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Scaling • Operations & Runbooks

Performance & Tuning

DocsscalingOperations & RunbooksPerformance & Tuning

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Performance & Tuning

Live technical documentation fetched from GitHub repository omghante/metapilot/docs/operations/performance.md

Performance Tuning & Optimization Guide

This guide details database indexing, Celery worker concurrency, Redis channel tuning, and caching strategies for MetaPilot.

1. Database Indexing & Query Optimizations

Critical Database Indexes

```
users_user.email
```
: Fast JWT login lookup.
```
messaging_conversation.customer_phone
```
: Instant phone number matching on incoming webhooks.
```
messaging_message.wamid
```
: Immediate status updates for incoming receipts (
```
delivered
```
,
```
read
```
).
```
tenants_tenantconfig.tenant_id
```
: Rapid key retrieval during broadcast execution.

Query Tuning Rules

Always use

select_related

and

prefetch_related

in ViewSets to prevent N+1 query overhead:

python

queryset = Message.objects.select_related('conversation', 'conversation__tenant').all()

2. Celery Worker Concurrency & Batch Sizing

Concurrency: Set
```
--concurrency=4
```
per worker container (or 2x CPU cores).
Batch Sizing:
```
SCHEDULER_BATCH_SIZE = 20
```
(optimal for Meta Graph API REST throughput without triggering memory overhead).
Rate Limit Token Bucket: Enforce 50 msgs/sec in
```
rate_limiter.py
```
.

3. Redis Channel Layer Optimization

Capacity Tuning: Set
```
capacity=1500
```
in
```
CHANNEL_LAYERS
```
setting to handle peak WebSocket frame spikes.
Message Expiry: Set
```
expiry=60
```
seconds to automatically purge stale disconnected WebSocket frames.