Custom Metrics Missing in Datadog

Missing custom metrics in Datadog can disrupt monitoring and decision-making. Here’s how to fix it:

• Check Agent Setup: Ensure API keys are valid, permissions are correct, and the agent is configured properly.
• Fix Custom Checks: Verify Python code for errors, correct metric types, and ensure proper YAML formatting.
• Review Metrics Explorer: Adjust queries, tag filters, and metric limits to ensure visibility.

Quick Commands:

• Verify agent status: sudo datadog-agent status
• Debug logs: grep -i "error\|warning" /var/log/datadog/agent.log
• Update JMX limits: avn service integration-update -c max_jmx_metrics=10000

Key Tip: Use Datadog's agent flare command to generate diagnostic reports for deeper troubleshooting. Proper configuration and proactive monitoring ensure reliable metric collection.

Sending Custom Metrics, tags to DataDog using Python

Why Custom Metrics Go Missing

Custom metrics can disappear due to problems with agent setup, custom checks, or display queries. According to Datadog's 2024 Agent Troubleshooting Guide, 43% of cases involving missing metrics are linked to API key misconfigurations. This section dives into common setup and configuration errors to help you identify and fix the root causes.

Agent Setup Problems

Agent misconfigurations often disrupt metric transmission. For example, invalid or expired API keys can lead to HTTP 403 errors, blocking data submission. A 2023 case study highlighted that upgrading from v6.32 to v7.52 (which added Python 3.10 support for custom checks) resolved missing metrics in several cases.

Another common issue is insufficient system permissions. Scripts in /etc/datadog-agent/checks.d/ must have chmod 755 permissions. To verify, use:

namei -l /path/to/check.py

Finally, review your custom check code for potential errors that could interfere with metric collection.

Custom Check Setup Issues

Code errors account for roughly one-third of custom metric failures. Datadog's documentation notes that 32% of custom check failures are caused by incorrect metric type method calls.

Check these areas carefully to ensure your custom checks are functioning as expected.

Metrics Explorer Display Issues

Sometimes, metrics are collected correctly but don’t show up due to query or filter mismatches. A common issue involves tag mismatches - filtering for env:prod won’t display metrics tagged as environment:production. Additionally, Datadog's default limits can restrict visibility:

• Default JMX metric limit: 2,000 metrics (can be increased to 100,000)
• Pro plan allocation: 100 custom metrics per host, pooled across infrastructure

For example, Aiven Kafka Service users in 2022 encountered warnings like "3,378 metrics dropped" after hitting the default JMX limit. They resolved the issue by increasing the limit using the CLI:

avn service integration-update -c max_jmx_metrics=10000

To ensure metrics display correctly, verify that collection intervals align with your query settings. Use the metric's metadata tab to check the collection frequency and adjust your queries if needed.

How to Check Metric Collection

You can confirm metric collection by checking the agent's status and reviewing log files.

Agent Status Checks

Use the datadog-agent status command to verify if metrics are being collected. Run the command with elevated privileges:

# Linux/Mac
sudo datadog-agent status

# Windows
.\datadog-agent status

A successful custom check will show active metrics with their current values. For example, this output confirms metrics are being collected:

app_orders (2.0.0)
-------------------
Instance ID: app_orders:abc123 [OK]
Total Runs: 42
Metrics:
- app.orders.count (gauge) → 57.0

For more detailed troubleshooting, you can trigger an immediate check collection:

sudo -u dd-agent datadog-agent check app_orders

After this, move on to reviewing the logs for additional details.

Log Analysis Steps

If the agent status looks fine, the next step is to analyze the logs. These logs provide detailed information about metric collection. Here are the standard log file locations:

A successful metric transmission will appear in the logs like this:

"2025-05-03 20:15:42 EDT | INFO | (pkg/forwarder/transaction.go:237) | Successfully posted payload to https://app.datadoghq.com/api/v1/check_run?api_key=***, response: {"status":"ok"}"

To find potential issues, search the logs for errors or warnings:

grep -i "error\|warning" /var/log/datadog/agent.log

If you need more detailed logs, set the log level to debug in your configuration file:

log_level: debug

If metrics don’t show up within five minutes of collection, generate a diagnostic report using the agent flare command:

sudo datadog-agent flare --email your@email.com

This command creates a bundle that includes a metrics.json file, which lists all collected metrics and their associated tags. Use this to pinpoint any formatting or transmission issues before they are sent to Datadog's backend.

How to Fix Common Problems

Once you've confirmed that metrics are being collected, use these steps to address common configuration and code issues.

Agent Configuration Fixes

Check and update your agent's configuration in the datadog.yaml file:

api_key: YOUR_ACTUAL_API_KEY
site: datadoghq.com

Make sure the file permissions are set correctly to avoid access problems:

# Linux/Mac
sudo chown -R dd-agent:dd-agent /etc/datadog-agent/
sudo chmod 640 /etc/datadog-agent/datadog.yaml

# Windows (PowerShell as Administrator)
$acl = Get-Acl "C:\ProgramData\Datadog\datadog.yaml"
$rule = New-Object System.Security.AccessControl.FileSystemAccessRule("ddagentuser","Read","Allow")
$acl.SetAccessRule($rule)
Set-Acl "C:\ProgramData\Datadog\datadog.yaml" $acl

Custom Check Code Fixes

Errors in your Python scripts can block metric collection. Address common issues like this:

# Use the correct naming format for metrics
def check(self, instance):
    # Correct example
    self.gauge('custom.metric.name', value)

    # Ensure tags are formatted properly
    tags = ['environment:production', 'service:orders']

If you're working with JSON-based custom metrics, ensure the formatting is accurate:

{
  "series": [
    {
      "metric": "custom.metric.name",
      "points": [[<timestamp>, <value>]],
      "tags": ["environment:production"],
      "type": "gauge"
    }
  ]
}

Metrics Explorer Query Fixes

Double-check metric names, adjust the time range, and simplify tag filters if needed.

Here’s an example of a correctly formatted query:

# Correct format
avg:custom.metric.name{environment:production} by {service}

# Incorrect format
avg:Custom_Metric_Name{*} # Avoid this

If you're troubleshooting, temporarily set the log level to debug in the datadog.yaml file to verify submission:

log_level: debug

Restart the agent to apply changes:

sudo systemctl restart datadog-agent  # Linux
Restart-Service -Name datadogagent    # Windows

Conclusion

Summary of Solutions

Custom metric issues often stem from agent misconfigurations, faulty custom checks, or query display errors. Research shows that applying proper tag filtering improves metric visibility by 67% and reduces custom metric noise by 58%. Additionally, 41% of large cluster monitoring problems are linked to incorrect JMX metric configurations. Adjusting the max_jmx_metrics setting can help avoid thousands of dropped metrics in such scenarios. These findings highlight actionable steps to improve metric reliability.

Recommended Actions

To ensure dependable metric collection, consider these steps:

• Conduct Regular Metric Audits
  Use Datadog's Metric Summary page to identify and remove redundant metrics. Set up automated alerts for metric submission failures and fine-tune collection intervals for less critical metrics.
• Adopt Proactive Monitoring
  Implement service check monitors to track agent health. Use log monitors to capture ERROR or WARN messages, and define service level objectives (SLOs) to maintain consistent metric collection.
• Streamline Configuration Management
  Validate YAML configurations and follow naming conventions before deployment. Leverage automation tools like Terraform to ensure consistent setups.

For example, increasing the max_jmx_metrics setting successfully resolved dropped metrics in a production Kafka cluster, demonstrating the impact of proper configuration.

FAQs

Why are my custom metrics not showing up in Datadog, and how can I fix this?

Custom metrics might not appear in Datadog due to a few common issues. First, ensure that the metrics are being sent correctly by verifying your code or integration setup. Check for typos in metric names, tags, or data types. Second, confirm that your account has not exceeded its custom metrics limit, as this could prevent new metrics from being processed. Third, make sure the metric data is being sent to the correct Datadog API endpoint.

To resolve these issues, double-check your instrumentation, monitor usage limits in your Datadog account, and review any error logs for clues. If the problem persists, reach out to Datadog support for further assistance.

Why aren't my custom metrics showing up in Datadog, and how can I check if they're being collected correctly?

If your custom metrics are not appearing in Datadog, there are a few key steps you can take to verify if they’re being collected properly:

1. Check your Datadog Agent logs: Ensure the Agent is running and configured correctly to collect custom metrics. Look for any error messages or warnings related to metric collection.
2. Verify your metric submission: Confirm that your application is sending metrics to Datadog using the correct API calls or libraries. Ensure the metric names follow Datadog’s naming conventions and avoid unsupported characters.
3. Inspect your Datadog Dashboard: Use the Metrics Explorer or the Live Tail tool to search for your custom metric. If it’s not visible, double-check that the metric is being sent to the correct Datadog account and region.

If you’ve verified all these steps and your metrics still aren’t appearing, it may help to consult Datadog’s documentation or reach out to support for further assistance.

Why aren't my custom metrics showing up in the Metrics Explorer, and how can I fix this?

If your custom metrics are not appearing in the Metrics Explorer, here are some steps you can take to troubleshoot and resolve the issue:

1. Verify metric submission: Ensure your application is correctly sending the custom metrics to Datadog. Check your logs or use the Datadog Agent status command to confirm the metrics are being submitted.
2. Check metric naming conventions: Custom metric names must follow Datadog's naming rules (e.g., no spaces, use underscores instead of special characters). Incorrect naming can prevent metrics from being processed.
3. Confirm API key configuration: Ensure your Datadog API key is correctly configured in your application or agent. An invalid or missing API key can block metrics from being sent to your account.
4. Adjust time range: In the Metrics Explorer, ensure the selected time range includes the period when the custom metrics were submitted. Metrics outside the selected range won’t be displayed.
5. Ensure proper tags: If you're filtering by tags in the Metrics Explorer, confirm that the custom metrics include the correct tags. Missing or mismatched tags can cause display issues.

If you've checked these steps and the issue persists, consider reviewing your Datadog Agent logs or contacting Datadog support for further assistance.

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