import logging
import os
import tempfile
import time
import uuid
from typing import TYPE_CHECKING, Iterable, Optional

import pyarrow.parquet as pq

if TYPE_CHECKING:
    import pyarrow as pa

import ray
from ray.data._internal.datasource import bigquery_datasource
from ray.data._internal.execution.interfaces import TaskContext
from ray.data._internal.remote_fn import cached_remote_fn
from ray.data._internal.util import _check_import
from ray.data.block import Block, BlockAccessor
from ray.data.datasource.datasink import Datasink

logger = logging.getLogger(__name__)

DEFAULT_MAX_RETRY_CNT = 10
RATE_LIMIT_EXCEEDED_SLEEP_TIME = 11


class BigQueryDatasink(Datasink[None]):
    def __init__(
        self,
        project_id: str,
        dataset: str,
        max_retry_cnt: int = DEFAULT_MAX_RETRY_CNT,
        overwrite_table: Optional[bool] = True,
    ) -> None:
        _check_import(self, module="google.cloud", package="bigquery")
        _check_import(self, module="google.cloud", package="bigquery_storage")
        _check_import(self, module="google.api_core", package="exceptions")

        self.project_id = project_id
        self.dataset = dataset
        self.max_retry_cnt = max_retry_cnt
        self.overwrite_table = overwrite_table

    def on_write_start(self, schema: Optional["pa.Schema"] = None) -> None:
        from google.api_core import exceptions

        if self.project_id is None or self.dataset is None:
            raise ValueError("project_id and dataset are required args")

        # Set up datasets to write
        client = bigquery_datasource._create_client(project_id=self.project_id)
        dataset_id = self.dataset.split(".", 1)[0]
        try:
            client.get_dataset(dataset_id)
        except exceptions.NotFound:
            client.create_dataset(f"{self.project_id}.{dataset_id}", timeout=30)
            logger.info("Created dataset " + dataset_id)

        # Delete table if overwrite_table is True
        if self.overwrite_table:
            logger.info(
                f"Attempting to delete table {self.dataset}"
                + " if it already exists since kwarg overwrite_table = True."
            )
            client.delete_table(f"{self.project_id}.{self.dataset}", not_found_ok=True)
        else:
            logger.info(
                f"The write will append to table {self.dataset}"
                + " if it already exists since kwarg overwrite_table = False."
            )

    def write(
        self,
        blocks: Iterable[Block],
        ctx: TaskContext,
    ) -> None:
        def _write_single_block(block: Block, project_id: str, dataset: str) -> None:
            from google.api_core import exceptions
            from google.cloud import bigquery

            block = BlockAccessor.for_block(block).to_arrow()

            client = bigquery_datasource._create_client(project_id=project_id)
            job_config = bigquery.LoadJobConfig(autodetect=True)
            job_config.source_format = bigquery.SourceFormat.PARQUET
            job_config.write_disposition = bigquery.WriteDisposition.WRITE_APPEND

            with tempfile.TemporaryDirectory() as temp_dir:
                fp = os.path.join(temp_dir, f"block_{uuid.uuid4()}.parquet")
                pq.write_table(block, fp, compression="SNAPPY")

                retry_cnt = 0
                while retry_cnt <= self.max_retry_cnt:
                    with open(fp, "rb") as source_file:
                        job = client.load_table_from_file(
                            source_file, dataset, job_config=job_config
                        )
                    try:
                        logger.info(job.result())
                        break
                    except (exceptions.Forbidden, exceptions.TooManyRequests) as e:
                        retry_cnt += 1
                        if retry_cnt > self.max_retry_cnt:
                            break
                        logger.info(
                            "A block write encountered a rate limit exceeded error"
                            + f" {retry_cnt} time(s). Sleeping to try again."
                        )
                        logging.debug(e)
                        time.sleep(RATE_LIMIT_EXCEEDED_SLEEP_TIME)

                # Raise exception if retry_cnt exceeds max_retry_cnt
                if retry_cnt > self.max_retry_cnt:
                    logger.info(
                        f"Maximum ({self.max_retry_cnt}) retry count exceeded. Ray"
                        + " will attempt to retry the block write via fault tolerance."
                    )
                    raise RuntimeError(
                        f"Write failed due to {retry_cnt}"
                        + " repeated API rate limit exceeded responses. Consider"
                        + " specifiying the max_retry_cnt kwarg with a higher value."
                    )

        _write_single_block = cached_remote_fn(_write_single_block)

        # Launch a remote task for each block within this write task
        ray.get(
            [
                _write_single_block.remote(block, self.project_id, self.dataset)
                for block in blocks
            ]
        )