playbook/antigravity-awesome-skills/skills/monte-carlo-push-ingestion/scripts/templates/snowflake/push_query_logs.py

249 lines
8.3 KiB
Python

#!/usr/bin/env python3
"""
Push query logs to Monte Carlo from a JSON manifest — push only.
Reads a manifest file produced by ``collect_query_logs.py`` and sends 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
-------------------
- MCD_INGEST_ID (env) / --key-id (CLI) : Monte Carlo ingestion key ID
- MCD_INGEST_TOKEN (env) / --key-token (CLI) : Monte Carlo ingestion key token
- MCD_RESOURCE_UUID (env) / --resource-uuid (CLI) : MC resource UUID for this connection
Prerequisites
-------------
pip install pycarlo
Usage
-----
python push_query_logs.py \\
--key-id <MCD_INGEST_ID> \\
--key-token <MCD_INGEST_TOKEN> \\
--resource-uuid <MCD_RESOURCE_UUID> \\
--input-file query_logs_output.json
"""
import argparse
import json
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
# ← SUBSTITUTE: set LOG_TYPE to match your warehouse type (query logs use log_type, not resource_type)
LOG_TYPE = "snowflake"
# 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:
start_time = q.get("start_time")
end_time = q.get("end_time")
query_text = q.get("query_text") or ""
query_id = q.get("query_id")
user_name = q.get("user")
warehouse_name = q.get("warehouse")
bytes_scanned = q.get("bytes_scanned")
rows_produced = q.get("rows_produced")
# 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 warehouse_name is not None:
extra["warehouse_name"] = warehouse_name
if bytes_scanned is not None:
extra["bytes_scanned"] = int(bytes_scanned)
entries.append(
QueryLogEntry(
start_time=isoparse(start_time) if start_time else None,
end_time=isoparse(end_time) if end_time else None,
query_text=query_text,
query_id=query_id,
user=user_name,
returned_rows=int(rows_produced) if rows_produced is not None else None,
extra=extra or None,
)
)
if truncated:
print(f" Truncated {truncated} query text(s) exceeding {_MAX_QUERY_TEXT_LEN} chars")
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)
print(f"Loaded {len(entries)} query log entry/entries from {input_file}")
if not entries:
print("No query log entries to push.")
push_result = {
"resource_uuid": resource_uuid,
"log_type": log_type,
"invocation_ids": [],
"pushed_at": datetime.now(tz=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)
print(f" Pushed batch {batch_num}/{total_batches} ({len(batch)} entries) — invocation_id={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:
print(f" ERROR pushing batch {idx + 1}: {exc}")
raise
print(f" All {total_batches} batches pushed ({max_workers} workers)")
push_result = {
"resource_uuid": resource_uuid,
"log_type": log_type,
"invocation_ids": invocation_ids,
"pushed_at": datetime.now(tz=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)
print(f"Push result written to {output_file}")
return push_result
def main() -> None:
parser = argparse.ArgumentParser(
description="Push Snowflake query logs from a manifest to Monte Carlo",
)
parser.add_argument(
"--key-id",
default=os.environ.get("MCD_INGEST_ID"),
help="Monte Carlo ingestion key ID (env: MCD_INGEST_ID)",
)
parser.add_argument(
"--key-token",
default=os.environ.get("MCD_INGEST_TOKEN"),
help="Monte Carlo ingestion key token (env: MCD_INGEST_TOKEN)",
)
parser.add_argument(
"--resource-uuid",
default=os.environ.get("MCD_RESOURCE_UUID"),
help="Monte Carlo resource UUID for this Snowflake connection (env: MCD_RESOURCE_UUID)",
)
parser.add_argument(
"--input-file",
default="query_logs_output.json",
help="Path to the collect manifest to read (default: query_logs_output.json)",
)
parser.add_argument(
"--output-file",
default="query_logs_push_result.json",
help="Path to write the push result (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()
missing = [
name
for name, val in [
("--key-id", args.key_id),
("--key-token", args.key_token),
("--resource-uuid", args.resource_uuid),
]
if not val
]
if missing:
parser.error(f"Missing required arguments: {', '.join(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,
)
print("Done.")
if __name__ == "__main__":
main()