cable network

Grafana metric spikes causing LLM context window overflow

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Problem Description

An AI‑driven observability agent receives raw Prometheus query results and Loki log streams from Grafana during a blue‑green traffic shift. The ingestion pipeline builds a single prompt for an OpenAI LLM. When metric spikes and duplicate log streams are unbounded, the prompt exceeds the model’s token limit, producing errors such as:

{
  "error": {
    "message": "This model's maximum context length is 8192 tokens",
    "type": "invalid_request_error"
  }
}

LangChain raises PromptTooLongError – "Prompt length (12345) exceeds the allowed maximum of 8192 tokens". Grafana webhooks return HTTP 413 “Payload Too Large”. The downstream LLM fails to generate a root‑cause analysis, causing alert routing failures and missed SRE triage.

Root Cause Analysis

Technical Background

  • Prometheus query limits: Grafana’s Prometheus datasource defaults to maxDataPoints = 10 000 and allows unlimited time ranges unless explicitly set (Grafana Docs – Prometheus Data Source).
  • Alert webhook payloads: Grafana enforces a maximum JSON payload size (default ≈ 5 MB) for alert notifications (Grafana Docs – Alerting & Notification Policies).
  • LLM token limits: OpenAI models such as gpt-3.5-turbo accept up to 8 192 tokens per request (OpenAI Token Limits).
  • LangChain prompt management: The library expects callers to keep prompts within the model’s token budget, offering chunking and summarization utilities (LangChain Prompt Management).

Why the overflow occurs

During a blue‑green deployment, traffic is split between the old (blue) and new (green) environments. Both environments continue to scrape the same Prometheus targets, producing duplicate series. Simultaneously, Loki streams logs from both clusters. The observability agent performs the following steps:

  1. Export the full dashboard JSON (.json) via Grafana’s /api/dashboards/uid/:uid endpoint.
  2. Execute a Prometheus range query for the past 5 minutes without a maxDataPoints cap.
  3. Append raw log lines from Loki’s /loki/api/v1/query_range response.
  4. Concatenate all raw data into a single string and pass it to LangChain’s LLMChain.

The combination of:

  • Unbounded maxDataPoints (often > 10 k points per series during spikes).
  • Duplicate series from blue and green clusters.
  • Large log payloads (tens of megabytes) as observed in "Duplicate log stream detected during blue‑green rollout – combined size 45 MB" (Grafana Loki issue #6321).

creates a prompt that can exceed 15 k tokens, far beyond the 8 192‑token ceiling, triggering the errors listed above.

Investigation and Debugging

Step‑by‑step diagnostics

# 1. Capture the raw webhook payload size
curl -s -X POST https://grafana.example.com/api/alertmanager/webhook \
  -H "Content-Type: application/json" \
  -d @payload.json \
  -w "\nSize: %{size_download} bytes\n"
# Expected: < 5 000 000 bytes (Grafana default limit)
# 2. Inspect Prometheus query response token count
PROM_QUERY='sum(rate(http_requests_total[5m])) by (instance)'
curl -s "http://prometheus.example.com/api/v1/query_range?query=${PROM_QUERY}&start=$(date -d '-5 min' +%s)&end=$(date +%s)&step=15"
| jq '.data.result[0].values | length'   # Number of datapoints
# 3. Estimate token count using tiktoken (Python)
import tiktoken
enc = tiktoken.encoding_for_model("gpt-3.5-turbo")
prompt = open("combined_prompt.txt").read()
print("Token count:", len(enc.encode(prompt)))

During the incident (2024‑03‑01), the len(enc.encode(prompt)) reported 15 800 tokens, matching the log entry:

LLM pipeline log entry: "Token budget exceeded – truncating metric series; original token count: 15800"

Log excerpts

Timestamp Component Message
2024‑03‑01T14:02:13Z LLM Pipeline Prompt length (12345) exceeds the allowed maximum of 8192 tokens
2024‑03‑01T14:02:14Z Grafana Webhook HTTP 413 Payload Too Large
2024‑03‑01T14:02:15Z Loki Plugin Duplicate log stream detected during blue‑green rollout – combined size 45 MB exceeds configured max_payload_size

Resolution

Key changes

  1. Enforce query limits in Grafana’s Prometheus datasource.
  2. Deduplicate series before building the prompt.
  3. Chunk and summarize metric and log data using LangChain utilities.
  4. Apply a hard token budget (e.g., 6 000 tokens) with graceful truncation.

Before – unbounded query configuration

{
  "datasource": {
    "type": "prometheus",
    "url": "http://prometheus.example.com",
    "jsonData": {
      "maxDataPoints": null,   // unlimited
      "timeInterval": null
    }
  }
}

After – bounded query with down‑sampling

{
  "datasource": {
    "type": "prometheus",
    "url": "http://prometheus.example.com",
    "jsonData": {
      "maxDataPoints": 5000,          // cap to 5 k points per series
      "timeInterval": "30s",          // down‑sample to 30‑second steps
      "queryTimeout": "30s"
    }
  }
}

Prompt construction with LangChain

from langchain.prompts import PromptTemplate
from langchain.text_splitter import TokenTextSplitter
from tiktoken import encoding_for_model

MAX_TOKENS = 6000
ENC = encoding_for_model("gpt-3.5-turbo")

def build_prompt(metrics_json: str, logs_json: str) -> str:
    # 1. Deduplicate metric series
    unique_series = {m["metric"]: m for m in json.loads(metrics_json)["data"]["result"]}.values()
    compact_metrics = json.dumps({"result": list(unique_series)})

    # 2. Summarize logs (first 200 lines)
    log_lines = logs_json.splitlines()
    summary = "\n".join(log_lines[:200])

    raw_prompt = f"""Context:
Metrics:
{compact_metrics}

Logs:
{summary}
"""
    # 3. Enforce token budget
    token_count = len(ENC.encode(raw_prompt))
    if token_count > MAX_TOKENS:
        splitter = TokenTextSplitter(chunk_size=MAX_TOKENS, chunk_overlap=0)
        chunks = splitter.split_text(raw_prompt)
        # Keep only the first chunk (most recent data)
        raw_prompt = chunks[0]
    return raw_prompt

Why the fix works

  • Setting maxDataPoints limits the number of datapoints fetched, directly reducing token count.
  • Down‑sampling via timeInterval aggregates points, preserving trend information while shrinking payload size.
  • Deduplication removes identical series from blue and green environments, halving the metric contribution.
  • Chunking and summarizing logs ensures only the most relevant portion reaches the LLM, staying under the token budget.

Validation

  1. Re‑run the same blue‑green traffic shift and capture the prompt size:
python -c "import tiktoken, json; prompt=open('prompt.txt').read(); print(len(tiktoken.encoding_for_model('gpt-3.5-turbo').encode(prompt)))"
# Expected output: ≤ 6000
  1. Verify the OpenAI API response no longer contains invalid_request_error:
curl -X POST https://api.openai.com/v1/chat/completions \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-3.5-turbo","messages":[{"role":"user","content":'"$(cat prompt.txt)"'}]}'
# Should return a 200 with a valid choices array.
  1. Check Grafana alert webhook logs for absence of HTTP 413:
journalctl -u grafana-server -f | grep "Payload Too Large"
# No matches expected.

Prevention and Best Practices

  • Token budgeting: Always compute an upper bound on token usage before calling the LLM. Reserve ~20 % headroom for system messages.
  • Prometheus query hygiene: Set maxDataPoints and explicit step values for all range queries in production dashboards.
  • Deduplication middleware: Implement a small service that normalizes metric series identifiers across environments during blue‑green deployments.
  • Log stream size limits: Configure Loki’s max_payload_size (e.g., 10 MB) and enable drop_duplicate_entries when multiple clusters push the same logs.
  • Monitoring: Add alerts on:
    • Prometheus query response size > 5 k points.
    • LLM pipeline logs containing PromptTooLongError or token‑budget warnings.
    • Grafana webhook HTTP 413 responses.
  • Testing: Include integration tests that simulate traffic spikes and verify that the prompt builder stays within token limits.

Related Topic Hub: Observability Troubleshooting Hub

FAQ

  1. Why does the overflow only happen during a blue‑green rollout?
    Both the old and new environments emit identical metric series and log streams. Without deduplication the payload roughly doubles, pushing token counts over the limit.
  2. Can I increase the OpenAI model’s context window?
    Some models (e.g., gpt-4-32k) offer larger windows, but they increase cost and still require careful budgeting. It’s better to reduce payload size at the source.
  3. How do I safely down‑sample a Prometheus range query?
    Use the step parameter (e.g., step=30s) and set maxDataPoints in Grafana’s datasource JSON. This aggregates data points while preserving trends.
  4. What is the recommended way to summarize large log streams for LLMs?
    Extract the most recent N lines (e.g., 200) or use a log summarizer (e.g., logreduce) before embedding them. LangChain’s TokenTextSplitter can enforce token limits automatically.
  5. Is there a Grafana setting to limit the size of exported dashboard JSON?
    Grafana recommends keeping dashboard JSON under 2 MB (Dashboard JSON Model). Export only the panels needed for the LLM and avoid embedding full raw series.