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Apache Aurora

Aurora User Guide


This document gives an overview of how Aurora works under the hood. It assumes you’ve already worked through the “hello world” example job in the Aurora Tutorial. Specifics of how to use Aurora are not given here, but pointers to documentation about how to use Aurora are provided.

Aurora is a Mesos framework used to schedule jobs onto Mesos. Mesos cares about individual tasks, but typical jobs consist of dozens or hundreds of task replicas. Aurora provides a layer on top of Mesos with its Job abstraction. An Aurora Job consists of a task template and instructions for creating near-identical replicas of that task (modulo things like “instance id” or specific port numbers which may differ from machine to machine).

How many tasks make up a Job is complicated. On a basic level, a Job consists of one task template and instructions for creating near-idential replicas of that task (otherwise referred to as “instances” or “shards”).

However, since Jobs can be updated on the fly, a single Job identifier or job key can have multiple job configurations associated with it.

For example, consider when I have a Job with 4 instances that each request 1 core of cpu, 1 GB of RAM, and 1 GB of disk space as specified in the configuration file hello_world.aurora. I want to update it so it requests 2 GB of RAM instead of 1. I create a new configuration file to do that called new_hello_world.aurora and issue a aurora job update <job_key_value>/0-1 new_hello_world.aurora command.

This results in instances 0 and 1 having 1 cpu, 2 GB of RAM, and 1 GB of disk space, while instances 2 and 3 have 1 cpu, 1 GB of RAM, and 1 GB of disk space. If instance 3 dies and restarts, it restarts with 1 cpu, 1 GB RAM, and 1 GB disk space.

So that means there are two simultaneous task configurations for the same Job at the same time, just valid for different ranges of instances.

This isn’t a recommended pattern, but it is valid and supported by the Aurora scheduler. This most often manifests in the “canary pattern” where instance 0 runs with a different configuration than instances 1-N to test different code versions alongside the actual production job.

A task can merely be a single process corresponding to a single command line, such as python2.6 However, a task can also consist of many separate processes, which all run within a single sandbox. For example, running multiple cooperating agents together, such as logrotate, installer, master, or slave processes. This is where Thermos comes in. While Aurora provides a Job abstraction on top of Mesos Tasks, Thermos provides a Process abstraction underneath Mesos Tasks and serves as part of the Aurora framework’s executor.

You define Jobs,Tasks, and Processes in a configuration file. Configuration files are written in Python, and make use of the Pystachio templating language. They end in a .aurora extension.

Pystachio is a type-checked dictionary templating library.


  • Aurora manages jobs made of tasks.
  • Mesos manages tasks made of processes.
  • Thermos manages processes.
  • All defined in .aurora configuration file.

Aurora hierarchy

Each Task has a sandbox created when the Task starts and garbage collected when it finishes. All of a Task's processes run in its sandbox, so processes can share state by using a shared current working directory.

The sandbox garbage collection policy considers many factors, most importantly age and size. It makes a best-effort attempt to keep sandboxes around as long as possible post-task in order for service owners to inspect data and logs, should the Task have completed abnormally. But you can’t design your applications assuming sandboxes will be around forever, e.g. by building log saving or other checkpointing mechanisms directly into your application or into your Job description.

Job Lifecycle

When Aurora reads a configuration file and finds a Job definition, it:

  1. Evaluates the Job definition.
  2. Splits the Job into its constituent Tasks.
  3. Sends those Tasks to the scheduler.
  4. The scheduler puts the Tasks into PENDING state, starting each Task’s life cycle.

Life Of A Task

Life of a task


When a Task is in the PENDING state, the scheduler constantly searches for machines satisfying that Task’s resource request requirements (RAM, disk space, CPU time) while maintaining configuration constraints such as “a Task must run on machines dedicated to a particular role” or attribute limit constraints such as “at most 2 Tasks from the same Job may run on each rack”. When the scheduler finds a suitable match, it assigns the Task to a machine and puts the Task into the ASSIGNED state.

From the ASSIGNED state, the scheduler sends an RPC to the slave machine containing Task configuration, which the slave uses to spawn an executor responsible for the Task’s lifecycle. When the scheduler receives an acknowledgement that the machine has accepted the Task, the Task goes into STARTING state.

STARTING state initializes a Task sandbox. When the sandbox is fully initialized, Thermos begins to invoke Processes. Also, the slave machine sends an update to the scheduler that the Task is in RUNNING state.

If a Task stays in ASSIGNED or STARTING for too long, the scheduler forces it into LOST state, creating a new Task in its place that’s sent into PENDING state. This is technically true of any active state: if the Mesos core tells the scheduler that a slave has become unhealthy (or outright disappeared), the Tasks assigned to that slave go into LOST state and new Tasks are created in their place. From PENDING state, there is no guarantee a Task will be reassigned to the same machine unless job constraints explicitly force it there.

If there is a state mismatch, (e.g. a machine returns from a netsplit and the scheduler has marked all its Tasks LOST and rescheduled them), a state reconciliation process kills the errant RUNNING tasks, which may take up to an hour. But to emphasize this point: there is no uniqueness guarantee for a single instance of a job in the presence of network partitions. If the Task requires that, it should be baked in at the application level using a distributed coordination service such as Zookeeper.

Task Updates

Job configurations can be updated at any point in their lifecycle. Usually updates are done incrementally using a process called a rolling upgrade, in which Tasks are upgraded in small groups, one group at a time. Updates are done using various Aurora Client commands.

For a configuration update, the Aurora Client calculates required changes by examining the current job config state and the new desired job config. It then starts a rolling batched update process by going through every batch and performing these operations:

  • If an instance is present in the scheduler but isn’t in the new config, then that instance is killed.
  • If an instance is not present in the scheduler but is present in the new config, then the instance is created.
  • If an instance is present in both the scheduler the new config, then the client diffs both task configs. If it detects any changes, it performs an instance update by killing the old config instance and adds the new config instance.

The Aurora client continues through the instance list until all tasks are updated, in RUNNING, and healthy for a configurable amount of time. If the client determines the update is not going well (a percentage of health checks have failed), it cancels the update.

Update cancellation runs a procedure similar to the described above update sequence, but in reverse order. New instance configs are swapped with old instance configs and batch updates proceed backwards from the point where the update failed. E.g.; (0,1,2) (3,4,5) (6,7, 8-FAIL) results in a rollback in order (8,7,6) (5,4,3) (2,1,0).

HTTP Health Checking and Graceful Shutdown

The Executor implements a protocol for rudimentary control of a task via HTTP. Tasks subscribe for this protocol by declaring a port named health. Take for example this configuration snippet:

nginx = Process(
  name = 'nginx',
  cmdline = './ -port {{thermos.ports[http]}}')

When this Process is included in a job, the job will be allocated a port, and the command line will be replaced with something like:

./ -port 42816

Where 42816 happens to be the allocated. port. Typically, the Executor monitors Processes within a task only by liveness of the forked process. However, when a health port was allocated, it will also send periodic HTTP health checks. A task requesting a health port must handle the following requests:

HTTP request Description
GET /health Inquires whether the task is healthy.
POST /quitquitquit Task should initiate graceful shutdown.
POST /abortabortabort Final warning task is being killed.

Please see the configuration reference for configuration options for this feature.

Snoozing Health Checks

If you need to pause your health check, you can do so by touching a file inside of your sandbox, named .healthchecksnooze

As long as that file is present, health checks will be disabled, enabling users to gather core dumps or other performance measurements without worrying about Aurora’s health check killing their process.

WARNING: Remember to remove this when you are done, otherwise your instance will have permanently disabled health checks.

Tearing a task down

The Executor follows an escalation sequence when killing a running task:

  1. If health port is not present, skip to (5)
  2. POST /quitquitquit
  3. wait 5 seconds
  4. POST /abortabortabort
  5. Send SIGTERM (kill)
  6. Send SIGKILL (kill -9)

If the Executor notices that all Processes in a Task have aborted during this sequence, it will not proceed with subsequent steps. Note that graceful shutdown is best-effort, and due to the many inevitable realities of distributed systems, it may not be performed.

Giving Priority to Production Tasks: PREEMPTING

Sometimes a Task needs to be interrupted, such as when a non-production Task’s resources are needed by a higher priority production Task. This type of interruption is called a pre-emption. When this happens in Aurora, the non-production Task is killed and moved into the PREEMPTING state when both the following are true:

  • The task being killed is a non-production task.
  • The other task is a PENDING production task that hasn’t been scheduled due to a lack of resources.

Since production tasks are much more important, Aurora kills off the non-production task to free up resources for the production task. The scheduler UI shows the non-production task was preempted in favor of the production task. At some point, tasks in PREEMPTING move to KILLED.

Note that non-production tasks consuming many resources are likely to be preempted in favor of production tasks.

Natural Termination: FINISHED, FAILED

A RUNNING Task can terminate without direct user interaction. For example, it may be a finite computation that finishes, even something as simple as echo hello world.Or it could be an exceptional condition in a long-lived service. If the Task is successful (its underlying processes have succeeded with exit status 0 or finished without reaching failure limits) it moves into FINISHED state. If it finished after reaching a set of failure limits, it goes into FAILED state.

Forceful Termination: KILLING, RESTARTING

You can terminate a Task by issuing an aurora job kill command, which moves it into KILLING state. The scheduler then sends the slave a request to terminate the Task. If the scheduler receives a successful response, it moves the Task into KILLED state and never restarts it.

The scheduler has access to a non-public RESTARTING state. If a Task is forced into the RESTARTING state, the scheduler kills the underlying task but in parallel schedules an identical replacement for it.


You define and configure your Jobs (and their Tasks and Processes) in Aurora configuration files. Their filenames end with the .aurora suffix, and you write them in Python making use of the Pystachio templating language, along with specific Aurora, Mesos, and Thermos commands and methods. See the Configuration Guide and Reference and Configuration Tutorial.

Service Discovery

It is possible for the Aurora executor to announce tasks into ServerSets for the purpose of service discovery. ServerSets use the Zookeeper group membership pattern of which there are several reference implementations:

These can also be used natively in Finagle using the ZookeeperServerSetCluster.

For more information about how to configure announcing, see the Configuration Reference.

Creating Jobs

You create and manipulate Aurora Jobs with the Aurora client, which starts all its command line commands with aurora. See Aurora Client Commands for details about the Aurora Client.

Interacting With Jobs

You interact with Aurora jobs either via:

  • Read-only Web UIs

Part of the output from creating a new Job is a URL for the Job’s scheduler UI page.

For example:

  vagrant@precise64:~$ aurora job create devcluster/www-data/prod/hello \
  INFO] Creating job hello
  INFO] Response from scheduler: OK (message: 1 new tasks pending for job www-data/prod/hello)
  INFO] Job url: http://precise64:8081/scheduler/www-data/prod/hello

The “Job url” goes to the Job’s scheduler UI page. To go to the overall scheduler UI page, stop at the “scheduler” part of the URL, in this case, http://precise64:8081/scheduler

You can also reach the scheduler UI page via the Client command aurora job open:

  aurora job open [<cluster>[/<role>[/<env>/<job_name>]]]

If only the cluster is specified, it goes directly to that cluster’s scheduler main page. If the role is specified, it goes to the top-level role page. If the full job key is specified, it goes directly to the job page where you can inspect individual tasks.

Once you click through to a role page, you see Jobs arranged separately by pending jobs, active jobs, and finished jobs. Jobs are arranged by role, typically a service account for production jobs and user accounts for test or development jobs.

  • The Aurora client

See client commands.