clari-performance-tuning

# Clari Performance Tuning ## Overview Optimize Clari export pipelines: reduce export times, cache forecast data, and parallelize multi-period exports. ## Instructions ### Parallel Multi-Period Export ```python from concurrent.futures import ThreadPoolExecutor, as_completed def parallel_export( client, forecast_name: str, periods: list[str], max_workers: int = 3, ) -> dict[str, list[dict]]: results = {} def export_period(period: str) -> tuple[str, list[dict]]: data = client.export_and_download(forecast_name, period) return period, data.get("entries", []) with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = { executor.submit(export_period, p): p for p in periods } for future in as_completed(futures): period, entries = future.result() results[period] = entries print(f" {period}: {len(entries)} entries") return results ``` ### Cache Export Results ```python import json import hashlib from pathlib import Path from datetime import datetime, timedelta class ExportCache: def __init__(self, cache_dir: str = ".cache/clari", ttl_hours: int = 4): self.cache_dir = Path(cache_dir) self.cache_dir.mkdir(parents=True, exist_ok=True) self.ttl = timedelta(hours=ttl_hours) def _key(self, forecast: str, period: str) -> str: return hashlib.md5(f"{forecast}:{period}".encode()).hexdigest() def get(self, ...