How to write Aurora configuration files, including feature descriptions
and best practices. When writing a configuration file, make use of
aurora job inspect. It takes the same job key and configuration file
arguments as aurora job create or aurora update start. It first ensures the
configuration parses, then outputs it in human-readable form.
You should read this after going through the general Aurora Tutorial.
To run a job on Aurora, you must specify a configuration file that tells
Aurora what it needs to know to schedule the job, what Mesos needs to
run the tasks the job is made up of, and what Thermos needs to run the
processes that make up the tasks. This file must have
a.aurora suffix.
A configuration file defines a collection of objects, along with parameter values for their attributes. An Aurora configuration file contains the following three types of objects:
A configuration also specifies a list of Job objects assigned
to the variable jobs.
The .aurora file format is just Python. However, Job, Task,
Process, and other classes are defined by a type-checked dictionary
templating library called Pystachio, a powerful tool for
configuration specification and reuse. Pystachio objects are tailored
via {{}} surrounded templates.
When writing your .aurora file, you may use any Pystachio datatypes, as
well as any objects shown in the Aurora Configuration
Reference, without import statements - the
Aurora config loader injects them automatically. Other than that, an .aurora
file works like any other Python script.
Aurora Configuration Reference has a full reference of all Aurora/Thermos defined Pystachio objects.
A well-structured configuration starts with structural templates (if
any). Structural templates encapsulate in their attributes all the
differences between Jobs in the configuration that are not directly
manipulated at the Job level, but typically at the Process or Task
level. For example, if certain processes are invoked with slightly
different settings or input.
After structural templates, define, in order, Processes, Tasks, and
Jobs.
Structural template names should be UpperCamelCased and their
instantiations are typically UPPER_SNAKE_CASED. Process, Task,
and Job names are typically lower_snake_cased. Indentation is typically 2
spaces.
The following is a typical configuration file. Don’t worry if there are parts you don’t understand yet, but you may want to refer back to this as you read about its individual parts. Note that names surrounded by curly braces {{}} are template variables, which the system replaces with bound values for the variables.
# --- templates here ---
class Profile(Struct):
package_version = Default(String, 'live')
java_binary = Default(String, '/usr/lib/jvm/java-1.7.0-openjdk/bin/java')
extra_jvm_options = Default(String, '')
parent_environment = Default(String, 'prod')
parent_serverset = Default(String,
'/foocorp/service/bird/{{parent_environment}}/bird')
# --- processes here ---
main = Process(
name = 'application',
cmdline = '{{profile.java_binary}} -server -Xmx1792m '
'{{profile.extra_jvm_options}} '
'-jar application.jar '
'-upstreamService {{profile.parent_serverset}}'
)
# --- tasks ---
base_task = SequentialTask(
name = 'application',
processes = [
Process(
name = 'fetch',
cmdline = 'curl -O
https://packages.foocorp.com/{{profile.package_version}}/application.jar'),
]
)
# not always necessary but often useful to have separate task
# resource classes
staging_task = base_task(resources =
Resources(cpu = 1.0,
ram = 2048*MB,
disk = 1*GB))
production_task = base_task(resources =
Resources(cpu = 4.0,
ram = 2560*MB,
disk = 10*GB))
# --- job template ---
job_template = Job(
name = 'application',
role = 'myteam',
contact = 'myteam-team@foocorp.com',
instances = 20,
service = True,
task = production_task
)
# -- profile instantiations (if any) ---
PRODUCTION = Profile()
STAGING = Profile(
extra_jvm_options = '-Xloggc:gc.log',
parent_environment = 'staging'
)
# -- job instantiations --
jobs = [
job_template(cluster = 'cluster1', environment = 'prod')
.bind(profile = PRODUCTION),
job_template(cluster = 'cluster2', environment = 'prod')
.bind(profile = PRODUCTION),
job_template(cluster = 'cluster1',
environment = 'staging',
service = False,
task = staging_task,
instances = 2)
.bind(profile = STAGING),
]
Processes are handled by the Thermos system. A process is a single executable step run as a part of an Aurora task, which consists of a bash-executable statement.
The key (and required) Process attributes are:
name: Any string which is a valid Unix filename (no slashes,
NULLs, or leading periods). The name value must be unique relative
to other Processes in a Task.cmdline: A command line run in a bash subshell, so you can use
bash scripts. Nothing is supplied for command-line arguments,
so $* is unspecified.Many tiny processes make managing configurations more difficult. For example, the following is a bad way to define processes.
copy = Process(
name = 'copy',
cmdline = 'curl -O https://packages.foocorp.com/app.zip'
)
unpack = Process(
name = 'unpack',
cmdline = 'unzip app.zip'
)
remove = Process(
name = 'remove',
cmdline = 'rm -f app.zip'
)
run = Process(
name = 'app',
cmdline = 'java -jar app.jar'
)
run_task = Task(
processes = [copy, unpack, remove, run],
constraints = order(copy, unpack, remove, run)
)
Since cmdline runs in a bash subshell, you can chain commands
with && or ||.
When defining a Task that is just a list of Processes run in a
particular order, use SequentialTask, as described in the Defining
Task Objects section. The following simplifies and combines the
above multiple Process definitions into just two.
stage = Process(
name = 'stage',
cmdline = 'curl -O https://packages.foocorp.com/app.zip && '
'unzip app.zip && rm -f app.zip')
run = Process(name = 'app', cmdline = 'java -jar app.jar')
run_task = SequentialTask(processes = [stage, run])
Process also has optional attributes to customize its behaviour. Details can be found in the Aurora Configuration Reference.
When using Aurora, you need to get your executable code into its “sandbox”, specifically the Task sandbox where the code executes for the Processes that make up that Task.
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.
Typically, you save this code somewhere. You then need to define a Process
in your .aurora configuration file that fetches the code from that somewhere
to where the agent can see it. For a public cloud, that can be anywhere public on
the Internet, such as S3. For a private cloud internal storage, you need to put in
on an accessible HDFS cluster or similar storage.
The template for this Process is:
<name> = Process(
name = '<name>'
cmdline = '<command to copy and extract code archive into current working directory>'
)
Note: Be sure the extracted code archive has an executable.
Every time a process is forked the Thermos executor checks for the existence of the
.thermos_profile file, if the .thermos_profile file exists it will be sourced.
You can utilize this process to pass environment variables to the sandbox.
An example for this Process is:
setup_env = Process(
name = 'setup',
cmdline = (
'cat <<EOF > .thermos_profile\n'
'export RESULT=hello\n'
'EOF\n'
)
)
read_env = Process(
name = 'read'
cmdline = 'echo $RESULT'
)
Tasks are handled by Mesos. A task is a collection of processes that runs in a shared sandbox. It’s the fundamental unit Aurora uses to schedule the datacenter; essentially what Aurora does is find places in the cluster to run tasks.
The key (and required) parts of a Task are:
name: A string giving the Task’s name. By default, if a Task is
not given a name, it inherits the first name in its Process list.
processes: An unordered list of Process objects bound to the Task.
The value of the optional constraints attribute affects the
contents as a whole. Currently, the only constraint, order, determines if
the processes run in parallel or sequentially.
resources: A Resource object defining the Task’s resource
footprint. A Resource object has three attributes:
- cpu: A Float, the fractional number of cores the Task
requires.
- ram: An Integer, RAM bytes the Task requires.
- disk: An integer, disk bytes the Task requires.
A basic Task definition looks like:
Task(
name="hello_world",
processes=[Process(name = "hello_world", cmdline = "echo hello world")],
resources=Resources(cpu = 1.0,
ram = 1*GB,
disk = 1*GB))
A Task has optional attributes to customize its behaviour. Details can be found in the Aurora Configuration Reference
By default, a Task with several Processes runs them in parallel. There are two ways to run Processes sequentially:
Include an order constraint in the Task definition’s constraints
attribute whose arguments specify the processes’ run order:
Task( ... processes=[process1, process2, process3],
constraints = order(process1, process2, process3), ...)
Use SequentialTask instead of Task; it automatically runs
processes in the order specified in the processes attribute. No
constraint parameter is needed:
SequentialTask( ... processes=[process1, process2, process3] ...)
For quickly creating simple tasks, use the SimpleTask helper. It
creates a basic task from a provided name and command line using a
default set of resources. For example, in a .aurora configuration
file:
SimpleTask(name="hello_world", command="echo hello world")
is equivalent to
Task(name="hello_world",
processes=[Process(name = "hello_world", cmdline = "echo hello world")],
resources=Resources(cpu = 1.0,
ram = 1*GB,
disk = 1*GB))
The simplest idiomatic Job configuration thus becomes:
import os
hello_world_job = Job(
task=SimpleTask(name="hello_world", command="echo hello world"),
role=os.getenv('USER'),
cluster="cluster1")
When written to hello_world.aurora, you invoke it with a simple
aurora job create cluster1/$USER/test/hello_world hello_world.aurora.
Tasks.concat(synonym,concat_tasks) and
Tasks.combine(synonym,combine_tasks) merge multiple Task definitions
into a single Task. It may be easier to define complex Jobs
as smaller constituent Tasks. But since a Job only includes a single
Task, the subtasks must be combined before using them in a Job.
Smaller Tasks can also be reused between Jobs, instead of having to
repeat their definition for multiple Jobs.
With both methods, the merged Task takes the first Task’s name. The difference between the two is the result Task’s process ordering.
Tasks.combine runs its subtasks’ processes in no particular order.
The new Task’s resource consumption is the sum of all its subtasks’
consumption.
Tasks.concat runs its subtasks in the order supplied, with each
subtask’s processes run serially between tasks. It is analogous to
the order constraint helper, except at the Task level instead of
the Process level. The new Task’s resource consumption is the
maximum value specified by any subtask for each Resource attribute
(cpu, ram and disk).
For example, given the following:
setup_task = Task(
...
processes=[download_interpreter, update_zookeeper],
# It is important to note that {{Tasks.concat}} has
# no effect on the ordering of the processes within a task;
# hence the necessity of the {{order}} statement below
# (otherwise, the order in which {{download_interpreter}}
# and {{update_zookeeper}} run will be non-deterministic)
constraints=order(download_interpreter, update_zookeeper),
...
)
run_task = SequentialTask(
...
processes=[download_application, start_application],
...
)
combined_task = Tasks.concat(setup_task, run_task)
The Tasks.concat command merges the two Tasks into a single Task and
ensures all processes in setup_task run before the processes
in run_task. Conceptually, the task is reduced to:
task = Task(
...
processes=[download_interpreter, update_zookeeper,
download_application, start_application],
constraints=order(download_interpreter, update_zookeeper,
download_application, start_application),
...
)
In the case of Tasks.combine, the two schedules run in parallel:
task = Task(
...
processes=[download_interpreter, update_zookeeper,
download_application, start_application],
constraints=order(download_interpreter, update_zookeeper) +
order(download_application, start_application),
...
)
In the latter case, each of the two sequences may operate in parallel.
Of course, this may not be the intended behavior (for example, if
the start_application Process implicitly relies
upon download_interpreter). Make sure you understand the difference
between using one or the other.
A job is a group of identical tasks that Aurora can run in a Mesos cluster.
A Job object is defined by the values of several attributes, some
required and some optional. The required attributes are:
task: Task object to bind to this job. Note that a Job can
only take a single Task.
role: Job’s role account; in other words, the user account to run
the job as on a Mesos cluster machine. A common value is
os.getenv('USER'); using a Python command to get the user who
submits the job request. The other common value is the service
account that runs the job, e.g. www-data.
environment: Job’s environment, typical values
are devel, test, or prod.
cluster: Aurora cluster to schedule the job in, defined in
/etc/aurora/clusters.json or ~/.clusters.json. You can specify
jobs where the only difference is the cluster, then at run time
only run the Job whose job key includes your desired cluster’s name.
You usually see a name parameter. By default, name inherits its
value from the Job’s associated Task object, but you can override this
default. For these four parameters, a Job definition might look like:
foo_job = Job( name = 'foo', cluster = 'cluster1',
role = os.getenv('USER'), environment = 'prod',
task = foo_task)
In addition to the required attributes, there are several optional attributes. Details can be found in the Aurora Configuration Reference.
At the end of your .aurora file, you need to specify a list of the
file’s defined Jobs. For example, the following exports the jobs job1,
job2, and job3.
jobs = [job1, job2, job3]
This allows the aurora client to invoke commands on those jobs, such as starting, updating, or killing them.
These are provided to give a basic understanding of simple Aurora jobs.
Put the following in a file named hello_world.aurora, substituting your own values
for values such as clusters.
import os
hello_world_process = Process(name = 'hello_world', cmdline = 'echo hello world')
hello_world_task = Task(
resources = Resources(cpu = 0.1, ram = 16 * MB, disk = 16 * MB),
processes = [hello_world_process])
hello_world_job = Job(
cluster = 'cluster1',
role = os.getenv('USER'),
task = hello_world_task)
jobs = [hello_world_job]
Then issue the following commands to create and kill the job, using your own values for the job key.
aurora job create cluster1/$USER/test/hello_world hello_world.aurora
aurora job kill cluster1/$USER/test/hello_world
Put the following in a file named hello_world_productionized.aurora, substituting your own values
for values such as clusters.
include('hello_world.aurora')
production_resources = Resources(cpu = 1.0, ram = 512 * MB, disk = 2 * GB)
staging_resources = Resources(cpu = 0.1, ram = 32 * MB, disk = 512 * MB)
hello_world_template = hello_world(
name = "hello_world-{{cluster}}"
task = hello_world(resources=production_resources))
jobs = [
# production jobs
hello_world_template(cluster = 'cluster1', instances = 25),
hello_world_template(cluster = 'cluster2', instances = 15),
# staging jobs
hello_world_template(
cluster = 'local',
instances = 1,
task = hello_world(resources=staging_resources)),
]
Then issue the following commands to create and kill the job, using your own values for the job key
aurora job create cluster1/$USER/test/hello_world-cluster1 hello_world_productionized.aurora
aurora job kill cluster1/$USER/test/hello_world-cluster1