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244 lines
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13 KiB
Markdown
244 lines
No EOL
13 KiB
Markdown
### Context
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**Issue:** Prometheus configured as a callback causes orders-of-magnitude worse performance (reported by Zurich).
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**Reproduction:** Intermittent—requires many runs and configuration changes. Hypothesis: provider response speed may influence reproducibility.
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### Next Steps
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[x] {1} **Latency comparison (2 runs):** callbacks on vs off
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**Run 1**
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- [x] Callbacks on → `callbacks_on_3.txt`
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- [x] Callbacks off → `callbacks_off_3.txt`
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**Run 2**
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- [x] Callbacks on → `callbacks_on_4.txt`
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- [x] Callbacks off → `callbacks_off_4.txt`
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#### Conclusion
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**Data summary**
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| Batch | Callbacks | avg | p95 | Above 1s |
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|----------------|-----------|--------|--------|----------|
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| 1 (yesterday) | on | 0.539s | 1.257s | 8.1% |
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| 1 | off | 0.382s | 0.429s | 1.6% |
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| 2 (yesterday) | on | 0.453s | 0.472s | 3.4% |
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| 2 | off | 0.383s | 0.450s | 1.0% |
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| 3 (today) | on | 0.410s | 0.499s | 0.8% |
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| 3 | off | 0.338s | 0.425s | 0.8% |
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| 4 (today) | on | 0.374s | 0.438s | 0.8% |
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| 4 | off | 0.316s | 0.391s | 0.8% |
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**Findings:** Yesterday (batches 1–2) showed a large impact: callbacks on had ~3× higher p95 and 5–8× more requests above 1s. Today (batches 3–4) shows only a modest overhead (~15–20% on avg/p95) with similar % above 1s. This supports the hypothesis that provider response speed influences reproducibility—faster responses today may have masked the issue.
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[x] {2} Use local provider + artificial delays to test whether slower responses make the Prometheus callback issue more reproducible
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**Run 1**
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- [x] Callbacks on → `callbacks_on_5.txt`
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- [x] Callbacks off → `callbacks_off_5.txt`
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#### Conclusion
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Adding artificial delays did not make the Prometheus callback impact more reproducible. The difference between callbacks on and off remained intermittent.
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[x] {3} Let's increase the concurrency of the test and the total number of requests **(200 concurrent users / 15000)**
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**Run 1**
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- [x] Callbacks on → `callbacks_on_6.txt`
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- [x] Callbacks off → `callbacks_off_6.txt`
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#### Conclusion
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Higher concurrency and request number made performance worse for both cases.
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[x] {4} Random ID generation for the user being passed onto the payload instead of sequential 1/2/3/4 ids.
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**Run 1** - Introduce the random ID generation
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- [x] Callbacks on → `callbacks_on_7.txt`
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- [x] Callbacks off → `callbacks_off_7.txt`
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**Run 2** - Remove the random ID generation
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- [x] Callbacks on → `callbacks_on_8.txt`
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- [x] Callbacks off → `callbacks_off_8.txt`
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**Run 3** - Bring back the random ID generation
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- [x] Callbacks on → `callbacks_on_9.txt`
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- [x] Callbacks off → `callbacks_off_9.txt`
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#### Conclusion
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**Data summary**
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| Run | User IDs | Callbacks | avg | p95 | Above 1s |
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|-------|------------|-----------|--------|--------|----------|
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| 1 | random | on | 0.415s | 1.014s | 5.1% |
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| 1 | random | off | 0.303s | 0.574s | 1.4% |
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| 2 | sequential | on | 0.343s | 0.389s | 0.8% |
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| 2 | sequential | off | 0.217s | 0.285s | 0.8% |
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| 3 | random | on | 0.484s | 1.052s | 5.4% |
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| 3 | random | off | 0.286s | 0.504s | 1.6% |
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**Findings:** With random user IDs, Prometheus callbacks clearly increase latency: ~5% of requests exceed 1s vs ~1.5% when callbacks are off. With sequential IDs, the difference disappears—both configs show ~0.8% above 1s. Random IDs make the Prometheus callback impact reproducible.
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### Breakpoint
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We now have a concrete reproduction. To maximize the chance of fixing Zurich's perf issues, we should address both:
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1. **Prometheus overhead** — Extra latency when Prometheus callbacks are enabled.
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2. **Baseline latency spikes** — Requests exceeding 1s even with callbacks off (~1.5% in our runs).
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### Prometheus overhead
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**Places to investigate (in order of suspicion):**
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1. **`litellm/integrations/prometheus.py`**
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- `async_log_success_event` (line ~877) — main entry; runs on every completion
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- `_increment_remaining_budget_metrics` (line ~1171) — async; calls `_assemble_key_object`, `_assemble_team_object`, `_assemble_user_object`, which may hit DB/cache
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- `_assemble_user_object` (line ~2877) — fetches from DB when metadata is incomplete
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- `_assemble_team_object` (line ~2662) — calls `get_team_object` (DB/cache)
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- `_assemble_key_object` (line ~2807) — fetches key from cache/DB
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2. **`prometheus_client` usage**
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- `.labels(**kwargs).inc()` / `.observe()` / `.set()` — sync, may contend on the registry lock when many label combinations are used
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- High cardinality from `end_user`, `user_api_key`, `model_id`, etc. can create many series
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3. **Callback invocation**
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- `litellm/litellm_core_utils/litellm_logging.py` (line ~2569) — callbacks run sequentially; Prometheus blocks the response until it finishes
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4. **`prometheus_system` callback** (if relevant)
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- `litellm/integrations/prometheus_services.py` — service-level metrics (Redis, Postgres); separate from completion flow
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**Action Plan**
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1. Early return from `async_log_success_event` to confirm Prometheus is the cause.
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| Config | File | avg | p95 | Above 1s |
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|---------------------|-------------------------|--------|--------|----------|
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| Callbacks on + early return | `callbacks_on_10a.txt` | 0.361s | 0.631s | 2.1% |
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| Callbacks on + full Prometheus | `callbacks_on_10b.txt` | 0.463s | 1.059s | 5.6% |
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**Conclusion:** Early return reduces requests above 1s from 5.6% to 2.1%. Prometheus callback is confirmed as the source of the overhead.
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2. **Bisect: async vs sync work** — Narrow down whether the overhead comes from async DB/cache lookups or sync metric updates.
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**Test A:** Early return *after* `_increment_remaining_budget_metrics` (skip sync metric updates). If latency improves → sync Prometheus updates are the culprit.
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**Run 1** - Introduce the random ID generation
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- [x] Callbacks on + early return → `callbacks_on_11a.txt`
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- [x] Callbacks on + no early return → `callbacks_on_11b.txt`
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**Conclusion:** No effect. Skipping sync metric updates did not improve latency—sync Prometheus updates are not the culprit.
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**Test B:** Early return *before* `_increment_remaining_budget_metrics` (skip async budget metrics only). If latency improves → DB/cache lookups in budget metrics are the culprit.
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**Run 1** - Introduce the random ID generation
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- [x] Callbacks on + early return → `callbacks_on_12a.txt`
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- [x] Callbacks on + no early return → `callbacks_on_12b.txt`
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**Conclusion:** Early return reduced requests above 1s from 4.8% to 1.9%. Root cause: `_increment_remaining_budget_metrics` (DB/cache lookups for key, team, and user budget).
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3. **Isolate: comment out `_increment_remaining_budget_metrics`** — Run everything else (including the sync metric updates below). Confirms whether the function is the main cause or if the code below contributes.
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- [x] Comment out the function call only, run everything below → `callbacks_on_13a.txt`
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- [x] Bring the function back on => `callbacks_on_13b.txt`
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**Conclusion:** Commenting out that function stoped the latency issue caused by prometheus.
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4. **Patch: run `_increment_remaining_budget_metrics` off critical path** — Use `asyncio.create_task()` instead of `await` so budget metrics run in background and no longer block request latency.
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**Run 1**
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- [x] Callbacks on + patch (create_task) → `callbacks_on_14a.txt`
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- [x] Callbacks on + no patch (baseline) → `callbacks_on_14b.txt`
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| Config | File | avg | p95 | Above 1s |
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|----------|-------------------------|--------|--------|----------|
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| Patch | `callbacks_on_14a.txt` | 0.468s | 0.911s | 4.4% |
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| Baseline | `callbacks_on_14b.txt` | 0.362s | 0.784s | 3.4% |
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**Conclusion:** In this run, the patch did not improve latency—baseline (3.4% above 1s) outperformed the patch (4.4%). Run-to-run variance may be a factor; additional runs recommended to confirm.
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5. **Baseline: 10 runs with Prometheus on, 10 with Prometheus off** — Measure latency to establish a strong baseline before testing the parallelization patch.
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**Prometheus on**
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- [x] Run 1 → `callbacks_on_15_1.txt`
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- [x] Run 2 → `callbacks_on_15_2.txt`
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- [x] Run 3 → `callbacks_on_15_3.txt`
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- [x] Run 4 → `callbacks_on_15_4.txt`
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- [x] Run 5 → `callbacks_on_15_5.txt`
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- [x] Run 6 → `callbacks_on_15_6.txt`
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| Run | File | avg | p95 | A1s | A2s | A3s | A4s | A5s | A6s | A7s | A8s |
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|-----|-------------------------|--------|--------|-------|-------|-------|-------|-------|-------|-------|-------|
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| 1 | `callbacks_on_15_1.txt` | 0.405s | 0.822s | 3.1% | 1.1% | 0.9% | 0.7% | 0.5% | 0.3% | 0.2% | 0.0% |
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| 2 | `callbacks_on_15_2.txt` | 0.442s | 0.887s | 4.2% | 1.7% | 1.0% | 0.8% | 0.6% | 0.4% | 0.3% | 0.1% |
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| 3 | `callbacks_on_15_3.txt` | 0.511s | 1.131s | 6.3% | 1.9% | 1.2% | 0.9% | 0.6% | 0.4% | 0.3% | 0.1% |
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| 4 | `callbacks_on_15_4.txt` | 0.381s | 0.952s | 4.6% | 1.5% | 0.9% | 0.7% | 0.5% | 0.3% | 0.2% | 0.0% |
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| 5 | `callbacks_on_15_5.txt` | 0.318s | 0.565s | 2.2% | 1.1% | 0.8% | 0.6% | 0.4% | 0.3% | 0.2% | 0.0% |
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| 6 | `callbacks_on_15_6.txt` | 0.393s | 1.106s | 5.7% | 1.9% | 1.0% | 0.7% | 0.4% | 0.3% | 0.2% | 0.0% |
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**Merged (6 runs, 30k requests):** mean avg 0.408s, mean p95 0.911s.
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Above 1s: 1307 (4.4%), 2s: 455 (1.5%), 3s: 292 (1.0%), 4s: 213 (0.7%), 5s: 152 (0.5%), 6s: 101 (0.3%), 7s: 62 (0.2%), 8s: 18 (0.1%), 9s: 1 (0.0%), 10s: 0 (0.0%).
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**Prometheus off**
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- [x] Run 1 → `callbacks_off_15_1.txt`
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- [x] Run 2 → `callbacks_off_15_2.txt`
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- [x] Run 3 → `callbacks_off_15_3.txt`
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- [x] Run 4 → `callbacks_off_15_4.txt`
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- [x] Run 5 → `callbacks_off_15_5.txt`
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- [x] Run 6 → `callbacks_off_15_6.txt`
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| Run | File | avg | p95 | A1s | A2s | A3s | A4s | A5s | A6s | A7s | A8s |
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|-----|--------------------------|--------|--------|-------|-------|-------|-------|-------|-------|-------|-------|
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| 1 | `callbacks_off_15_1.txt` | 0.319s | 0.693s | 2.0% | 0.9% | 0.8% | 0.6% | 0.5% | 0.4% | 0.3% | 0.1% |
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| 2 | `callbacks_off_15_2.txt` | 0.328s | 0.696s | 1.8% | 0.9% | 0.8% | 0.6% | 0.4% | 0.3% | 0.2% | 0.0% |
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| 3 | `callbacks_off_15_3.txt` | 0.276s | 0.540s | 1.2% | 0.9% | 0.7% | 0.6% | 0.4% | 0.3% | 0.2% | 0.0% |
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| 4 | `callbacks_off_15_4.txt` | 0.376s | 0.741s | 2.1% | 0.9% | 0.8% | 0.6% | 0.5% | 0.4% | 0.2% | 0.1% |
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| 5 | `callbacks_off_15_5.txt` | 0.347s | 0.580s | 1.7% | 0.9% | 0.8% | 0.6% | 0.5% | 0.3% | 0.2% | 0.0% |
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| 6 | `callbacks_off_15_6.txt` | 0.288s | 0.598s | 1.5% | 0.9% | 0.7% | 0.6% | 0.4% | 0.3% | 0.1% | 0.0% |
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**Merged (6 runs, 30k requests):** mean avg 0.322s, mean p95 0.641s.
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Above 1s: 521 (1.7%), 2s: 271 (0.9%), 3s: 225 (0.8%), 4s: 180 (0.6%), 5s: 139 (0.5%), 6s: 98 (0.3%), 7s: 59 (0.2%), 8s: 15 (0.1%), 9s: 0 (0.0%), 10s: 0 (0.0%).
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**Comparison (Prometheus on vs off)**
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| Metric | Prometheus on | Prometheus off | Δ |
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|--------------|---------------|----------------|----------|
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| Mean avg | 0.408s | 0.322s | +27% |
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| Mean p95 | 0.911s | 0.641s | +42% |
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| Above 1s | 1307 (4.4%) | 521 (1.7%) | +2.6× |
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| Above 2s | 455 (1.5%) | 271 (0.9%) | +1.7× |
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| Above 3s | 292 (1.0%) | 225 (0.8%) | +1.3× |
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Requests above threshold (bar length ∝ %):
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```
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Above 1s on ██████████████████████ 4.4% off ████████ 1.7%
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Above 2s on ███████ 1.5% off ████ 0.9%
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Above 3s on █████ 1.0% off ████ 0.8%
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```
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**Expectation (targets when root cause is fixed)**
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After addressing `_increment_remaining_budget_metrics` (e.g. parallelization, caching, or moving off critical path), Prometheus-on should approach Prometheus-off performance:
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| Metric | Current (on) | Target (on) | Baseline (off) |
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|------------|--------------|--------------|----------------|
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| Mean avg | 0.408s | ≤ 0.35s | 0.322s |
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| Mean p95 | 0.911s | ≤ 0.70s | 0.641s |
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| Above 1s | 4.4% | ≤ 2.5% | 1.7% |
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| Above 2s | 1.5% | ≤ 1.0% | 0.9% |
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| Above 3s | 1.0% | ≤ 0.9% | 0.8% |
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Success = Prometheus-on metrics within ~10–15% of Prometheus-off, so enabling Prometheus has minimal impact on user-facing latency.
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**Conclusion:**
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6. [x] **Patch: parallelize the three budget metric lookups** — Use `asyncio.gather()` instead of sequential `await` so key, team, and user lookups run in parallel.
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### Final Conclusion
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The issue was caused by hitting the database on every request when Prometheus was used as a callback. Commit `30534d7e82` fixes this by setting `check_db_only` to `False`.
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> It was hard to diagnose because Prometheus was catching database errors and only logging them at debug level. Fixed by commit `d37796662` (adds `_log_budget_lookup_failure` to surface errors). This error is very important—it stops the cache from working properly. |