Module 16 Cheat Sheet
OBSERVABILITY
=============
Logs
what happened
Metrics
how often/how much
Traces
where request spent time
CORE SIGNALS
============
traffic
errors
latency
saturation
LATENCY
=======
measure:
p50
p95
p99
break down:
Mule
Salesforce
HTTP APIs
DB
queue wait
CORRELATION
===========
correlationId
= transaction
customerId
= business entity
TIMEOUTS
========
derive from
overall latency budget
RETRIES
=======
bounded
backoff
idempotent
watch for:
retry storms
monitor retry rate
SALESFORCE
==========
watch:
latency
timeouts
limits
locking
API usage
ASYNC
=====
queue depth
oldest message age
DLQ
processing lag
BATCH
=====
processed
failed
records/sec
duration
Bulk job status
ALERTING
========
alert on meaningful thresholds
not every individual error
TROUBLESHOOTING
===============
“Mule is slow”
↓
find exact latency segment
↓
check downstream
↓
check retries
↓
check saturation
↓
check traffic
↓
check deployment changes
One interview sentence worth memorizing:
“I don't troubleshoot ‘Mule is slow’ as one black box. I decompose the request into Mule processing, downstream latency, retries, queueing and resource saturation, use correlation IDs to trace individual requests, and monitor p95/p99 plus dependency-specific metrics so I can identify the actual bottleneck rather than tuning blindly.”
And one current detail you can casually mention if observability comes up: Mule Runtime 4.12 added OpenTelemetry metrics support to Direct Telemetry Stream, so Mule telemetry can increasingly fit into enterprise-wide OpenTelemetry observability strategies.
Next is Module 17 — Interview System-Design Scenarios. Instead of introducing many new concepts, we'll put everything together and practice 5–6 realistic prompts like “build Mule → Salesforce customer sync,” “500k nightly records,” “Salesforce outage,” “Account + Contacts + Billing orchestration,” and “event-driven Salesforce updates.” This is where I'd focus heavily because it's likely closest to what they'll actually ask you.