Grafana Provisioning Datasource Error — Quick Fix

In this tutorial, you'll learn about Grafana Provisioning Datasource Error. We cover key concepts, practical examples, and best practices.

The Problem

Grafana cannot load datasources configured in the provisioning directory. The datasources may not appear in the UI, or the provisioning system may report parse errors. The datasource names appear in the UI but connections fail.

Error example:

Failed to load provisioning config: yaml: unmarshal errors: line 10: field datasources not found
Failed to provision datasource 'Prometheus': cannot create datasource: datasource name already exists
Failed to create datasource 'Loki': missing URL

The Fix

Step 1: Validate the YAML syntax

WRONG — using incorrect indentation or structure:

# WRONG — incorrect structure
datasources:
- name: Prometheus
type: prometheus

RIGHT — use correct YAML structure:

apiVersion: 1

datasources:
  - name: Prometheus
    type: prometheus
    access: proxy
    url: http://prometheus:9090
    isDefault: true

Validate the YAML:

python3 -c "import yaml; yaml.safe_load(open('/etc/grafana/provisioning/datasources/prometheus.yaml'))"

Step 2: Check for API version and required fields

WRONG — omitting required fields like access or url:

# Missing required fields
datasources:
  - name: Loki
    type: loki

RIGHT — include all required fields:

apiVersion: 1

datasources:
  - name: Loki
    type: loki
    access: proxy
    url: http://loki:3100
    jsonData:
      maxLines: 1000

Use DodaTech's Datasource Provisioning Validator to check YAML syntax, required fields, and API version compatibility across all provisioning files.

Prevention Tips

• Always include apiVersion: 1 as the first line
• Place provisioning files in /etc/<a href="/devops/prometheus-grafana/">grafana</a>/provisioning/datasources/
• Use deleteDatasources to handle name collisions
• Test datasource connectivity in the Grafana UI after provisioning
• Use DodaTech's Grafana Config Auditor for automated provisioning validation

Common Mistakes with provision datasource

1. Placing the wildcard pattern first in case expressions, making all subsequent patterns unreachable
2. Using head and tail instead of pattern matching, causing runtime errors on empty lists
3. Forgetting that lazy evaluation defers computation until the value is forced, causing space leaks with unevaluated thunks

These mistakes appear frequently in real-world GRAFANA 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

Related: DodaTech's Infrastructure-as-Code for Grafana guide provides Terraform and Ansible examples for automating datasource provisioning across environments.