⚡ Bolt: Implement telemetry caching and optimize status helpers#82
⚡ Bolt: Implement telemetry caching and optimize status helpers#82heidi-dang wants to merge 1 commit intofeat/bootstrap-scaffoldfrom
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Implemented a thread-safe `StateCache` for `state.json` and added TTL-based caching for `get_gpu_summary` and `get_last_event_ts`. - `get_state` is now ~56% faster by avoiding redundant JSON parsing. - `nvidia-smi` calls are reduced by caching (TTL 5s). - `get_last_event_ts` is safer and more efficient. - Moved status helpers to module level for better maintainability.
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Summary of ChangesHello @heidi-dang, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request significantly enhances the performance of telemetry and status endpoints by introducing a multi-layered caching strategy. The changes aim to reduce redundant disk I/O and CPU overhead caused by frequent polling, making the system more responsive and efficient, particularly for dashboards and TUI applications. The optimizations focus on caching file reads and subprocess call results, leading to faster data retrieval for critical status information. Highlights
Changelog
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Code Review
This pull request introduces several caching optimizations to heidi_engine/telemetry.py to improve the performance of status polling, which is a great enhancement. The implementation of StateCache and the TTL-based caches for GPU summary and event timestamps are well-structured. My review focuses on improving the robustness of the new caching mechanisms, particularly around error handling and ensuring failures are also cached to prevent redundant I/O. I've suggested changes to log exceptions instead of ignoring them silently and to refine the caching logic in get_last_event_ts.
| except Exception: | ||
| return None |
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Swallowing all exceptions silently can make it difficult to diagnose problems with reading or parsing state.json. For instance, a malformed JSON file would cause this to return None without any indication of the root cause. It's better to log the exception to stderr for debugging, which is consistent with other error handling in this file.
| except Exception: | |
| return None | |
| except Exception as e: | |
| print(f"[WARN] StateCache failed to read or parse state file {state_file}: {e}", file=sys.stderr) | |
| return None |
| except Exception: | ||
| pass |
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Silently passing on all exceptions can hide underlying issues with nvidia-smi (e.g., it's not installed, command fails, parsing error). It's better to log the exception to stderr for easier debugging.
| except Exception: | |
| pass | |
| except Exception as e: | |
| print(f"[WARN] Failed to get GPU summary: {e}", file=sys.stderr) |
| if ( | ||
| _last_event_ts_cache["data"] | ||
| and (now - _last_event_ts_cache["last_polled"]) < LAST_EVENT_TS_TTL | ||
| ): | ||
| return _last_event_ts_cache["data"] | ||
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| try: | ||
| events_file = get_events_path() | ||
| if events_file.exists(): | ||
| file_size = events_file.stat().st_size | ||
| if file_size > 0: | ||
| with open(events_file, "rb") as f: | ||
| # BOLT OPTIMIZATION: Safely seek even for small files | ||
| f.seek(-min(500, file_size), 2) | ||
| lines = f.read().decode().strip().split("\n") | ||
| if lines: | ||
| last_line = lines[-1] | ||
| event = json.loads(last_line) | ||
| ts = event.get("ts") | ||
| _last_event_ts_cache = {"data": ts, "last_polled": now} | ||
| return ts | ||
| except Exception: | ||
| pass | ||
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| return None |
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This function can be improved in two ways:
- Cache
Noneresults: The current implementation doesn't cacheNoneresults (e.g., when the event file is empty or an error occurs). This leads to repeated file I/O on every call in these scenarios. CachingNonefor the TTL duration would be more efficient. - Error logging: The
except Exception: passsilently swallows errors, making it hard to debug issues with reading the event file.
This refactoring addresses both points by modifying the cache check to be time-based, ensuring None results are cached, adding logging for exceptions, and safely handling potentially partial JSON lines.
if (
_last_event_ts_cache["last_polled"] > 0
and (now - _last_event_ts_cache["last_polled"]) < LAST_EVENT_TS_TTL
):
return _last_event_ts_cache["data"]
ts = None
try:
events_file = get_events_path()
if events_file.exists():
file_size = events_file.stat().st_size
if file_size > 0:
with open(events_file, "rb") as f:
# BOLT OPTIMIZATION: Safely seek even for small files
f.seek(-min(500, file_size), 2)
lines = f.read().decode(errors="ignore").strip().split("\n")
if lines and lines[-1]:
try:
event = json.loads(lines[-1])
ts = event.get("ts")
except json.JSONDecodeError:
# Last line might be partial, ignore.
pass
except Exception as e:
print(f"[WARN] Failed to get last event timestamp: {e}", file=sys.stderr)
_last_event_ts_cache = {"data": ts, "last_polled": now}
return ts1 participant
💡 What: Implemented three levels of caching in
heidi_engine/telemetry.py:StateCachesingleton to cachestate.jsoncontent using file metadata validation.get_gpu_summary(5s) to avoid expensivenvidia-smisubprocess calls.get_last_event_ts(1s) to reduce redundant file reads.🎯 Why: The
/statusendpoint and TUI dashboard poll these functions frequently (2Hz+), leading to significant redundant IO and CPU overhead from JSON parsing and subprocess execution.📊 Impact:
get_state: 0.09ms -> 0.04ms (~56% faster).nvidia-smi: Significant reduction in subprocess overhead (cached for 5s).🔬 Measurement: Verified with benchmark scripts (
bench_telemetry.py,bench_gpu.py,bench_events.py) and existing tests.Other: Handled edge cases like small log files in
get_last_event_tsand useddeepcopyinStateCachefor safety.PR created automatically by Jules for task 6381428177306477881 started by @heidi-dang