Picture this: It’s 3:00 AM. Your pager goes off. You stumble to your laptop, eyes blurry, only to find a wall of red on your Grafana dashboard. High CPU usage? Memory leak? No—according to the logs, a "strange vapor shaped like a white dog" has emerged from the server room, followed closely by a "glowing red fireball" crossing the southern rack.
While that sounds like a hallucination from a sleep-deprived DevOps engineer, it is actually a real alert from one of the most fascinating open-source projects to hit the developer community recently: Joseon-Omens. Created by developer Minwoo Lee (ryanking13), this project maps 500 years of court omens from the Joseon Dynasty (1392–1897) in Korea directly into a modern Grafana observability dashboard, complete with Prometheus-style metrics, severity levels, and alerting rules.
At first glance, it’s a brilliant, tongue-in-cheek historical hack. But as I spent a few hours digging into the architecture, parsing the datasets, and looking at how these "alerts" were categorized, I realized something profound: the royal court of medieval Korea was running the exact same incident management lifecycle we use today.
In this post, we’re going to dive into how this project was built, look at some of its incredible data pipelines, and extract some genuinely valuable lessons about modern observability, alert fatigue, and why "omened-driven development" might not be as dead as we think.
The Stack: Translating Annals to Prometheus Metrics
To understand how this works, we have to look at the data source. The Veritable Records of the Joseon Dynasty (조선왕조실록) is a massive, meticulously kept historical record spanning 472 years. Within these records, royal astrologers and chroniclers recorded "omens"—anomalies in the sky, weather, zoology, and human behavior that were believed to reflect the heavens' judgment on the king's rule.
To turn this into a DevOps dashboard, the creator built a pipeline that mirrors a modern cloud-native telemetry setup:
- Data Source: The National Institute of Korean History's open API.
- Ingestion & Parsing: A Python parser that extracts dates, omen types, and descriptions, converting historical dates into UNIX timestamps.
- Exporter (The "Collector"): A custom Prometheus exporter that exposes these omens as gauge and counter metrics.
- Visualization: A Grafana dashboard that queries Prometheus to display trends over time, anomaly heatmaps, and active "incident" panels.
Here is a simplified architectural representation of how this historical data pipeline functions:
+------------------------------------+
| Annals of Joseon Dynasty API | <-- 500 Years of Historical Texts
+------------------------------------+
|
v (Python Scraping & Parsing)
+------------------------------------+
| JSON/CSV Dataset of Omens | <-- Normalized schema (Date, Type, Severity)
+------------------------------------+
|
v (Custom Prometheus Exporter)
+------------------------------------+
| Prometheus Metrics Endpoint | <-- Exposes 'joseon_omen_count_total'
+------------------------------------+
|
v (Piped via Prometheus)
+------------------------------------+
| Grafana Dashboard | <-- Time-series charts, Heatmaps, Alerting
+------------------------------------+
Defining the Metrics: What is a "Severe" Omen?
In modern site reliability engineering (SRE), we classify incidents by severity: P0 (site down), P1 (core feature broken), P2 (degraded performance), and so on. In the Joseon-Omens dashboard, the creator mapped historical phenomena to these exact modern concepts.
For example, how do you categorize a solar eclipse versus a local report of a three-legged chicken? The project maps them using custom Prometheus metrics like joseon_omen_count_total{type="astronomy", severity="critical"}.
The Metric Schema
If we look at how a custom exporter might expose this data to Prometheus, the payload looks surprisingly familiar:
# HELP joseon_omen_count_total Total number of recorded omens.
# TYPE joseon_omen_count_total counter
joseon_omen_count_total{king="Seonjo", type="astronomy", subtype="solar_eclipse", severity="critical"} 42
joseon_omen_count_total{king="Seonjo", type="weather", subtype="frost_in_summer", severity="warning"} 18
joseon_omen_count_total{king="Seonjo", type="zoology", subtype="hen_turns_into_rooster", severity="info"} 5
Let's look at a quick Python snippet demonstrating how this data is parsed and registered as Prometheus metrics using the official client library:
from prometheus_client import start_http_server, Counter
import time
import json
# Define the metric
OMEN_COUNTER = Counter(
'joseon_omen_count_total',
'Total historical court omens recorded',
['king', 'type', 'severity']
)
def bootstrap_historical_metrics():
# Simulated load of historical parsed JSON data
historical_data = [
{"king": "Seonjo", "type": "astronomy", "severity": "critical"},
{"king": "Seonjo", "type": "weather", "severity": "warning"},
{"king": "Taejo", "type": "zoology", "severity": "info"}
]
for omen in historical_data:
OMEN_COUNTER.labels(
king=omen["king"],
type=omen["type"],
severity=omen["severity"]
).inc()
if __name__ == '__main__':
# Start Prometheus metrics server on port 8000
start_http_server(8000)
bootstrap_historical_metrics()
print("Prometheus historical exporter running on port :8000")
while True:
time.sleep(1)
Lesson 1: Alert Fatigue is 500 Years Old
One of the most hilarious, yet educational, aspects of this project is observing the spikes in the Grafana dashboard during specific reigns. For instance, during the reign of King Jungjong (1506–1544), the number of reported omens sky-rocketed.
Why? Was the universe actually more angry at Jungjong than other kings?
Not necessarily. In historical context, Jungjong was placed on the throne by a coup. He was politically weak, and his court officials constantly used "omens" (like a dog sitting on the palace roof) as political leverage to pressure the king into changing his policies. In DevOps terms, the court officials were tuning down the alert threshold to generate noise and force a change in the system's state.
This is the classic Alert Fatigue problem. When everything is a warning, nothing is a warning. If your Slack channel is flooded with [WARNING] CPU usage is 81% fifty times a day, you will eventually mute the channel. When a genuine P0 event occurs, you'll miss it—just like historical kings eventually started ignoring their ministers' warnings of "impending doom" because they heard it every single Tuesday.
Lesson 2: The Importance of a "Post-Mortem"
When an anomaly (omen) occurred in the Joseon court, they didn't just log it and move on. They had a structured, highly regulated incident response pipeline:
- Triage: The Office of Astronomy (Gwansanggam) identified the anomaly (e.g., an unpredicted solar eclipse) and logged the incident.
- Impact Analysis: The King's advisors determined what this meant for the state of the kingdom (the "system").
- Mitigation: The King would issue a self-reproaching decree, reduce the complexity of royal meals (essentially going into "safe mode" or "read-only mode"), and temporarily halt major public works to conserve resources.
- Retrospective (Post-Mortem): Scholars gathered to discuss what policy failures (code bugs) led to this cosmic displeasure, and how to patch the political system to prevent future alerts.
If we strip away the superstition, this is an incredibly robust, blameless post-mortem framework. The focus was not just on pointing fingers (though there was plenty of political maneuvering), but on identifying the root cause of systemic instability and applying a fix to stabilize the environment.
Lesson 3: Semantics Matter in Observability
When looking at the dashboard, we see categories like "Atmosphere," "Earthquakes," "Flora/Fauna Anomaly," and "Astronomy." In modern observability, we categorise our data into Metrics, Logs, and Traces (the three pillars), supplemented by Events.
An omen is fundamentally an Event. Unlike continuous metrics (like CPU temperature or system memory), an event is discrete, contextual, and rich in metadata.
If we were to model a Joseon Omen as an Event payload in a modern platform like OpenTelemetry or Datadog, it would look like this:
{
"timestamp": "1592-06-15T02:30:00Z",
"event_type": "omen.meteorology.hail",
"severity": "ERROR",
"attributes": {
"location": "Hanyang (Seoul), Palace Courtyard",
"size_metric": "size_of_chestnuts",
"damage_reported": true,
"ruling_monarch": "Seonjo",
"dynasty_year": 25
},
"message": "Hail fell in the courtyard, of the size of chestnuts. Peaches and plums were damaged, destroying the autumn harvest prospects."
}
When building your own logging pipelines, are you just dumping unstructured strings into your logging daemon, or are you structuring your events with rich metadata like the JSON above? Having tags like location, ruling_monarch (service owner), and size_metric (impact scale) makes querying, filtering, and debugging vastly faster when things go sideways.
The Takeaway: What is Your "Three-Legged Chicken"?
The Joseon-Omens dashboard is more than just a clever data visualization project; it is a brilliant reminder that humans have always been obsessed with monitoring complex systems. The universe was the medieval world's microservice architecture—unpredictable, occasionally erratic, and desperately in need of monitoring.
As you go back to your day job of managing Kubernetes clusters, database replication lags, and API response times, ask yourself:
- Are we configuring alerts based on meaningful system health, or are we just alerting on "strange vapors" that don't actually impact our users?
- Is our alert threshold configured by political anxiety (like King Jungjong's court) or by actual statistical anomalies?
- Do we have a clear, documented mitigation pipeline when our dashboard lights up red?
Shout out to ryanking13 on GitHub for creating this masterpiece of historical tech art. If you want to run the dashboard yourself, head over to the repository, spin up the Docker Compose file, and start monitoring the ancient heavens.
What’s the weirdest "omen" you’ve ever found in your production logs? Let me know in the comments below, or share your favorite historical anomalies with me on Twitter/X at @sysseder!