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Performance Profiling

Overview

iOS app performance problems fall into distinct categories, each with a specific diagnosis tool. This skill helps you choose the right tool , use it effectively , and interpret results correctly under pressure.

Core principle : Measure before optimizing. Guessing about performance wastes more time than profiling.

Requires : Xcode 15+, iOS 14+ Related skills : axiom-swiftui-performance (SwiftUI-specific profiling with Instruments 26), axiom-memory-debugging (memory leak diagnosis)

When to Use Performance Profiling

Use this skill when

• ✅ App feels slow (UI lags, loads take 5+ seconds)
• ✅ Memory grows over time (Xcode shows increasing memory usage)
• ✅ Battery drains fast (device gets hot, battery depletes in hours)
• ✅ You want to profile proactively (before users complain)
• ✅ You're unsure which Instruments tool to use
• ✅ Profiling results are confusing or contradictory

Use axiom-memory-debugging instead when

• Investigating specific memory leaks with retain cycles
• Using Instruments Allocations in detail mode

Use axiom-swiftui-performance instead when

• Analyzing SwiftUI view body updates
• Using SwiftUI Instrument specifically

Performance Decision Tree

Before opening Instruments, narrow down what you're actually investigating.

Step 1: What's the Symptom?

App performance problem?
├─ App feels slow or lags (UI interactions stall, scrolling stutters)
│  └─ → Use Time Profiler (measure CPU usage)
├─ Memory grows over time (Xcode shows increasing memory)
│  └─ → Use Allocations (measure object creation)
├─ Data loading is slow (parsing, database queries, API calls)
│  └─ → Use Core Data instrument (if using Core Data)
│  └─ → Use Time Profiler (if it's computation)
└─ Battery drains fast (device gets hot, depletes in hours)
   └─ → Use Energy Impact (measure power consumption)

Step 2: Can You Reproduce It?

YES – Use Instruments to measure it (profiling is most accurate)

NO – Use profiling proactively

• Enable Core Data SQL debugging to catch N+1 queries
• Profile app during normal use (scrolling, loading, navigation)
• Establish baseline metrics before changes

Step 3: Which Instruments Tool?

Time Profiler – Slowness, UI lag, CPU spikes Allocations – Memory growth, memory pressure, object counts Core Data – Query performance, fetch times, fault fires Energy Impact – Battery drain, sustained power draw Network Link Conditioner – Connection-related slowness System Trace – Thread blocking, main thread blocking, scheduling

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Time Profiler Deep Dive

Use Time Profiler when your app feels slow or laggy. It measures CPU time spent in each function.

Workflow: Record and Analyze

Step 1: Launch Instruments

open -a Instruments

Select "Time Profiler" template.

Step 2: Attach to Running App

1. Start your app in simulator or device
2. In Instruments, select your app from the target dropdown
3. Click Record (red circle)
4. Interact with the slow part (scroll, tap buttons, load data)
5. Stop recording after 10-30 seconds of interaction

Step 3: Read the Call Stack

The top panel shows a timeline of CPU usage over time. Look for:

• Tall spikes – Brief CPU-intensive operations
• Sustained high usage – Continuous expensive work
• Main thread blocking – UI thread doing work (causes UI lag)

Step 4: Drill Down to Hot Spots

In the call tree, click "Heaviest Stack Trace" to see which functions use the most CPU:

Time Profiler Results

MyViewController.viewDidLoad() – 500ms (40% of total)
  ├─ DataParser.parse() – 350ms
  │  └─ JSONDecoder.decode() – 320ms
  └─ UITableView.reloadData() – 150ms

Self Time = Time spent IN that function (not in functions it calls) Total Time = Time spent in that function + everything it calls

Common Mistakes & Fixes

❌ Mistake 1: Blaming the Wrong Function

// ❌ WRONG: Profile shows DataParser.parse() is 80% CPU
// Conclusion: "DataParser is slow, let me optimize it"

// ✅ RIGHT: Check what DataParser is calling
// If JSONDecoder.decode() is doing 99% of the work,
// optimize JSON decoding, not DataParser

The issue : A function with high Total Time might be calling slow code, not doing slow work itself.

Fix : Look at Self Time, not Total Time. Drill down to see what each function calls.

❌ Mistake 2: Profiling the Wrong Code Path

// ❌ WRONG: Profile app in Simulator
// Simulator CPU is different than real device
// Results don't reflect actual device performance

// ✅ RIGHT: Profile on actual device
// Device settings: Developer Mode enabled, Xcode attached

Fix : Always profile on actual device for accurate CPU measurements.

❌ Mistake 3: Not Isolating the Problem

// ❌ WRONG: Profile entire app startup
// Sees 2000ms startup time, many functions involved

// ✅ RIGHT: Profile just the slow part
// "App feels slow when scrolling" → profile only scrolling
// Separate concerns: startup slow vs interaction slow

Fix : Reproduce the specific slow operation, not the entire app.

Pressure Scenario: "Profile Shows Function X is 80% CPU"

The temptation : "I must optimize function X!"

The reality : Function X might be:

• Calling expensive code (optimize the called function, not X)
• Running on main thread (move to background, it's already optimized)
• Necessary work that looks slow (baseline is acceptable, user won't notice)

What to do instead :

1. Check Self Time, not Total Time

   • Self Time 80%? Function is actually doing expensive work
   • Self Time 5%, Total Time 80%? Function is calling slow code

2. Drill down one level

   • What is this function calling?
   • Is the slow code in a library you control?

3. Check the timeline

   • Is this 80% sustained (steady slow) or spikes (occasional stalls)?
   • Sustained = optimization needed
   • Spikes = caching might help

4. Ask: Will users notice?

   • 500ms background work = user won't notice
   • 500ms on main thread = UI stall, user sees it
   • 50ms on main thread per frame = smooth UI (60fps)

Time cost : 5 min (read results) + 2 min (drill down) = 7 minutes to understand

Cost of guessing : 2 hours optimizing wrong function + 1 hour realizing it didn't help + back to square one = 3+ hours wasted

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Allocations Deep Dive

Use Allocations when memory grows over time or you suspect memory pressure issues.

Workflow: Record and Analyze

Step 1: Launch Instruments

open -a Instruments

Select "Allocations" template.

Step 2: Attach and Record

1. Start your app
2. In Instruments, select your app
3. Click Record
4. Perform actions that use memory (load data, display images, navigate)
5. Stop recording after memory stabilizes or peaks

Step 3: Find Memory Growth

Look at the main chart:

• Blue line = Total allocations
• Sharp climb = Memory being allocated
• Flat line = Memory stable (good)
• No decline after stopping actions = Possible leak (or caching)

Step 4: Identify Persistent Objects

Under "Statistics":

• Sort by "Persistent" (objects still alive)

• Look for surprisingly large object counts:

  UIImage: 500 instances (300MB) – Should be <50 for normal app
  NSString: 50000 instances – Should be <1000
  CustomDataModel: 10000 instances – Should be <100
  

Common Mistakes & Fixes

❌ Mistake 1: Confusing "Memory Grew" with "Memory Leak"

// ❌ WRONG: Memory went from 100MB to 500MB
// Conclusion: "There's a leak, memory keeps growing!"

// ✅ RIGHT: Check what caused the growth
// Loaded 1000 images (normal)
// Cached API responses (normal)
// User has 5000 contacts (normal)
// Memory is being used correctly

The issue : Growing memory ≠ leak. Apps legitimately use more memory when loading data.

Fix : Check Allocations for object counts. If images/data count matches what you loaded, it's normal. If object count keeps growing without actions, that's a leak.

❌ Mistake 2: Not Accounting for Caching

// ❌ WRONG: Allocations shows 1000 UIImages in memory
// Conclusion: "Memory leak, too many images!"

// ✅ RIGHT: Check if this is intentional caching
// ImageCache holds up to 1000 images by design
// When memory pressure happens, cache is cleared
// Normal behavior

Fix : Distinguish between intended caching and actual leaks. Leaks don't release under memory pressure.

❌ Mistake 3: Profiling Too Short

// ❌ WRONG: Record for 5 seconds, see 200MB
// Conclusion: "App uses 200MB, optimize memory"

// ✅ RIGHT: Record for 2-3 minutes, see full lifecycle
// Load data: 200MB
// Navigate away: 180MB (20MB still cached)
// Navigate back: 190MB (cache reused)
// Real baseline: ~190MB at steady state

Fix : Profile long enough to see memory stabilize. Short recordings capture transient spikes.

Pressure Scenario: "Memory is 500MB, That's a Leak!"

The temptation : "Delete caching, reduce object creation, optimize data structures"

The reality : Is 500MB actually large?

• iPhone 14 Pro has 6GB RAM
• Instagram uses 400-600MB on load
• Photos app uses 500MB+ when browsing large library
• 500MB might be completely normal

What to do instead :

1. Establish baseline on real device

   # On device, open Memory view in Xcode
   Xcode → Debug → Memory Debugger → Check "Real Memory" at app launch
   

2. Check object counts, not total memory

   • Allocations → Statistics → "Persistent"
   • Are images, views, or data objects 10x expected count?
   • If yes, investigate that object type
   • If no, memory is probably fine

3. Test under memory pressure

   • Xcode → Debug → Simulate Memory Warning
   • Does memory drop by 50%+? It's caching (normal)
   • Does memory stay high? Investigate persistent objects

4. Profile real user journey

   • Load data (like user does)
   • Navigate around (like user does)
   • Return to app (from background)
   • Check memory at each step

Time cost : 5 min (launch Allocations) + 3 min (record app usage) + 2 min (analyze) = 10 minutes

Cost of guessing : Delete caching to "reduce memory" → app reloads data every screen → slower app → users complain → revert changes = 2+ hours wasted

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Core Data Deep Dive

Use Core Data instrument when your app uses Core Data and data loading is slow.

Workflow: Enable SQL Debugging and Profile

Step 1: Enable Core Data SQL Logging

Add to your launch arguments in Xcode:

Edit Scheme → Run → Arguments Passed On Launch
Add: -com.apple.CoreData.SQLDebug 1

Now SQLite queries print to console:

CoreData: sql: SELECT ... FROM tracks WHERE artist = ? (time: 0.015s)
CoreData: sql: SELECT ... FROM albums WHERE id = ? (time: 0.002s)

Step 2: Identify N+1 Query Problem

Watch the console during a typical user action (load list, scroll, filter):

❌ BAD: Loading 100 tracks, then querying album for each
SELECT * FROM tracks (time: 0.050s) → 100 tracks
SELECT * FROM albums WHERE id = 1 (time: 0.005s)
SELECT * FROM albums WHERE id = 2 (time: 0.005s)
SELECT * FROM albums WHERE id = 3 (time: 0.005s)
... 97 more queries
Total: 0.050s + (100 × 0.005s) = 0.550s

✅ GOOD: Fetch tracks WITH album relationship (eager loading)
SELECT tracks.*, albums.* FROM tracks
LEFT JOIN albums ON tracks.albumId = albums.id
(time: 0.050s)
Total: 0.050s

Step 3: Profile with Core Data Instrument

open -a Instruments

Select "Core Data" template.

Record while performing slow action:

Core Data Results

Fetch Requests: 102
Average Fetch Time: 12ms
Slow Fetch: "SELECT * FROM tracks" (180ms)

Fault Fires: 5000
  → Object accessed, requires fetch from database
  → Should use prefetching

Common Mistakes & Fixes

❌ Mistake 1: Not Using Relationships Correctly

// ❌ WRONG: Fetch tracks, then access album for each
let tracks = try context.fetch(Track.fetchRequest())
for track in tracks {
    print(track.album.title)  // Fires individual query for each
}
// Total: 1 + N queries

// ✅ RIGHT: Fetch with relationship prefetching
let request = Track.fetchRequest()
request.returnsObjectsAsFaults = false
request.relationshipKeyPathsForPrefetching = ["album"]
let tracks = try context.fetch(request)
for track in tracks {
    print(track.album.title)  // Already loaded
}
// Total: 1 query

Fix : Use relationshipKeyPathsForPrefetching to load related objects upfront.

❌ Mistake 2: Not Using Batching

// ❌ WRONG: Fetch 50,000 records all at once
let request = Track.fetchRequest()
let allTracks = try context.fetch(request)  // Huge memory spike

// ✅ RIGHT: Batch fetch in chunks
let request = Track.fetchRequest()
request.fetchBatchSize = 500  // Fetch 500 at a time
let allTracks = try context.fetch(request)  // Memory efficient

Fix : Use fetchBatchSize for large datasets.

❌ Mistake 3: Not Using Faulting to Reduce Memory

// ❌ WRONG: Keep all objects in memory
let request = Track.fetchRequest()
request.returnsObjectsAsFaults = false  // Keep all in memory
let allTracks = try context.fetch(request)  // 50,000 objects
// Memory spike if you don't use all of them

// ✅ RIGHT: Use faults (lazy loading)
let request = Track.fetchRequest()
// request.returnsObjectsAsFaults = true (default)
let allTracks = try context.fetch(request)  // Just references
// Only load objects you actually access

Fix : Leave returnsObjectsAsFaults as default (true) unless you need all objects upfront.

Pressure Scenario: "Core Data Queries Are Slow, Redesign Schema!"

The temptation : "The schema is wrong, I need to restructure everything"

The reality : 99% of "slow Core Data" is due to:

• ❌ Missing indexes
• ❌ N+1 query problem
• ❌ Fetching too much data at once
• ❌ Not using batch size or prefetching

Redesigning the schema is the LAST thing to try.

What to do instead :

1. Enable SQL debugging (2 min)

   • Add -com.apple.CoreData.SQLDebug 1 launch argument
   • Watch what queries execute

2. Look for N+1 pattern (3 min)

   • Fetching 100 objects, then individual queries for related data?
   • Add relationship prefetching

3. Add indexes if needed (5 min)

   • @NSManaged var artist: String with frequent filtering?
   • Add @Index in schema

4. Test improvement (2 min)

   • Re-run the same action
   • Compare query count and total time
   • If 10x faster, you're done
   • If still slow, go to step 5

5. Only THEN consider schema changes (30+ min)

   • But you probably won't get here

Time cost : 12 minutes to diagnose + fix = 12 minutes

Cost of schema redesign : 8 hours design + 4 hours migration + 2 hours testing + 1 hour rollback = 15 hours total

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Quick Reference: Other Tools

Energy Impact (Battery Drain)

When to use : App drains battery fast, device gets hot

Workflow :

1. Launch Instruments → Energy Impact template
2. Run app normally for 5+ minutes
3. Look for red/orange sustained usage (bad)
4. Drill down to see which subsystems drain battery

Key metrics :

• Sustained Power – Ongoing energy use (should be minimal)
• Peaks – Brief high usage (acceptable)
• CPU – Process CPU time
• GPU – Graphics rendering
• Network – Cellular/WiFi radio
• Location – GPS usage

Common issues :

• Continuous location updates with 1m accuracy (should be 100m)
• Running timers that wake the device repeatedly
• Excessive network calls (batch requests instead)
• Animating views while not visible

Network Link Conditioner (Connection Simulation)

When to use : App seems slow on 4G, want to test without traveling

Setup :

1. Download Additional Tools for Xcode
2. Install Network Link Conditioner
3. Open System Preferences → Network Link Conditioner
4. Choose profile (3G, LTE, WiFi Slow, etc.)
5. Enable and activate profile
6. Run app to test

Key profiles :

• 3G – 1.6Mbps down, 768Kbps up, 150ms latency
• LTE – 10Mbps down, 5Mbps up, 20ms latency
• WiFi Slow – 10Mbps, 100ms latency
• Custom – Set your own parameters

Note : Also covered in ui-testing for network-dependent test scenarios.

System Trace (Thread Blocking, Scheduling)

When to use : UI freezes or is janky, but Time Profiler shows low CPU

Common cause : Main thread blocked by background task waiting on lock

Workflow :

1. Launch Instruments → System Trace template
2. Record while reproducing issue
3. Look for main thread gaps (blocked, not running)
4. Drill down to see what's blocking it

Key metrics :

• Main thread gaps – Empty spaces = main thread idle/blocked
• Core scheduling – Which threads run when
• Lock contention – Threads waiting for locks

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OSSignposter — Custom Performance Instrumentation

While Time Profiler shows where CPU time goes generally, OSSignposter lets you measure specific operations you define. It's the primary tool for custom performance instrumentation on Apple platforms.

When to Use

• Measuring duration of specific operations (data load, image processing, sync cycle)
• Creating custom Instruments lanes for your app's operations
• Bridging to automated performance testing (XCTOSSignpostMetric)
• Measuring operations that span multiple threads or await points

Basic API

import os

let signposter = OSSignposter(subsystem: "com.app", category: "DataLoad")

// Interval measurement (start → end)
func loadData() async throws -> [Item] {
    let signpostID = signposter.makeSignpostID()
    let state = signposter.beginInterval("Load Items", id: signpostID)
    defer { signposter.endInterval("Load Items", state) }

    return try await fetchItems()
}

// Point of interest (single event)
func cacheHit(for key: String) {
    signposter.emitEvent("Cache Hit")
}

Integration with Instruments

1. Launch Instruments → add "os_signpost" or "Points of Interest" instrument
2. Record your app performing the instrumented operations
3. Signpost intervals appear as colored bars in the timeline
4. Filter by subsystem/category to focus on your operations

When to Use Signposts vs Time Profiler

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Pressure Scenarios

Scenario 1: "Profiling Shows Different Results Each Run"

The problem : You run Time Profiler 3 times, get 200ms, 150ms, 280ms. Which is correct?

Red flags you might think :

• "Results are unreliable, profiling isn't accurate"
• "Let me just average them"
• "This is too variable, I can't optimize"

The reality : Variance is NORMAL. Different runs hit different:

• Cache states (cold cache = slower)
• System load (other apps running)
• CPU frequency (boost/throttle)

What to do instead :

1. Warm up the cache (first run always slower)

   • Perform the action once (cold cache)
   • Perform again (warm cache) – use this measurement

2. Control system load

   • Close other apps
   • Don't touch device during profiling
   • Profile on device (not simulator)

3. Look for the pattern

   • Multiple runs: 150ms, 160ms, 155ms (consistent = good)
   • Multiple runs: 150ms, 280ms, 240ms (inconsistent = investigate)
   • Inconsistency = intermittent problem, find it

4. Trust the slowest run (worst case scenario)

   • If range is 150-280ms, assume 280ms is real
   • Optimize for worst case

Time cost : 10 min (run profiler 3x) + 2 min (interpret) = 12 minutes

Cost of ignoring variance : Miss intermittent performance issue → users see occasional freezes → bad reviews

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Scenario 2: "Time Profiler and Allocations Show Different Problems"

The problem : Time Profiler shows JSON parsing is slow. Allocations show memory use is normal. Which to fix?

The answer : Both are real, prioritize differently.

Time Profiler: JSONDecoder.decode() = 500ms
Allocations: Memory = 250MB (normal for app size)

Result: App is slow AND memory is fine
Action: Optimize JSON decoding (not memory)

Common conflicts :

What to do :

1. Prioritize by user impact

   • Slowness (UI lag) = fix first
   • Memory (background issue) = fix second

2. Check if they're related

   • Does JSON parsing leak memory? (No → separate issues)
   • Does memory growth slow CPU? (Maybe → fix memory first)

3. Fix in order of impact

   • Slow JSON parsing: Affects every data load
   • Normal memory: No user impact
   • → Fix JSON parsing

Time cost : 5 min (analyze both results) = 5 minutes

Cost of fixing wrong problem : Spend 4 hours optimizing memory that's fine → no improvement to user experience

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Scenario 3: "Profiling Under Deadline Pressure"

The situation : Manager says "We ship in 2 hours. Is performance acceptable?"

Red flags you might think :

• "Profiling takes too long, let me just ask users"
• "I don't have time to profile properly, ship as-is"
• "One quick run will tell me if it's fine"

The reality : Profiling takes 15-20 minutes total. That's 1% of your remaining time.

What to do instead :

1. Profile the critical path (3 min)

   • What users do most (load list, scroll, search)
   • Not the entire app, just the slow part

2. Record one proper run (5 min)

   • Cold cache first time
   • Warm cache second time
   • Use warm cache results

3. Interpret quickly (5 min)

   • Time Profiler: Any >100ms on main thread? (If no, fine)
   • Allocations: Any memory growing? (If no, fine)

4. Ship with confidence (2 min)

   • If results are acceptable, ship
   • If not, you have 90 minutes to fix or delay

Time cost : 15 min profiling + 5 min analysis = 20 minutes

Cost of not profiling : Ship with unknown performance → Users hit slowness → Bad reviews → Emergency hotfix 2 weeks later

Math : 20 minutes of profiling now << 2+ weeks of post-launch support

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Quick Reference

Common Operations

// Time Profiler: Launch Instruments
open -a Instruments

// Core Data: Enable SQL logging
// Edit Scheme → Run → Arguments Passed On Launch
-com.apple.CoreData.SQLDebug 1

// Allocations: Check persistent objects
Instruments → Allocations → Statistics → sort "Persistent"

// Memory warning: Simulate pressure
Xcode → Debug → Simulate Memory Warning

// Energy Impact: Profile battery drain
Instruments → Energy Impact template

// Network Link Conditioner: Simulate 3G
System Preferences → Network Link Conditioner → 3G profile

Decision Tree Summary

Performance problem?
├─ App feels slow/laggy?
│  └─ → Time Profiler (measure CPU)
├─ Memory grows over time?
│  └─ → Allocations (find object growth)
├─ Data loading is slow?
│  └─ → Core Data instrument (if using Core Data)
│  └─ → Time Profiler (if computation slow)
└─ Battery drains fast?
   └─ → Energy Impact (measure power)

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Real-World Examples

Example 1: Identifying N+1 Query Problem in Core Data

Scenario : Your app loads a list of albums with artist names. It's slow (5+ seconds for 100 albums). You suspect Core Data.

Setup : Enable SQL logging first

# Edit Scheme → Run → Arguments Passed On Launch
-com.apple.CoreData.SQLDebug 1

What you see in console :

CoreData: sql: SELECT ... FROM albums WHERE ... (time: 0.050s)
CoreData: sql: SELECT ... FROM artists WHERE id = 1 (time: 0.003s)
CoreData: sql: SELECT ... FROM artists WHERE id = 2 (time: 0.003s)
... 98 more individual queries
Total: 0.050s + (100 × 0.003s) = 0.350s

Diagnosis using the skill :

• Fetching 100 albums, then individual query for each album's artist = N+1 query problem (Core Data Deep Dive, lines 302-325)

Fix :

// ❌ WRONG: Each album access triggers separate artist query
let request = Album.fetchRequest()
let albums = try context.fetch(request)
for album in albums {
    print(album.artist.name)  // Extra query for each
}

// ✅ RIGHT: Prefetch the relationship
let request = Album.fetchRequest()
request.returnsObjectsAsFaults = false
request.relationshipKeyPathsForPrefetching = ["artist"]
let albums = try context.fetch(request)
for album in albums {
    print(album.artist.name)  // Already loaded
}

Result : 0.350s → 0.050s (7x faster)

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Example 2: Finding Where UI Lag Really Comes From

Scenario : Your app UI stalls for 1-2 seconds when loading a view. Your co-lead says "Add background threading everywhere." You want to measure first.

Workflow using the skill (Time Profiler Deep Dive, lines 82-118):

1. Open Instruments :

open -a Instruments

# Select "Time Profiler"

2. Record the stall :

App launches
Time Profiler records
View loads
Stall happens (observe the spike in Time Profiler)
Stop recording

3. Examine results :

Call Stack shows:

viewDidLoad() – 1500ms
  ├─ loadJSON() – 1200ms (Self Time: 50ms)
  │   └─ loadImages() – 1150ms (Self Time: 1150ms) ← HERE'S THE CULPRIT
  ├─ parseData() – 200ms
  └─ layoutUI() – 100ms

4. Apply the skill (lines 173-175):

loadJSON() has Self Time: 50ms, Total Time: 1200ms
→ loadJSON() isn't slow, something it CALLS is slow
→ loadImages() has Self Time: 1150ms
→ loadImages() is the actual bottleneck

5. Fix the right thing :

// ❌ WRONG: Thread everything
DispatchQueue.global().async { loadJSON() }

// ✅ RIGHT: Thread only the slow part
func loadJSON() {
    let data = parseJSON()  // 50ms, fine on main

    // Move ONLY the slow part to background
    DispatchQueue.global().async {
        let images = loadImages()  // 1150ms, now background
        DispatchQueue.main.async {
            updateUI(with: images)
        }
    }
}

Result : 1500ms → 350ms (4x faster, main thread unblocked)

Why this matters : You fixed the ACTUAL bottleneck (1150ms), not guessing blindly about threading.

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Example 3: Memory Growing vs Memory Leak

Scenario : Allocations shows memory growing from 150MB to 600MB over 30 minutes of app use. Your manager says "Memory leak!" You need to know if it's real.

Workflow using the skill (Allocations Deep Dive, lines 199-277):

1. Launch Allocations in Instruments

2. Record normal app usage for 3 minutes :

User loads data → memory grows to 400MB

User navigates around → memory stays at 400MB
User goes to Settings → memory at 400MB
User comes back → memory at 400MB

3. Check Allocations Statistics :

Persistent Objects:
- UIImage: 1200 instances (300MB) ← Large count
- NSString: 5000 instances (4MB)
- CustomDataModel: 800 instances (15MB)

4. Ask the skill questions (lines 220-240):

• Are 1200 images legitimately loaded? (User loaded photo library with 1000 photos) → YES
• Does memory drop if you trigger memory warning? (Simulate with Xcode) → YES, drops to 180MB
• Is this caching working as designed? → YES

Diagnosis : NOT a leak. This is normal caching (lines 235-248)

Memory growing = apps using data users asked for
Memory dropping under pressure = cache working correctly
Memory staying high indefinitely = possible leak

5. Conclusion :

// ✅ This is working correctly
let imageCache = NSCache<NSString, UIImage>()
// Holds up to 1200 images by design
// Clears when system memory pressure happens
// No leak

Result : No action needed. The "leak" is actually the cache doing its job.

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Regression-Proofing Pipeline

Performance work isn't done when the fix ships. Without regression detection, optimizations quietly degrade over time. The three-stage pipeline catches regressions at every phase.

The Three Stages

Stage 1: Instrument Your Code (OSSignposter)

See OSSignposter section above. Add signpost intervals to performance-critical code paths.

Stage 2: Automate with XCTest Performance Tests

func testDataLoadPerformance() throws {
    let options = XCTMeasureOptions()
    options.iterationCount = 10

    measure(metrics: [
        XCTClockMetric(),        // Wall clock time
        XCTCPUMetric(),          // CPU time and cycles
        XCTMemoryMetric(),       // Peak physical memory
    ], options: options) {
        loadData()
    }
}

Available XCTMetric Types

• XCTClockMetric — Wall clock duration
• XCTCPUMetric — CPU time, instructions retired, cycles
• XCTMemoryMetric — Peak physical memory during test
• XCTStorageMetric — Logical writes to storage
• XCTOSSignpostMetric — Duration of signposted intervals (bridges Stage 1 → Stage 2)
• XCTApplicationLaunchMetric — App launch time (cold/warm/optimized)
• XCTHitchMetric — Hitch time ratio (scrolling and animation hitches)

Setting Baselines

After running once, click the value in Xcode's test results → "Set Baseline". Subsequent runs compare against baseline and fail if regression exceeds tolerance (default 10%).

Anti-Pattern: Baseline-Less Performance Tests

// ❌ Test always passes — no baseline set
func testPerformance() {
    measure { doWork() }
}

// ✅ Set baseline in Xcode after first run
// Tests fail when performance regresses beyond tolerance

Bridging Signposts to Tests (XCTOSSignpostMetric)

// In production code
let signposter = OSSignposter(subsystem: "com.app", category: "Sync")

func syncData() {
    let id = signposter.makeSignpostID()
    let state = signposter.beginInterval("Full Sync", id: id)
    defer { signposter.endInterval("Full Sync", state) }
    // ... sync logic
}

// In test
func testSyncPerformance() {
    let metric = XCTOSSignpostMetric(
        subsystem: "com.app",
        category: "Sync",
        name: "Full Sync"
    )
    measure(metrics: [metric]) {
        syncData()
    }
}

Stage 3: Monitor in Production (MetricKit)

See axiom-metrickit-ref for comprehensive MetricKit integration. Key metrics to monitor:

• MXAppLaunchMetric — Launch time regression
• MXAppResponsivenessMetric — Hang rate increase
• MXCPUMetric — CPU time per foreground session
• MXMemoryMetric — Peak memory growth across versions

---

Resources

WWDC : 2023-10160, 2024-10217, 2025-308, 2025-312

Docs : /library/archive/documentation/cocoa/conceptual/coredataperformance, /library/archive/technotes/tn2224, /os/ossignposter, /xctest/xctestcase/measure

Skills : axiom-memory-debugging, axiom-swiftui-performance, axiom-swift-concurrency, axiom-metrickit-ref

---

Targets: iOS 14+, Swift 5.5+ Tools: Instruments, Core Data History: See git log for changes

Weekly Installs

97

Repository

charleswiltgen/axiom

GitHub Stars

601

First Seen

Jan 21, 2026

Security Audits

Gen Agent Trust HubFailSocketPassSnykPass

Installed on

opencode82

codex76

claude-code76

gemini-cli75

cursor73

github-copilot71