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When exporting AEM Content Fragments as JSON Offers to Adobe Target, you may encounter an error preventing the successful integration. This post details the issue, its root cause, and the steps to resolve it.

Issue

The AEM Content Fragment Export to Adobe Target failed with the following exception:

06.03.2025 12:59:31.223 *DEBUG* [[0:0:0:0:0:0:0:1] [1741287571223] GET /content/dam/content-fragments/test/test-cf/.permissions.json HTTP/1.1]  
com.test.core.filters.LoggingFilter request for /content/dam/content-fragments/test/test-cf/, with selector permissions  

06.03.2025 12:59:40.706 *DEBUG* [[0:0:0:0:0:0:0:1] [1741287580703] POST /content/dam/content-fragments/test/test-cf.cfm.targetexport HTTP/1.1]  
com.test.core.filters.LoggingFilter request for /content/dam/content-fragments/test/test-cf, with selector cfm  

06.03.2025 12:59:40.710 *ERROR* [[0:0:0:0:0:0:0:1] [1741287580703] POST /content/dam/content-fragments/test/test-cf.cfm.targetexport HTTP/1.1]  
com.adobe.cq.dam.cfm.graphql.extensions.querygen.impl.service.QueryGeneratorServiceImpl  
Cannot find Sites GraphQL endpoint resource, cannot generate GraphQL query  

Root Cause

This issue occurs because no GraphQL endpoint is defined for Adobe Target to fetch Content Fragment details. The export process requires a valid GraphQL endpoint to retrieve structured content from AEM and send it to Adobe Target.

Solution

To resolve this issue, follow these steps to define a global GraphQL endpoint in AEM:

1. Log into AEM and navigate to Tools → General → GraphQL.

2. Create a new GraphQL endpoint and associate it with /conf/global.

3. Save and publish the endpoint to make it accessible.

Once configured, the AEM Content Fragment export to Adobe Target will be successful, allowing the fragments to be used in Adobe Target Activities for personalized content experiences.

Introduction

As modern applications increasingly rely on microservices architectures, managing communication between services becomes crucial. Two key technologies that help address these challenges are API Gateways and Service Meshes. While both manage service-to-service communication, they serve different purposes and operate at different layers of an application architecture. This blog explores their differences, use cases, and how to decide which one to use.

1. What is an API Gateway?

An API Gateway is an entry point for external clients to interact with an application’s backend services. It acts as a reverse proxy that routes requests to the appropriate microservices while handling concerns like authentication, rate limiting, logging, and caching.

Key Features of an API Gateway

• Traffic Routing & Load Balancing – Directs external requests to the correct microservice.
• Authentication & Authorization – Enforces security policies using OAuth, JWT, or API keys.
• Rate Limiting & Throttling – Prevents abuse by limiting the number of requests per client.
• Request Transformation – Modifies request/response formats to ensure compatibility.
• Logging & Monitoring – Tracks API calls for analytics and debugging.
• Caching – Stores frequently accessed responses to improve performance.

Popular API Gateway Solutions

• Kong (Open-source and enterprise API management)

• Amazon API Gateway (AWS-managed API gateway)

• Apigee (Google Cloud API management platform)

• Nginx (Lightweight API Gateway & reverse proxy)

• Traefik (Cloud-native API Gateway)

2. What is a Service Mesh?

A Service Mesh is a dedicated infrastructure layer for managing service-to-service communication within a microservices architecture. Unlike API Gateways, which handle north-south traffic (client-to-service requests), a Service Mesh focuses on east-west traffic (internal service-to-service communication).

Key Features of a Service Mesh

• Service Discovery & Load Balancing – Automatically detects services and distributes traffic efficiently.
• mTLS (Mutual TLS) Encryption – Secures communication between services.
• Observability & Tracing – Provides deep insights into service interactions.
• Traffic Management – Enables request routing, retries, and fault tolerance.
• Policy Enforcement – Manages service access policies, authentication, and authorization.
• Circuit Breaking & Failover – Prevents cascading failures by limiting retries and isolating failing services.

Popular Service Mesh Solutions

• Istio (One of the most popular service meshes, integrates with Kubernetes)

• Linkerd (Lightweight service mesh for Kubernetes)

• Consul (Service mesh and service discovery solution by HashiCorp)

• AWS App Mesh (Managed service mesh for AWS environments)

3. API Gateway vs Service Mesh: Key Differences

Feature	API Gateway	Service Mesh
Primary Focus	External traffic (north-south)	Internal service-to-service traffic (east-west)
Traffic Management	Request routing, load balancing	Service discovery, retries, circuit breaking
Security Features	Authentication, rate limiting	Mutual TLS, fine-grained service access control
Performance Optimization	Caching, compression	Traffic shaping, observability, tracing
Deployment	Edge of the network	Embedded within the infrastructure
Best for	Exposing APIs to external users	Managing inter-service communication

4. When to Use an API Gateway vs. a Service Mesh?

Use an API Gateway When:

• You need to expose your APIs securely to external clients.
• You require authentication, rate limiting, or request transformation.
• You want to improve performance with caching and load balancing.
• You need to monetize APIs or apply API lifecycle management.

Use a Service Mesh When:

• You have multiple microservices that need secure communication between them.
• You need observability, tracing, and traffic management across services.
• You want mTLS-based encryption for secure service-to-service communication.
• You need fine-grained policy enforcement between microservices.

5. Can API Gateways and Service Mesh Work Together?

Yes! API Gateways and Service Mesh complement each other rather than compete. Many modern architectures combine both to achieve end-to-end traffic management.

Example Architecture with API Gateway & Service Mesh

1. API Gateway (Edge Layer): Handles external client requests, authentication, rate limiting, and API exposure.

2. Service Mesh (Internal Layer): Manages service-to-service communication, security, and observability.

This combination allows for better security, scalability, and resilience in microservices architectures.

Conclusion

Both API Gateways and Service Meshes play essential roles in microservices architectures, but they serve different purposes. While API Gateways manage external traffic, Service Meshes optimize internal service-to-service communication. Organizations should evaluate their architecture needs and consider using both for a comprehensive microservices communication strategy.

I configured the AEM-Adobe Target integration by enabling:

• IMS configuration for authentication
• Legacy Adobe Target Cloud Config and New Adobe Target Cloud Config
• Default workspace assignment in Adobe Developer Console Project
• Approver permissions for the API credential in the Admin Console

However, when attempting to create an A/B test or Experience Targeting (XT) activity from AEM and sync it to Adobe Target, the Target Configuration dropdown was empty, indicating that no configurations were detected.

Root Cause & Resolution

Upon further analysis, I identified that the issue was due to the user not being part of the target-activity-authors group. After adding the user to this group, the activity creation process began recognizing all available Adobe Target configurations, including both legacy and new configurations.

Now, activities can be successfully created and synced to the default workspace once the experience is defined.

When we try to create A/B Testing or Experience Targeting activities from the AEM Activities Console and sync them to Adobe Target

The synchronization fails with the following error, and the status is shown as 'Not Synced'.

The root cause of the issue is that no experience variations were defined for the activity. We created different experiences but did not apply any pages to the activity or enable the required experience changes. To resolve the issue, select the page where this activity should be enabled and target the required components and assign the changes for appropriate experiences. This should allow the activity to sync successfully with Adobe Target.

Introduction

Music generation with Python has become more accessible with powerful libraries that allow us to compose melodies, generate MIDI files, and convert them into audio formats like WAV and MP3. This guide walks through the process of creating a MIDI file, synthesizing it into WAV using FluidSynth, and finally converting it to MP3 using pydub.

By the end of this guide, you will have a fully functional Python script that generates music and exports it as an MP3 file.

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Why Use Python for Music Generation?

Python provides several libraries that make it easy to create and manipulate music:

• MIDIUtil – Generates MIDI files programmatically.
• Mingus – Provides music theory functions and chord generation.
• FluidSynth – A real-time synthesizer that converts MIDI to WAV.
• pydub – Converts audio formats, such as WAV to MP3.

Using these libraries, we can generate music from scratch and export it into an audio format that can be played on any device.

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Setting Up the Environment

Before running the script, install the necessary dependencies:

Install Python Libraries

Run the following command in your terminal:

pip install midiutil mingus pyfluidsynth pydub

Install FluidSynth

1. Download FluidSynth from the official repository:
   FluidSynth Releases
2. Extract it to C:\tools\fluidsynth
3. Add C:\tools\fluidsynth\bin to your system PATH (for command-line access).
4. Verify the installation by running:

   fluidsynth --version

Download a SoundFont (.sf2) File

FluidSynth requires a SoundFont file to map MIDI notes to instrument sounds.

• Download FluidR3_GM.sf2:
  FluidR3_GM SoundFont from member.keymusician.com/Member/FluidR3_GM/index.html
• Move it to the same folder as your Python script or any preferred directory.

How Music is Generated in Python

Music generation in Python follows these key principles:

Understanding MIDI File Structure

A MIDI (Musical Instrument Digital Interface) file contains:

• Note Data – The pitches and durations of notes.
• Velocity – The intensity of each note.
• Instrument Information – Which instruments to use for playback.

Unlike audio formats like MP3 or WAV, MIDI does not contain actual sound data, meaning it must be played back using a synthesizer like FluidSynth.

Breaking Down the Composition Process

1. Chords and Progressions

   • Chords are groups of notes played together.
   • A chord progression is a sequence of chords that forms a harmonic structure for the music.
   • Example: "C → G → Am → F" is a common progression.

2. Melody Generation

   • A melody is a sequence of individual notes that create a recognizable tune.
   • The script selects notes from a chord to create a simple melodic line.

3. Bassline Generation

   • The bassline is usually the root note of each chord, played in a lower octave.
   • It provides rhythm and harmonic stability.

4. MIDI to Audio Conversion

   • Since MIDI files do not contain actual sound, FluidSynth uses a SoundFont to generate audio.
   • Finally, we convert the generated WAV file to MP3 using pydub.

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Python Script to Generate MIDI and Convert to MP3

This script will:

1. Generate a MIDI file with chord progressions, a melody, and a bassline.
2. Convert MIDI to WAV using FluidSynth and a SoundFont.
3. Convert WAV to MP3 using pydub.

Python Script

import random
import os
import subprocess
from midiutil import MIDIFile
from mingus.core import chords
from pydub import AudioSegment

# Define paths
SOUNDFONT_PATH =os.path.join(os.getcwd(), "FluidR3_GM.sf2")  # Update your SoundFont path
MIDI_FILENAME = "generated_music.mid"
WAV_FILENAME = "generated_music.wav"
MP3_FILENAME = "generated_music.mp3"

# Define chord progressions
verse = ["C", "G", "Am", "F"]
chorus = ["F", "C", "G", "Am"]
bridge = ["Dm", "A7", "G", "C"]
song_structure = [verse, verse, chorus, verse, bridge, chorus]

# MIDI settings
track = 0
channel = 0
time = 0  # Start time in beats
tempo = 120  # BPM
volume = 100  # MIDI velocity

# Create a MIDI file
MyMIDI = MIDIFile(1)
MyMIDI.addTempo(track, time, tempo)

# Assign instruments
instrument_chords = 0  # Acoustic Piano
instrument_melody = 40  # Violin
instrument_bass = 33  # Acoustic Bass

MyMIDI.addProgramChange(track, channel, time, instrument_chords)
MyMIDI.addProgramChange(track, channel + 1, time, instrument_melody)
MyMIDI.addProgramChange(track, channel + 2, time, instrument_bass)

# Convert note names to MIDI numbers
def note_to_number(note: str, octave: int) -> int:
    NOTES = ['C', 'C#', 'D', 'Eb', 'E', 'F', 'F#', 'G', 'Ab', 'A', 'Bb', 'B']
    NOTES_IN_OCTAVE = len(NOTES)
    return NOTES.index(note) + (NOTES_IN_OCTAVE * octave)

# Generate music
for section in song_structure:
    for chord in section:
        chord_notes = chords.from_shorthand(chord)
        random.shuffle(chord_notes)
        rhythm_pattern = [0, 0.5, 1, 1.5, 2, 2.5, 3]

        # Add chords
        for i, note in enumerate(chord_notes):
            octave = 3
            midi_note = note_to_number(note, octave)
            MyMIDI.addNote(track, channel, midi_note, time + rhythm_pattern[i % len(rhythm_pattern)], 1, volume)

        # Add bassline
        bass_note = note_to_number(chord_notes[0], 2)
        MyMIDI.addNote(track, channel + 2, bass_note, time, 4, volume)

        # Add melody
        melody_note = note_to_number(random.choice(chord_notes), 5)
        melody_duration = random.choice([0.5, 1, 1.5])
        MyMIDI.addNote(track, channel + 1, melody_note, time + 2, melody_duration, volume)

        time += 4

# Save MIDI file
with open(MIDI_FILENAME, "wb") as output_file:
    MyMIDI.writeFile(output_file)

# Convert MIDI to WAV using FluidSynth
subprocess.run(f'fluidsynth -ni -F {WAV_FILENAME} -r 44100 {SOUNDFONT_PATH} {MIDI_FILENAME}', shell=True, check=True)

# Convert WAV to MP3 using pydub
AudioSegment.from_wav(WAV_FILENAME).export(MP3_FILENAME, format="mp3")

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Running the Script

Once dependencies are installed, run:

python generate_music.py

This generates:

• generated_music.mid (MIDI file)
• generated_music.wav (WAV file)
• generated_music.mp3 (MP3 file)

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Next Steps

• Customize the chord progressions
• Experiment with different instruments
• Generate longer compositions
• Integrate AI-generated melodies

Start generating music with Python today!