The Prometheus Grep That Wasn't: Debugging KV-Cache Metrics in a Production SGLang Deployment

Introduction

In the middle of an intense engineering campaign to optimize DeepSeek-V4-Flash on 8× RTX PRO 6000 Blackwell GPUs, a seemingly simple question from the user triggered a debugging chain that illuminates the hidden complexity of production monitoring. The user asked: "do we have any metrics / prometheus / grafana that we can use to see how full kv-cache is?" ([msg 12724]). What followed was a multi-message investigation spanning metric discovery, server reconfiguration, service restarts, and—in the message that is the subject of this article—a subtle debugging insight about the gap between how humans think Prometheus metrics look and how they actually appear in the wire format.

This article examines message 12731 in the conversation, where the assistant diagnoses why its KV-cache metrics query returned empty, corrects a grep pattern that was too strict, sends a warmup request to populate the gauges, and successfully retrieves the sglang:max_total_num_tokens metric from the decode server. The message is a compact case study in diagnostic reasoning, format assumptions, and the kind of "small" debugging insight that often separates a working system from a mysteriously silent one.

The Context: A Production PD-Disaggregated Deployment

To understand message 12731, one must first understand the infrastructure it operates within. The assistant had deployed DeepSeek-V4-Flash using prefill-decode (PD) disaggregation across all 8 GPUs. The architecture split the workload: GPUs 0–3 on NUMA node 0 handled prefill (the compute-intensive processing of new prompts), while GPUs 4–7 on NUMA node 1 handled decode (the memory-bandwidth-bound generation of tokens). A router service on port 30001 proxied OpenAI-format requests to the appropriate backend. The decode server held the KV cache—the critical memory structure that stores key-value tensors from previous attention computations, enabling the model to maintain context across generated tokens. The KV cache's capacity was configured at 2.58 million tokens with a 512K context window, consuming approximately 83 GB of GPU memory on each of the four decode GPUs.

The user's question about KV cache fullness was entirely reasonable. In any production LLM serving system, monitoring KV cache utilization is essential: when the cache fills up, the system must either evict old tokens (degrading quality for long conversations) or reject new requests. Without visibility into this metric, operators are flying blind.

The Investigation Before Message 12731

The assistant's investigation began in earnest at [msg 12725], where it discovered that the router exposed Prometheus metrics on port 29001—but only router-level metrics like request counts and worker health, not KV cache utilization. The decode server's /metrics endpoint returned "Not Found" because metrics were disabled by default. Over the next several messages ([msg 12726] through [msg 12729]), the assistant located the relevant source files in the SGLang codebase, identified the specific gauge names (sglang:full_token_usage, sglang:kv_used_tokens, sglang:kv_available_tokens, sglang:max_total_num_tokens, sglang:cache_hit_rate), and added --enable-metrics to both the prefill and decode server launch scripts.

At [msg 12730], the assistant restarted both systemd services and waited for them to come back online. After 80 seconds, both services reported "fired up and ready." Then came the query:

curl -s --max-time 8 http://127.0.0.1:30002/metrics | grep -E "^sglang:(full_token_usage|kv_used_tokens|kv_available_tokens|max_total_num_tokens|cache_hit_rate|num_running_reqs) " | head

The result: nothing. Empty output. The KV-cache metrics section showed blank.

The Core Insight: Message 12731

Message 12731 is where the assistant stops, reflects, and corrects its approach. The reasoning section reveals a two-step diagnostic process:

"The KV metrics query came back empty, which could mean the gauges haven't been populated yet since they're only set during scheduling, or the metrics might be exposed under a different format or path."

This is a classic debugging bifurcation: the assistant considers two mutually exclusive hypotheses. Hypothesis A: the metrics exist but haven't been populated (a runtime issue—the scheduler only updates gauges when processing decode batches). Hypothesis B: the metrics exist but the query method is wrong (a format/pattern issue). Either could explain the empty result, and the assistant has no immediate evidence to favor one over the other.

Then comes the crucial realization:

"Actually, I realize the issue — Prometheus format includes labels in curly braces after the metric name, so it's sglang:full_token_usage{model=..., tp_rank=...} 0.0, not just sglang:full_token_usage with a space. My grep pattern won't match because it's expecting a space right after the metric name."

This is the key insight. The assistant's grep pattern was:

^sglang:(full_token_usage|kv_used_tokens|...) 

Note the trailing space before the closing parenthesis. In the grep regex, this space anchors the match to lines where the metric name is immediately followed by a space. But Prometheus exposition format doesn't put a space after the metric name—it puts an opening curly brace { followed by label key-value pairs. The actual wire format looks like:

sglang:max_total_num_tokens{engine_type="decode",model_name="/root/models/DeepSeek-V4-Flash-NVFP4",moe_ep_rank="0",pp_rank="0",tp_rank="3"} 2.581504e+06

The metric name sglang:max_total_num_tokens is followed by {, not a space. The assistant's grep pattern was looking for sglang:max_total_num_tokens (with trailing space), which would never match the actual line sglang:max_total_num_tokens{...}. The pattern was correct in spirit but wrong in the specific character it expected after the metric name.

This is a subtle but instructive mistake. The assistant knew the Prometheus metric names—it had discovered them from source code. It knew the endpoint was live (the server was running and responding). But it made an implicit assumption about the wire format: that the metric name would be followed by a space and then the value, as in a simple key-value format. In reality, Prometheus uses a richer format with optional labels in curly braces before the value. The space does appear, but only after the closing brace of the label set, not immediately after the metric name.

The Two-Part Fix

The assistant's response combines two corrective actions. First, it sends a warmup request to the router:

curl -s --max-time 15 http://127.0.0.1:30001/v1/chat/completions -H "Content-Type: application/json" -d '{"model":"x","messages":[{"role":"user","content":"hi"}],"max_tokens":8}' -o /dev/null

This addresses Hypothesis A: if the gauges are only populated during scheduling, sending a request forces a scheduling cycle, which should update the metrics. The assistant correctly identifies that "the scheduler reports them on decode batches," so a live request is needed to populate the gauges.

Second, it fixes the grep pattern:

curl -s --max-time 8 http://127.0.0.1:30002/metrics | grep -vE "^#" | grep -iE "full_token_usage|kv_used_tokens|kv_available_tokens|max_total_num_tokens|cache_hit_rate|num_running_reqs|num_queue_reqs|token_usage" | head -20

The corrected pattern removes the ^sglang:... anchor and the trailing space. Instead, it uses -iE (case-insensitive extended regex) to match the metric name substring anywhere on the line, and prepends grep -vE "^#" to strip Prometheus comment lines (which start with #). This is a more robust approach: instead of assuming the exact format of the line, it searches for the metric name as a substring.

The Result: Metrics Confirmed Live

The corrected query succeeds immediately. The output shows:

sglang:max_total_num_tokens{engine_type="decode",model_name="/root/models/DeepSeek-V4-Flash-NVFP4",moe_ep_rank="0",pp_rank="0",tp_rank="3"} 2.581504e+06
sglang:max_total_num_tokens{engine_type="decode",model_name="/root/models/DeepSeek-V4-Flash-NVFP4",moe_ep_rank="0",pp_rank="0",tp_rank="2"} 2.581504e+06
sglang:max_total_num_tokens{engine_type="decode",model_name="/root/models/DeepSeek-V4-Flash-NVFP4",moe_ep_rank="0",pp_rank="0",tp_rank="1"} 2.581504e+06

The metric is exposed per TP (tensor parallelism) rank, with each of the four decode GPUs reporting its own max_total_num_tokens value of 2,581,504. The labels reveal important architectural details: engine_type="decode" confirms this is the decode server, model_name points to the NVFP4-quantized model path, moe_ep_rank="0" indicates single expert-parallel rank, pp_rank="0" indicates single pipeline-parallel rank, and tp_rank iterates from 0 to 3 for the four tensor-parallel GPUs.

Notably, only max_total_num_tokens appears in the output. The other metrics (full_token_usage, kv_used_tokens, kv_available_tokens, cache_hit_rate) are absent. This is consistent with the assistant's Hypothesis A: these gauges are only populated during active scheduling. Since the warmup request completed quickly and the system returned to idle, the "used" and "available" counters may have been reset or only updated during batch processing. The capacity metric (max_total_num_tokens) is a static configuration value that is always available.

What This Message Reveals About the System

Beyond the immediate debugging narrative, message 12731 illuminates several deeper aspects of the deployment.

First, the PD-disaggregation architecture is confirmed operational at the metrics level. The decode server exposes engine-type labels, demonstrating that the disaggregation layer correctly separates prefill and decode roles. The TP-rank labels confirm that tensor parallelism is active across four GPUs on the decode side.

Second, the metric infrastructure is minimal but functional. SGLang's --enable-metrics flag exposes a standard Prometheus /metrics endpoint with labeled gauges, but the system does not include a built-in Prometheus server or Grafana dashboard. The metrics are available for scraping but require external infrastructure to collect, store, and visualize.

Third, the KV cache capacity of 2.58M tokens is confirmed. This is a substantial cache—enough to serve dozens of concurrent long-context conversations. Combined with the 512K context window, it represents a deliberate tradeoff: maximum context length at the cost of reduced concurrent capacity (256 max running requests).

Assumptions, Mistakes, and Learning

The assistant made two implicit assumptions that proved incorrect. The first was about Prometheus wire format: it assumed a simple metric_name value format without labels. This is a reasonable assumption for someone familiar with basic metrics protocols but not the full Prometheus exposition format. The mistake was not in the concept but in the grep pattern's specificity—anchoring to a trailing space made the pattern too brittle.

The second assumption was that metrics would be populated at server startup. In reality, many SGLang metrics are "lazy"—they only appear after the first scheduling cycle. This is a common pattern in monitoring systems: static configuration metrics are always available, while dynamic runtime metrics require at least one event to initialize their values.

The assistant's learning process is visible in real time. It starts with two hypotheses, rejects the format hypothesis initially (or at least tests the population hypothesis first by sending a warmup), then realizes the format issue is the primary culprit. The corrected grep pattern is more robust and would handle both labeled and unlabeled metrics.

Knowledge Flow

Input knowledge required to understand this message includes: familiarity with Prometheus metrics exposition format (especially the name{labels} value syntax), understanding of PD-disaggregated LLM serving architecture, knowledge of SGLang's metrics configuration (--enable-metrics), and awareness that KV cache metrics are per-server gauges that require active scheduling to populate.

Output knowledge created by this message includes: confirmation that the decode server's /metrics endpoint is live and exposing sglang:max_total_num_tokens with TP-rank labels, verification that the KV cache capacity is 2,581,504 tokens, evidence that other KV-usage metrics require active requests to appear, and a working query pattern for extracting metrics from the Prometheus endpoint.

Conclusion

Message 12731 is a compact demonstration of diagnostic debugging in a complex distributed system. The assistant encounters an empty query result, generates competing hypotheses, identifies a subtle format mismatch between its grep pattern and the actual Prometheus wire format, corrects both the query and the runtime state (by sending a warmup request), and successfully retrieves the target metrics. The entire process—from empty result to confirmed data—unfolds in a single message, but the reasoning path reveals the kind of systematic thinking that characterizes effective debugging.

The lesson is universal: when a query returns nothing, the problem is often not that the data is absent, but that the question was asked in a language the system doesn't understand. In this case, the assistant was speaking "simple key-value" while the server was speaking "Prometheus with labels." Learning to listen in the right format turned silence into signal.