---
layout: docs
page_title: Monitoring Nomad
description: |-
  Overview of runtime metrics available in Nomad along with monitoring and
  alerting.
---

# Monitoring Nomad

The Nomad client and server agents collect a wide range of runtime metrics.
These metrics are useful for monitoring the health and performance of Nomad
clusters. Careful monitoring can spot trends before they cause issues and help
debug issues if they arise.

All Nomad agents, both servers and clients, report basic system and Go runtime
metrics.

Nomad servers all report many metrics, but some metrics are specific to the
leader server. Since leadership may change at any time, these metrics should be
monitored on all servers. Missing (or 0) metrics from non-leaders may be safely
ignored.

Nomad clients have separate metrics for the host they are running on as well as
for each allocation being run. Both of these metrics [must be explicitly
enabled][telemetry-stanza].

By default, the Nomad agent collects telemetry data at a [1 second
interval][collection-interval]. Note that Nomad supports [gauges, counters, and
timers][metric-types].

There are three ways to obtain metrics from Nomad:

- Query the [/v1/metrics API endpoint][metrics-api-endpoint] to return metrics
  for the current Nomad process. This endpoint supports Prometheus formatted
  metrics.

- Send the USR1 signal to the Nomad process. This will dump the current
  telemetry information to STDERR (on Linux).

- Configure Nomad to automatically forward metrics to a third-party provider
  such as [DataDog][datadog-telem], [Prometheus][prometheus-telem],
  [statsd][statsd-telem], and [Circonus][circonus-telem].

## Alerting

The recommended practice for alerting is to leverage the alerting capabilities
of your monitoring provider. Nomad’s intention is to surface metrics that enable
users to configure the necessary alerts using their existing monitoring systems
as a scaffold, rather than to natively support alerting. Here are a few common
patterns.

- Export metrics from Nomad to Prometheus using the [StatsD
  exporter][statsd-exporter], define [alerting rules][alerting-rules] in
  Prometheus, and use [Alertmanager][alertmanager] for summarization and
  routing/notifications (to PagerDuty, Slack, etc.). A similar workflow is
  supported for [Datadog][datadog-alerting].

- Periodically submit test jobs into Nomad to determine if your application
  deployment pipeline is working end-to-end. This pattern is well-suited to
  batch processing workloads.

- Deploy Nagios on Nomad. Centrally manage Nomad job files and add the Nagios
  monitor when a new Nomad job is added. When a job is removed, remove the
  Nagios monitor. Map Consul alerts to the Nagios monitor. This provides a
  job-specific alerting system.

- Write a script that looks at the history of each batch job to determine
  whether or not the job is in an unhealthy state, updating your monitoring
  system as appropriate. In many cases, it may be ok if a given batch job fails
  occasionally, as long as it goes back to passing.

# Key Performance Indicators

Nomad servers' memory, CPU, disk, and network usage all scales linearly with
cluster size and scheduling throughput. The most important aspect of ensuring
Nomad operates normally is monitoring these system resources to ensure the
servers are not encountering resource constraints.

The sections below cover a number of other important metrics.

## Consensus Protocol (Raft)

Nomad uses the Raft consensus protocol for leader election and state
replication. Spurious leader elections can be caused by networking
issues between the servers, insufficient CPU resources, or
insufficient disk IOPS. Users in cloud environments often bump their
servers up to the next instance class with improved networking and CPU
to stabilize leader elections, or switch to higher-performance disks.

The `nomad.raft.leader.lastContact` metric is a general indicator of
Raft latency which can be used to observe how Raft timing is
performing and guide infrastructure provisioning. If this number
trends upwards, look at CPU, disk IOPs, and network
latency. `nomad.raft.leader.lastContact` should not get too close to
the leader lease timeout of 500ms.

The `nomad.raft.replication.appendEntries` metric is an indicator of
the time it takes for a Raft transaction to be replicated to a quorum
of followers. If this number trends upwards, check the disk I/O on the
followers and network latency between the leader and the followers.

The details for how to examine CPU, IO operations, and networking are
specific to your platform and environment. On Linux, the `sysstat`
package contains a number of useful tools. Here are examples to
consider.

- **CPU** - `vmstat 1`, cloud provider metrics for "CPU %"

- **IO** - `iostat`, `sar -d`, cloud provider metrics for "volume
  write/read ops" and "burst balance"

- **Network** - `sar -n`, `netstat -s`, cloud provider metrics for
  interface "allowance"

The `nomad.raft.fsm.apply` metric is an indicator of the time it takes
for a server to apply Raft entries to the internal state machine. If
this number trends upwards, look at the `nomad.nomad.fsm.*` metrics to
see if a specific Raft entry is increasing in latency. You can compare
this to warn-level logs on the Nomad servers for `attempting to apply
large raft entry`. If a specific type of message appears here, there
may be a job with a large job specification or dispatch payload that
is increasing the time it takes to apply Raft messages. Try shrinking the size
of the job either by putting distinct task groups into separate jobs,
downloading templates instead of embedding them, or reducing the `count` on
task groups.

## Scheduling

The [Scheduling] documentation describes the workflow of how evaluations become
scheduled plans and placed allocations.

### Progress

There is a class of bug possible in Nomad where the two parts of the scheduling
pipeline, the workers and the leader's plan applier, *disagree* about the
validity of a plan. In the pathological case this can cause a job to never
finish scheduling, as workers produce the same plan and the plan applier
repeatedly rejects it.

While this class of bug is very rare, it can be detected by repeated log lines
on the Nomad servers containing `plan for node rejected`:

```
nomad: plan for node rejected: node_id=0fa84370-c713-b914-d329-f6485951cddc reason="reserved port collision" eval_id=098a5
```

While it is possible for these log lines to occur infrequently due to normal
cluster conditions, they should not appear repeatedly and prevent the job from
eventually running (look up the evaluation ID logged to find the job).

If this log *does* appear repeatedly with the same `node_id` referenced, try
[draining] the node and shutting it down. Misconfigurations not caught by
validation can cause nodes to enter this state: [#11830][gh-11830].

### Performance

The following metrics allow observing changes in throughput at the various
points in the scheduling process.

- **nomad.worker.invoke_scheduler.<type\>** - The time to run the
  scheduler of the given type. Each scheduler worker handles one
  evaluation at a time, entirely in-memory. If this metric increases,
  examine the CPU and memory resources of the scheduler.

- **nomad.broker.total_blocked** - The number of blocked
  evaluations. Blocked evaluations are created when the scheduler
  cannot place all allocations as part of a plan. Blocked evaluations
  will be re-evaluated so that changes in cluster resources can be
  used for the blocked evaluation's allocations. An increase in
  blocked evaluations may mean that the cluster's clients are low in
  resources or that job have been submitted that can never have all
  their allocations placed. Nomad also emits a similar metric for each
  individual scheduler. For example `nomad.broker.batch_blocked` shows
  the number of blocked evaluations for the batch scheduler.

- **nomad.broker.total_unacked** - The number of unacknowledged
  evaluations. When an evaluation has been processed, the worker sends
  an acknowledgment RPC to the leader to signal to the eval broker
  that processing is complete. The unacked evals are those that are
  in-flight in the scheduler and have not yet been acknowledged. An
  increase in unacknowledged evaluations may mean that the schedulers
  have a large queue of evaluations to process. See the
  `invoke_scheduler` metric (above) and examine the CPU and memory
  resources of the scheduler. Nomad also emits a similar metric for
  each individual scheduler. For example `nomad.broker.batch_unacked`
  shows the number of unacknowledged evaluations for the batch
  scheduler.

- **nomad.plan.evaluate** - The time to evaluate a scheduler plan
  submitted by a worker. This operation happens on the leader to
  serialize the plans of all the scheduler workers. This happens
  entirely in memory on the leader. If this metric increases, examine
  the CPU and memory resources of the leader.

- **nomad.plan.wait_for_index** - The time required for the planner to wait for
  the Raft index of the plan to be processed. If this metric increases, refer
  to the [Consensus Protocol (Raft)] section above. If this metric approaches 5
  seconds, scheduling operations may fail and be retried. If possible reduce
  scheduling load until metrics improve.

- **nomad.plan.submit** - The time to submit a scheduler plan from the
  worker to the leader. This operation requires writing to Raft and
  includes the time from `nomad.plan.evaluate` and
  `nomad.plan.wait_for_index` (above). If this metric increases, refer
  to the [Consensus Protocol (Raft)] section above.

- **nomad.plan.queue_depth** - The number of scheduler plans waiting
  to be evaluated after being submitted. If this metric increases,
  examine the `nomad.plan.evaluate` and `nomad.plan.submit` metrics to
  determine if the problem is in general leader resources or Raft
  performance.

Upticks in any of the above metrics indicate a decrease in scheduler
throughput.

## Capacity

The importance of monitoring resource availability is workload specific. Batch
processing workloads often operate under the assumption that the cluster should
be at or near capacity, with queued jobs running as soon as adequate resources
become available. Clusters that are primarily responsible for long running
services with an uptime requirement may want to maintain headroom at 20% or
more. The following metrics can be used to assess capacity across the cluster on
a per client basis.

- **nomad.client.allocated.cpu**
- **nomad.client.unallocated.cpu**
- **nomad.client.allocated.disk**
- **nomad.client.unallocated.disk**
- **nomad.client.allocated.iops**
- **nomad.client.unallocated.iops**
- **nomad.client.allocated.memory**
- **nomad.client.unallocated.memory**

## Task Resource Consumption

The metrics listed [here][allocation-metrics] can be used to track resource
consumption on a per task basis. For user facing services, it is common to alert
when the CPU is at or above the reserved resources for the task.

## Job and Task Status

See [Job Summary Metrics] for monitoring the health and status of workloads
running on Nomad.

## Runtime Metrics

Runtime metrics apply to all clients and servers. The following metrics are
general indicators of load and memory pressure.

- **nomad.runtime.num_goroutines**
- **nomad.runtime.heap_objects**
- **nomad.runtime.alloc_bytes**

It is recommended to alert on upticks in any of the above, server memory usage
in particular.

## Federated Deployments (Serf)

Nomad uses the membership and failure detection capabilities of the Serf library
to maintain a single, global gossip pool for all servers in a federated
deployment. An uptick in `member.flap` and/or `msg.suspect` is a reliable indicator
that membership is unstable.

If these metrics increase, look at CPU load on the servers and network
latency and packet loss for the [Serf] address.

[alerting-rules]: https://prometheus.io/docs/prometheus/latest/configuration/alerting_rules/
[alertmanager]: https://prometheus.io/docs/alerting/alertmanager/
[allocation-metrics]: /docs/telemetry/metrics#allocation-metrics
[circonus-telem]: /docs/configuration/telemetry#circonus
[collection-interval]: /docs/configuration/telemetry#collection_interval
[datadog-alerting]: https://www.datadoghq.com/blog/monitoring-101-alerting/
[datadog-telem]: /docs/configuration/telemetry#datadog
[draining]: https://learn.hashicorp.com/tutorials/nomad/node-drain
[gh-11830]: https://github.com/hashicorp/nomad/pull/11830
[metric-types]: /docs/telemetry/metrics#metric-types
[metrics-api-endpoint]: /api-docs/metrics
[prometheus-telem]: /docs/configuration/telemetry#prometheus
[serf]: /docs/configuration#serf-1
[statsd-exporter]: https://github.com/prometheus/statsd_exporter
[statsd-telem]: /docs/configuration/telemetry#statsd
[statsite-telem]: /docs/configuration/telemetry#statsite
[tagged-metrics]: /docs/telemetry/metrics#tagged-metrics
[telemetry-stanza]: /docs/configuration/telemetry
[Consensus Protocol (Raft)]: /docs/operations/telemetry#consensus-protocol-raft
[Job Summary Metrics]: /docs/operations/metrics-reference#job-summary-metrics
[Scheduling]: /docs/internals/scheduling/scheduling
