"""
BigQuery — Query Log Push (push only)
======================================
Reads a manifest file produced by ``collect_query_logs.py`` and pushes the query
log entries to Monte Carlo using the pycarlo push ingestion API.  Large payloads
are split into batches to stay under the 1 MB compressed limit.

Can be run standalone via CLI or imported (use the ``push()`` function).

Substitution points (search for "← SUBSTITUTE"):
  - MCD_INGEST_ID / MCD_INGEST_TOKEN : Monte Carlo API credentials
  - MCD_RESOURCE_UUID      : UUID of the BigQuery connection in Monte Carlo

Prerequisites:
  pip install pycarlo
"""

from __future__ import annotations

import argparse
import json
import logging
import os
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime, timezone

from dateutil.parser import isoparse
from pycarlo.core import Client, Session
from pycarlo.features.ingestion import IngestionService
from pycarlo.features.ingestion.models import QueryLogEntry

logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
log = logging.getLogger(__name__)

LOG_TYPE = "bigquery"

# Maximum entries per batch — conservative default to keep compressed payload under 1 MB.
# Query logs include full SQL text — keep batches small to stay under the 1 MB
# compressed payload limit.  50 entries can trigger 413 on active warehouses.
# ← SUBSTITUTE: tune based on average query length
_BATCH_SIZE = 100

# Truncate query_text longer than this to prevent 413 errors.
# Some SQL statements (e.g., generated by BI tools) can be 100KB+ and blow up
# compressed payloads even at small batch sizes.
_MAX_QUERY_TEXT_LEN = 10_000


def _build_query_log_entries(queries: list[dict]) -> list[QueryLogEntry]:
    """Convert manifest query dicts into QueryLogEntry objects."""
    entries = []
    truncated = 0
    for q in queries:
        query_text = q.get("query_text") or ""

        # Truncate very long SQL to prevent 413 Request Too Large
        if len(query_text) > _MAX_QUERY_TEXT_LEN:
            query_text = query_text[:_MAX_QUERY_TEXT_LEN] + "... [TRUNCATED]"
            truncated += 1

        extra = {}
        if q.get("total_bytes_billed") is not None:
            extra["total_bytes_billed"] = q["total_bytes_billed"]
        if q.get("statement_type") is not None:
            extra["statement_type"] = q["statement_type"]

        start_time = q.get("start_time")
        end_time = q.get("end_time")

        entry = QueryLogEntry(
            query_id=q.get("query_id"),
            query_text=query_text,
            start_time=isoparse(start_time) if start_time else None,
            end_time=isoparse(end_time) if end_time else None,
            user=q.get("user"),
            extra=extra or None,
        )
        entries.append(entry)
    if truncated:
        log.info("Truncated %d query text(s) exceeding %d chars", truncated, _MAX_QUERY_TEXT_LEN)
    return entries


def push(
    input_file: str,
    resource_uuid: str,
    key_id: str,
    key_token: str,
    batch_size: int = _BATCH_SIZE,
    output_file: str = "query_logs_push_result.json",
) -> dict:
    """
    Read a query log manifest and push entries to Monte Carlo in batches.

    Returns a result dict with invocation IDs for each batch.
    """
    with open(input_file) as fh:
        manifest = json.load(fh)

    queries = manifest.get("queries", [])
    log_type = manifest.get("log_type", LOG_TYPE)
    entries = _build_query_log_entries(queries)
    log.info("Loaded %d query log entry/entries from %s", len(entries), input_file)

    if not entries:
        log.info("No query log entries to push.")
        push_result = {
            "resource_uuid": resource_uuid,
            "log_type": log_type,
            "invocation_ids": [],
            "pushed_at": datetime.now(timezone.utc).isoformat(),
            "total_entries": 0,
            "batch_count": 0,
            "batch_size": batch_size,
        }
        with open(output_file, "w") as fh:
            json.dump(push_result, fh, indent=2)
        return push_result

    # Split into batches
    batches = []
    for i in range(0, len(entries), batch_size):
        batches.append(entries[i : i + batch_size])
    total_batches = len(batches)

    def _push_batch(batch: list, batch_num: int) -> str | None:
        """Push a single batch using a dedicated Session (thread-safe)."""
        client = Client(session=Session(mcd_id=key_id, mcd_token=key_token, scope="Ingestion"))
        service = IngestionService(mc_client=client)
        result = service.send_query_logs(
            resource_uuid=resource_uuid,
            log_type=log_type,
            events=batch,
        )
        invocation_id = service.extract_invocation_id(result)
        log.info("Pushed batch %d/%d (%d entries) — invocation_id=%s", batch_num, total_batches, len(batch), invocation_id)
        return invocation_id

    # Push batches in parallel (each thread gets its own pycarlo Session)
    max_workers = min(4, total_batches)
    invocation_ids: list[str | None] = [None] * total_batches

    with ThreadPoolExecutor(max_workers=max_workers) as pool:
        futures = {
            pool.submit(_push_batch, batch, i + 1): i
            for i, batch in enumerate(batches)
        }
        for future in as_completed(futures):
            idx = futures[future]
            try:
                invocation_ids[idx] = future.result()
            except Exception as exc:
                log.error("ERROR pushing batch %d: %s", idx + 1, exc)
                raise

    log.info("All %d batches pushed (%d workers)", total_batches, max_workers)

    push_result = {
        "resource_uuid": resource_uuid,
        "log_type": log_type,
        "invocation_ids": invocation_ids,
        "pushed_at": datetime.now(timezone.utc).isoformat(),
        "total_entries": len(entries),
        "batch_count": total_batches,
        "batch_size": batch_size,
    }
    with open(output_file, "w") as fh:
        json.dump(push_result, fh, indent=2)
    log.info("Push result written to %s", output_file)

    return push_result


def main() -> None:
    parser = argparse.ArgumentParser(
        description="Push BigQuery query logs from a manifest to Monte Carlo",
    )
    parser.add_argument("--resource-uuid", default=os.getenv("MCD_RESOURCE_UUID"))
    parser.add_argument("--key-id", default=os.getenv("MCD_INGEST_ID"))
    parser.add_argument("--key-token", default=os.getenv("MCD_INGEST_TOKEN"))
    parser.add_argument("--input-file", default="query_logs_output.json")
    parser.add_argument("--output-file", default="query_logs_push_result.json")
    parser.add_argument(
        "--batch-size",
        type=int,
        default=_BATCH_SIZE,
        help=f"Max entries per push batch (default: {_BATCH_SIZE})",
    )
    args = parser.parse_args()

    required = ["resource_uuid", "key_id", "key_token"]
    missing = [k for k in required if getattr(args, k) is None]
    if missing:
        parser.error(f"Missing required arguments/env vars: {missing}")

    push(
        input_file=args.input_file,
        resource_uuid=args.resource_uuid,
        key_id=args.key_id,
        key_token=args.key_token,
        batch_size=args.batch_size,
        output_file=args.output_file,
    )


if __name__ == "__main__":
    main()