"""
Databricks — Query Log Push (push-only)
=========================================
Reads a JSON manifest file produced by collect_query_logs.py and pushes the query
log entries to Monte Carlo via the push ingestion API, with configurable batching
to keep compressed payloads under 1 MB.

Substitution points (search for "← SUBSTITUTE"):
  - MCD_INGEST_ID / MCD_INGEST_TOKEN : Monte Carlo API credentials
  - MCD_RESOURCE_UUID      : UUID of the Databricks connection in Monte Carlo
  - PUSH_BATCH_SIZE       : number of entries per API call (default 100)

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 typing import Any

from dateutil.parser import isoparse
from pycarlo.core import Client, Session
from pycarlo.features.ingestion import IngestionService
from pycarlo.features.ingestion.models import QueryLogEntry
from _safe_paths import safe_existing_directory, safe_input_json_path, safe_output_json_path, read_json_file, write_json_file

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

LOG_TYPE = "databricks"
DEFAULT_BATCH_SIZE = 100  # ← SUBSTITUTE: conservative default to stay under 1 MB compressed

# 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(entry_dicts: list[dict[str, Any]]) -> list[QueryLogEntry]:
    """Convert manifest query dicts into QueryLogEntry objects."""
    entries = []
    truncated = 0
    for d in entry_dicts:
        query_text = d.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 d.get("total_task_duration_ms") is not None:
            extra["total_task_duration_ms"] = d["total_task_duration_ms"]
        if d.get("read_rows") is not None:
            extra["read_rows"] = d["read_rows"]
        if d.get("read_bytes") is not None:
            extra["read_bytes"] = d["read_bytes"]

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

        entries.append(
            QueryLogEntry(
                query_id=d.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=d.get("user"),
                returned_rows=d.get("returned_rows"),
                extra=extra or None,
            )
        )
    if truncated:
        log.info("Truncated %d query text(s) exceeding %d chars", truncated, _MAX_QUERY_TEXT_LEN)
    return entries


def push(
    manifest_path: str,
    resource_uuid: str,
    key_id: str,
    key_token: str,
    batch_size: int = DEFAULT_BATCH_SIZE,
) -> dict[str, Any]:
    """Read a collect manifest and push query log entries to Monte Carlo in batches.

    Returns a summary dict with invocation IDs and counts.
    """
    manifest = read_json_file(manifest_path)

    entry_dicts: list[dict[str, Any]] = manifest["entries"]
    entries = _build_query_log_entries(entry_dicts)
    log.info("Loaded %d query log entries from %s", len(entries), manifest_path)

    if not entries:
        log.info("No query log entries to push.")
        summary = {
            "resource_uuid": resource_uuid,
            "log_type": LOG_TYPE,
            "invocation_ids": [],
            "pushed_at": datetime.now(timezone.utc).isoformat(),
            "query_log_count": 0,
            "batch_count": 0,
            "batch_size": batch_size,
        }
        push_manifest_path = manifest_path.replace(".json", "_push_result.json")
        write_json_file(push_manifest_path, summary)
        return summary

    # 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)

    pushed_at = datetime.now(timezone.utc).isoformat()
    summary = {
        "resource_uuid": resource_uuid,
        "log_type": LOG_TYPE,
        "invocation_ids": invocation_ids,
        "pushed_at": pushed_at,
        "query_log_count": len(entries),
        "batch_count": total_batches,
        "batch_size": batch_size,
        "lookback_hours": manifest.get("lookback_hours"),
        "lookback_lag_hours": manifest.get("lookback_lag_hours"),
    }

    push_manifest_path = manifest_path.replace(".json", "_push_result.json")
    write_json_file(push_manifest_path, summary)
    log.info("Push result written to %s", push_manifest_path)

    return summary


def main() -> None:
    parser = argparse.ArgumentParser(description="Push Databricks query logs to Monte Carlo from manifest")
    parser.add_argument("--manifest", default="manifest_query_logs.json")
    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("--batch-size", type=int, default=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(
        manifest_path=args.manifest,
        resource_uuid=args.resource_uuid,
        key_id=args.key_id,
        key_token=args.key_token,
        batch_size=args.batch_size,
    )


if __name__ == "__main__":
    main()