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.
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: 5) |
| final | Boolean | When True, this process is a finalizing one that should run last. (Default: False) |
| logger | Logger | Struct defining the log behavior for the process. (Default: Empty) |
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.
The default behavior of Thermos is to store stderr/stdout logs in files which grow unbounded. In the event that you have large log volume, you may want to configure Thermos to automatically rotate logs after they grow to a certain size, which can prevent your job from using more than its allocated disk space.
Logger objects specify a destination for Process logs which is, by default, file - a pair of
stdout and stderr files. Its also possible to specify console to get logs output to
the Process stdout and stderr streams, none to suppress any logs output or both to send logs to
files and console streams.
The default Logger mode is standard which lets the stdout and stderr streams grow without bound.
| Attribute Name | Type | Description |
|---|---|---|
| destination | LoggerDestination | Destination of logs. (Default: file) |
| mode | LoggerMode | Mode of the logger. (Default: standard) |
| rotate | RotatePolicy | An optional rotation policy. (Default: Empty) |
A RotatePolicy describes log rotation behavior for when mode is set to rotate and it is ignored
otherwise. If rotate is Empty or RotatePolicy() when the mode is set to rotate the
defaults below are used.
| Attribute Name | Type | Description |
|---|---|---|
| log_size | Integer | Maximum size (in bytes) of an individual log file. (Default: 100 MiB) |
| backups | Integer | The maximum number of backups to retain. (Default: 5) |
An example process configuration is as follows:
process = Process(
name='process',
logger=Logger(
destination=LoggerDestination('both'),
mode=LoggerMode('rotate'),
rotate=RotatePolicy(log_size=5*MB, backups=5)
)
)
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()])]
The order function accepts Process name strings ('foo', 'bar') or the processes
themselves, e.g. foo=Process(name='foo', ...), bar=Process(name='bar', ...),
constraints=order(foo, bar).
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 failed processes needed for the Task to be
marked as failed.
For example, assume a Task has two Processes and a max_failures value of 2:
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 could succeed on the first run. However,
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. Both processes
would have to fail for the Task to fail.
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)
Process execution is organizued into 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.)
When running on Aurora, the finalization_wait is capped at 60 seconds.
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. |
gpu |
Integer | Number of GPU cores required by the task |
Note: Specifying a Container object as the value of the container property is
deprecated in favor of setting its value directly to the appropriate Docker or Mesos
container type
Note: Specifying preemption behavior of tasks through production flag is deprecated in favor of
electing appropriate task tier via tier attribute.
| 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. By default must be one of prod, devel, test or staging<number> but it can be changed by the Cluster operator using the scheduler option allowed_job_environments. |
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. KILL_EXISTING Kill the previous run, and schedule the new run CANCEL_NEW Let the previous run continue, and cancel the new run. (Default: KILL_EXISTING) |
update_config |
UpdateConfig 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 | (Deprecated) Whether or not this is a production task that may preempt other tasks (Default: False). Production job role must have the appropriate quota. |
health_check_config |
HealthCheckConfig object |
Parameters for controlling a task's health checks. HTTP health check is only used if a health port was assigned with a command line wildcard. |
container |
Choice of Container, Docker or Mesos object |
An optional container to run all processes inside of. |
lifecycle |
LifecycleConfig object |
An optional task lifecycle configuration that dictates commands to be executed on startup/teardown. HTTP lifecycle is enabled by default if the "health" port is requested. See LifecycleConfig Objects for more information. |
tier |
String | Task tier type. The default scheduler tier configuration allows for 3 tiers: revocable, preemptible, and preferred. If a tier is not elected, Aurora assigns the task to a tier based on its choice of production (that is preferred for production and preemptible for non-production jobs). See the section on Configuration Tiers for more information. |
announce |
Announcer object |
Optionally enable Zookeeper ServerSet announcements. See [Announcer Objects] for more information. |
enable_hooks |
Boolean | Whether to enable Client Hooks for this job. (Default: False) |
partition_policy |
PartitionPolicy object |
An optional partition policy that allows job owners to define how to handle partitions for running tasks (in partition-aware Aurora clusters) |
metadata |
list of Metadata objects |
list of Metadata objects for user's customized metadata information. |
executor_config |
ExecutorConfig object |
Allows choosing an alternative executor defined in custom_executor_config to be used instead of Thermos. Tasks will be launched with Thermos as the executor by default. See Custom Executors for more info. |
sla_policy |
Choice of CountSlaPolicy, PercentageSlaPolicy or CoordinatorSlaPolicy object |
An optional SLA policy that allows job owners to describe the SLA requirements for the job. See SlaPolicy Objects for more information. |
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) |
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) |
update_strategy |
Choice of QueueUpdateStrategy, BatchUpdateStrategy, or VariableBatchUpdateStrategy object |
Indicate which update strategy to use for this update. |
sla_aware |
boolean | When True, updates will only update an instance if it does not break the task's specified SLA Requirements. (Default: None) |
Update strategy which will keep the active updating instances at size batch_size throughout the update until there are no more instances left to update.
| object | type | description |
|---|---|---|
batch_size |
Integer | Maximum number of shards to be updated in one iteration (Default: 1) |
Update strategy which will wait until a maximum of batch_size number of instances are updated before continuing on to the next group until all instances are updated.
| object | type | description |
|---|---|---|
batch_size |
Integer | Maximum number of shards to be updated in one iteration (Default: 1) |
autopause_after_batch |
Boolean | Automatically pauses update after completing a batch. (Default: False) |
Similar to Batch Update strategy, this strategy will wait until all instances in a current group are updated before updating more instances. However, instead of maintaining a static group size, the size of each group may change as the update progresses. For example, an update which modifies a total of 10 instances may be done in batch sizes of 2, 3, and 5. If the number of instances to be updated are greater than the sum of the groups, the last group size will be used in perpetuity until all instances are updated. Following the previous example, if instead of 10 instances 20 instances are modified, the update groups would become: 2, 3, 5, 5, 5.
| object | type | description |
|---|---|---|
batch_sizes |
List(Integer) | Maximum number of shards to be updated per iteration. As each iteration completes, the next iteration's group size may change. If there are still instances that need to be updated after all sizes are used, the last size will be reused for the remainder of the update. |
autopause_after_batch |
Boolean | Automatically pauses update before starting a new batch. (Default: False) |
There are some nuances around the sla_aware option that users should be aware of:
- SLA-aware updates work in tandem with maintenance. Draining a host that has an instance of the job being updated affects the SLA and thus will be taken into account when the update determines whether or not it is safe to update another instance.
- SLA-aware updates will use the SLAPolicy of the
newest configuration when determining whether or not it is safe to update an instance. For
example, if the current configuration specifies a
PercentageSlaPolicy that allows for
5% of instances to be down and the updated configuration increaes this value to 10%, the SLA
calculation will be done using the 10% policy. Be mindful of this when doing an update that
modifies the
SLAPolicysince it may be possible to put the old configuration in a bad state that the new configuration would not be affected by. Additionally, if the update is rolled back, then the rollback will use the oldSLAPolicy(or none if there was not one previously). - If using the CoordinatorSlaPolicy,
it is important to pay attention to the
batch_sizeof the update. If you have a complex SLA requirement, then you may be limiting the throughput of your updates with an insufficientbatch_size. For example, imagine you have a job with 9 instance that represents three replicated caches, and you can only update one instance per replica set:[0 1 2] [3 4 5] [6 7 8](the number indicates the instance ID and the brackets represent replica sets). If yourbatch_sizeis 3, then you will slowly update one replica set at a time. If yourbatch_sizeis 9, then you can update all replica sets in parallel and thus speeding up the update. - If an instance fails an SLA check for an update, then it will be rechecked starting at a delay
from
sla_aware_kill_retry_min_delayand exponentially increasing up tosla_aware_kill_retry_max_delay. These are cluster-operator set values.
Parameters for controlling a task's health checks via HTTP or a shell command.
| param | type | description |
|---|---|---|
health_checker |
HealthCheckerConfig | Configure what kind of health check to use. |
initial_interval_secs |
Integer | Initial grace period (during which health-check failures are ignored) while performing health checks. (Default: 15) |
interval_secs |
Integer | Interval on which to check the task's health. (Default: 10) |
max_consecutive_failures |
Integer | Maximum number of consecutive failures that will be tolerated before considering a task unhealthy (Default: 0) |
min_consecutive_successes |
Integer | Minimum number of consecutive successful health checks required before considering a task healthy (Default: 1) |
timeout_secs |
Integer | Health check timeout. (Default: 1) |
| param | type | description |
|---|---|---|
http |
HttpHealthChecker | Configure health check to use HTTP. (Default) |
shell |
ShellHealthChecker | Configure health check via a shell command. |
| param | type | description |
|---|---|---|
endpoint |
String | HTTP endpoint to check (Default: /health) |
expected_response |
String | If not empty, fail the HTTP health check if the response differs. Case insensitive. (Default: ok) |
expected_response_code |
Integer | If not zero, fail the HTTP health check if the response code differs. (Default: 0) |
| param | type | description |
|---|---|---|
shell_command |
String | An alternative to HTTP health checking. Specifies a shell command that will be executed. Any non-zero exit status will be interpreted as a health check failure. |
| param | type | description |
|---|---|---|
reschedule |
Boolean | Whether or not to reschedule when running tasks become partitioned (Default: True) |
delay_secs |
Integer | How long to delay transitioning to LOST when running tasks are partitioned. (Default: 0) |
Describes a piece of user metadata in a key value pair
| param | type | description |
|---|---|---|
key |
String | Indicate which metadata the user provides |
value |
String | Provide the metadata content for corresponding key |
Describes an Executor name and data to pass to the Mesos Task
| param | type | description |
|---|---|---|
name |
String | Name of the executor to use for this task. Must match the name of an executor in custom_executor_config or Thermos (AuroraExecutor). (Default: AuroraExecutor) |
data |
String | Data blob to pass on to the executor. (Default: "") |
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 (which can be optionally overriden by specifying
zk_path parameter). If no Announcer object is specified,
no announcement will take place. For more information about ServerSets, see the Service Discover
documentation.
By default, the hostname in the registered endpoints will be the --hostname parameter
that is passed to the mesos agent. To override the hostname value, the executor can be started
with --announcer-hostname=<overriden_value>. If you decide to use --announcer-hostname and if
the overriden value needs to change for every executor, then the executor has to be started inside a wrapper, see Executor Wrapper.
For example, if you want the hostname in the endpoint to be an IP address instead of the hostname,
the --hostname parameter to the mesos agent can be set to the machine IP or the executor can
be started with --announcer-hostname=<host_ip> while wrapping the executor inside a script.
| 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 be announced in the ServerSet (Default: { 'aurora': '{{primary_port}}' }) |
zk_path |
String | Zookeeper serverset path override (executor must be started with the --announcer-allow-custom-serverset-path parameter) |
The 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 agent attributes should be used to enforce such guarantees should they be needed.
Describes the container the job's processes will run inside. If not using Docker or the Mesos unified-container, the container can be omitted from your job config.
| param | type | description |
|---|---|---|
mesos |
Mesos | A native Mesos container to use. |
docker |
Docker | A Docker container to use (via Docker engine) |
| param | type | description |
|---|---|---|
image |
Choice(AppcImage, DockerImage) | An optional filesystem image to use within this container. |
volumes |
List(Volume) | An optional list of volume mounts for this container. |
| param | type | description |
|---|---|---|
container_path |
String | Path on the host to mount. |
host_path |
String | Mount point in the container. |
mode |
Enum | Mode of the mount, can be 'RW' or 'RO'. |
Describes an AppC filesystem image.
| param | type | description |
|---|---|---|
name |
String | The name of the appc image. |
image_id |
String | The image id of the appc image. |
Describes a Docker filesystem image.
| param | type | description |
|---|---|---|
name |
String | The name of the docker image. |
tag |
String | The tag that identifies the docker image. |
Note: In order to correctly execute processes inside a job, the Docker container must have Python 2.7 installed.
Note: For private docker registry, mesos mandates the docker credential file to be named as .dockercfg, even though docker may create a credential file with a different name on various platforms. Also, the .dockercfg file needs to be copied into the sandbox using the -thermos_executor_resources flag, specified while starting Aurora.
| 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. |
parameters |
List(Parameter) | Additional parameters to pass to the Docker engine. |
Docker CLI parameters. This needs to be enabled by the scheduler -allow_docker_parameters option.
See Docker Command Line Reference for valid parameters.
| param | type | description |
|---|---|---|
name |
String | The name of the docker parameter. E.g. volume |
value |
String | The value of the parameter. E.g. /usr/local/bin:/usr/bin:rw |
Note: The only lifecycle configuration supported is the HTTP lifecycle via the HttpLifecycleConfig.
| param | type | description |
|---|---|---|
http |
HttpLifecycleConfig | Configure the lifecycle manager to send lifecycle commands to the task via HTTP. |
Note: The combined graceful_shutdown_wait_secs and shutdown_wait_secs is implicitly upper bounded by the --stop_timeout_in_secs flag exposed by the executor (see options here, default is 2 minutes). Therefore, if the user specifies values that add up to more than --stop_timeout_in_secs, the task will be killed earlier than the user anticipates (see the termination lifecycle here). Furthermore, stop_timeout_in_secs itself is implicitly upper bounded by two scheduler options: transient_task_state_timeout and preemption_slot_hold_time (see reference here. If the stop_timeout_in_secs exceeds either of these scheduler options, tasks could be designated as LOST or tasks utilizing preemption could lose their desired slot respectively. Cluster operators should be aware of these timings should they change the defaults.
| param | type | description |
|---|---|---|
port |
String | The named port to send POST commands. (Default: health) |
graceful_shutdown_endpoint |
String | Endpoint to hit to indicate that a task should gracefully shutdown. (Default: /quitquitquit) |
shutdown_endpoint |
String | Endpoint to hit to give a task its final warning before being killed. (Default: /abortabortabort) |
graceful_shutdown_wait_secs |
Integer | The amount of time (in seconds) to wait after hitting the graceful_shutdown_endpoint before proceeding with the task termination lifecycle. (Default: 5) |
shutdown_wait_secs |
Integer | The amount of time (in seconds) to wait after hitting the shutdown_endpoint before proceeding with the task termination lifecycle. (Default: 5) |
If the Job is listening on the port as specified by the HttpLifecycleConfig
(default: health), a HTTP POST request will be sent over localhost to this
endpoint to request that the task gracefully shut itself down. This is a
courtesy call before the shutdown_endpoint is invoked
graceful_shutdown_wait_secs seconds later.
If the Job is listening on the port as specified by the HttpLifecycleConfig
(default: health), a HTTP POST request will be sent over localhost to this
endpoint to request as a final warning before being shut down. If the task
does not shut down on its own after shutdown_wait_secs seconds, it will be
forcefully killed.
Configuration for specifying custom SLA requirements for a job. There are 3 supported SLA policies
namely, CountSlaPolicy, PercentageSlaPolicy and CoordinatorSlaPolicy.
| param | type | description |
|---|---|---|
count |
Integer | The number of active instances required every durationSecs. |
duration_secs |
Integer | Minimum time duration a task needs to be RUNNING to be treated as active. |
| param | type | description |
|---|---|---|
percentage |
Float | The percentage of active instances required every durationSecs. |
duration_secs |
Integer | Minimum time duration a task needs to be RUNNING to be treated as active. |
| param | type | description |
|---|---|---|
coordinator_url |
String | The URL to the Coordinator service to be contacted before performing SLA affecting actions (job updates, host drains etc). |
status_key |
String | The field in the Coordinator response that indicates the SLA status for working on the task. (Default: drain) |
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.
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 ! . |
Further details can be found in the Scheduling Constraints feature description.
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 variables which relate to the mesos agent
which launched the task. The instance variable 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. |
hostname |
String | The instance hostname that the task was launched on. |
Please note, there is no uniqueness guarantee for instance in the presence of
network partitions. If that is required, it should be baked in at the application
level using a distributed coordination service such as Zookeeper.
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.