Posthog performance tuning
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'Optimize PostHog performance: local flag evaluation, client batching config,
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SKILL.md
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PostHog Performance Tuning
Overview
Optimize PostHog for production workloads. The biggest performance wins are: local feature flag evaluation (eliminates network calls), proper batching configuration, event sampling for high-volume apps, and efficient HogQL queries with date filters.
Prerequisites
posthog-nodeand/orposthog-jsinstalled- Personal API key (
phx_...) for local flag evaluation - Feature flags configured (if applicable)
Instructions
Step 1: Enable Local Feature Flag Evaluation
The single biggest performance improvement. Without local evaluation, every getFeatureFlag() call makes a network request (~50-200ms). With local evaluation, flag definitions are cached and evaluation is instant (~0.1ms).
import { PostHog } from 'posthog-node';
const posthog = new PostHog(process.env.NEXT_PUBLIC_POSTHOG_KEY!, {
host: 'https://us.i.posthog.com',
// This is the key: personal API key enables local flag evaluation
personalApiKey: process.env.POSTHOG_PERSONAL_API_KEY,
// Flag definitions are polled every 30 seconds by default
// Adjust if you need faster flag updates:
// featureFlagsPollingInterval: 10000, // 10 seconds
});
// With personalApiKey set, this evaluates locally (no network call)
const variant = await posthog.getFeatureFlag('pricing-experiment', 'user-123', {
personProperties: { plan: 'pro', country: 'US' },
});
// Get all flags at once (still local, still fast)
const allFlags = await posthog.getAllFlags('user-123', {
personProperties: { plan: 'pro' },
groupProperties: { company: { industry: 'SaaS' } },
});
Step 2: Optimize Client Batching
// Production: batch events for network efficiency
const posthog = new PostHog(process.env.NEXT_PUBLIC_POSTHOG_KEY!, {
host: 'https://us.i.posthog.com',
flushAt: 20, // Send batch when 20 events accumulated (default)
flushInterval: 10000, // Or flush every 10 seconds (default)
requestTimeout: 10000, // 10 second timeout per request
maxRetries: 3, // Retry failed sends
});
// Serverless: flush immediately (function may exit)
const serverless = new PostHog(process.env.NEXT_PUBLIC_POSTHOG_KEY!, {
host: 'https://us.i.posthog.com',
flushAt: 1, // Send every event immediately
flushInterval: 0, // Don't wait
});
// CRITICAL: Always shutdown before process exits
process.on('SIGTERM', async () => {
await posthog.shutdown();
process.exit(0);
});
Step 3: Event Sampling (Browser)
import posthog from 'posthog-js';
posthog.init(process.env.NEXT_PUBLIC_POSTHOG_KEY!, {
api_host: 'https://us.i.posthog.com',
before_send: (event) => {
// Always capture business-critical events
const alwaysCapture = ['purchase', 'signup', 'subscription_started', 'subscription_canceled'];
if (alwaysCapture.includes(event.event)) return event;
// Sample high-volume events
const sampleRates: Record<string, number> = {
'$pageview': 1.0, // Keep all pageviews
'$pageleave': 0.5, // Sample 50%
'$autocapture': 0.1, // Sample 10% of autocapture
'scroll_depth': 0.05, // Sample 5%
};
const rate = sampleRates[event.event] ?? 0.5;
if (Math.random() >= rate) return null; // Drop event
// Tag sampled events so you can adjust in analysis
event.properties = { ...event.properties, $sample_rate: rate };
return event;
},
});
Step 4: Efficient HogQL Queries
async function queryPostHog(hogql: string) {
const response = await fetch(
`https://app.posthog.com/api/projects/${process.env.POSTHOG_PROJECT_ID}/query/`,
{
method: 'POST',
headers: {
'Content-Type': 'application/json',
Authorization: `Bearer ${process.env.POSTHOG_PERSONAL_API_KEY}`,
},
body: JSON.stringify({
query: { kind: 'HogQLQuery', query: hogql },
}),
}
);
return response.json();
}
// FAST: Filtered by time, limited results
const fast = await queryPostHog(`
SELECT
properties.$current_url AS url,
count() AS views,
uniq(distinct_id) AS visitors
FROM events
WHERE event = '$pageview'
AND timestamp > now() - interval 7 day
GROUP BY url
ORDER BY views DESC
LIMIT 50
`);
// SLOW (avoid): No time filter, scans entire table
// SELECT * FROM events WHERE event = '$pageview'
// OPTIMIZED: Use subqueries for complex analysis
const retention = await queryPostHog(`
SELECT
dateTrunc('week', first_seen) AS cohort_week,
dateTrunc('week', timestamp) AS activity_week,
uniq(distinct_id) AS users
FROM events
INNER JOIN (
SELECT distinct_id, min(timestamp) AS first_seen
FROM events
WHERE event = 'user_signed_up'
AND timestamp > now() - interval 90 day
GROUP BY distinct_id
) AS cohorts ON events.distinct_id = cohorts.distinct_id
WHERE timestamp > now() - interval 90 day
GROUP BY cohort_week, activity_week
ORDER BY cohort_week, activity_week
`);
Step 5: Session Recording Performance
// Limit session recording to reduce data volume and cost
posthog.init(process.env.NEXT_PUBLIC_POSTHOG_KEY!, {
api_host: 'https://us.i.posthog.com',
session_recording: {
// Only record 10% of sessions
sampleRate: 0.1,
// Minimum session duration to record (skip quick bounces)
minimumDurationMilliseconds: 5000,
// Mask all text inputs by default
maskAllInputs: true,
// Mask specific CSS selectors
maskTextSelector: '.sensitive-data',
},
});
Performance Benchmarks
| Operation | Without Optimization | With Optimization |
|---|---|---|
| Feature flag evaluation | 50-200ms (network) | <1ms (local eval) |
| Event capture | Individual sends | Batched (20 events/req) |
| HogQL query (7d) | 2-5s | <1s (with filters) |
| HogQL query (no filter) | 30-60s (timeout risk) | N/A (always filter) |
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Events dropped on exit | No shutdown hook | Add posthog.shutdown() to SIGTERM handler |
| Flag evaluation slow | No personalApiKey | Add personal API key for local evaluation |
| High event cost | Capturing everything | Implement before_send sampling |
| HogQL timeout | No date filter | Always include timestamp > now() - interval N day |
| Session recordings large | Recording all sessions | Set sampleRate to 0.1-0.25 |
Output
- Local feature flag evaluation (<1ms per check)
- Optimized batching configuration
- Event sampling with
before_send - Efficient HogQL query patterns
- Session recording sampling