Hi everyone, I had a scary moment last week with a data pipeline that I think highlights a classic newbie oversight. I was working on an orchestration agent that processes customer support chat logs from BigQuery, does some light sentiment tagging in Python, and loads summaries back into a table. The agent ran fine in dev with small samples, but when I pointed it at production data, it gradually consumed all the memory on the VM and was killed by the OS after about six hours.
The root cause was embarrassingly simple: I didn't set any memory limits on the tasks, and the agent was caching the *entire* chat history in memory as it processed each day. It was loading the raw text for each chat into a list for batch processing, but that list just grew and grew. I was using the Python `multiprocessing` module to parallelize by day, and each process was holding its own copy of data for its date range, but my code wasn't clearing intermediate results.
Here’s a simplified snippet of the problematic pattern I used:
```python
def process_day(day):
# This query fetches all chats for a single day
chats = bigquery_client.query(day_query).result()
chat_texts = []
for row in chats:
chat_texts.append(row['full_chat_text']) # This list gets huge!
# ... do processing on chat_texts ...
summaries = analyze_chats(chat_texts)
# ... write summaries back ...
```
I’ve since learned to use generators and to be explicit about memory boundaries. For my Airflow DAG, I now set `executor_config` on the PythonOperator to limit memory, and I process in smaller batches. My fix looks more like this:
```python
def process_day_batched(day, batch_size=100):
query_job = bigquery_client.query(day_query)
rows = query_job.result(page_size=batch_size)
for page in rows.pages:
batch = [row['full_chat_text'] for row in page]
summaries = analyze_chats(batch)
write_summaries(summaries)
# batch goes out of scope here
```
Has anyone else run into similar issues with memory creeping up in long-running data tasks? Are there other safe patterns or configuration settings in Airflow or with Python workers that you'd recommend to prevent this? I'm especially nervous about tasks that run for hours and might have hidden state like this.
Oof, that's rough. I'm working on something similar with a vendor's chat API. Makes me wonder, when you parallelized by day, were you also hitting any BigQuery usage quotas? I've heard of bills spiking from concurrent queries.
What was the final fix? Did you switch to a generator or just add a manual clear step?
Still learning.
The BigQuery quota question is a good catch, but honestly, the compute costs from a choked VM were the bigger surprise for me. Parallelizing without memory limits is like launching more boats without checking if the harbor's already full.
> What was the final fix?
I went with a generator pattern for the raw text processing, but that's only half the story. The real fix was also setting explicit memory limits in the orchestration framework itself (I was using Prefect). Otherwise, the generator's just a nicer looking leak.
You should run a quick break-even: will the time saved by parallel processing actually offset the increased query costs and risk of quota errors? For chat logs, I've found that batching by week and streaming results often wins.
Show me the bill
That pattern with the accumulating list is a textbook memory leak, especially since Python's garbage collector won't free that memory while the process is still alive and the list's reference remains in scope. Even without multiprocessing, a single-threaded version would eventually hit the same wall, just later.
Your multiprocessing angle adds an important dimension people often miss. On Linux, `multiprocessing` uses `fork()` by default, which can lead to each child process holding a *copy* of the parent's memory at the moment of forking. If you've already loaded a large dataset before spawning processes, you've multiplied your memory footprint before any work even begins. The fix is to structure the code so the data is loaded *inside* the child process, after the fork, or better yet, switch to using `spawn` as the start method to ensure clean memory separation.
Generators help, but as noted elsewhere, they're only effective if you aren't inadvertently materializing the entire iterable elsewhere in your pipeline, like passing it to a library function that expects a list.
Data never lies.
Right, the multiprocessing memory bloat is a real gotcha. But I'm curious about the *cost* of that six-hour runaway before the OOM killer stepped in.
You said you were parallelizing by day on a VM. Were you tracking the CPU credits or sustained usage charges while it slowly choked itself? A process holding memory hostage often leaves the CPU idle, but the billing clock keeps ticking. That's a direct translation to wasted spend.
The generator pattern helps the code, but have you actually seen the memory limit configuration in your orchestration tool reflected in lower cloud bills? Sometimes the limits are soft and the hypervisor just lets things slide until the hard stop. I'd want to see the VM's monitored memory graph next to the cost report for that job.
cost_observer_42
Good point on the `fork()` memory copy, but I'd push back on calling it a "textbook memory leak". A leak implies the memory can't be reclaimed, but a persistent reference to a growing list is just inefficient allocation. The process still knows about the memory, it's just using it poorly.
The `spawn` method fix is correct, but it's often a band-aid for bad architecture. If you're forking with a large dataset already in memory, your data loading strategy is the real problem. You should be streaming from the source directly into the worker, not pre-loading.
Also, `spawn` adds overhead. For a short-lived process with small data, `fork` is fine. The issue here is scale, not the start method itself.
show me the bill