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| layout | page_title | sidebar_current | description |
|---|---|---|---|
| intro | Jobs | getting-started-jobs | Learn how to submit, modify and stop jobs in Nomad. |
Jobs
Jobs are the primary configuration that users interact with when using Nomad. A job is a declarative specification of tasks that Nomad should run. Jobs have a globally unique name, one or many task groups, which are themselves collections of one or many tasks.
The format of the jobs is documented in the job specification. They can either be specified in HashiCorp Configuration Language or JSON, however we recommend only using JSON when the configuration is generated by a machine.
Running a Job
To get started, we will use the job init command which
generates a skeleton job file:
$ nomad job init
Example job file written to example.nomad
You can view the contents of this file by running cat example.nomad. In this
example job file, we have declared a single task 'redis' which is using
the Docker driver to run the task. The primary way you interact with Nomad
is with the job run command. The run command takes
a job file and registers it with Nomad. This is used both to register new
jobs and to update existing jobs.
We can register our example job now:
$ nomad job run example.nomad
==> Monitoring evaluation "13ebb66d"
Evaluation triggered by job "example"
Allocation "883269bf" created: node "e42d6f19", group "cache"
Evaluation within deployment: "b0a84e74"
Evaluation status changed: "pending" -> "complete"
==> Evaluation "13ebb66d" finished with status "complete"
Anytime a job is updated, Nomad creates an evaluation to determine what actions need to take place. In this case, because this is a new job, Nomad has determined that an allocation should be created and has scheduled it on our local agent.
To inspect the status of our job we use the status command:
$ nomad status example
ID = example
Name = example
Submit Date = 10/31/17 22:58:40 UTC
Type = service
Priority = 50
Datacenters = dc1
Status = running
Periodic = false
Parameterized = false
Summary
Task Group Queued Starting Running Failed Complete Lost
cache 0 0 1 0 0 0
Latest Deployment
ID = b0a84e74
Status = successful
Description = Deployment completed successfully
Deployed
Task Group Desired Placed Healthy Unhealthy
cache 1 1 1 0
Allocations
ID Node ID Task Group Version Desired Status Created Modified
8ba85cef 171a583b cache 0 run running 5m ago 5m ago
Here we can see that the result of our evaluation was the creation of an allocation that is now running on the local node.
An allocation represents an instance of Task Group placed on a node. To inspect
an allocation we use the alloc status command:
$ nomad alloc status 8ba85cef
ID = 8ba85cef
Eval ID = 13ebb66d
Name = example.cache[0]
Node ID = e42d6f19
Job ID = example
Job Version = 0
Client Status = running
Client Description = <none>
Desired Status = run
Desired Description = <none>
Created = 5m ago
Modified = 5m ago
Deployment ID = fa882a5b
Deployment Health = healthy
Task "redis" is "running"
Task Resources
CPU Memory Disk IOPS Addresses
8/500 MHz 6.3 MiB/256 MiB 300 MiB 0 db: 127.0.0.1:22672
Task Events:
Started At = 10/31/17 22:58:49 UTC
Finished At = N/A
Total Restarts = 0
Last Restart = N/A
Recent Events:
Time Type Description
10/31/17 22:58:49 UTC Started Task started by client
10/31/17 22:58:40 UTC Driver Downloading image redis:3.2
10/31/17 22:58:40 UTC Task Setup Building Task Directory
10/31/17 22:58:40 UTC Received Task received by client
We can see that Nomad reports the state of the allocation as well as its
current resource usage. By supplying the -stats flag, more detailed resource
usage statistics will be reported.
To see the logs of a task, we can use the logs command:
$ nomad alloc logs 8ba85cef redis
_._
_.-``__ ''-._
_.-`` `. `_. ''-._ Redis 3.2.1 (00000000/0) 64 bit
.-`` .-```. ```\/ _.,_ ''-._
( ' , .-` | `, ) Running in standalone mode
|`-._`-...-` __...-.``-._|'` _.-'| Port: 6379
| `-._ `._ / _.-' | PID: 1
`-._ `-._ `-./ _.-' _.-'
|`-._`-._ `-.__.-' _.-'_.-'|
| `-._`-._ _.-'_.-' | http://redis.io
`-._ `-._`-.__.-'_.-' _.-'
|`-._`-._ `-.__.-' _.-'_.-'|
| `-._`-._ _.-'_.-' |
`-._ `-._`-.__.-'_.-' _.-'
`-._ `-.__.-' _.-'
`-._ _.-'
`-.__.-'
...
Modifying a Job
The definition of a job is not static, and is meant to be updated over time. You may update a job to change the docker container, to update the application version, or to change the count of a task group to scale with load.
For now, edit the example.nomad file to update the count and set it to 3:
# The "count" parameter specifies the number of the task groups that should
# be running under this group. This value must be non-negative and defaults
# to 1.
count = 3
Once you have finished modifying the job specification, use the job plan
command to invoke a dry-run of the scheduler to see
what would happen if you ran the updated job:
$ nomad job plan example.nomad
+/- Job: "example"
+/- Task Group: "cache" (2 create, 1 in-place update)
+/- Count: "1" => "3" (forces create)
Task: "redis"
Scheduler dry-run:
- All tasks successfully allocated.
Job Modify Index: 7
To submit the job with version verification run:
nomad job run -check-index 7 example.nomad
When running the job with the check-index flag, the job will only be run if the
server side version matches the job modify index returned. If the index has
changed, another user has modified the job and the plan's results are
potentially invalid.
We can see that the scheduler detected the change in count and informs us that
it will cause 2 new instances to be created. The in-place update that will
occur is to push the updated job specification to the existing allocation and
will not cause any service interruption. We can then run the job with the run
command the plan emitted.
By running with the -check-index flag, Nomad checks that the job has not
been modified since the plan was run. This is useful if multiple people are
interacting with the job at the same time to ensure the job hasn't changed
before you apply your modifications.
$ nomad job run -check-index 7 example.nomad
==> Monitoring evaluation "93d16471"
Evaluation triggered by job "example"
Evaluation within deployment: "0d06e1b6"
Allocation "3249e320" created: node "e42d6f19", group "cache"
Allocation "453b210f" created: node "e42d6f19", group "cache"
Allocation "883269bf" modified: node "e42d6f19", group "cache"
Evaluation status changed: "pending" -> "complete"
==> Evaluation "93d16471" finished with status "complete"
Because we set the count of the task group to three, Nomad created two additional allocations to get to the desired state. It is idempotent to run the same job specification again and no new allocations will be created.
Now, let's try to do an application update. In this case, we will simply change
the version of redis we want to run. Edit the example.nomad file and change
the Docker image from "redis:3.2" to "redis:4.0":
# Configure Docker driver with the image
config {
image = "redis:4.0"
}
We can run plan again to see what will happen if we submit this change:
+/- Job: "example"
+/- Task Group: "cache" (1 create/destroy update, 2 ignore)
+/- Task: "redis" (forces create/destroy update)
+/- Config {
+/- image: "redis:3.2" => "redis:4.0"
port_map[0][db]: "6379"
}
Scheduler dry-run:
- All tasks successfully allocated.
Job Modify Index: 1127
To submit the job with version verification run:
nomad job run -check-index 1127 example.nomad
When running the job with the check-index flag, the job will only be run if the
server side version matches the job modify index returned. If the index has
changed, another user has modified the job and the plan's results are
potentially invalid.
The plan output shows us that one allocation will be updated and that the other
two will be ignored. This is due to the max_parallel setting in the update
stanza, which is set to 1 to instruct Nomad to perform only a single change at
a time.
Once ready, use run to push the updated specification:
$ nomad job run example.nomad
==> Monitoring evaluation "293b313a"
Evaluation triggered by job "example"
Evaluation within deployment: "f4047b3a"
Allocation "27bd4a41" created: node "e42d6f19", group "cache"
Evaluation status changed: "pending" -> "complete"
==> Evaluation "293b313a" finished with status "complete"
After running, the rolling upgrade can be followed by running nomad status and
watching the deployed count.
We can see that Nomad handled the update in three phases, only updating a single
allocation in each phase and waiting for it to be healthy for min_healthy_time
of 10 seconds before moving on to the next. The update strategy can be
configured, but rolling updates makes it easy to upgrade an application at large
scale.
Stopping a Job
So far we've created, run and modified a job. The final step in a job lifecycle
is stopping the job. This is done with the job stop command:
$ nomad job stop example
==> Monitoring evaluation "6d4cd6ca"
Evaluation triggered by job "example"
Evaluation within deployment: "f4047b3a"
Evaluation status changed: "pending" -> "complete"
==> Evaluation "6d4cd6ca" finished with status "complete"
When we stop a job, it creates an evaluation which is used to stop all
the existing allocations. If we now query the job status, we can see it is
now marked as dead (stopped), indicating that the job has been stopped and
Nomad is no longer running it:
$ nomad status example
ID = example
Name = example
Submit Date = 11/01/17 17:30:40 UTC
Type = service
Priority = 50
Datacenters = dc1
Status = dead (stopped)
Periodic = false
Parameterized = false
Summary
Task Group Queued Starting Running Failed Complete Lost
cache 0 0 0 0 6 0
Latest Deployment
ID = f4047b3a
Status = successful
Description = Deployment completed successfully
Deployed
Task Group Desired Placed Healthy Unhealthy
cache 3 3 3 0
Allocations
ID Node ID Task Group Version Desired Status Created Modified
8ace140d 2cfe061e cache 2 stop complete 5m ago 5m ago
8af5330a 2cfe061e cache 2 stop complete 6m ago 6m ago
df50c3ae 2cfe061e cache 2 stop complete 6m ago 6m ago
If we wanted to start the job again, we could simply run it again.
Next Steps
Users of Nomad primarily interact with jobs, and we've now seen how to create and scale our job, perform an application update, and do a job tear down. Next we will add another Nomad client to create our first cluster