node

A troubleshooting guide for StarRocks database servers. It covers backend (BE) crashes and out-of-memory errors, as well as frontend (FE) deadlocks, long garbage-collection pauses, and memory leaks.

In plain words
What is it for?
Use it to inspect memory breakdowns, connect large allocations to query IDs, trace deadlocks, analyze garbage-collection logs, and investigate crashes.
Why use it?
It gives a structured way to identify whether a query, lock, insufficient heap, or leaking module is causing failures or hanging queries.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/starrocks/starrocks-debug-skills/node
Any agent
npx skills add StarRocks/starrocks-debug-skills --skill node
Clone the repo
git clone --depth 1 https://github.com/StarRocks/starrocks-debug-skills

Made for: Claude Code, Codex.

Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,151 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00080 $0.08151
Opus 5 $0.00040 $0.04075
Sonnet 5 $0.00016 $0.01630
Haiku 4.5 $0.00008 $0.00815

Measured 2d ago against content hash 2435083e37f1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

node scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/analyze_logs.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

description: "Use when a BE or FE node is abnormal: BE out-of-memory or crash, FE deadlock causing all queries to hang, FE Full GC pauses, or FE heap memory growth and leak. Covers mem_tracker curl analysis, large-memory
node/SKILL.md · 804 lines

How it starts

The opening of the file, as written. The whole thing — 804 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Node Troubleshooting (BE Crash / OOM, FE Deadlock / GC)

Investigation guide for BE-level crashes and out-of-memory events, FE deadlocks, Full GC pauses, and FE heap memory analysis.

Five root causes account for the vast majority of cases:

  • Cause A — Query-caused BE OOM (memory limit exceeded by large query)
  • Cause B — BE crash (segfault / internal fatal error)
  • Cause C — FE deadlock caused by inconsistent lock ordering
  • Cause D — FE Full GC caused by heap too small or memory leak
  • Cause E — FE OOM from specific module memory leak

Metric Taxonomy — Read This First

Before using any metrics, understand the two-layer structure:

BE-Side Metrics

Metric / Source Meaning
curl http://<BE>:<port>/mem_tracker Per-module memory breakdown (current allocations by subsystem)
curl http://<BE>:<port>/memz tcmalloc allocator internal status — heap vs OS footprint
starrocks_be_.*_mem_bytes Per-module memory gauge in Prometheus/Grafana (e.g., starrocks_be_query_mem_bytes)
be.WARNINGlarge memory alloc Single large allocation events, includes query_id and stack trace
dmesg — OOM Killer Linux kernel OOM Killer records listing killed process and memory pressure

FE-Side Metrics

Metric / Source Meaning
fe.gc.log — Full GC entries JVM Full GC frequency, stop-the-world pause duration, Old Gen occupancy
fe.WARNINGFE ShutDown! Running Query Queries running at moment of FE OOM shutdown, each with QueryFEAllocatedMemory
fe.logLockManager / DeadlockChecker Structured JSON deadlock report: lock chain, waiter threads, hold durations
Memory Allocated Profile (flame graph) HTML flame graph in fe/log/proc_profile/ — per-callsite allocation breakdown
SHOW PROC '/current_queries' Live view of running queries with memUsageBytes (FE) and memUsageBytes (BE)

How to Retrieve Metrics

BE memory breakdown

# Per-module memory (most useful for OOM investigation)
curl -s http://<BE_IP>:<BE_HTTP_PORT>/mem_tracker

# All BE memory gauge metrics
curl -s http://<BE_IP>:<BE_HTTP_PORT>/metrics | grep "^starrocks_be_.*_mem_bytes"

# tcmalloc allocator status
curl -s http://<BE_IP>:<BE_HTTP_PORT>/memz

# Memory-heavy queries from mem_tracker
curl -s http://<BE_IP>:<BE_HTTP_PORT>/mem_tracker | grep "query"

Read the full file on GitHub · 804 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 804 lines · 80 tokens per session scan A 2435083e37f1

Subscribe to this mod's changes

node is a skill published in the GitHub repository StarRocks/starrocks-debug-skills (75 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 80 tokens to every session and 8,151 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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