Restructuring a Day-Long Debug Log

Problem Description

A solo developer has been debugging a performance regression in a Python data pipeline for a full day, keeping a time-stamped personal log as they worked. Now at end-of-day, they want to turn the log into something they can share with their tech lead in tomorrow's sync — something that communicates the state of the investigation efficiently without making the tech lead read all the false starts and context-switching.

Produce a structured investigation document from the log below and save it as perf_investigation.md.

Input Files

The following file is provided as input. Extract it before beginning.

=============== FILE: inputs/dev_log.md ===============

Daily Debug Log — 2024-04-10

09:00

Started the day. The pipeline that processes customer transaction CSV files overnight is now taking 4.2 hours, up from 1.8 hours last week. No code changes were merged last week. Need to figure out why.

09:15

Checked git log. No merges since April 3rd. But there was a dependency update via Dependabot on April 5th — updated pandas from 1.5.3 to 2.0.1. Maybe the pandas upgrade is the culprit?

09:30

Ran the pipeline with pandas 1.5.3 in a virtual env to compare. Same data, same code — runtime was 1.9 hours. So the pandas upgrade is definitely involved.

10:00

Read pandas 2.0 migration notes. They deprecated the .append() method (now raises a warning and falls back to concat). Our pipeline uses .append() in a loop to build a result DataFrame — 50,000 iterations. In pandas 2.0, this involves extra copy overhead each iteration.

10:45

Replaced .append() loop with a list-accumulate-then-concat pattern. Local test on a 1% sample: old code 4.8s, new code 0.3s. 16x improvement on sample.

11:30

Full pipeline test on staging with the fix. Runtime: 1.7 hours. Faster than original! Fix confirmed.

12:00 - 12:45

Lunch break. Also had a 1:1 with manager about Q2 roadmap, not relevant.

13:00

While the fix is confirmed, I'm curious: why did it take 4.2 hours but with pandas 1.5.3 it was 1.9 hours? The sample showed 16x improvement, which would imply the full pipeline should have been much slower. Let me check if there are other bottlenecks.

13:30

Profiled the original (broken) pipeline end to end. Surprisingly: the pandas section is only 35% of the total runtime. The file I/O section — reading and writing CSV files — accounts for 55% of total time.

14:00

Investigated file I/O section. Found: pipeline reads each of 1,200 CSV files individually using pd.read_csv() in a sequential loop, then merges. No parallelism. Each read takes ~10ms on average but with 1,200 files that's 12 seconds just for reads. Seems normal though.

14:45

Noticed the CSV files are stored on an NFS mount. NFS latency has been higher than usual this week — ops team says a network switch was replaced on April 6th and there might be some packet loss. Maybe the I/O slowness is also contributing.

15:30

Checked NFS metrics. Packet loss is 0.02% since April 6th. For a 10ms operation, this is unlikely to meaningfully affect latency. NFS probably not the culprit for the I/O bottleneck.

16:00

Hypothesis: The CSV reading could be parallelized with multiprocessing or async I/O. Quick prototype with 4 workers: sample reads went from 48s to 14s. Significant potential.

16:30

Ran out of time to test the parallel I/O fix properly — needs more careful implementation to avoid race conditions in the merge step. Leaving as a future optimization idea rather than a confirmed fix.

17:00

Wrapping up. Main fix (pandas append → concat) is confirmed and ready to deploy. The parallel CSV reading is still just an idea.