This is a very well-structured and robust script. The use of `asyncio`, `httpx`, and `polars` is excellent, and the logging and checkpointing show great attention to detail. I've reviewed the code and found one critical logical error, a major performance issue, and a few minor problems. Here is a breakdown of my findings: ### 🚩 Critical Issue 1. **Incorrect Settlement Periods for DST Start** * **Problem:** Your `get_settlement_periods` method correctly identifies the "long day" (50 periods) when the clocks go back (`self.dst_end`). However, it does not account for the "short day" when the clocks "spring forward" (`self.dst_start`). * **Details:** On `2024-03-31` (the last Sunday in March), the UK springs forward. This day only has **46 settlement periods** (SPs 1-46, as the hour from 1:00-2:00 AM doesn't exist). Your script currently assumes this day has 48 periods. * **Impact:** This will cause your script to make requests for `SP 47` and `SP 48` on `2024-03-31`, which do not exist. The API will return 404s for these, which your code will log as warnings, but it's still incorrect and wastes requests. * **Solution:** Update `get_settlement_periods` to handle all three cases: ```python def get_settlement_periods(self, settlement_date: date) -> List[int]: """ Get settlement periods for a given date. - Short days (DST start): 1-46 - Normal days: 1-48 - Long days (DST end): 1-50 """ if settlement_date == self.dst_start: # Clocks go forward - short day return list(range(1, 47)) # 1-46 elif settlement_date == self.dst_end: # Clocks go back - long day return list(range(1, 51)) # 1-50 else: # Normal day return list(range(1, 49)) # 1-48 ``` ----- ### ⚠️ Major Problems & Suggestions 1. **Inefficient Use of `asyncio` (Serial Execution)** * **Problem:** You are using an `async` client (`httpx.AsyncClient`) but are not making any concurrent requests. Your code runs serially, one request at a time. * **Details:** In your `retrieve_all_data` loop, this pattern: ```python for settlement_period in settlement_periods: ... data = await self.make_api_request(...) # <--- Stops and waits here ... await asyncio.sleep(self.request_delay) # <--- Stops and waits here ``` ...means that the script makes one request, waits for it to finish, processes it, and then sleeps. The `asyncio` event loop is not being used for concurrency at all. * **Impact:** The script will be **very slow**. You have \~17,564 requests to make (364*48 + 1*46 + 1\*50). At `0.15s` delay *per request* (plus network time), this script will take at least **44 minutes** to run. * **Solution (Option A - Keep it Simple & Serial):** If you are happy with this runtime and just want to be server-friendly, you can simplify your code by removing `asyncio` entirely. Use the synchronous `httpx.Client()` and `time.sleep()`. The code would be simpler and achieve the same result. * **Solution (Option B - Use True Concurrency):** To actually get the speed benefit of `asyncio`, you need to run requests in parallel, while still respecting rate limits. The best way is with an `asyncio.Semaphore` to limit concurrent requests. ```python async def retrieve_all_data(self) -> pl.DataFrame: dates = self.generate_date_range() all_data_nested = [] # Store results in order checkpoint_date, checkpoint_period = self.load_checkpoint() # --- Concurrency setup --- semaphore = asyncio.Semaphore(10) # Allow 10 concurrent requests tasks = [] # ------------------------- logger.info(f"Starting data retrieval...") async with httpx.AsyncClient() as client: # This wrapper will manage concurrency and rate-limiting async def fetch_with_semaphore(settlement_date, settlement_period): async with semaphore: # Skip if already processed if self.should_skip_request(settlement_date, settlement_period, checkpoint_date, checkpoint_period): return [] # Return empty list, not None data = await self.make_api_request(client, settlement_date, settlement_period) # Rate limiting *after* a request is done await asyncio.sleep(self.request_delay) return data, settlement_date, settlement_period # Create all tasks first for settlement_date in dates: settlement_periods = self.get_settlement_periods(settlement_date) for settlement_period in settlement_periods: tasks.append(fetch_with_semaphore(settlement_date, settlement_period)) # Run tasks and log progress total_tasks = len(tasks) results = [] for i, f in enumerate(asyncio.as_completed(tasks)): result = await f if result is not None: data, s_date, s_period = result if data: all_data_nested.extend(data) # Save checkpoint every 100 requests if i % 100 == 0: self.save_checkpoint(s_date, s_period) if i % 1000 == 0 or i == total_tasks - 1: progress = (i / total_tasks) * 100 logger.info(f"Progress: {i}/{total_tasks} ({progress:.1f}%)") logger.info(f"Data retrieval complete. Retrieved {len(all_data_nested)} records") # Flatten the list and proceed with DataFrame creation all_data = all_data_nested # ... (rest of your DataFrame creation code) ... ``` *Note: This concurrent logic is more complex. You'll need to adapt the checkpointing logic, but it will be dramatically faster.* 2. **Flawed Checkpoint Cleanup Logic** * **Problem:** The script deletes the checkpoint file (`checkpoint_path.unlink()`) as long as the *CSV write is successful*. * **Impact:** Imagine the script runs, but the API request for `2024-12-31 SP48` fails all its retries. `make_api_request` will return `None`. Your loop will log the error and continue. The script will then save a CSV that is **missing data** for that final period. Because the CSV save was successful, `save_to_csv` will **delete the checkpoint file**. If you run the script again, it will not know it missed a period and will not retry. * **Solution:** Track failures. Only delete the checkpoint if *all* requests were successful. ```python # In retrieve_all_data ... all_requests_succeeded = True # Track failures async with httpx.AsyncClient() as client: for settlement_date in dates: for settlement_period in settlement_periods: ... data = await self.make_api_request(...) if data is None: all_requests_succeeded = False # Mark as failed else: all_data.extend(data) ... ... return df, all_requests_succeeded # Return the flag # In save_to_csv def save_to_csv(self, df: pl.DataFrame, all_succeeded: bool): try: df.write_csv(self.output_file) logger.info(f"Data saved to {self.output_file}") # Only clean up checkpoint on 100% success if all_succeeded: checkpoint_path = Path(self.checkpoint_file) if checkpoint_path.exists(): checkpoint_path.unlink() logger.info("Checkpoint file removed after successful completion") else: logger.warning("Data saved, but some requests failed. Checkpoint file retained for retry.") except Exception as e: logger.error(f"Error saving to CSV: {e}") raise # In run async def run(self): ... df, all_succeeded = await self.retrieve_all_data() if not df.is_empty(): self.save_to_csv(df, all_succeeded) ... ``` ----- ### 🔍 Minor Issues & Nitpicks 1. **Fragile Datetime Parsing:** The format `"%Y-%m-%dT%H:%M:%S"` for `halfHourEndTime` is very strict. Elexon's API often includes a `Z` (for UTC) at the end (e.g., `...T14:30:00Z`). This strict format will fail to parse that. * **Solution:** Let Polars' powerful parser infer the format. It's much more robust. ```python df = df.with_columns([ ... # Let Polars handle the format, it's very good at ISO-8601 pl.col("halfHourEndTime").str.to_datetime() ]) ``` 2. **Inaccurate Progress Tracking:** Your `total_requests` calculation is slightly off because it doesn't account for the "short day" (it will be fixed by implementing the fix for the critical DST bug). 3. **Broad Exceptions:** In `load_checkpoint` and `save_checkpoint`, you use `except Exception as e:`. This is fine for a script, but for more robust code, you'd catch specific errors like `FileNotFoundError`, `json.JSONDecodeError`, or `IOError`. Overall, this is a very strong script. Fixing the DST logic and the checkpoint cleanup will make it production-ready. Deciding on the `asyncio` strategy (serial vs. concurrent) depends on whether you value speed or simplicity more.