == args.step_id)
    if args.action_type:
        df = df.where(F.col("action_type") == args.action_type)

    rows = (
        df.orderBy(F.col("created_at_utc").desc_nulls_last())
        .limit(max(1, min(args.limit, 500)))
        .collect()
    )

    out = [r.asDict(recursive=True) for r in rows]
    print(json.dumps(out, ensure_ascii=False))


if __name__ == "__main__":
    main()