Building Self-Healing Data Pipelines: How ARKRAFT Maintains 99.97% Uptime
At 13:52 KST on February 18th, the KRX primary data feed dropped 342 tickers from its equity universe. No warning, no error code — just silence on those symbols. Our self-healing system detected the gap in 6 seconds, triangulated the missing data from two alternative sources, and restored full coverage in 14 seconds total. Zero signals were interrupted.
This post explains how that system works.
The Problem with Traditional Monitoring
Most data pipeline monitoring follows the alert-investigate-fix pattern: something breaks, an alert fires, a human investigates, a human fixes it. This works when you have hours. In systematic trading, you have seconds.
The KRX incident happened during the Asian equity session opening — one of the highest-activity windows of the day. A 5-minute gap in Korea equity data would have cascaded: signals would have generated on incomplete data, portfolio rebalancing would have used stale weights, and execution would have sent orders based on outdated prices.
ARKRAFT's Self-Healing Architecture
Our approach replaces the human-in-the-loop with three autonomous layers:
Layer 1: Continuous Completeness Checks
Every 2 seconds, a completeness agent scans each data feed against its expected universe. "Expected" is learned — the agent maintains a rolling 30-day history of what each feed normally provides. When the actual payload drops below 95% of expected, it triggers investigation.
Layer 2: Root Cause Classification
The investigation agent classifies the gap into one of five categories: - Feed delay (most common, 70% of incidents — wait and retry) - Partial feed failure (what happened with KRX — triangulate from alternatives) - Full feed outage (switch to backup provider) - Data quality issue (values present but anomalous — quarantine and flag) - Universe change (legitimate addition/removal — update expected baseline)
Classification uses a decision tree trained on 18 months of incident data. Accuracy: 94%.
Layer 3: Autonomous Resolution
Each category has a resolution playbook. For partial feed failure: identify which alternative sources carry the missing symbols, query them in parallel, merge results with latency-priority weighting (prefer the source with lower latency when multiple have the data), and validate that the reconstructed feed matches expected statistical properties.
The Numbers
Since deploying self-healing pipelines 14 months ago:
- •Incidents detected: 2,847
- •Autonomously resolved: 2,791 (98%)
- •Required human intervention: 56 (2%)
- •Mean detection time: 4.2 seconds
- •Mean resolution time: 11.8 seconds
- •System uptime: 99.97%
The 56 human-escalated incidents were all novel failure modes the system hadn't seen before. After human resolution, each was catalogued and added to the classification model — the system literally learns from every incident it can't solve.
Lessons Learned
- 1.Completeness is more important than correctness — A missing data point is worse than a slightly inaccurate one. You can hedge against noise; you can't trade on absence.
- 2.Latency budgets matter — Our 14-second resolution time consumes 7% of our 200ms latency SLA. Every second of detection time is a second of stale data.
- 3.Alternative source quality varies — Not all backup feeds are created equal. We maintain a real-time quality score for each source, updated every hour, so the triangulation engine knows which sources to trust.