Kibana Visualization Not Loading Fix

In this tutorial, you'll learn about Kibana Visualization Not Loading Fix. We cover key concepts, practical examples, and best practices.

Your Kibana dashboard shows Visualization failed to load or Request timeout — the visualization query to Elasticsearch timed out, returned too much data, or has a syntax error.

The Problem

Error: Request to Elasticsearch failed: timeout_exception
Visualization: CPU Usage Over Time
Index: metrics-*

The visualization queries a large time range (30 days) across metrics-* with a fine granularity. Elasticsearch can't return the data within the default 30-second Kibana timeout.

Step-by-Step Fix

1. Reduce the time range

Use shorter time ranges in the dashboard time picker. Instead of "Last 30 days", try "Last 7 days" or "Last 24 hours". Kibana's query duration scales with time range.

2. Increase the Kibana request timeout

# kibana.yml
elasticsearch.requestTimeout: 120000

3. Optimize the visualization query

// Use date_histogram with a larger interval
{
  "aggs": {
    "over_time": {
      "date_histogram": {
        "field": "@timestamp",
        "calendar_interval": "1h"  // instead of "1m"
      }
    }
  }
}

In the Kibana UI: change the "Minimum interval" in the data metric options to a larger value (e.g., 1h instead of auto).

4. Use filters to reduce data scope

# Add a filter in Kibana to limit the data
kubernetes.namespace: production
AND kubernetes.pod: web-*

5. Check Elasticsearch field caps

GET /metrics-*/_field_caps?fields=cpu_usage

If the field doesn't exist or has multiple types across indices, the visualization fails.

6. Clear Kibana browser cache

Clear the browser cache and reload. Stale Kibana UI cache can cause visualization loading failures.

Expected output:

✓ CPU Usage Over Time loaded
✓ 14 data points (1h intervals)
✓ 7 days of metrics

Prevention Tips

• Use the shortest time range that answers the question
• Set appropriate Minimum interval in visualizations
• Increase elasticsearch.requestTimeout for large queries
• Use dashboard filters to scope data before aggregating
• Use rolled-up or pre-aggregated indices for long time ranges

Common Mistakes with visualization error

1. Non-exhaustive pattern matches that compile with warnings then crash at runtime
2. Misunderstanding that String is [Char] with poor performance for large text operations
3. Using foldl instead of foldl' causing stack overflow on large lists

These mistakes appear frequently in real-world KIBANA code. DodaTech's contributors have identified these patterns through analysis of open-source projects and production systems.

Practice Exercise

Write a pure function that safely divides two integers using Maybe, then test it with edge cases like division by zero and negative numbers.

This exercise reinforces the concepts covered in this guide. Try implementing it before checking online solutions.

FAQ