Visualizing Edge Application Performance with Amazon QuickSight Sankey Diagrams
By Braincuber Team
Published on February 12, 2026
In modern gaming and edge computing, latency is the silent killer. When players in Tokyo experience lag connecting to a server in Singapore, time-series line graphs often fail to show where the bottleneck is. Is it the regional gateway? The edge node? Or the core database?
This tutorial demonstrates how to build a Network Performance Pipeline for a fictional gaming company, LatencyLabs. We will visualize traffic flows and latency between distributed nodes using Amazon QuickSight Sankey Diagrams, powered by Athena and S3.
Why Sankey Diagrams?
Unlike standard bar or line charts, Sankey diagrams excel at showing flow:
- Volume: The width of the line visualizes the amount of traffic (or latency magnitude).
- Path: Immediately see which Source Nodes are overloading specific Destination Nodes.
- Relationships: Understand the many-to-many relationships in a mesh network.
The Architecture
-
1. Data Collection
EC2 instances (Game Servers) run a Python script that pings other nodes and logs latency metrics to a CSV file.
-
2. Storage (Pipeline)
Logs are uploaded to Amazon S3 under
s3://latencylabs-logs/. -
3. Query (Athena)
Amazon Athena maps the CSV files to a SQL table structure.
-
4. Visualize (QuickSight)
Amazon QuickSight reads the Athena table and renders the live Sankey Diagram.
Step 1: Athena Table Configuration
First, we tell Athena how to interpret our S3 CSV logs.
CREATE DATABASE network_performance;
CREATE EXTERNAL TABLE network_performance.ping_logs (
timestamp_utc STRING,
protocol STRING,
source_ip STRING,
source_region STRING, -- e.g., 'us-east-1'
dest_ip STRING,
dest_region STRING, -- e.g., 'ap-northeast-1'
rtt_msec DOUBLE
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
LOCATION 's3://latencylabs-logs/ping-data/';
Step 2: Configuring QuickSight
Once the data is in Athena, setting up QuickSight is a drag-and-drop process.
- Data Source: Select "Athena" and choose the
network_performancedatabase. - Visual Type: Click the "Sankey Diagram" icon in the Visuals pane.
- Field Mapping:
- Source: Drag
source_regionhere. - Destination: Drag
dest_regionhere. - Weight: Drag
rtt_msechere.
- Source: Drag
Result: You will see thick lines connecting regions with high latency (high RTT), and thin lines for fast connections. High latency paths immediately stand out visually as "fat pipes" of delay.
Conclusion
By combining S3, Athena, and QuickSight, LatencyLabs created a "Mission Control" dashboard that visualizes global network health at a glance. What used to be a spreadsheet of 10,000 latency rows is now a single, interactive diagram that highlights exactly where the lag is coming from.
Monitoring Distributed Systems?
Stop staring at CSVs. Let us help you build a real-time visualization pipeline for your edge applications.
