sentry-performance-tuning
Tunes Sentry performance settings to improve signal-to-noise ratio and reduce monitoring costs.
Install
mkdir -p .claude/skills/sentry-performance-tuning && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/8417" && unzip -o skill.zip -d .claude/skills/sentry-performance-tuning && rm skill.zipInstalls to .claude/skills/sentry-performance-tuning
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Optimize Sentry performance monitoring for lower overhead and higherKey capabilities
- →Replace static sampling with dynamic tracesSampler
- →Configure profiling sample rates
- →Parameterize transaction names to prevent cardinality explosion
- →Add custom span measurements
- →Set Web Vitals thresholds
How it works
It replaces static sampling rates with a dynamic sampler callback to prioritize critical transactions and uses custom span measurements for granular performance tracking.
Inputs & outputs
When to use sentry-performance-tuning
- →Implement dynamic trace sampling
- →Reduce Sentry SDK performance overhead
- →Configure custom span measurements
- →Set web vitals thresholds
About this skill
Sentry Performance Tuning
Overview
Optimize Sentry's performance monitoring pipeline to maximize signal quality while minimizing SDK overhead and event volume costs. Covers the v8 SDK API for @sentry/node, @sentry/browser, and sentry-sdk (Python), targeting sentry.io or self-hosted Sentry 24.1+.
Prerequisites
- Sentry SDK v8+ installed (
@sentry/node>= 8.0.0 orsentry-sdk>= 2.0.0) Sentry.init()called with a valid DSN before any application code runs- Performance monitoring enabled (
tracesSampleRate > 0or atracesSamplerfunction) - Access to the Sentry Performance dashboard to verify changes
Instructions
Step 1 — Replace Static tracesSampleRate with Dynamic tracesSampler
A flat tracesSampleRate: 0.1 samples all routes equally. The tracesSampler callback makes per-transaction decisions based on route, operation type, and upstream trace context.
import * as Sentry from '@sentry/node';
Sentry.init({
dsn: process.env.SENTRY_DSN,
// tracesSampler replaces tracesSampleRate — do not set both
tracesSampler: (samplingContext) => {
const { name, attributes, parentSampled } = samplingContext;
// Honor parent sampling for distributed trace consistency
if (parentSampled !== undefined) return parentSampled ? 1.0 : 0;
// Drop noise — health probes, static assets
if (name?.match(/\/(health|ready|alive|ping|metrics)$/)) return 0;
if (name?.match(/\.(js|css|png|jpg|svg|woff2?|ico)$/)) return 0;
// Always sample business-critical paths
if (name?.includes('/checkout') || name?.includes('/payment')) return 1.0;
// Higher sampling for write operations (mutations are riskier)
if (name?.startsWith('POST ') || name?.startsWith('PUT ')) return 0.25;
// Moderate sampling for read APIs
if (name?.startsWith('GET /api/')) return 0.1;
// Low sampling for background work
if (name?.startsWith('job:') || name?.startsWith('queue:')) return 0.05;
// User-tier sampling (via custom attributes from middleware)
if (attributes?.['user.plan'] === 'enterprise') return 0.5;
return 0.05; // Default: 5%
},
});
Step 2 — Configure Profiling with profilesSampleRate
The profilesSampleRate controls what fraction of traced transactions get profiled. Setting it to 1.0 with a 5% tracesSampler means 5% of traffic is profiled.
import { nodeProfilingIntegration } from '@sentry/profiling-node';
Sentry.init({
dsn: process.env.SENTRY_DSN,
integrations: [nodeProfilingIntegration()],
tracesSampler: (ctx) => { /* ... from Step 1 ... */ },
// Effective rate = tracesSampler rate * profilesSampleRate
profilesSampleRate: 1.0,
// Alternative: Continuous profiling (v8.7.0+) — profiles the entire process
// profileSessionSampleRate: 0.1, // 10% of server instances
});
Tuning: Start at profilesSampleRate: 0.1 in production. Profiling adds ~3-5% CPU overhead per profiled transaction. Continuous profiling (profileSessionSampleRate) has lower per-transaction cost but runs on sampled instances continuously.
Step 3 — Fix Transaction Naming (Prevent Cardinality Explosion)
Names with dynamic IDs (/api/users/12345) create thousands of unique entries, degrading dashboard performance and inflating quota. Route templates go in the name, dynamic values go in attributes.
// BAD — creates thousands of unique transaction entries
// GET /api/users/12345, GET /api/users/67890, ...
// GOOD — Sentry auto-parameterizes Express/Koa/Fastify routes
// GET /api/users/:userId
// For custom spans, always parameterize:
Sentry.startSpan(
{
name: 'order.process', // No dynamic IDs in name
op: 'task',
attributes: {
'order.id': orderId, // Filterable in Discover queries
'order.total_cents': totalCents,
'customer.tier': customerTier,
},
},
async (span) => {
const result = await processOrder(orderId);
span.setAttribute('order.status', result.status);
return result;
}
);
Detect cardinality issues with a Discover query:
SELECT count(), transaction FROM transactions GROUP BY transaction ORDER BY count() DESC
Step 4 — Add Custom Measurements
Custom measurements appear in the Performance dashboard and can be charted, alerted on, and queried in Discover. Unit types: 'millisecond', 'byte', 'none' (count), 'percent'.
await Sentry.startSpan(
{ name: 'search.execute', op: 'function' },
async (span) => {
const start = performance.now();
const results = await searchService.query(term);
Sentry.setMeasurement('search.latency', performance.now() - start, 'millisecond');
Sentry.setMeasurement('search.result_count', results.length, 'none');
Sentry.setMeasurement('search.memory_delta',
process.memoryUsage().heapUsed - memBefore, 'byte');
span.setAttribute('search.cache_hit', results.fromCache);
return results;
}
);
| Measurement | Unit | Use case |
|---|---|---|
cart.total_cents | none | Revenue correlation with latency |
query.rows_scanned | none | Database query efficiency |
cache.hit_rate | percent | Cache performance per route |
upload.file_size | byte | File upload impact on response time |
Step 5 — Reduce SDK Overhead
For high-throughput services (>1000 req/s), every integration and breadcrumb counts.
Sentry.init({
dsn: process.env.SENTRY_DSN,
maxBreadcrumbs: 20, // Default: 100. Each ~0.5-2KB.
maxValueLength: 500, // Truncate long string values
maxAttachmentSize: 5_242_880, // 5MB (default: 20MB)
// Remove noisy integrations
integrations: (defaults) => defaults.filter(
(i) => i.name !== 'Console'
),
// Trim oversized stack traces
beforeSend: (event) => {
if (event.exception?.values) {
for (const exc of event.exception.values) {
if (exc.stacktrace?.frames && exc.stacktrace.frames.length > 30) {
exc.stacktrace.frames = [
...exc.stacktrace.frames.slice(0, 10),
...exc.stacktrace.frames.slice(-20),
];
}
}
}
return event;
},
// Drop internal/noise spans
beforeSendSpan: (span) => {
if (span.description?.startsWith('internal.')) return null;
return span;
},
});
Browser SDK lazy loading (saves ~30KB gzipped from critical path):
async function initSentry() {
const Sentry = await import('@sentry/browser');
Sentry.init({
dsn: process.env.SENTRY_DSN,
integrations: [Sentry.browserTracingIntegration()],
tracesSampleRate: 0.1,
});
}
window.addEventListener('load', initSentry, { once: true });
Step 6 — Span Best Practices (Avoid Span Explosion)
Only wrap operations with measurable latency (>1ms). Never span synchronous lookups or individual loop iterations.
// BAD — sub-microsecond config read; span overhead exceeds operation cost
function getConfig(key: string) {
return Sentry.startSpan({ name: 'config.get', op: 'function' }, () => config[key]);
}
// BAD — N spans per request from loop iterations
for (const item of items) {
await Sentry.startSpan({ name: 'process.item', op: 'function' }, () => processItem(item));
}
// GOOD — span the batch, count in attributes
await Sentry.startSpan(
{ name: 'process.batch', op: 'function', attributes: { 'batch.size': items.length } },
async () => Promise.all(items.map(processItem))
);
// GOOD — span external I/O with real latency
async function fetchUserProfile(userId: string) {
return Sentry.startSpan(
{ name: 'user.fetch_profile', op: 'http.client', attributes: { 'user.id': userId } },
async () => fetch(`${USER_SERVICE_URL}/users/${userId}`).then(r => r.json())
);
}
Step 7 — Web Vitals Monitoring
The Browser SDK auto-captures Core Web Vitals. Filter span creation to avoid noise from third-party scripts.
Sentry.init({
dsn: process.env.SENTRY_DSN,
integrations: [
Sentry.browserTracingIntegration({
shouldCreateSpanForRequest: (url) =>
!url.includes('googleapis.com') && !url.includes('analytics'),
}),
],
tracesSampleRate: 0.1,
});
| Metric | Good | Poor | Measures |
|---|---|---|---|
| LCP | < 2.5s | > 4.0s | Visual load completion |
| INP | < 200ms | > 500ms | Input responsiveness (replaced FID) |
| CLS | < 0.1 | > 0.25 | Visual stability |
| TTFB | < 800ms | > 1800ms | Server response time |
Alert thresholds: LCP p75 > 2.5s (5 min), INP p75 > 200ms (5 min), CLS p75 > 0.1 (15 min).
Step 8 — Dashboard Queries for Performance Trends
-- Slowest transactions (p95)
SELECT transaction, p95(transaction.duration), count()
FROM transactions WHERE transaction.duration:>1000
ORDER BY p95(transaction.duration) DESC
-- Regression detection (20%+ slower vs last week)
SELECT transaction, p75(transaction.duration),
compare(p75(transaction.duration), -7d) as vs_last_week
FROM transactions GROUP BY transaction
HAVING compare(p75(transaction.duration), -7d) > 1.2
-- Span breakdown for a route
SELECT span.op, span.description, p75(span.duration), count()
FROM spans WHERE transaction:/api/checkout
ORDER BY p75(span.duration) DESC
Output
- Dynamic sampling active — health checks at 0%, payments at 100%, defaults at 5%
- Profiling enabled with
profilesSampleRateor continuousprofileSessionSampleRate - Transaction names parameterized — cardinality under 500 unique names
- Custom measurements tracking business KPIs alongside latency
- SDK overhead reduced — fewer breadcrumbs, filtered integrations, trimmed payloads
- Web Vitals monitored with alerts at Google's recommended thresholds
Verify at Sentry Stats (Settings > Stats) — volume should drop while data quality improves.
Error Handling
| Symptom | Root Cause | Fix |
|---|---|---|
| Performance tab emp |
Content truncated.
When not to use it
- →When using static tracesSampleRate and tracesSampler simultaneously
Prerequisites
Limitations
- →Profiling adds 3-5% CPU overhead per transaction
- →Continuous profiling runs on sampled instances
How it compares
Unlike static sampling, this approach uses a dynamic sampler to adjust data collection based on route, operation type, and trace context.
Compared to similar skills
sentry-performance-tuning side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| sentry-performance-tuning (this skill) | 0 | 25d | Review | Intermediate |
| analyzing-logs | 14 | 25d | Review | Beginner |
| obsidian-observability | 5 | 25d | Review | Intermediate |
| instruments-profiling | 3 | 2mo | No flags | Advanced |
Try saying
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