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Learn how to check what metrics are available for monitoring your service using Prometheus, and find out which of the available metrics are particularly worth monitoring and why.

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About the Prometheus integration

The Prometheus integration allows you to monitor your Aiven services and understand the resource usage. Using this integration, you can track non-service-specific system metrics that provide insights into infrastructure performance. To start using Prometheus for monitoring metrics, configure the Prometheus integration and set up the Prometheus server.

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Get a list of available service metrics

To discover the metrics available for your services, make an HTTP GET request to your Prometheus service endpoint.
1

Collect connection information

Once your Prometheus integration is configured, collect the following details from Aiven Console:
  1. Navigate to the Overview page of your service
  2. Go to the Connection information section
  3. Open the Prometheus tab
  4. Copy:
    • Prometheus URL
    • Username
    • Password
2

Request metrics snapshot

Make a request to get a snapshot of your metrics:
curl -k --user USERNAME:PASSWORD PROMETHEUS_URL/metrics
Replace USERNAME, PASSWORD, and PROMETHEUS_URL with your service values.
3

Review available metrics

The output provides a full list of metrics available for your service.

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Key system metrics

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CPU usage metrics

CPU usage metrics help determine if the CPU is constantly being maxed out.

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High-level CPU usage

For a single CPU service, get an overview with: 
100 - cpu_usage_idle{cpu="cpu-total"}
A process with a nice value larger than 0 is categorized as cpu_usage_nice, which is not included in cpu_usage_user.

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CPU I/O wait monitoring

Monitor cpu_usage_iowait{cpu="cpu-total"} to detect I/O bottlenecks. A high value indicates that the service node is working on something I/O intensive.For example, if cpu_usage_iowait{cpu="cpu-total"} equals 40, the CPU is idle waiting for disk or network I/O operations for 40% of the time.

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CPU metrics reference

These metrics are generated from the Telegraf CPU plugin:
MetricDescription
cpu_usage_idlePercentage of time the CPU is idle
cpu_usage_systemPercentage of time the Kernel code is consuming the CPU
cpu_usage_userPercentage of time the CPU is in user-space programs with nice ≤ 0
cpu_usage_nicePercentage of time the CPU is in user-space programs with nice > 0
cpu_usage_iowaitPercentage of time the CPU is idle when the system has pending disk I/O operations
cpu_usage_stealPercentage of time waiting for the hypervisor to give CPU cycles to the VM
cpu_usage_irqPercentage of time the system is handling interrupts
cpu_usage_softirqPercentage of time the system is handling software interrupts
cpu_usage_guestPercentage of time the CPU is running for a guest OS
cpu_usage_guest_nicePercentage of time the CPU is running for a guest OS with low priority

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Disk usage metrics

Monitoring disk usage ensures that applications or processes don’t fail due to insufficient disk storage.
Consider monitoring disk_used_percent and disk_free to prevent storage-related issues.

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Disk metrics reference

MetricDescription
disk_freeFree space on the service disk
disk_usedUsed space on the disk (e.g., 1.0e+9 = 8,000,000,000 bytes)
disk_totalTotal space on the disk (free and used)
disk_used_percentPercentage of disk space used: disk_used / disk_total * 100 (e.g., 80 = 80% usage)
disk_inodes_freeNumber of index nodes available on the service disk
disk_inodes_usedNumber of index nodes used on the service disk
disk_inodes_totalTotal number of index nodes on the service disk

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Memory usage metrics

Memory consumption metrics are essential to ensure the performance of your service.
Consider monitoring mem_available (in bytes) or mem_available_percent, as this represents the estimated amount of memory available for applications without swapping.

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Key memory metrics

  • mem_available - Available memory for applications (bytes)
  • mem_available_percent - Available memory as a percentage
  • mem_total - Total system memory
  • mem_used - Used memory
  • mem_free - Free memory
  • mem_cached - Cached memory
  • mem_buffered - Buffered memory

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Network usage metrics

Monitoring the network provides visibility of your network utilization and traffic, allowing you to act immediately in case of network issues.
It may be worth monitoring the number of established TCP sessions available in the netstat_tcp_established metric.

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Key network metrics

  • netstat_tcp_established - Number of established TCP connections
  • net_bytes_sent - Bytes sent over the network
  • net_bytes_recv - Bytes received from the network
  • net_packets_sent - Packets sent over the network
  • net_packets_recv - Packets received from the network
  • net_err_in - Inbound network errors
  • net_err_out - Outbound network errors

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Monitoring best practices

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Alerting thresholds

Consider setting alerts for:
  • CPU usage - Alert when 100 - cpu_usage_idle > 80% for extended periods
  • CPU I/O wait - Alert when cpu_usage_iowait > 30% consistently
  • Disk usage - Alert when disk_used_percent > 85%
  • Memory available - Alert when mem_available_percent < 15%
  • TCP connections - Alert on unusual changes in netstat_tcp_established

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Query examples

100 - cpu_usage_idle{cpu="cpu-total"}

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Visualization recommendations

For effective monitoring dashboards:
  1. Overview panel - Display CPU, memory, disk, and network at a glance
  2. CPU breakdown - Show user, system, and I/O wait separately
  3. Disk trends - Plot usage over time to predict capacity needs
  4. Memory pressure - Track available memory and swap usage
  5. Network traffic - Monitor bytes sent/received and error rates

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Troubleshooting

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No metrics appearing

  • Verify the Prometheus integration is enabled on your service
  • Check credentials (username and password) are correct
  • Confirm the Prometheus URL is accessible
  • Allow a few minutes for initial metrics to populate

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Incomplete metrics

  • Some metrics may not be available for all service types
  • Check service logs for any integration errors
  • Verify the Prometheus server is configured correctly

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High cardinality warnings

  • Limit the number of unique label combinations
  • Use Prometheus recording rules for frequently queried metrics
  • Consider adjusting retention policies

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