How to write Aurora configuration files, including feature descriptions
and best practices. When writing a configuration file, make use of
aurora inspect. It takes the same job key and configuration file
arguments as aurora create or aurora update. 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+Thermos 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+Thermos 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 five optional attributes, each with a default value
if one isn’t specified in the configuration:
max_failures: Defaulting to 1, the maximum number of failures
(non-zero exit statuses) before this Process is marked permanently
failed and not retried. If a Process permanently fails, Thermos
checks the Process object’s containing Task for the task’s
failure limit (usually 1) to determine whether or not the Task
should be failed. Setting max_failuresto 0 means that this
process will keep retrying until a successful (zero) exit status is
achieved. Retries happen at most once every min_duration seconds
to prevent effectively mounting a denial of service attack against
the coordinating scheduler.
daemon: Defaulting to False, if daemon is set to True, a
successful (zero) exit status does not prevent future process runs.
Instead, the Process reinvokes after min_duration seconds.
However, the maximum failure limit (max_failures) still
applies. A combination of daemon=True and max_failures=0 retries
a Process indefinitely regardless of exit status. This should
generally be avoided for very short-lived processes because of the
accumulation of checkpointed state for each process run. When
running in Aurora, max_failures is capped at
100.
ephemeral: Defaulting to False, if ephemeral is True, the
Process’ status is not used to determine if its bound Task has
completed. For example, consider a Task with a
non-ephemeral webserver process and an ephemeral logsaver process
that periodically checkpoints its log files to a centralized data
store. The Task is considered finished once the webserver process
finishes, regardless of the logsaver’s current status.
min_duration: Defaults to 15. Processes may succeed or fail
multiple times during a single Task. Each result is called a
process run and this value is the minimum number of seconds the
scheduler waits before re-running the same process.
final: Defaulting to False, this is a finalizing Process that
should run last. Processes can be grouped into two classes:
ordinary and finalizing. By default, Thermos Processes are
ordinary. They run as long as the Task is considered
healthy (i.e. hasn’t reached a failure limit). But once all regular
Thermos Processes have either finished or the Task has reached a
certain failure threshold, Thermos moves into a finalization stage
and runs all finalizing Processes. These are typically necessary for
cleaning up after the Task, such as log checkpointers, or perhaps
e-mail notifications of a completed Task. Finalizing processes may
not depend upon ordinary processes or vice-versa, however finalizing
processes may depend upon other finalizing processes and will
otherwise run as a typical process schedule.
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 slave 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.
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))
There are four optional Task attributes:
constraints: A list of Constraint objects that constrain the
Task’s processes. Currently there is only one type, the order
constraint. For example the following requires that the processes
run in the order foo, then bar.
constraints = [Constraint(order=['foo', 'bar'])]
There is an order() function that takes order('foo', 'bar', 'baz')
and converts it into [Constraint(order=['foo', 'bar', 'baz'])].
order() accepts Process name strings ('foo', 'bar') or the processes
themselves, e.g. foo=Process(name='foo', ...), bar=Process(name='bar', ...),
constraints=order(foo, bar)
Note that Thermos rejects tasks with process cycles.
max_failures: Defaulting to 1, the number of failed processes
needed for the Task to be marked as failed. Note how this
interacts with individual Processes’ max_failures values. Assume a
Task has two Processes and a max_failures value of 2. So both
Processes must fail for the Task to fail. Now, assume each of the
Task’s Processes has its own max_failures value of 10. If
Process “A” fails 5 times before succeeding, and Process “B” fails
10 times and is then marked as failing, their parent Task succeeds.
Even though there were 15 individual failures by its Processes, only
1 of its Processes was finally marked as failing. Since 1 is less
than the 2 that is the Task’s max_failures value, the Task does
not fail.
max_concurrency: Defaulting to 0, the maximum number of
concurrent processes in the Task. 0 specifies unlimited
concurrency. For Tasks with many expensive but otherwise independent
processes, you can limit the amount of concurrency Thermos schedules
instead of artificially constraining them through order
constraints. For example, a test framework may generate a Task with
100 test run processes, but runs it in a Task with
resources.cpus=4. Limit the amount of parallelism to 4 by setting
max_concurrency=4.
finalization_wait: Defaulting to 30, the number of seconds
allocated for finalizing the Task’s processes. A Task starts in
ACTIVE state when Processes run and stays there as long as the Task
is healthy and Processes run. When all Processes finish successfully
or the Task reaches its maximum process failure limit, it goes into
CLEANING state. In CLEANING, it sends SIGTERMS to any still running
Processes. When all Processes terminate, the Task goes into
FINALIZING state and invokes the schedule of all processes whose
final attribute has a True value. Everything from the end of ACTIVE
to the end of FINALIZING must happen within finalization_wait
number of seconds. If not, all still running Processes are sent
SIGKILLs (or if dependent on yet to be completed Processes, are
never invoked).
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 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. The first (strongly recommended) optional attribute is:
contact: An email address for the Job’s owner. For production
jobs, it is usually a team mailing list.Two more attributes deal with how to handle failure of the Job’s Task:
max_task_failures: An integer, defaulting to 1, of the maximum
number of Task failures after which the Job is considered failed.
-1 allows for infinite failures.
service: A boolean, defaulting to False, which if True
restarts tasks regardless of whether they succeeded or failed. In
other words, if True, after the Job’s Task completes, it
automatically starts again. This is for Jobs you want to run
continuously, rather than doing a single run.
Three attributes deal with configuring the Job’s Task:
instances: Defaulting to 1, the number of
instances/replicas/shards of the Job’s Task to create.
priority: Defaulting to 0, the Job’s Task’s preemption priority,
for which higher values may preempt Tasks from Jobs with lower
values.
production: a Boolean, defaulting to False, specifying that this
is a production job backed by quota. Tasks from production Jobs may
preempt tasks from any non-production job, and may only be preempted
by tasks from production jobs in the same role with higher
priority. WARNING: To run Jobs at this level, the Job role must
have the appropriate quota.
The final three Job attributes each take an object as their value.
update_config: An UpdateConfig
object provides parameters for controlling the rate and policy of
rolling updates. The UpdateConfig parameters are:
batch_size: An integer, defaulting to 1, specifying the
maximum number of shards to update in one iteration.restart_threshold: An integer, defaulting to 60, specifying
the maximum number of seconds before a shard must move into the
RUNNING state before considered a failure.watch_secs: An integer, defaulting to 45, specifying the
minimum number of seconds a shard must remain in the RUNNING
state before considered a success.max_per_shard_failures: An integer, defaulting to 0,
specifying the maximum number of restarts per shard during an
update. When the limit is exceeded, it increments the total
failure count.max_total_failures: An integer, defaulting to 0, specifying
the maximum number of shard failures tolerated during an update.
Cannot be equal to or greater than the job’s total number of
tasks.health_check_config: A HealthCheckConfig object that provides
parameters for controlling a Task’s health checks via HTTP. Only
used if a health port was assigned with a command line wildcard. The
HealthCheckConfig parameters are:
initial_interval_secs: An integer, defaulting to 15,
specifying the initial delay for doing an HTTP health check.interval_secs: An integer, defaulting to 10, specifying the
number of seconds in the interval between checking the Task’s
health.timeout_secs: An integer, defaulting to 1, specifying the
number of seconds the application must respond to an HTTP health
check with OK before it is considered a failure.max_consecutive_failures: An integer, defaulting to 0,
specifying the maximum number of consecutive failures before a
task is unhealthy.constraints: A dict Python object, specifying Task scheduling
constraints. Most users will not need to specify constraints, as the
scheduler automatically inserts reasonable defaults. Please do not
set this field unless you are sure of what you are doing. See the
section in the Aurora + Thermos Reference manual on Specifying
Scheduling Constraints for more information.At the end of your .aurora file, you need to specify a list of the
file’s defined Jobs to run in the order listed. For example, the
following runs first job1, then job2, then job3.
jobs = [job1, job2, job3]
The .aurora file format is just Python. However, Job, Task,
Process, and other classes are defined by a templating library called
Pystachio, a powerful tool for configuration specification and reuse.
Aurora+Thermos Configuration Reference has a full reference of all Aurora/Thermos defined Pystachio objects.
When writing your .aurora file, you may use any Pystachio datatypes, as
well as any objects shown in the Aurora+Thermos Configuration
Reference without import statements - the Aurora config loader
injects them automatically. Other than that the .aurora format
works like any other Python script.
Pystachio uses the visually distinctive {{}} to indicate template variables. These are often called “mustache variables” after the similarly appearing variables in the Mustache templating system and because the curly braces resemble mustaches.
If you are familiar with the Mustache system, templates in Pystachio have significant differences. They have no nesting, joining, or inheritance semantics. On the other hand, when evaluated, templates are evaluated iteratively, so this affords some level of indirection.
Let’s start with the simplest template; text with one
variable, in this case name;
Hello {{name}}
If we evaluate this as is, we’d get back:
Hello
If a template variable doesn’t have a value, when evaluated it’s replaced with nothing. If we add a binding to give it a value:
{ "name" : "Tom" }
We’d get back:
Hello Tom
We can also use {{}} variables as sectional variables. Let’s say we have:
{{#x}} Testing... {{/x}}
If x evaluates to True, the text between the sectional tags is
shown. If there is no value for x or it evaluates to False, the
between tags text is not shown. So, at a basic level, a sectional
variable acts as a conditional.
However, if the sectional variable evaluates to a list, array, etc. it
acts as a foreach. For example,
{{#x}} {{name}} {{/x}}
with
{ "x": [ { "name" : "tic" } { "name" : "tac" } { "name" : "toe" } ] }
evaluates to
tic tac toe
Every Pystachio object has an associated .bind method that can bind
values to {{}} variables. Bindings are not immediately evaluated.
Instead, they are evaluated only when the interpolated value of the
object is necessary, e.g. for performing equality or serializing a
message over the wire.
Objects with and without mustache templated variables behave differently:
>>> Float(1.5)
Float(1.5)
>>> Float('{{x}}.5')
Float({{x}}.5)
>>> Float('{{x}}.5').bind(x = 1)
Float(1.5)
>>> Float('{{x}}.5').bind(x = 1) == Float(1.5)
True
>>> contextual_object = String('{{metavar{{number}}}}').bind(
... metavar1 = "first", metavar2 = "second")
>>> contextual_object
String({{metavar{{number}}}})
>>> contextual_object.bind(number = 1)
String(first)
>>> contextual_object.bind(number = 2)
String(second)
You usually bind simple key to value pairs, but you can also bind three other objects: lists, dictionaries, and structurals. These will be described in detail later.
Most Aurora/Thermos users don’t ever (knowingly) interact with String,
Float, or Integer Pystashio objects directly. Instead they interact
with derived structural (Struct) objects that are collections of
fundamental and structural objects. The structural object components are
called attributes. Aurora’s most used structural objects are Job,
Task, and Process:
class Process(Struct):
cmdline = Required(String)
name = Required(String)
max_failures = Default(Integer, 1)
daemon = Default(Boolean, False)
ephemeral = Default(Boolean, False)
min_duration = Default(Integer, 5)
final = Default(Boolean, False)
Construct default objects by following the object’s type with (). If you want an attribute to have a value different from its default, include the attribute name and value inside the parentheses.
>>> Process()
Process(daemon=False, max_failures=1, ephemeral=False,
min_duration=5, final=False)
Attribute values can be template variables, which then receive specific values when creating the object.
>>> Process(cmdline = 'echo {{message}}')
Process(daemon=False, max_failures=1, ephemeral=False, min_duration=5,
cmdline=echo {{message}}, final=False)
>>> Process(cmdline = 'echo {{message}}').bind(message = 'hello world')
Process(daemon=False, max_failures=1, ephemeral=False, min_duration=5,
cmdline=echo hello world, final=False)
A powerful binding property is that all of an object’s children inherit its bindings:
>>> List(Process)([
... Process(name = '{{prefix}}_one'),
... Process(name = '{{prefix}}_two')
... ]).bind(prefix = 'hello')
ProcessList(
Process(daemon=False, name=hello_one, max_failures=1, ephemeral=False, min_duration=5, final=False),
Process(daemon=False, name=hello_two, max_failures=1, ephemeral=False, min_duration=5, final=False)
)
Remember that an Aurora Job contains Tasks which contain Processes. A Job level binding is inherited by its Tasks and all their Processes. Similarly a Task level binding is available to that Task and its Processes but is not visible at the Job level (inheritance is a one-way street.)
When you define a Struct schema, one powerful, but confusing, feature
is that all of that structure’s attributes are Mustache variables within
the enclosing scope once they have been populated.
For example, when Process is defined above, all its attributes such as
{{name}}, {{cmdline}}, {{max_failures}} etc., are all immediately
defined as Mustache variables, implicitly bound into the Process, and
inherit all child objects once they are defined.
Thus, you can do the following:
>>> Process(name = "installer", cmdline = "echo {{name}} is running")
Process(daemon=False, name=installer, max_failures=1, ephemeral=False, min_duration=5,
cmdline=echo installer is running, final=False)
WARNING: This binding only takes place in one direction. For example,
the following does NOT work and does not set the Process name
attribute’s value.
>>> Process().bind(name = "installer")
Process(daemon=False, max_failures=1, ephemeral=False, min_duration=5, final=False)
The following is also not possible and results in an infinite loop that
attempts to resolve Process.name.
>>> Process(name = '{{name}}').bind(name = 'installer')
Do not confuse Structural attributes with bound Mustache variables. Attributes are implicitly converted to Mustache variables but not vice versa.
A second templating method is both as powerful as the aforementioned and often confused with it. This method is due to automatic conversion of Struct attributes to Mustache variables as described above.
Suppose you create a Process object:
>>> p = Process(name = "process_one", cmdline = "echo hello world")
>>> p
Process(daemon=False, name=process_one, max_failures=1, ephemeral=False, min_duration=5,
cmdline=echo hello world, final=False)
This Process object, “p”, can be used wherever a Process object is
needed. It can also be reused by changing the value(s) of its
attribute(s). Here we change its name attribute from process_one to
process_two.
>>> p(name = "process_two")
Process(daemon=False, name=process_two, max_failures=1, ephemeral=False, min_duration=5,
cmdline=echo hello world, final=False)
Template creation is a common use for this technique:
>>> Daemon = Process(daemon = True)
>>> logrotate = Daemon(name = 'logrotate', cmdline = './logrotate conf/logrotate.conf')
>>> mysql = Daemon(name = 'mysql', cmdline = 'bin/mysqld --safe-mode')
As described above, .bind() binds simple strings or numbers to
Mustache variables. In addition to Structural types formed by combining
atomic types, Pystachio has two container types; List and Map which
can also be bound via .bind().
The bind() function can take Python dictionaries or kwargs
interchangeably (when “kwargs” is in a function definition, kwargs
receives a Python dictionary containing all keyword arguments after the
formal parameter list).
>>> String('{{foo}}').bind(foo = 'bar') == String('{{foo}}').bind({'foo': 'bar'})
True
Bindings done “closer” to the object in question take precedence:
>>> p = Process(name = '{{context}}_process')
>>> t = Task().bind(context = 'global')
>>> t(processes = [p, p.bind(context = 'local')])
Task(processes=ProcessList(
Process(daemon=False, name=global_process, max_failures=1, ephemeral=False, final=False,
min_duration=5),
Process(daemon=False, name=local_process, max_failures=1, ephemeral=False, final=False,
min_duration=5)
))
>>> fibonacci = List(Integer)([1, 1, 2, 3, 5, 8, 13])
>>> String('{{fib[4]}}').bind(fib = fibonacci)
String(5)
>>> first_names = Map(String, String)({'Kent': 'Clark', 'Wayne': 'Bruce', 'Prince': 'Diana'})
>>> String('{{first[Kent]}}').bind(first = first_names)
String(Clark)
>>> String('{{p.cmdline}}').bind(p = Process(cmdline = "echo hello world"))
String(echo hello world)
Use structural templates when binding more than two or three individual values at the Job or Task level. For fewer than two or three, standard key to string binding is sufficient.
Structural binding is a very powerful pattern and is most useful in
Aurora/Thermos for doing Structural configuration. For example, you can
define a job profile. The following profile uses HDFS, the Hadoop
Distributed File System, to designate a file’s location. HDFS does
not come with Aurora, so you’ll need to either install it separately
or change the way the dataset is designated.
class Profile(Struct):
version = Required(String)
environment = Required(String)
dataset = Default(String, hdfs://home/aurora/data/{{environment}}')
PRODUCTION = Profile(version = 'live', environment = 'prod')
DEVEL = Profile(version = 'latest',
environment = 'devel',
dataset = 'hdfs://home/aurora/data/test')
TEST = Profile(version = 'latest', environment = 'test')
JOB_TEMPLATE = Job(
name = 'application',
role = 'myteam',
cluster = 'cluster1',
environment = '{{profile.environment}}',
task = SequentialTask(
name = 'task',
resources = Resources(cpu = 2, ram = 4*GB, disk = 8*GB),
processes = [
Process(name = 'main', cmdline = 'java -jar application.jar -hdfsPath
{{profile.dataset}}')
]
)
)
jobs = [
JOB_TEMPLATE(instances = 100).bind(profile = PRODUCTION),
JOB_TEMPLATE.bind(profile = DEVEL),
JOB_TEMPLATE.bind(profile = TEST),
]
In this case, a custom structural “Profile” is created to self-document
the configuration to some degree. This also allows some schema
“type-checking”, and for default self-substitution, e.g. in
Profile.dataset above.
So rather than a .bind() with a half-dozen substituted variables, you
can bind a single object that has sensible defaults stored in a single
place.
When creating your .aurora configuration, try to keep all versions of
a particular job within the same .aurora file. For example, if you
have separate jobs for cluster1, cluster1 staging, cluster1
testing, andcluster2, keep them as close together as possible.
Constructs shared across multiple jobs owned by your team (e.g.
team-level defaults or structural templates) can be split into separate
.aurorafiles and included via the include directive.
If you see repetition or find yourself copy and pasting any parts of your configuration, it’s likely an opportunity for templating. Take the example below:
redundant.aurora contains:
download = Process(
name = 'download',
cmdline = 'wget http://www.python.org/ftp/python/2.7.3/Python-2.7.3.tar.bz2',
max_failures = 5,
min_duration = 1)
unpack = Process(
name = 'unpack',
cmdline = 'rm -rf Python-2.7.3 && tar xzf Python-2.7.3.tar.bz2',
max_failures = 5,
min_duration = 1)
build = Process(
name = 'build',
cmdline = 'pushd Python-2.7.3 && ./configure && make && popd',
max_failures = 1)
email = Process(
name = 'email',
cmdline = 'echo Success | mail feynman@tmc.com',
max_failures = 5,
min_duration = 1)
build_python = Task(
name = 'build_python',
processes = [download, unpack, build, email],
constraints = [Constraint(order = ['download', 'unpack', 'build', 'email'])])
As you’ll notice, there’s a lot of repetition in the Process
definitions. For example, almost every process sets a max_failures
limit to 5 and a min_duration to 1. This is an opportunity for factoring
into a common process template.
Furthermore, the Python version is repeated everywhere. This can be bound via structural templating as described in the Advanced Binding section.
less_redundant.aurora contains:
class Python(Struct):
version = Required(String)
base = Default(String, 'Python-{{version}}')
package = Default(String, '{{base}}.tar.bz2')
ReliableProcess = Process(
max_failures = 5,
min_duration = 1)
download = ReliableProcess(
name = 'download',
cmdline = 'wget http://www.python.org/ftp/python/{{python.version}}/{{python.package}}')
unpack = ReliableProcess(
name = 'unpack',
cmdline = 'rm -rf {{python.base}} && tar xzf {{python.package}}')
build = ReliableProcess(
name = 'build',
cmdline = 'pushd {{python.base}} && ./configure && make && popd',
max_failures = 1)
email = ReliableProcess(
name = 'email',
cmdline = 'echo Success | mail {{role}}@foocorp.com')
build_python = SequentialTask(
name = 'build_python',
processes = [download, unpack, build, email]).bind(python = Python(version = "2.7.3"))
Many tiny Processes makes for harder to manage configurations.
copy = Process(
name = 'copy',
cmdline = 'rcp user@my_machine:my_application .'
)
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)
)
Each cmdline runs in a bash subshell, so you have the full power of
bash. Chaining commands with && or || is almost always the right
thing to do.
Also for Tasks that are simply a list of processes that run one after
another, consider using the SequentialTask helper which applies a
linear ordering constraint for you.
stage = Process(
name = 'stage',
cmdline = 'rcp user@my_machine:my_application . && unzip app.zip && rm -f app.zip')
run = Process(name = 'app', cmdline = 'java -jar app.jar')
run_task = SequentialTask(processes = [stage, run])
90% of the time you define a function in a .aurora file, you’re
probably Doing It Wrong™.
def get_my_task(name, user, cpu, ram, disk):
return Task(
name = name,
user = user,
processes = [STAGE_PROCESS, RUN_PROCESS],
constraints = order(STAGE_PROCESS, RUN_PROCESS),
resources = Resources(cpu = cpu, ram = ram, disk = disk)
)
task_one = get_my_task('task_one', 'feynman', 1.0, 32*MB, 1*GB)
task_two = get_my_task('task_two', 'feynman', 2.0, 64*MB, 1*GB)
This one is more idiomatic. Forced keyword arguments prevents accidents, e.g. constructing a task with “32*MB” when you mean 32MB of ram and not disk. Less proliferation of task-construction techniques means easier-to-read, quicker-to-understand, and a more composable configuration.
TASK_TEMPLATE = SequentialTask(
user = 'wickman',
processes = [STAGE_PROCESS, RUN_PROCESS],
)
task_one = TASK_TEMPLATE(
name = 'task_one',
resources = Resources(cpu = 1.0, ram = 32*MB, disk = 1*GB) )
task_two = TASK_TEMPLATE(
name = 'task_two',
resources = Resources(cpu = 2.0, ram = 64*MB, disk = 1*GB)
)