Don’t know where to start? The Aurora configuration schema is very powerful, and configurations can become quite complex for advanced use cases.
For examples of simple configurations to get something up and running quickly, check out the Tutorial. When you feel comfortable with the basics, move on to the Configuration Tutorial for more in-depth coverage of configuration design.
For additional basic configuration examples, see the end of this document.
Process objects consist of required name and cmdline attributes. You can customize Process
behavior with its optional attributes. Remember, Processes are handled by Thermos.
| Attribute Name | Type | Description |
|---|---|---|
| name | String | Process name (Required) |
| cmdline | String | Command line (Required) |
| max_failures | Integer | Maximum process failures (Default: 1) |
| daemon | Boolean | When True, this is a daemon process. (Default: False) |
| ephemeral | Boolean | When True, this is an ephemeral process. (Default: False) |
| min_duration | Integer | Minimum duration between process restarts in seconds. (Default: 15) |
| final | Boolean | When True, this process is a finalizing one that should run last. (Default: False) |
The name is any valid UNIX filename string (specifically no slashes, NULLs or leading periods). Within a Task object, each Process name must be unique.
The command line run by the process. The command line is invoked in a bash
subshell, so can involve fully-blown bash scripts. However, nothing is
supplied for command-line arguments so $* is unspecified.
The maximum number of failures (non-zero exit statuses) this process can have before being marked permanently failed and not retried. If a process permanently fails, Thermos looks at the failure limit of the task containing the process (usually 1) to determine if the task has failed as well.
Setting max_failures to 0 makes the process retry
indefinitely until it achieves a successful (zero) exit status.
It retries at most once every min_duration seconds to prevent
an effective denial of service attack on the coordinating Thermos scheduler.
By default, Thermos processes are non-daemon. 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 still applies. A combination of
daemon=True and max_failures=0 causes a process to retry
indefinitely regardless of exit status. This should be avoided
for very short-lived processes because of the accumulation of
checkpointed state for each process run. When running in Mesos
specifically, max_failures is capped at 100.
By default, Thermos processes are non-ephemeral. If ephemeral is set to
True, the process’ status is not used to determine if its containing 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 has
completed, regardless of the logsaver’s current status.
Processes may succeed or fail multiple times during a single task’s
duration. Each of these is called a process run. min_duration is
the minimum number of seconds the scheduler waits before running the
same process.
Processes can be grouped into two classes: ordinary processes and finalizing processes. By default, Thermos processes are ordinary. They run as long as the task is considered healthy (i.e., no failure limits have been reached.) But once all regular Thermos processes finish or the task reaches a certain failure threshold, it moves into a “finalization” stage and runs all finalizing processes. These are typically processes necessary for cleaning up the task, such as log checkpointers, or perhaps e-mail notifications that the task completed.
Finalizing processes may not depend upon ordinary processes or vice-versa, however finalizing processes may depend upon other finalizing processes and otherwise run as a typical process schedule.
Tasks fundamentally consist of a name and a list of Process objects stored as the
value of the processes attribute. Processes can be further constrained with
constraints. By default, name’s value inherits from the first Process in the
processes list, so for simple Task objects with one Process, name
can be omitted. In Mesos, resources is also required.
| param | type | description |
|---|---|---|
name |
String | Process name (Required) (Default: processes0.name) |
processes |
List of Process objects |
List of Process objects bound to this task. (Required) |
constraints |
List of Constraint objects |
List of Constraint objects constraining processes. |
resources |
Resource object |
Resource footprint. (Required) |
max_failures |
Integer | Maximum process failures before being considered failed (Default: 1) |
max_concurrency |
Integer | Maximum number of concurrent processes (Default: 0, unlimited concurrency.) |
finalization_wait |
Integer | Amount of time allocated for finalizing processes, in seconds. (Default: 30) |
name is a string denoting the name of this task. It defaults to the name of the first Process in
the list of Processes associated with the processes attribute.
processes is an unordered list of Process objects. To constrain the order
in which they run, use constraints.
A list of Constraint objects. Currently it supports only one type,
the order constraint. order is a list of process names
that should run in the order given. For example,
process = Process(cmdline = "echo hello {{name}}")
task = Task(name = "echoes",
processes = [process(name = "jim"), process(name = "bob")],
constraints = [Constraint(order = ["jim", "bob"]))
Constraints can be supplied ad-hoc and in duplicate. Not all Processes need be constrained, however Tasks with cycles are rejected by the Thermos scheduler.
Use the order function as shorthand to generate Constraint lists.
The following:
order(process1, process2)
is shorthand for
[Constraint(order = [process1.name(), process2.name()])]
Takes a Resource object, which specifies the amounts of CPU, memory, and disk space resources
to allocate to the Task.
max_failures is the number of times processes that are part of this
Task can fail before the entire Task is marked for failure.
For example:
template = Process(max_failures=10)
task = Task(
name = "fail",
processes = [
template(name = "failing", cmdline = "exit 1"),
template(name = "succeeding", cmdline = "exit 0")
],
max_failures=2)
The failing Process could fail 10 times before being marked as
permanently failed, and the succeeding Process would succeed on the
first run. The task would succeed despite only allowing for two failed
processes. To be more specific, there would be 10 failed process runs
yet 1 failed process.
For Tasks with a number of expensive but otherwise independent
processes, you may want to limit the amount of concurrency
the Thermos scheduler provides rather than artificially constraining
it via order constraints. For example, a test framework may
generate a task with 100 test run processes, but wants to run it on
a machine with only 4 cores. You can limit the amount of parallelism to
4 by setting max_concurrency=4 in your task configuration.
For example, the following task spawns 180 Processes (“mappers”) to compute individual elements of a 180 degree sine table, all dependent upon one final Process (“reducer”) to tabulate the results:
def make_mapper(id):
return Process(
name = "mapper%03d" % id,
cmdline = "echo 'scale=50;s(%d\*4\*a(1)/180)' | bc -l >
temp.sine_table.%03d" % (id, id))
def make_reducer():
return Process(name = "reducer", cmdline = "cat temp.\* | nl \> sine\_table.txt
&& rm -f temp.\*")
processes = map(make_mapper, range(180))
task = Task(
name = "mapreduce",
processes = processes + [make\_reducer()],
constraints = [Constraint(order = [mapper.name(), 'reducer']) for mapper
in processes],
max_concurrency = 8)
Tasks have three active stages: ACTIVE, CLEANING, and FINALIZING. The
ACTIVE stage is when ordinary processes run. This stage lasts as
long as Processes are running and the Task is healthy. The moment either
all Processes have finished successfully or the Task has reached a
maximum Process failure limit, it goes into CLEANING stage and send
SIGTERMs to all currently running Processes and their process trees.
Once all Processes have terminated, the Task goes into FINALIZING stage
and invokes the schedule of all Processes with the “final” attribute set to True.
This whole process from the end of ACTIVE stage to the end of FINALIZING
must happen within finalization_wait seconds. If it does not
finish during that time, all remaining Processes are sent SIGKILLs
(or if they depend upon uncompleted Processes, are
never invoked.)
Client applications with higher priority may force a shorter
finalization wait (e.g. through parameters to thermos kill), so this
is mostly a best-effort signal.
Current constraint objects only support a single ordering constraint, order,
which specifies its processes run sequentially in the order given. By
default, all processes run in parallel when bound to a Task without
ordering constraints.
| param | type | description |
|---|---|---|
| order | List of String | List of processes by name (String) that should be run serially. |
Specifies the amount of CPU, Ram, and disk resources the task needs. See the Resource Isolation document for suggested values and to understand how resources are allocated.
| param | type | description |
|---|---|---|
cpu |
Float | Fractional number of cores required by the task. |
ram |
Integer | Bytes of RAM required by the task. |
disk |
Integer | Bytes of disk required by the task. |
| name | type | description |
|---|---|---|
task |
Task | The Task object to bind to this job. Required. |
name |
String | Job name. (Default: inherited from the task attribute’s name) |
role |
String | Job role account. Required. |
cluster |
String | Cluster in which this job is scheduled. Required. |
environment |
String | Job environment, default devel. Must be one of prod, devel, test or staging<number>. |
contact |
String | Best email address to reach the owner of the job. For production jobs, this is usually a team mailing list. |
instances |
Integer | Number of instances (sometimes referred to as replicas or shards) of the task to create. (Default: 1) |
cron_schedule |
String | Cron schedule in cron format. May only be used with non-service jobs. See Cron Jobs for more information. Default: None (not a cron job.) |
cron_collision_policy |
String | Policy to use when a cron job is triggered while a previous run is still active. KILLEXISTING Kill the previous run, and schedule the new run CANCELNEW Let the previous run continue, and cancel the new run. (Default: KILL_EXISTING) |
update_config |
update_config object |
Parameters for controlling the rate and policy of rolling updates. |
constraints |
dict | Scheduling constraints for the tasks. See the section on the constraint specification language |
service |
Boolean | If True, restart tasks regardless of success or failure. (Default: False) |
max_task_failures |
Integer | Maximum number of failures after which the task is considered to have failed (Default: 1) Set to -1 to allow for infinite failures |
priority |
Integer | Preemption priority to give the task (Default 0). Tasks with higher priorities may preempt tasks at lower priorities. |
production |
Boolean | Whether or not this is a production task backed by quota (Default: False). Production jobs may preempt any non-production job, and may only be preempted by production jobs in the same role and of higher priority. To run jobs at this level, the job role must have the appropriate quota. To grant quota to a particular role in production, operators use the aurora_admin set_quota command. |
health_check_config |
heath_check_config object |
Parameters for controlling a task’s health checks via HTTP. Only used if a health port was assigned with a command line wildcard. |
container |
Container object |
An optional container to run all processes inside of. |
Jobs with the service flag set to True are called Services. The Service
alias can be used as shorthand for Job with service=True.
Services are differentiated from non-service Jobs in that tasks
always restart on completion, whether successful or unsuccessful.
Jobs without the service bit set only restart up to
max_task_failures times and only if they terminated unsuccessfully
either due to human error or machine failure.
Parameters for controlling the rate and policy of rolling updates.
| object | type | description |
|---|---|---|
batch_size |
Integer | Maximum number of shards to be updated in one iteration (Default: 1) |
restart_threshold |
Integer | Maximum number of seconds before a shard must move into the RUNNING state before considered a failure (Default: 60) |
watch_secs |
Integer | Minimum number of seconds a shard must remain in RUNNING state before considered a success (Default: 45) |
max_per_shard_failures |
Integer | Maximum number of restarts per shard during update. Increments total failure count when this limit is exceeded. (Default: 0) |
max_total_failures |
Integer | Maximum number of shard failures to be tolerated in total during an update. Cannot be greater than or equal to the total number of tasks in a job. (Default: 0) |
rollback_on_failure |
boolean | When False, prevents auto rollback of a failed update (Default: True) |
wait_for_batch_completion |
boolean | When True, all threads from a given batch will be blocked from picking up new instances until the entire batch is updated. This essentially simulates the legacy sequential updater algorithm. (Default: False) |
pulse_interval_secs |
Integer | Indicates a coordinated update. If no pulses are received within the provided interval the update will be blocked. Beta-updater only. Will fail on submission when used with client updater. (Default: None) |
Parameters for controlling a task’s health checks via HTTP.
| object | type | description |
|---|---|---|
initial_interval_secs |
Integer | Initial delay for performing an HTTP health check. (Default: 15) |
interval_secs |
Integer | Interval on which to check the task’s health via HTTP. (Default: 10) |
timeout_secs |
Integer | HTTP request timeout. (Default: 1) |
max_consecutive_failures |
Integer | Maximum number of consecutive failures that tolerated before considering a task unhealthy (Default: 0) |
If the announce field in the Job configuration is set, each task will be
registered in the ServerSet /aurora/role/environment/jobname in the
zookeeper ensemble configured by the executor. If no Announcer object is specified,
no announcement will take place. For more information about ServerSets, see the User Guide.
| object | type | description |
|---|---|---|
primary_port |
String | Which named port to register as the primary endpoint in the ServerSet (Default: http) |
portmap |
dict | A mapping of additional endpoints to announced in the ServerSet (Default: { 'aurora': '{{primary_port}}' }) |
portmapThe primary endpoint registered in the ServerSet is the one allocated to the port
specified by the primary_port in the Announcer object, by default
the http port. This port can be referenced from anywhere within a configuration
as {{thermos.ports[http]}}.
Without the port map, each named port would be allocated a unique port number.
The portmap allows two different named ports to be aliased together. The default
portmap aliases the aurora port (i.e. {{thermos.ports[aurora]}}) to
the http port. Even though the two ports can be referenced independently,
only one port is allocated by Mesos. Any port referenced in a Process object
but which is not in the portmap will be allocated dynamically by Mesos and announced as well.
It is possible to use the portmap to alias names to static port numbers, e.g.
{'http': 80, 'https': 443, 'aurora': 'http'}. In this case, referencing
{{thermos.ports[aurora]}} would look up {{thermos.ports[http]}} then
find a static port 80. No port would be requested of or allocated by Mesos.
Static ports should be used cautiously as Aurora does nothing to prevent two tasks with the same static port allocations from being co-scheduled. External constraints such as slave attributes should be used to enforce such guarantees should they be needed.
Note: The only container type currently supported is “docker”. Docker support is currently EXPERIMENTAL. Note: In order to correctly execute processes inside a job, the Docker container must have python 2.7 installed.
Describes the container the job’s processes will run inside.
| param | type | description |
|---|---|---|
docker |
Docker | A docker container to use. |
| param | type | description |
|---|---|---|
image |
String | The name of the docker image to execute. If the image does not exist locally it will be pulled with docker pull. |
Most users will not need to specify constraints explicitly, as the
scheduler automatically inserts reasonable defaults that attempt to
ensure reliability without impacting schedulability. For example, the
scheduler inserts a host: limit:1 constraint, ensuring
that your shards run on different physical machines. Please do not
set this field unless you are sure of what you are doing.
In the Job object there is a map constraints from String to String
allowing the user to tailor the schedulability of tasks within the job.
Each slave in the cluster is assigned a set of string-valued
key/value pairs called attributes. For example, consider the host
cluster1-aaa-03-sr2 and its following attributes (given in key:value
format): host:cluster1-aaa-03-sr2 and rack:aaa.
The constraint map’s key value is the attribute name in which we constrain Tasks within our Job. The value is how we constrain them. There are two types of constraints: limit constraints and value constraints.
| constraint | description |
|---|---|
| Limit | A string that specifies a limit for a constraint. Starts with 'limit: followed by an Integer and closing single quote, such as 'limit:1'. |
| Value | A string that specifies a value for a constraint. To include a list of values, separate the values using commas. To negate the values of a constraint, start with a ! . |
You can also control machine diversity using constraints. The below constraint ensures that no more than two instances of your job may run on a single host. Think of this as a “group by” limit.
constraints = {
'host': 'limit:2',
}
Likewise, you can use constraints to control rack diversity, e.g. at most one task per rack:
constraints = {
'rack': 'limit:1',
}
Use these constraints sparingly as they can dramatically reduce Tasks’ schedulability.
Currently, a few Pystachio namespaces have special semantics. Using them in your configuration allow you to tailor application behavior through environment introspection or interact in special ways with the Aurora client or Aurora-provided services.
The mesos namespace contains the instance variable that can be used
to distinguish between Task replicas.
| variable name | type | description |
|---|---|---|
instance |
Integer | The instance number of the created task. A job with 5 replicas has instance numbers 0, 1, 2, 3, and 4. |
The thermos namespace contains variables that work directly on the
Thermos platform in addition to Aurora. This namespace is fully
compatible with Tasks invoked via the thermos CLI.
| variable | type | description |
|---|---|---|
ports |
map of string to Integer | A map of names to port numbers |
task_id |
string | The task ID assigned to this task. |
The thermos.ports namespace is automatically populated by Aurora when
invoking tasks on Mesos. When running the thermos command directly,
these ports must be explicitly mapped with the -P option.
For example, if ’{{thermos.ports[http]}}’ is specified in a Process
configuration, it is automatically extracted and auto-populated by
Aurora, but must be specified with, for example, thermos -P http:12345
to map http to port 12345 when running via the CLI.
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