Automating Cloud Troubleshooting

During my internship at IBM Cloud, I tackled a problem that had long plagued engineers: diagnosing unassigned Elasticsearch shards. Before my work, shard assignment failures threw generic error messages, leaving engineers to manually sift through logs and system metrics to pinpoint root causes. My goal? Automate the pain away.

The Problem: Unassigned Shards and Manual Debugging

Elasticsearch, the backbone of many search and analytics applications, relies on a distributed architecture where data is divided into shards and spread across multiple nodes. This design enables scalability and fault tolerance, but it also introduces challenges: shards sometimes refuse to assign themselves to nodes. Why? Could be insufficient disk space, a misconfiguration, a failed node—take your pick. The worst part? Elasticsearch wasn’t exactly handing out clear answers.

Previously, engineers had to manually troubleshoot unassigned shards by combing through logs, running API calls, and making educated guesses. This process wasn’t just tedious—it was inefficient. The longer it took to diagnose the issue, the longer databases remained in a degraded state.

The Solution: Enhancing icdctl with eseu

My work focused on automating shard diagnostics within icdctl (IBM Cloud Database Control), a command-line tool for managing IBM Cloud databases. I developed and integrated eseu (Elasticsearch Explain Unassigned), a command designed to take the guesswork out of troubleshooting.

Here’s what eseu brought to the table:

• Automated Diagnostics: Instead of leaving engineers to decipher cryptic error logs, eseu analyzed Elasticsearch’s internal state and provided human-readable explanations for unassigned shards.
• Actionable Insights: Rather than just saying “Shard unassigned,” eseu would report: “Shard cannot be assigned due to insufficient disk space on Node X. Consider freeing up space or reconfiguring shard allocation settings.”
• Efficiency Gains: By reducing the need for manual investigation, eseu significantly sped up the time-to-resolution for shard issues.

From Manual Debugging to Intelligent Automation

Before automation, troubleshooting shard assignments was a slow and reactive process. Engineers had to:

1. Identify the affected database.
2. Check Elasticsearch cluster health.
3. Run multiple API calls to get allocation explanations.
4. Cross-reference logs and system metrics.
5. Determine the root cause manually.

With eseu, this workflow transformed into a streamlined, proactive approach:

1. Run icdctl eseu.
2. Receive an immediate, detailed diagnostic report.
3. Take informed corrective action.

Lessons Learned and Impact

Building automation for Elasticsearch troubleshooting wasn’t just a coding exercise—it required a deep dive into Elasticsearch’s internals, error reporting mechanisms, and cloud database management. One of the biggest challenges? Ensuring accuracy in diagnostics while keeping error messages concise and actionable.

While my contribution was just a piece of the larger IBM Cloud ecosystem, it made a real difference. Engineers could now resolve shard assignment issues faster, reducing database downtime and improving overall operational efficiency.

Final Thoughts

This project reinforced the power of automation in cloud infrastructure. By taking a previously manual, error-prone process and turning it into a fast, reliable command-line tool, I got firsthand experience in building practical tools that enhance developer productivity.

More importantly, I saw how even small improvements can ripple through a system, saving engineers valuable time and making cloud database management just a little bit smoother.