"""Orpheus asset for processing DynamoDB orpheus data and triggering Glue export."""

from datetime import datetime
import warnings

import dagster as dg
from dagster_snowflake import SnowflakeResource

from src.utils.automation_conditions import hourly_cron_condition
from src.utils.snowflake.constants import TIME_WINDOW_FRESHNESS_POLICY_WARN_1H_FAIL_2H, PartitionExpr, Team, Group
from src.utils.glue_utils import trigger_glue_job

warnings.filterwarnings("ignore", category=dg.BetaWarning)

ORPHEUS_PROJECT_START_DATE = datetime.strptime('2025-01-01:00', '%Y-%m-%d:%H')
ORPHEUS_PROJECT_TABLE_NAME = "RAW_S3_DDB_ORPHEUS"


@dg.asset(
    name="ddb_orpheus",
    description="DynamoDB S3 dump of orpheus data with JSON metadata",
    group_name=Group.DYNAMODB.value,
    partitions_def=dg.HourlyPartitionsDefinition(start_date=ORPHEUS_PROJECT_START_DATE, end_offset=0),
    backfill_policy=dg.BackfillPolicy.multi_run(max_partitions_per_run=24),
    automation_condition=hourly_cron_condition,
    owners=[Team.CORE_POD.value],
    metadata={
        "table_name": ORPHEUS_PROJECT_TABLE_NAME,
        "data_start_date": ORPHEUS_PROJECT_START_DATE.strftime("%Y-%m-%d"),
        "cluster_by": "[p_date, p_hour]",
        "partition_expr": PartitionExpr.HOURLY.value,
        "sla_minutes": 60,
    },
    freshness_policy=TIME_WINDOW_FRESHNESS_POLICY_WARN_1H_FAIL_2H,
)
def ddb_orpheus(
    context: dg.AssetExecutionContext,
) -> dg.MaterializeResult:
    """
    Process orpheus data from DDB and trigger Glue export.

    Steps:
    1. Trigger Glue job to export data to S3
    """
    run_id = context.run.run_id
    logger = dg.get_dagster_logger()

    # Get partition time window for processing
    partition_start = context.partition_time_window.start
    partition_end = context.partition_time_window.end

    fetch_params = {
        "partition_start_date": partition_start.strftime("%Y-%m-%d"),
        "partition_end_date": partition_end.strftime("%Y-%m-%d"),
        "partition_start_hour": partition_start.hour,
        "partition_end_hour": partition_end.hour,
        "orpheus_table_name": ORPHEUS_PROJECT_TABLE_NAME
    }

    logger.info(f"Processing orpheus for partition: {partition_start} to {partition_end}")
    logger.info(f"Fetch params: {fetch_params}")

    try:
        glue_result = trigger_glue_job(
            job_name="ddb_to_s3_orpheus_hourly_insert",
            context=context,
            arguments={
                "--partition_date": partition_start.strftime("%Y-%m-%d"),
                "--partition_hour": str(partition_start.hour),
            },
            poll_interval=20,
            wait_for_completion=True,
            timeout=1800,  # 30 minutes
        )
        logger.info(f"Glue job completed successfully: {glue_result}")
        glue_job_status = "SUCCESS"
        glue_job_id = glue_result.get("job_run_id", "N/A")
        glue_execution_time = glue_result.get("execution_time", 0)
    except Exception as e:
        logger.error(f"Glue job failed: {str(e)}")
        glue_job_status = "FAILED"
        glue_job_id = "N/A"
        glue_execution_time = 0
        # Re-raise the exception to fail the asset materialization
        raise e

    return dg.MaterializeResult(
        metadata={
            "run_id": dg.MetadataValue.text(run_id),
            "table_name": ORPHEUS_PROJECT_TABLE_NAME,
            "partition_time_window_start": dg.MetadataValue.text(partition_start.isoformat()),
            "partition_time_window_end": dg.MetadataValue.text(partition_end.isoformat()),
            "glue_job_status": dg.MetadataValue.text(glue_job_status),
            "glue_job_run_id": dg.MetadataValue.text(glue_job_id),
            "glue_execution_time_seconds": glue_execution_time,
        },
    )