Added: aurora/site/source/documentation/0.22.0/features/containers.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.22.0/features/containers.md?rev=1871319&view=auto ============================================================================== --- aurora/site/source/documentation/0.22.0/features/containers.md (added) +++ aurora/site/source/documentation/0.22.0/features/containers.md Fri Dec 13 05:37:33 2019 @@ -0,0 +1,130 @@ +Containers +========== + +Aurora supports several containerizers, notably the Mesos containerizer and the Docker +containerizer. The Mesos containerizer uses native OS features directly to provide isolation between +containers, while the Docker containerizer delegates container management to the Docker engine. + +The support for launching container images via both containerizers has to be +[enabled by a cluster operator](../../operations/configuration/#containers). + +Mesos Containerizer +------------------- + +The Mesos containerizer is the native Mesos containerizer solution. It allows tasks to be +run with an array of [pluggable isolators](../resource-isolation/) and can launch tasks using +[Docker](https://github.com/docker/docker/blob/master/image/spec/v1.md) images, +[AppC](https://github.com/appc/spec/blob/master/SPEC.md) images, or directly on the agent host +filesystem. + +The following example (available in our [Vagrant environment](../../getting-started/vagrant/)) +launches a hello world example within a `debian/jessie` Docker image: + + $ cat /vagrant/examples/jobs/hello_docker_image.aurora + hello_loop = Process( + name = 'hello', + cmdline = """ + while true; do + echo hello world + sleep 10 + done + """) + + task = Task( + processes = [hello_loop], + resources = Resources(cpu=1, ram=1*MB, disk=8*MB) + ) + + jobs = [ + Service( + cluster = 'devcluster', + environment = 'devel', + role = 'www-data', + name = 'hello_docker_image', + task = task, + container = Mesos(image=DockerImage(name='debian', tag='jessie')) + ) + ] + +Docker and Appc images are designated using an appropriate `image` property of the `Mesos` +configuration object. If either `container` or `image` is left unspecified, the host filesystem +will be used. Further details of how to specify images can be found in the +[Reference Documentation](../../reference/configuration/#mesos-object). + +By default, Aurora launches processes as the Linux user named like the used role (e.g. `www-data` +in the example above). This user has to exist on the host filesystem. If it does not exist within +the container image, it will be created automatically. Otherwise, this user and its primary group +has to exist in the image with matching uid/gid. + +For more information on the Mesos containerizer filesystem, namespace, and isolator features, visit +[Mesos Containerizer](http://mesos.apache.org/documentation/latest/mesos-containerizer/) and +[Mesos Container Images](http://mesos.apache.org/documentation/latest/container-image/). + + +Docker Containerizer +-------------------- + +The Docker containerizer launches container images using the Docker engine. It may often provide +more advanced features than the native Mesos containerizer, but has to be installed separately to +Mesos on each agent host. + +Starting with the 0.17.0 release, `image` can be specified with a `{{docker.image[name][tag]}}` binder so that +the tag can be resolved to a concrete image digest. This ensures that the job always uses the same image +across restarts, even if the version identified by the tag has been updated, guaranteeing that only job +updates can mutate configuration. + +Example (available in the [Vagrant environment](../../getting-started/vagrant/)): + + $ cat /vagrant/examples/jobs/hello_docker_engine.aurora + hello_loop = Process( + name = 'hello', + cmdline = """ + while true; do + echo hello world + sleep 10 + done + """) + + task = Task( + processes = [hello_loop], + resources = Resources(cpu=1, ram=1*MB, disk=8*MB) + ) + + jobs = [ + Service( + cluster = 'devcluster', + environment = 'devel', + role = 'www-data', + name = 'hello_docker', + task = task, + container = Docker(image = 'python:2.7') + ), Service( + cluster = 'devcluster', + environment = 'devel', + role = 'www-data', + name = 'hello_docker_engine_binding', + task = task, + container = Docker(image = '{{docker.image[library/python][2.7]}}') + ) + ] + +Note, this feature requires a v2 Docker registry. If using a private Docker registry its url +must be specified in the `clusters.json` configuration file under the key `docker_registry`. +If not specified `docker_registry` defaults to `https://registry-1.docker.io` (Docker Hub). + +Example: + # clusters.json + [{ + "name": "devcluster", + ... + "docker_registry": "https://registry.example.com"; + }] + +Details of how to use Docker via the Docker engine can be found in the +[Reference Documentation](../../reference/configuration/#docker-object). Please note that in order to +correctly execute processes inside a job, the Docker container must have Python 2.7 and potentitally +further Mesos dependencies installed. This limitation does not hold for Docker containers used via +the Mesos containerizer. + +For more information on launching Docker containers through the Docker containerizer, visit +[Docker Containerizer](http://mesos.apache.org/documentation/latest/docker-containerizer/)
Added: aurora/site/source/documentation/0.22.0/features/cron-jobs.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.22.0/features/cron-jobs.md?rev=1871319&view=auto ============================================================================== --- aurora/site/source/documentation/0.22.0/features/cron-jobs.md (added) +++ aurora/site/source/documentation/0.22.0/features/cron-jobs.md Fri Dec 13 05:37:33 2019 @@ -0,0 +1,124 @@ +# Cron Jobs + +Aurora supports execution of scheduled jobs on a Mesos cluster using cron-style syntax. + +- [Overview](#overview) +- [Collision Policies](#collision-policies) +- [Failure recovery](#failure-recovery) +- [Interacting with cron jobs via the Aurora CLI](#interacting-with-cron-jobs-via-the-aurora-cli) + - [cron schedule](#cron-schedule) + - [cron deschedule](#cron-deschedule) + - [cron start](#cron-start) + - [job killall, job restart, job kill](#job-killall-job-restart-job-kill) +- [Technical Note About Syntax](#technical-note-about-syntax) +- [Caveats](#caveats) + - [Failovers](#failovers) + - [Collision policy is best-effort](#collision-policy-is-best-effort) + - [Timezone Configuration](#timezone-configuration) + +## Overview + +A job is identified as a cron job by the presence of a +`cron_schedule` attribute containing a cron-style schedule in the +[`Job`](../../reference/configuration/#job-objects) object. Examples of cron schedules +include "every 5 minutes" (`*/5 * * * *`), "Fridays at 17:00" (`* 17 * * FRI`), and +"the 1st and 15th day of the month at 03:00" (`0 3 1,15 *`). + +Example (available in the [Vagrant environment](../../getting-started/vagrant/)): + + $ cat /vagrant/examples/jobs/cron_hello_world.aurora + # A cron job that runs every 5 minutes. + jobs = [ + Job( + cluster = 'devcluster', + role = 'www-data', + environment = 'test', + name = 'cron_hello_world', + cron_schedule = '*/5 * * * *', + task = SimpleTask( + 'cron_hello_world', + 'echo "Hello world from cron, the time is now $(date --rfc-822)"'), + ), + ] + +## Collision Policies + +The `cron_collision_policy` field specifies the scheduler's behavior when a new cron job is +triggered while an older run hasn't finished. The scheduler has two policies available: + +* `KILL_EXISTING`: The default policy - on a collision the old instances are killed and a instances with the current +configuration are started. +* `CANCEL_NEW`: On a collision the new run is cancelled. + +Note that the use of `CANCEL_NEW` is likely a code smell - interrupted cron jobs should be able +to recover their progress on a subsequent invocation, otherwise they risk having their work queue +grow faster than they can process it. + +## Failure recovery + +Unlike with services, which aurora will always re-execute regardless of exit status, instances of +cron jobs retry according to the `max_task_failures` attribute of the +[Task](../../reference/configuration/#task-object) object. To get "run-until-success" semantics, +set `max_task_failures` to `-1`. + +## Interacting with cron jobs via the Aurora CLI + +Most interaction with cron jobs takes place using the `cron` subcommand. See `aurora cron -h` +for up-to-date usage instructions. + +### cron schedule +Schedules a new cron job on the Aurora cluster for later runs or replaces the existing cron template +with a new one. Only future runs will be affected, any existing active tasks are left intact. + + $ aurora cron schedule devcluster/www-data/test/cron_hello_world /vagrant/examples/jobs/cron_hello_world.aurora + +### cron deschedule +Deschedules a cron job, preventing future runs but allowing current runs to complete. + + $ aurora cron deschedule devcluster/www-data/test/cron_hello_world + +### cron start +Start a cron job immediately, outside of its normal cron schedule. + + $ aurora cron start devcluster/www-data/test/cron_hello_world + +### job killall, job restart, job kill +Cron jobs create instances running on the cluster that you can interact with like normal Aurora +tasks with `job kill` and `job restart`. + + +## Technical Note About Syntax + +`cron_schedule` uses a restricted subset of BSD crontab syntax. While the +execution engine currently uses Quartz, the schedule parsing is custom, a subset of FreeBSD +[crontab(5)](http://www.freebsd.org/cgi/man.cgi?crontab(5)) syntax. See +[the source](https://github.com/apache/aurora/blob/master/src/main/java/org/apache/aurora/scheduler/cron/CrontabEntry.java#L106-L124) +for details. + + +## Caveats + +### Failovers +No failover recovery. Aurora does not record the latest minute it fired +triggers for across failovers. Therefore it's possible to miss triggers +on failover. Note that this behavior may change in the future. + +It's necessary to sync time between schedulers with something like `ntpd`. +Clock skew could cause double or missed triggers in the case of a failover. + +### Collision policy is best-effort +Aurora aims to always have *at least one copy* of a given instance running at a time - it's +an AP system, meaning it chooses Availability and Partition Tolerance at the expense of +Consistency. + +If your collision policy was `CANCEL_NEW` and a task has terminated but +Aurora has not noticed this Aurora will go ahead and create your new +task. + +If your collision policy was `KILL_EXISTING` and a task was marked `LOST` +but not yet GCed Aurora will go ahead and create your new task without +attempting to kill the old one (outside the GC interval). + +### Timezone Configuration +Cron timezone is configured indepdendently of JVM timezone with the `-cron_timezone` flag and +defaults to UTC. Added: aurora/site/source/documentation/0.22.0/features/custom-executors.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.22.0/features/custom-executors.md?rev=1871319&view=auto ============================================================================== --- aurora/site/source/documentation/0.22.0/features/custom-executors.md (added) +++ aurora/site/source/documentation/0.22.0/features/custom-executors.md Fri Dec 13 05:37:33 2019 @@ -0,0 +1,166 @@ +Custom Executors +================ + +If the need arises to use a Mesos executor other than the Thermos executor, the scheduler can be +configured to utilize a custom executor by specifying the `-custom_executor_config` flag. +The flag must be set to the path of a valid executor configuration file. + +The configuration file must be a valid **JSON array** and contain, at minimum, +one executor configuration including the name, command and resources fields and +must be pointed to by the `-custom_executor_config` flag when the scheduler is +started. + +### Array Entry + +Property | Description +----------------------- | --------------------------------- +executor (required) | Description of executor. +task_prefix (required) ) | Prefix given to tasks launched with this executor's configuration. +volume_mounts (optional) | Volumes to be mounted in container running executor. + +#### executor + +Property | Description +----------------------- | --------------------------------- +name (required) | Name of the executor. +command (required) | How to run the executor. +resources (required) | Overhead to use for each executor instance. + +#### command + +Property | Description +----------------------- | --------------------------------- +value (required) | The command to execute. +arguments (optional) | A list of arguments to pass to the command. +uris (optional) | List of resources to download into the task sandbox. +shell (optional) | Run executor via shell. + +A note on the command property (from [mesos.proto](https://github.com/apache/mesos/blob/master/include/mesos/mesos.proto)): + +``` +1) If 'shell == true', the command will be launched via shell + (i.e., /bin/sh -c 'value'). The 'value' specified will be + treated as the shell command. The 'arguments' will be ignored. +2) If 'shell == false', the command will be launched by passing + arguments to an executable. The 'value' specified will be + treated as the filename of the executable. The 'arguments' + will be treated as the arguments to the executable. This is + similar to how POSIX exec families launch processes (i.e., + execlp(value, arguments(0), arguments(1), ...)). +``` + +##### uris (list) +* Follows the [Mesos Fetcher schema](http://mesos.apache.org/documentation/latest/fetcher/) + +Property | Description +----------------------- | --------------------------------- +value (required) | Path to the resource needed in the sandbox. +executable (optional) | Change resource to be executable via chmod. +extract (optional) | Extract files from packed or compressed archives into the sandbox. +cache (optional) | Use caching mechanism provided by Mesos for resources. + +#### resources (list) + +Property | Description +------------------- | --------------------------------- +name (required) | Name of the resource: cpus or mem. +type (required) | Type of resource. Should always be SCALAR. +scalar (required) | Value in float for cpus or int for mem (in MBs) + +### volume_mounts (list) + +Property | Description +--------------------------- | --------------------------------- +host_path (required) | Host path to mount inside the container. +container_path (required) | Path inside the container where `host_path` will be mounted. +mode (required) | Mode in which to mount the volume, Read-Write (RW) or Read-Only (RO). + +A sample configuration is as follows: + +```json +[ + { + "executor": { + "name": "myExecutor", + "command": { + "value": "myExecutor.a", + "shell": "false", + "arguments": [ + "localhost:2181", + "-verbose", + "-config myConfiguration.config" + ], + "uris": [ + { + "value": "/dist/myExecutor.a", + "executable": true, + "extract": false, + "cache": true + }, + { + "value": "/home/user/myConfiguration.config", + "executable": false, + "extract": false, + "cache": false + } + ] + }, + "resources": [ + { + "name": "cpus", + "type": "SCALAR", + "scalar": { + "value": 1.00 + } + }, + { + "name": "mem", + "type": "SCALAR", + "scalar": { + "value": 512 + } + } + ] + }, + "volume_mounts": [ + { + "mode": "RO", + "container_path": "/path/on/container", + "host_path": "/path/to/host/directory" + }, + { + "mode": "RW", + "container_path": "/container", + "host_path": "/host" + } + ], + "task_prefix": "my-executor-" + } +] +``` + +It should be noted that if you do not use Thermos or a Thermos based executor, links in the scheduler's +Web UI for tasks will not work (at least for the time being). +Some information about launched tasks can still be accessed via the Mesos Web UI or via the Aurora Client. + +### Using a custom executor + +To launch tasks using a custom executor, +an [ExecutorConfig](../../reference/configuration/#executorconfig-objects) object must be added to +the Job or Service object. The `name` parameter of ExecutorConfig must match the name of an executor +defined in the JSON object provided to the scheduler at startup time. + +For example, if we desire to launch tasks using `myExecutor` (defined above), we may do so in +the following manner: + +``` +jobs = [Service( + task = task, + cluster = 'devcluster', + role = 'www-data', + environment = 'prod', + name = 'hello', + executor_config = ExecutorConfig(name='myExecutor'))] +``` + +This will create a Service Job which will launch tasks using myExecutor instead of Thermos. Added: aurora/site/source/documentation/0.22.0/features/job-updates.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.22.0/features/job-updates.md?rev=1871319&view=auto ============================================================================== --- aurora/site/source/documentation/0.22.0/features/job-updates.md (added) +++ aurora/site/source/documentation/0.22.0/features/job-updates.md Fri Dec 13 05:37:33 2019 @@ -0,0 +1,123 @@ +Aurora Job Updates +================== + +`Job` configurations can be updated at any point in their lifecycle. +Usually updates are done incrementally using a process called a *rolling +upgrade*, in which Tasks are upgraded in small groups, one group at a +time. Updates are done using various Aurora Client commands. + + +Rolling Job Updates +------------------- + +There are several sub-commands to manage job updates: + + aurora update start <job key> <configuration file> + aurora update info <job key> + aurora update pause <job key> + aurora update resume <job key> + aurora update abort <job key> + aurora update list <cluster> + +When you `start` a job update, the command will return once it has sent the +instructions to the scheduler. At that point, you may view detailed +progress for the update with the `info` subcommand, in addition to viewing +graphical progress in the web browser. You may also get a full listing of +in-progress updates in a cluster with `list`. + +Once an update has been started, you can `pause` to keep the update but halt +progress. This can be useful for doing things like debug a partially-updated +job to determine whether you would like to proceed. You can `resume` to +proceed. + +You may `abort` a job update regardless of the state it is in. This will +instruct the scheduler to completely abandon the job update and leave the job +in the current (possibly partially-updated) state. + +For a configuration update, the Aurora Scheduler calculates required changes +by examining the current job config state and the new desired job config. +It then starts a *rolling batched update process* by going through every batch +and performing these operations, in order: + +- If an instance is not present in the scheduler but is present in + the new config, then the instance is created. +- If an instance is present in both the scheduler and the new config, then + the scheduler diffs both task configs. If it detects any changes, it + performs an instance update by killing the old config instance and adds + the new config instance. +- If an instance is present in the scheduler but isn't in the new config, + then that instance is killed. + +The Aurora Scheduler continues through the instance list until all tasks are +updated and in `RUNNING`. If the scheduler determines the update is not going +well (based on the criteria specified in the UpdateConfig), it cancels the update. + +Update cancellation runs a procedure similar to the described above +update sequence, but in reverse order. New instance configs are swapped +with old instance configs and batch updates proceed backwards +from the point where the update failed. E.g.; (0,1,2) (3,4,5) (6,7, +8-FAIL) results in a rollback in order (8,7,6) (5,4,3) (2,1,0). + +For details on how to control a job update, please see the +[UpdateConfig](../../reference/configuration/#updateconfig-objects) configuration object. + + +Coordinated Job Updates +------------------------ + +Some Aurora services may benefit from having more control over updates by explicitly +acknowledging ("heartbeating") job update progress. This may be helpful for mission-critical +service updates where explicit job health monitoring is vital during the entire job update +lifecycle. Such job updates would rely on an external service (or a custom client) periodically +pulsing an active coordinated job update via a +[pulseJobUpdate RPC](https://github.com/apache/aurora/blob/rel/0.22.0/api/src/main/thrift/org/apache/aurora/gen/api.thrift). + +A coordinated update is defined by setting a positive +[pulse_interval_secs](../../reference/configuration/#updateconfig-objects) value in job configuration +file. If no pulses are received within specified interval the update will be blocked. A blocked +update is unable to continue rolling forward (or rolling back) but retains its active status. +It may only be unblocked by a fresh `pulseJobUpdate` call. + +NOTE: A coordinated update starts in `ROLL_FORWARD_AWAITING_PULSE` state and will not make any +progress until the first pulse arrives. However, a paused update (`ROLL_FORWARD_PAUSED` or +`ROLL_BACK_PAUSED`) is still considered active and upon resuming will immediately make progress +provided the pulse interval has not expired. + + +SLA-Aware Updates +----------------- + +Updates can take advantage of [Custom SLA Requirements](../../features/sla-requirements/) and +specify the `sla_aware=True` option within +[UpdateConfig](../../reference/configuration/#updateconfig-objects) to only update instances if +the action will maintain the task's SLA requirements. This feature allows updates to avoid killing +too many instances in the face of unexpected failures outside of the update range. + +See the [Using the `sla_aware` option](../../reference/configuration/#using-the-sla-aware-option) +for more information on how to use this feature. + + +Canary Deployments +------------------ + +Canary deployments are a pattern for rolling out updates to a subset of job instances, +in order to test different code versions alongside the actual production job. +It is a risk-mitigation strategy for job owners and commonly used in a form where +job instance 0 runs with a different configuration than the instances 1-N. + +For example, consider a job with 4 instances that each +request 1 core of cpu, 1 GB of RAM, and 1 GB of disk space as specified +in the configuration file `hello_world.aurora`. If you want to +update it so it requests 2 GB of RAM instead of 1. You can create a new +configuration file to do that called `new_hello_world.aurora` and +issue + + aurora update start <job_key_value>/0-1 new_hello_world.aurora + +This results in instances 0 and 1 having 1 cpu, 2 GB of RAM, and 1 GB of disk space, +while instances 2 and 3 have 1 cpu, 1 GB of RAM, and 1 GB of disk space. If instance 3 +dies and restarts, it restarts with 1 cpu, 1 GB RAM, and 1 GB disk space. + +So that means there are two simultaneous task configurations for the same job +at the same time, just valid for different ranges of instances. While this isn't a recommended +pattern, it is valid and supported by the Aurora scheduler. Added: aurora/site/source/documentation/0.22.0/features/mesos-fetcher.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.22.0/features/mesos-fetcher.md?rev=1871319&view=auto ============================================================================== --- aurora/site/source/documentation/0.22.0/features/mesos-fetcher.md (added) +++ aurora/site/source/documentation/0.22.0/features/mesos-fetcher.md Fri Dec 13 05:37:33 2019 @@ -0,0 +1,46 @@ +Mesos Fetcher +============= + +Mesos has support for downloading resources into the sandbox through the +use of the [Mesos Fetcher](http://mesos.apache.org/documentation/latest/fetcher/) + +Aurora supports passing URIs to the Mesos Fetcher dynamically by including +a list of URIs in job submissions. + +How to use +---------- +The scheduler flag `-enable_mesos_fetcher` must be set to true. + +Currently only the scheduler side of this feature has been implemented +so a modification to the existing client, or a custom Thrift client are required +to make use of this feature. + +If using a custom Thrift client, the list of URIs must be included in TaskConfig +as the `mesosFetcherUris` field. + +Each Mesos Fetcher URI has the following data members: + +|Property | Description| +|---------|------| +|value (required) |Path to the resource needed in the sandbox.| +|extract (optional)|Extract files from packed or compressed archives into the sandbox.| +|cache (optional) | Use caching mechanism provided by Mesos for resources.| + +Note that this structure is very similar to the one provided for downloading +resources needed for a [custom executor](../../operations/configuration/). + +This is because both features use the Mesos fetcher to retrieve resources into +the sandbox. However, one, the custom executor feature, has a static set of URIs +set in the server side, and the other, the Mesos Fetcher feature, is a dynamic set +of URIs set at the time of job submission. + +Security Implications +--------------------- +There are security implications that must be taken into account when enabling this feature. +**Enabling this feature may potentially enable any job submitting user to perform a privilege escalation.** + +Until a more through solution is created, one step that has been taken to mitigate this issue +is to statically mark every user submitted URI as non-executable. This is in contrast to the set of URIs +set in the custom executor feature which may mark any URI as executable. + +If the need arises to mark a downloaded URI as executable, please consider using the custom executor feature. \ No newline at end of file Added: aurora/site/source/documentation/0.22.0/features/multitenancy.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.22.0/features/multitenancy.md?rev=1871319&view=auto ============================================================================== --- aurora/site/source/documentation/0.22.0/features/multitenancy.md (added) +++ aurora/site/source/documentation/0.22.0/features/multitenancy.md Fri Dec 13 05:37:33 2019 @@ -0,0 +1,82 @@ +Multitenancy +============ + +Aurora is a multi-tenant system that can run jobs of multiple clients/tenants. +Going beyond the [resource isolation on an individual host](../resource-isolation/), it is +crucial to prevent those jobs from stepping on each others toes. + + +Job Namespaces +-------------- + +The namespace for jobs in Aurora follows a hierarchical structure. This is meant to make it easier +to differentiate between different jobs. A job key consists of four parts. The four parts are +`<cluster>/<role>/<environment>/<jobname>` in that order: + +* Cluster refers to the name of a particular Aurora installation. +* Role names are user accounts. +* Environment names are namespaces. +* Jobname is the custom name of your job. + +Role names correspond to user accounts. They are used for +[authentication](../../operations/security/), as the linux user used to run jobs, and for the +assignment of [quota](#preemption). If you don't know what accounts are available, contact your +sysadmin. + +The environment component in the job key, serves as a namespace. The values for +environment are validated in the scheduler. By default allowing any of `devel`, `test`, +`production`, and any value matching the regular expression `staging[0-9]*`. This validation can be +changed to allow any arbitrary regular expression by setting the scheduler option `allowed_job_environments`. + +None of the values imply any difference in the scheduling behavior. Conventionally, the +"environment" is set so as to indicate a certain level of stability in the behavior of the job +by ensuring that an appropriate level of testing has been performed on the application code. e.g. +in the case of a typical Job, releases may progress through the following phases in order of +increasing level of stability: `devel`, `test`, `staging`, `production`. + + +Configuration Tiers +------------------- + +Tier is a predefined bundle of task configuration options. Aurora schedules tasks and assigns them +resources based on their tier assignment. The default scheduler tier configuration allows for +3 tiers: + + - `revocable`: The `revocable` tier requires the task to run with [revocable](../resource-isolation/#oversubscription) + resources. + - `preemptible`: Setting the taskâs tier to `preemptible` allows for the possibility of that task + being [preempted](#preemption) by other tasks when cluster is running low on resources. + - `preferred`: The `preferred` tier prevents the task from using [revocable](../resource-isolation/#oversubscription) + resources and from being [preempted](#preemption). + +Since it is possible that a cluster is configured with a custom tier configuration, users should +consult their cluster administrator to be informed of the tiers supported by the cluster. Attempts +to schedule jobs with an unsupported tier will be rejected by the scheduler. + + +Preemption +---------- + +In order to guarantee that important production jobs are always running, Aurora supports +preemption. + +Let's consider we have a pending job that is candidate for scheduling but resource shortage pressure +prevents this. Active tasks can become the victim of preemption, if: + + - both candidate and victim are owned by the same role and the + [priority](../../reference/configuration/#job-objects) of a victim is lower than the + [priority](../../reference/configuration/#job-objects) of the candidate. + - OR a victim is a `preemptible` or `revocable` [tier](#configuration-tiers) task and the candidate + is a `preferred` [tier](#configuration-tiers) task. + +In other words, tasks from `preferred` [tier](../../reference/configuration/#job-objects) jobs may +preempt tasks from any `preemptible` or `revocable` job. However, a `preferred` task may only be +preempted by tasks from `preferred` jobs in the same role with higher [priority](../../reference/configuration/#job-objects). + +Aurora requires resource quotas for [production non-dedicated jobs](../../reference/configuration/#job-objects). +Quota is enforced at the job role level and when set, defines a non-preemptible pool of compute resources within +that role. All job types (service, adhoc or cron) require role resource quota unless a job has +[dedicated constraint set](../constraints/#dedicated-attribute). + +To grant quota to a particular role in production, an operator can use the command +`aurora_admin set_quota`. Added: aurora/site/source/documentation/0.22.0/features/resource-isolation.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.22.0/features/resource-isolation.md?rev=1871319&view=auto ============================================================================== --- aurora/site/source/documentation/0.22.0/features/resource-isolation.md (added) +++ aurora/site/source/documentation/0.22.0/features/resource-isolation.md Fri Dec 13 05:37:33 2019 @@ -0,0 +1,181 @@ +Resources Isolation and Sizing +============================== + +This document assumes Aurora and Mesos have been configured +using our [recommended resource isolation settings](../../operations/configuration/#resource-isolation). + +- [Isolation](#isolation) +- [Sizing](#sizing) +- [Oversubscription](#oversubscription) + + +Isolation +--------- + +Aurora is a multi-tenant system; a single software instance runs on a +server, serving multiple clients/tenants. To share resources among +tenants, it leverages Mesos for isolation of: + +* CPU +* GPU +* memory +* disk space +* ports + +CPU is a soft limit, and handled differently from memory and disk space. +Too low a CPU value results in throttling your application and +slowing it down. Memory and disk space are both hard limits; when your +application goes over these values, it's killed. + +### CPU Isolation + +Mesos can be configured to use a quota based CPU scheduler (the *Completely* +*Fair Scheduler*) to provide consistent and predictable performance. +This is effectively a guarantee of resources -- you receive at least what +you requested, but also no more than you've requested. + +The scheduler gives applications a CPU quota for every 100 ms interval. +When an application uses its quota for an interval, it is throttled for +the rest of the 100 ms. Usage resets for each interval and unused +quota does not carry over. + +For example, an application specifying 4.0 CPU has access to 400 ms of +CPU time every 100 ms. This CPU quota can be used in different ways, +depending on the application and available resources. Consider the +scenarios shown in this diagram. + + + +* *Scenario A*: the application can use up to 4 cores continuously for +every 100 ms interval. It is never throttled and starts processing +new requests immediately. + +* *Scenario B* : the application uses up to 8 cores (depending on +availability) but is throttled after 50 ms. The CPU quota resets at the +start of each new 100 ms interval. + +* *Scenario C* : is like Scenario A, but there is a garbage collection +event in the second interval that consumes all CPU quota. The +application throttles for the remaining 75 ms of that interval and +cannot service requests until the next interval. In this example, the +garbage collection finished in one interval but, depending on how much +garbage needs collecting, it may take more than one interval and further +delay service of requests. + +*Technical Note*: Mesos considers logical cores, also known as +hyperthreading or SMT cores, as the unit of CPU. + +### Memory Isolation + +Mesos uses dedicated memory allocation. Your application always has +access to the amount of memory specified in your configuration. The +application's memory use is defined as the sum of the resident set size +(RSS) of all processes in a shard. Each shard is considered +independently. + +In other words, say you specified a memory size of 10GB. Each shard +would receive 10GB of memory. If an individual shard's memory demands +exceed 10GB, that shard is killed, but the other shards continue +working. + +*Technical note*: Total memory size is not enforced at allocation time, +so your application can request more than its allocation without getting +an ENOMEM. However, it will be killed shortly after. + +### Disk Space + +Disk space used by your application is defined as the sum of the files' +disk space in your application's directory, including the `stdout` and +`stderr` logged from your application. Each shard is considered +independently. You should use off-node storage for your application's +data whenever possible. + +In other words, say you specified disk space size of 100MB. Each shard +would receive 100MB of disk space. If an individual shard's disk space +demands exceed 100MB, that shard is killed, but the other shards +continue working. + +After your application finishes running, its allocated disk space is +reclaimed. Thus, your job's final action should move any disk content +that you want to keep, such as logs, to your home file system or other +less transitory storage. Disk reclamation takes place an undefined +period after the application finish time; until then, the disk contents +are still available but you shouldn't count on them being so. + +*Technical note* : Disk space is not enforced at write so your +application can write above its quota without getting an ENOSPC, but it +will be killed shortly after. This is subject to change. + +### GPU Isolation + +GPU isolation will be supported for Nvidia devices starting from Mesos 1.0. +Access to the allocated units will be exclusive with no sharing between tasks +allowed (e.g. no fractional GPU allocation). For more details, see the +[Mesos design document](https://docs.google.com/document/d/10GJ1A80x4nIEo8kfdeo9B11PIbS1xJrrB4Z373Ifkpo/edit#heading=h.w84lz7p4eexl) +and the [Mesos agent configuration](http://mesos.apache.org/documentation/latest/configuration/). + +### Other Resources + +Other resources, such as network bandwidth, do not have any performance +guarantees. For some resources, such as memory bandwidth, there are no +practical sharing methods so some application combinations collocated on +the same host may cause contention. + + +Sizing +------- + +### CPU Sizing + +To correctly size Aurora-run Mesos tasks, specify a per-shard CPU value +that lets the task run at its desired performance when at peak load +distributed across all shards. Include reserve capacity of at least 50%, +possibly more, depending on how critical your service is (or how +confident you are about your original estimate : -)), ideally by +increasing the number of shards to also improve resiliency. When running +your application, observe its CPU stats over time. If consistently at or +near your quota during peak load, you should consider increasing either +per-shard CPU or the number of shards. + +## Memory Sizing + +Size for your application's peak requirement. Observe the per-instance +memory statistics over time, as memory requirements can vary over +different periods. Remember that if your application exceeds its memory +value, it will be killed, so you should also add a safety margin of +around 10-20%. If you have the ability to do so, you may also want to +put alerts on the per-instance memory. + +## Disk Space Sizing + +Size for your application's peak requirement. Rotate and discard log +files as needed to stay within your quota. When running a Java process, +add the maximum size of the Java heap to your disk space requirement, in +order to account for an out of memory error dumping the heap +into the application's sandbox space. + +## GPU Sizing + +GPU is highly dependent on your application requirements and is only limited +by the number of physical GPU units available on a target box. + + +Oversubscription +---------------- + +Mesos supports [oversubscription of machine resources](http://mesos.apache.org/documentation/latest/oversubscription/) +via the concept of revocable tasks. In contrast to non-revocable tasks, revocable tasks are best-effort. +Mesos reserves the right to throttle or even kill them if they might affect existing high-priority +user-facing services. + +As of today, the only revocable resource supported by Aurora are CPU and RAM resources. A job can +opt-in to use those by specifying the `revocable` [Configuration Tier](../../features/multitenancy/#configuration-tiers). +A revocable job will only be scheduled using revocable resources, even if there are plenty of +non-revocable resources available. + +The Aurora scheduler must be [configured to receive revocable offers](../../operations/configuration/#resource-isolation) +from Mesos and accept revocable jobs. If not configured properly revocable tasks will never get +assigned to hosts and will stay in `PENDING`. + +For details on how to mark a job as being revocable, see the +[Configuration Reference](../../reference/configuration/). Added: aurora/site/source/documentation/0.22.0/features/service-discovery.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.22.0/features/service-discovery.md?rev=1871319&view=auto ============================================================================== --- aurora/site/source/documentation/0.22.0/features/service-discovery.md (added) +++ aurora/site/source/documentation/0.22.0/features/service-discovery.md Fri Dec 13 05:37:33 2019 @@ -0,0 +1,43 @@ +Service Discovery +================= + +It is possible for the Aurora executor to announce tasks into ServerSets for +the purpose of service discovery. ServerSets use the Zookeeper [group membership pattern](http://zookeeper.apache.org/doc/trunk/recipes.html#sc_outOfTheBox) +of which there are several reference implementations: + + - [C++](https://github.com/apache/mesos/blob/master/src/zookeeper/group.cpp) + - [Java](https://github.com/twitter/commons/blob/master/src/java/com/twitter/common/zookeeper/ServerSetImpl.java#L221) + - [Python](https://github.com/twitter/commons/blob/master/src/python/twitter/common/zookeeper/serverset/serverset.py#L51) + +These can also be used natively in Finagle using the [ZookeeperServerSetCluster](https://github.com/twitter/finagle/blob/master/finagle-serversets/src/main/scala/com/twitter/finagle/zookeeper/ZookeeperServerSetCluster.scala). + +For more information about how to configure announcing, see the [Configuration Reference](../../reference/configuration/). + +Using Mesos DiscoveryInfo +------------------------- +Experimental support for populating DiscoveryInfo in Mesos is introduced in Aurora. This can be used to build +custom service discovery system not using zookeeper. Please see `Service Discovery` section in +[Mesos Framework Development guide](http://mesos.apache.org/documentation/latest/app-framework-development-guide/) for +explanation of the protobuf message in Mesos. + +To use this feature, please enable `--populate_discovery_info` flag on scheduler. All jobs started by scheduler +afterwards will have their portmap populated to Mesos and discoverable in `/state` endpoint in Mesos master and agent. + +### Using Mesos DNS +An example is using [Mesos-DNS](https://github.com/mesosphere/mesos-dns), which is able to generate multiple DNS +records. With current implementation, the example job with key `devcluster/vagrant/test/http-example` generates at +least the following: + +1. An A record for `http_example.test.vagrant.aurora.mesos` (which only includes IP address); +2. A [SRV record](https://en.wikipedia.org/wiki/SRV_record) for + `_http_example.test.vagrant._tcp.aurora.mesos`, which includes IP address and every port. This should only + be used if the service has one port. +3. A SRV record `_{port-name}._http_example.test.vagrant._tcp.aurora.mesos` for each port name + defined. This should be used when the service has multiple ports. To have this working properly it's needed to + add `-populate_discovery_info` to scheduler's configuration. + +Things to note: + +1. The domain part (".mesos" in above example) can be configured in [Mesos DNS](http://mesosphere.github.io/mesos-dns/docs/configuration-parameters.html); +2. Right now, portmap and port aliases in announcer object are not reflected in DiscoveryInfo, therefore not visible in + Mesos DNS records either. This is because they are only resolved in thermos executors. Added: aurora/site/source/documentation/0.22.0/features/services.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.22.0/features/services.md?rev=1871319&view=auto ============================================================================== --- aurora/site/source/documentation/0.22.0/features/services.md (added) +++ aurora/site/source/documentation/0.22.0/features/services.md Fri Dec 13 05:37:33 2019 @@ -0,0 +1,116 @@ +Long-running Services +===================== + +Jobs that are always restart on completion, whether successful or unsuccessful, +are called services. This is useful for long-running processes +such as webservices that should always be running, unless stopped explicitly. + + +Service Specification +--------------------- + +A job is identified as a service by the presence of the flag +``service=True` in the [`Job`](../../reference/configuration/#job-objects) object. +The `Service` alias can be used as shorthand for `Job` with `service=True`. + +Example (available in the [Vagrant environment](../../getting-started/vagrant/)): + + $ cat /vagrant/examples/jobs/hello_world.aurora + hello = Process( + name = 'hello', + cmdline = """ + while true; do + echo hello world + sleep 10 + done + """) + + task = SequentialTask( + processes = [hello], + resources = Resources(cpu = 1.0, ram = 128*MB, disk = 128*MB) + ) + + jobs = [ + Service( + task = task, + cluster = 'devcluster', + role = 'www-data', + environment = 'prod', + name = 'hello' + ) + ] + + +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 (see the +[`Job`](../../reference/configuration/#job-objects) object for details). + + +Ports +----- + +In order to be useful, most services have to bind to one or more ports. Aurora enables this +usecase via the [`thermos.ports` namespace](../../reference/configuration/#thermos-namespace) that +allows to request arbitrarily named ports: + + + nginx = Process( + name = 'nginx', + cmdline = './run_nginx.sh -port {{thermos.ports[http]}}' + ) + + +When this process is included in a job, the job will be allocated a port, and the command line +will be replaced with something like: + + ./run_nginx.sh -port 42816 + +Where 42816 happens to be the allocated port. + +For details on how to enable clients to discover this dynamically assigned port, see our +[Service Discovery](../service-discovery/) documentation. + + +Health Checking +--------------- + +Typically, the Thermos executor monitors processes within a task only by liveness of the forked +process. In addition to that, Aurora has support for rudimentary health checking: Either via HTTP +via custom shell scripts. + +For example, simply by requesting a `health` port, a process can request to be health checked +via repeated calls to the `/health` endpoint: + + nginx = Process( + name = 'nginx', + cmdline = './run_nginx.sh -port {{thermos.ports[health]}}' + ) + +Please see the +[configuration reference](../../reference/configuration/#healthcheckconfig-objects) +for configuration options for this feature. + +Starting with the 0.17.0 release, job updates rely only on task health-checks by introducing +a `min_consecutive_successes` parameter on the HealthCheckConfig object. This parameter represents +the number of successful health checks needed before a task is moved into the `RUNNING` state. Tasks +that do not have enough successful health checks within the first `n` attempts, are moved to the +`FAILED` state, where `n = ceil(initial_interval_secs/interval_secs) + max_consecutive_failures + +min_consecutive_successes`. In order to accommodate variability during task warm up, `initial_interval_secs` +will act as a grace period. Any health-check failures during the first `m` attempts are ignored and +do not count towards `max_consecutive_failures`, where `m = ceil(initial_interval_secs/interval_secs)`. + +As [job updates](../job-updates/) are based only on health-checks, it is not necessary to set +`watch_secs` to the worst-case update time, it can instead be set to 0. The scheduler considers a +task that is in the `RUNNING` to be healthy and proceeds to updating the next batch of instances. +For details on how to control health checks, please see the +[HealthCheckConfig](../../reference/configuration/#healthcheckconfig-objects) configuration object. +Existing jobs that do not configure a health-check can fall-back to using `watch_secs` to +monitor a task before considering it healthy. + +You can pause health checking by touching a file inside of your sandbox, named `.healthchecksnooze`. +As long as that file is present, health checks will be disabled, enabling users to gather core +dumps or other performance measurements without worrying about Aurora's health check killing +their process. + +WARNING: Remember to remove this when you are done, otherwise your instance will have permanently +disabled health checks. Added: aurora/site/source/documentation/0.22.0/features/sla-metrics.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.22.0/features/sla-metrics.md?rev=1871319&view=auto ============================================================================== --- aurora/site/source/documentation/0.22.0/features/sla-metrics.md (added) +++ aurora/site/source/documentation/0.22.0/features/sla-metrics.md Fri Dec 13 05:37:33 2019 @@ -0,0 +1,215 @@ +Aurora SLA Measurement +====================== + +- [Overview](#overview) +- [Metric Details](#metric-details) + - [Platform Uptime](#platform-uptime) + - [Job Uptime](#job-uptime) + - [Median Time To Assigned (MTTA)](#median-time-to-assigned-\(mtta\)) + - [Median Time To Starting (MTTS)](#median-time-to-starting-\(mtts\)) + - [Median Time To Running (MTTR)](#median-time-to-running-\(mttr\)) +- [Limitations](#limitations) + +## Overview + +The primary goal of the feature is collection and monitoring of Aurora job SLA (Service Level +Agreements) metrics that defining a contractual relationship between the Aurora/Mesos platform +and hosted services. + +The Aurora SLA feature is by default only enabled for service (non-cron) +production jobs (`"production=True"` in your `.aurora` config). It can be enabled for +non-production services by an operator via the scheduler command line flag `-sla_non_prod_metrics`. + +Counters that track SLA measurements are computed periodically within the scheduler. +The individual instance metrics are refreshed every minute (configurable via +`sla_stat_refresh_interval`). The instance counters are subsequently aggregated by +relevant grouping types before exporting to scheduler `/vars` endpoint (when using `vagrant` +that would be `http://192.168.33.7:8081/vars`) + + +## Metric Details + +### Platform Uptime + +*Aggregate amount of time a job spends in a non-runnable state due to platform unavailability +or scheduling delays. This metric tracks Aurora/Mesos uptime performance and reflects on any +system-caused downtime events (tasks LOST or DRAINED). Any user-initiated task kills/restarts +will not degrade this metric.* + +**Collection scope:** + +* Per job - `sla_<job_key>_platform_uptime_percent` +* Per cluster - `sla_cluster_platform_uptime_percent` + +**Units:** percent + +A fault in the task environment may cause the Aurora/Mesos to have different views on the task state +or lose track of the task existence. In such cases, the service task is marked as LOST and +rescheduled by Aurora. For example, this may happen when the task stays in ASSIGNED or STARTING +for too long or the Mesos agent becomes unhealthy (or disappears completely). The time between +task entering LOST and its replacement reaching RUNNING state is counted towards platform downtime. + +Another example of a platform downtime event is the administrator-requested task rescheduling. This +happens during planned Mesos agent maintenance when all agent tasks are marked as DRAINED and +rescheduled elsewhere. + +To accurately calculate Platform Uptime, we must separate platform incurred downtime from user +actions that put a service instance in a non-operational state. It is simpler to isolate +user-incurred downtime and treat all other downtime as platform incurred. + +Currently, a user can cause a healthy service (task) downtime in only two ways: via `killTasks` +or `restartShards` RPCs. For both, their affected tasks leave an audit state transition trail +relevant to uptime calculations. By applying a special "SLA meaning" to exposed task state +transition records, we can build a deterministic downtime trace for every given service instance. + +A task going through a state transition carries one of three possible SLA meanings +(see [SlaAlgorithm.java](https://github.com/apache/aurora/blob/rel/0.22.0/src/main/java/org/apache/aurora/scheduler/sla/SlaAlgorithm.java) for +sla-to-task-state mapping): + +* Task is UP: starts a period where the task is considered to be up and running from the Aurora + platform standpoint. + +* Task is DOWN: starts a period where the task cannot reach the UP state for some + non-user-related reason. Counts towards instance downtime. + +* Task is REMOVED from SLA: starts a period where the task is not expected to be UP due to + user initiated action or failure. We ignore this period for the uptime calculation purposes. + +This metric is recalculated over the last sampling period (last minute) to account for +any UP/DOWN/REMOVED events. It ignores any UP/DOWN events not immediately adjacent to the +sampling interval as well as adjacent REMOVED events. + +### Job Uptime + +*Percentage of the job instances considered to be in RUNNING state for the specified duration +relative to request time. This is a purely application side metric that is considering aggregate +uptime of all RUNNING instances. Any user- or platform initiated restarts directly affect +this metric.* + +**Collection scope:** We currently expose job uptime values at 5 pre-defined +percentiles (50th,75th,90th,95th and 99th): + +* `sla_<job_key>_job_uptime_50_00_sec` +* `sla_<job_key>_job_uptime_75_00_sec` +* `sla_<job_key>_job_uptime_90_00_sec` +* `sla_<job_key>_job_uptime_95_00_sec` +* `sla_<job_key>_job_uptime_99_00_sec` + +**Units:** seconds +You can also get customized real-time stats from aurora client. See `aurora sla -h` for +more details. + +### Median Time To Assigned (MTTA) + +*Median time a job spends waiting for its tasks to be assigned to a host. This is a combined +metric that helps track the dependency of scheduling performance on the requested resources +(user scope) as well as the internal scheduler bin-packing algorithm efficiency (platform scope).* + +**Collection scope:** + +* Per job - `sla_<job_key>_mtta_ms` +* Per cluster - `sla_cluster_mtta_ms` +* Per instance size (small, medium, large, x-large, xx-large). Size are defined in: +[ResourceBag.java](https://github.com/apache/aurora/blob/rel/0.22.0/src/main/java/org/apache/aurora/scheduler/resources/ResourceBag.java) + * By CPU: + * `sla_cpu_small_mtta_ms` + * `sla_cpu_medium_mtta_ms` + * `sla_cpu_large_mtta_ms` + * `sla_cpu_xlarge_mtta_ms` + * `sla_cpu_xxlarge_mtta_ms` + * By RAM: + * `sla_ram_small_mtta_ms` + * `sla_ram_medium_mtta_ms` + * `sla_ram_large_mtta_ms` + * `sla_ram_xlarge_mtta_ms` + * `sla_ram_xxlarge_mtta_ms` + * By DISK: + * `sla_disk_small_mtta_ms` + * `sla_disk_medium_mtta_ms` + * `sla_disk_large_mtta_ms` + * `sla_disk_xlarge_mtta_ms` + * `sla_disk_xxlarge_mtta_ms` + +**Units:** milliseconds + +MTTA only considers instances that have already reached ASSIGNED state and ignores those +that are still PENDING. This ensures straggler instances (e.g. with unreasonable resource +constraints) do not affect metric curves. + +### Median Time To Starting (MTTS) + +*Median time a job waits for its tasks to reach STARTING state. This is a comprehensive metric +reflecting on the overall time it takes for the Aurora/Mesos to start initializing the sandbox +for a task.* + +**Collection scope:** + +* Per job - `sla_<job_key>_mtts_ms` +* Per cluster - `sla_cluster_mtts_ms` +* Per instance size (small, medium, large, x-large, xx-large). Size are defined in: +[ResourceBag.java](https://github.com/apache/aurora/blob/rel/0.22.0/src/main/java/org/apache/aurora/scheduler/resources/ResourceBag.java) + * By CPU: + * `sla_cpu_small_mtts_ms` + * `sla_cpu_medium_mtts_ms` + * `sla_cpu_large_mtts_ms` + * `sla_cpu_xlarge_mtts_ms` + * `sla_cpu_xxlarge_mtts_ms` + * By RAM: + * `sla_ram_small_mtts_ms` + * `sla_ram_medium_mtts_ms` + * `sla_ram_large_mtts_ms` + * `sla_ram_xlarge_mtts_ms` + * `sla_ram_xxlarge_mtts_ms` + * By DISK: + * `sla_disk_small_mtts_ms` + * `sla_disk_medium_mtts_ms` + * `sla_disk_large_mtts_ms` + * `sla_disk_xlarge_mtts_ms` + * `sla_disk_xxlarge_mtts_ms` + +**Units:** milliseconds + +MTTS only considers instances in STARTING state. This ensures straggler instances (e.g. with +unreasonable resource constraints) do not affect metric curves. + +### Median Time To Running (MTTR) + +*Median time a job waits for its tasks to reach RUNNING state. This is a comprehensive metric +reflecting on the overall time it takes for the Aurora/Mesos to start executing user content.* + +**Collection scope:** + +* Per job - `sla_<job_key>_mttr_ms` +* Per cluster - `sla_cluster_mttr_ms` +* Per instance size (small, medium, large, x-large, xx-large). Size are defined in: +[ResourceBag.java](https://github.com/apache/aurora/blob/rel/0.22.0/src/main/java/org/apache/aurora/scheduler/resources/ResourceBag.java) + * By CPU: + * `sla_cpu_small_mttr_ms` + * `sla_cpu_medium_mttr_ms` + * `sla_cpu_large_mttr_ms` + * `sla_cpu_xlarge_mttr_ms` + * `sla_cpu_xxlarge_mttr_ms` + * By RAM: + * `sla_ram_small_mttr_ms` + * `sla_ram_medium_mttr_ms` + * `sla_ram_large_mttr_ms` + * `sla_ram_xlarge_mttr_ms` + * `sla_ram_xxlarge_mttr_ms` + * By DISK: + * `sla_disk_small_mttr_ms` + * `sla_disk_medium_mttr_ms` + * `sla_disk_large_mttr_ms` + * `sla_disk_xlarge_mttr_ms` + * `sla_disk_xxlarge_mttr_ms` + +**Units:** milliseconds + +MTTR only considers instances in RUNNING state. This ensures straggler instances (e.g. with +unreasonable resource constraints) do not affect metric curves. + +## Limitations + +* The availability of Aurora SLA metrics is bound by the scheduler availability. + +* All metrics are calculated at a pre-defined interval (currently set at 1 minute). + Scheduler restarts may result in missed collections. Added: aurora/site/source/documentation/0.22.0/features/sla-requirements.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.22.0/features/sla-requirements.md?rev=1871319&view=auto ============================================================================== --- aurora/site/source/documentation/0.22.0/features/sla-requirements.md (added) +++ aurora/site/source/documentation/0.22.0/features/sla-requirements.md Fri Dec 13 05:37:33 2019 @@ -0,0 +1,185 @@ +SLA Requirements +================ + +- [Overview](#overview) +- [Default SLA](#default-sla) +- [Custom SLA](#custom-sla) + - [Count-based](#count-based) + - [Percentage-based](#percentage-based) + - [Coordinator-based](#coordinator-based) + +## Overview + +Aurora guarantees SLA requirements for jobs. These requirements limit the impact of cluster-wide +maintenance operations on the jobs. For instance, when an operator upgrades +the OS on all the Mesos agent machines, the tasks scheduled on them needs to be drained. +By specifying the SLA requirements a job can make sure that it has enough instances to +continue operating safely without incurring downtime. + +> SLA is defined as minimum number of active tasks required for a job every duration window. +A task is active if it was in `RUNNING` state during the last duration window. + +There is a [default](#default-sla) SLA guarantee for +[preferred](../../features/multitenancy/#configuration-tiers) tier jobs and it is also possible to +specify [custom](#custom-sla) SLA requirements. + +## Default SLA + +Aurora guarantees a default SLA requirement for tasks in +[preferred](../../features/multitenancy/#configuration-tiers) tier. + +> 95% of tasks in a job will be `active` for every 30 mins. + + +## Custom SLA + +For jobs that require different SLA requirements, Aurora allows jobs to specify their own +SLA requirements via the `SlaPolicies`. There are 3 different ways to express SLA requirements. + +### [Count-based](../../reference/configuration/#countslapolicy-objects) + +For jobs that need a minimum `number` of instances to be running all the time, +[`CountSlaPolicy`](../../reference/configuration/#countslapolicy-objects) +provides the ability to express the minimum number of required active instances (i.e. number of +tasks that are `RUNNING` for at least `duration_secs`). For instance, if we have a +`replicated-service` that has 3 instances and needs at least 2 instances every 30 minutes to be +treated healthy, the SLA requirement can be expressed with a +[`CountSlaPolicy`](../../reference/configuration/#countslapolicy-objects) like below, + +```python +Job( + name = 'replicated-service', + role = 'www-data', + instances = 3, + sla_policy = CountSlaPolicy( + count = 2, + duration_secs = 1800 + ) + ... +) +``` + +### [Percentage-based](../../reference/configuration/#percentageslapolicy-objects) + +For jobs that need a minimum `percentage` of instances to be running all the time, +[`PercentageSlaPolicy`](../../reference/configuration/#percentageslapolicy-objects) provides the +ability to express the minimum percentage of required active instances (i.e. percentage of tasks +that are `RUNNING` for at least `duration_secs`). For instance, if we have a `webservice` that +has 10000 instances for handling peak load and cannot have more than 0.1% of the instances down +for every 1 hr, the SLA requirement can be expressed with a +[`PercentageSlaPolicy`](../../reference/configuration/#percentageslapolicy-objects) like below, + +```python +Job( + name = 'frontend-service', + role = 'www-data', + instances = 10000, + sla_policy = PercentageSlaPolicy( + percentage = 99.9, + duration_secs = 3600 + ) + ... +) +``` + +### [Coordinator-based](../../reference/configuration/#coordinatorslapolicy-objects) + +When none of the above methods are enough to describe the SLA requirements for a job, then the SLA +calculation can be off-loaded to a custom service called the `Coordinator`. The `Coordinator` needs +to expose an endpoint that will be called to check if removal of a task will affect the SLA +requirements for the job. This is useful to control the number of tasks that undergoes maintenance +at a time, without affected the SLA for the application. + +Consider the example, where we have a `storage-service` stores 2 replicas of an object. Each replica +is distributed across the instances, such that replicas are stored on different hosts. In addition +a consistent-hash is used for distributing the data across the instances. + +When an instance needs to be drained (say for host-maintenance), we have to make sure that at least 1 of +the 2 replicas remains available. In such a case, a `Coordinator` service can be used to maintain +the SLA guarantees required for the job. + +The job can be configured with a +[`CoordinatorSlaPolicy`](../../reference/configuration/#coordinatorslapolicy-objects) to specify the +coordinator endpoint and the field in the response JSON that indicates if the SLA will be affected +or not affected, when the task is removed. + +```python +Job( + name = 'storage-service', + role = 'www-data', + sla_policy = CoordinatorSlaPolicy( + coordinator_url = 'http://coordinator.example.com', + status_key = 'drain' + ) + ... +) +``` + + +#### Coordinator Interface [Experimental] + +When a [`CoordinatorSlaPolicy`](../../reference/configuration/#coordinatorslapolicy-objects) is +specified for a job, any action that requires removing a task +(such as drains) will be required to get approval from the `Coordinator` before proceeding. The +coordinator service needs to expose a HTTP endpoint, that can take a `task-key` param +(`<cluster>/<role>/<env>/<name>/<instance>`) and a json body describing the task +details, force maintenance countdown (ms) and other params and return a response json that will +contain the boolean status for allowing or disallowing the task's removal. + +##### Request: +```javascript +POST / + ?task=<cluster>/<role>/<env>/<name>/<instance> + +{ + "forceMaintenanceCountdownMs": "604755646", + "task": "cluster/role/devel/job/1", + "taskConfig": { + "assignedTask": { + "taskId": "taskA", + "slaveHost": "a", + "task": { + "job": { + "role": "role", + "environment": "devel", + "name": "job" + }, + ... + }, + "assignedPorts": { + "http": 1000 + }, + "instanceId": 1 + ... + }, + ... + } +} +``` + +##### Response: +```json +{ + "drain": true +} +``` + +If Coordinator allows removal of the task, then the taskâs +[termination lifecycle](../../reference/configuration/#httplifecycleconfig-objects) +is triggered. If Coordinator does not allow removal, then the request will be retried again in the +future. + +#### Coordinator Actions + +Coordinator endpoint get its own lock and this is used to serializes calls to the Coordinator. +It guarantees that only one concurrent request is sent to a coordinator endpoint. This allows +coordinators to simply look the current state of the tasks to determine its SLA (without having +to worry about in-flight and pending requests). However if there are multiple coordinators, +maintenance can be done in parallel across all the coordinators. + +_Note: Single concurrent request to a coordinator endpoint does not translate as exactly-once +guarantee. The coordinator must be able to handle duplicate drain +requests for the same task._ + + + Added: aurora/site/source/documentation/0.22.0/features/webhooks.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.22.0/features/webhooks.md?rev=1871319&view=auto ============================================================================== --- aurora/site/source/documentation/0.22.0/features/webhooks.md (added) +++ aurora/site/source/documentation/0.22.0/features/webhooks.md Fri Dec 13 05:37:33 2019 @@ -0,0 +1,112 @@ +Webhooks +======== + +Aurora has an optional feature which allows operator to specify a file to configure a HTTP webhook +to receive task state change events. It can be enabled with a scheduler flag eg +`-webhook_config=/path/to/webhook.json`. At this point, webhooks are still considered *experimental*. + +Below is a sample configuration: + +```json +{ + "headers": { + "Content-Type": "application/vnd.kafka.json.v1+json", + "Producer-Type": "reliable" + }, + "targetURL": "http://localhost:5000/";, + "timeoutMsec": 5 +} +``` + +And an example of a response that you will get back: + +```json +{ + "task": + { + "cachedHashCode":0, + "assignedTask": { + "cachedHashCode":0, + "taskId":"vagrant-test-http_example-8-a6cf7ec5-d793-49c7-b10f-0e14ab80bfff", + "task": { + "cachedHashCode":-1819348376, + "job": { + "cachedHashCode":803049425, + "role":"vagrant", + "environment":"test", + "name":"http_example" + }, + "owner": { + "cachedHashCode":226895216, + "user":"vagrant" + }, + "isService":true, + "numCpus":0.1, + "ramMb":16, + "diskMb":8, + "priority":0, + "maxTaskFailures":1, + "production":false, + "resources":[ + {"cachedHashCode":729800451,"setField":"NUM_CPUS","value":0.1}, + {"cachedHashCode":552899914,"setField":"RAM_MB","value":16}, + {"cachedHashCode":-1547868317,"setField":"DISK_MB","value":8}, + {"cachedHashCode":1957328227,"setField":"NAMED_PORT","value":"http"}, + {"cachedHashCode":1954229436,"setField":"NAMED_PORT","value":"tcp"} + ], + "constraints":[], + "requestedPorts":["http","tcp"], + "taskLinks":{"http":"http://%host%:%port:http%"}, + "contactEmail":"vagrant@localhost", + "executorConfig": { + "cachedHashCode":-1194797325, + "name":"AuroraExecutor", + "data": "{\"environment\": \"test\", \"health_check_config\": {\"initial_interval_secs\": 5.0, \"health_checker\": { \"http\": {\"expected_response_code\": 0, \"endpoint\": \"/health\", \"expected_response\": \"ok\"}}, \"max_consecutive_failures\": 0, \"timeout_secs\": 1.0, \"interval_secs\": 1.0}, \"name\": \"http_example\", \"service\": true, \"max_task_failures\": 1, \"cron_collision_policy\": \"KILL_EXISTING\", \"enable_hooks\": false, \"cluster\": \"devcluster\", \"task\": {\"processes\": [{\"daemon\": false, \"name\": \"echo_ports\", \"ephemeral\": false, \"max_failures\": 1, \"min_duration\": 5, \"cmdline\": \"echo \\\"tcp port: {{thermos.ports[tcp]}}; http port: {{thermos.ports[http]}}; alias: {{thermos.ports[alias]}}\\\"\", \"final\": false}, {\"daemon\": false, \"name\": \"stage_server\", \"ephemeral\": false, \"max_failures\": 1, \"min_duration\": 5, \"cmdline\": \"cp /vagrant/src/test/sh/org/apache/aurora/e2e/http_example.py .\", \"final\": false}, {\ "daemon\": false, \"name\": \"run_server\", \"ephemeral\": false, \"max_failures\": 1, \"min_duration\": 5, \"cmdline\": \"python http_example.py {{thermos.ports[http]}}\", \"final\": false}], \"name\": \"http_example\", \"finalization_wait\": 30, \"max_failures\": 1, \"max_concurrency\": 0, \"resources\": {\"disk\": 8388608, \"ram\": 16777216, \"cpu\": 0.1}, \"constraints\": [{\"order\": [\"echo_ports\", \"stage_server\", \"run_server\"]}]}, \"production\": false, \"role\": \"vagrant\", \"contact\": \"vagrant@localhost\", \"announce\": {\"primary_port\": \"http\", \"portmap\": {\"alias\": \"http\"}}, \"lifecycle\": {\"http\": {\"graceful_shutdown_endpoint\": \"/quitquitquit\", \"port\": \"health\", \"shutdown_endpoint\": \"/abortabortabort\"}}, \"priority\": 0}"}, + "metadata":[], + "container":{ + "cachedHashCode":-1955376216, + "setField":"MESOS", + "value":{"cachedHashCode":31}} + }, + "assignedPorts":{}, + "instanceId":8 + }, + "status":"PENDING", + "failureCount":0, + "taskEvents":[ + {"cachedHashCode":0,"timestamp":1464992060258,"status":"PENDING","scheduler":"aurora"}] + }, + "oldState":{}} +``` + +By default, the webhook watches all TaskStateChanges and sends events to configured endpoint. If you +are only interested in certain types of TaskStateChange (transition to `LOST` or `FAILED` statuses), +you can specify a whitelist of the desired task statuses in webhook.json. The webhook will only send +the corresponding events for the whitelisted statuses to the configured endpoint. + +```json +{ + "headers": { + "Content-Type": "application/vnd.kafka.json.v1+json", + "Producer-Type": "reliable" + }, + "targetURL": "http://localhost:5000/";, + "timeoutMsec": 50, + "statuses": ["LOST", "FAILED"] +} +``` + +If you want to whitelist all TaskStateChanges, you can add a wildcard character `*` to your whitelist +like below, or simply leave out the `statuses` field in webhook.json. + +```json +{ + "headers": { + "Content-Type": "application/vnd.kafka.json.v1+json", + "Producer-Type": "reliable" + }, + "targetURL": "http://localhost:5000/";, + "timeoutMsec": 50, + "statuses": ["*"] +} +``` Added: aurora/site/source/documentation/0.22.0/getting-started/overview.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.22.0/getting-started/overview.md?rev=1871319&view=auto ============================================================================== --- aurora/site/source/documentation/0.22.0/getting-started/overview.md (added) +++ aurora/site/source/documentation/0.22.0/getting-started/overview.md Fri Dec 13 05:37:33 2019 @@ -0,0 +1,112 @@ +Aurora System Overview +====================== + +Apache Aurora is a service scheduler that runs on top of Apache Mesos, enabling you to run +long-running services, cron jobs, and ad-hoc jobs that take advantage of Apache Mesos' scalability, +fault-tolerance, and resource isolation. + + +Components +---------- + +It is important to have an understanding of the components that make up +a functioning Aurora cluster. + + + +* **Aurora scheduler** + The scheduler is your primary interface to the work you run in your cluster. You will + instruct it to run jobs, and it will manage them in Mesos for you. You will also frequently use + the scheduler's read-only web interface as a heads-up display for what's running in your cluster. + +* **Aurora client** + The client (`aurora` command) is a command line tool that exposes primitives that you can use to + interact with the scheduler. The client operates on + + Aurora also provides an admin client (`aurora_admin` command) that contains commands built for + cluster administrators. You can use this tool to do things like manage user quotas and manage + graceful maintenance on machines in cluster. + +* **Aurora executor** + The executor (a.k.a. Thermos executor) is responsible for carrying out the workloads described in + the Aurora DSL (`.aurora` files). The executor is what actually executes user processes. It will + also perform health checking of tasks and register tasks in ZooKeeper for the purposes of dynamic + service discovery. + +* **Aurora observer** + The observer provides browser-based access to the status of individual tasks executing on worker + machines. It gives insight into the processes executing, and facilitates browsing of task sandbox + directories. + +* **ZooKeeper** + [ZooKeeper](http://zookeeper.apache.org) is a distributed consensus system. In an Aurora cluster + it is used for reliable election of the leading Aurora scheduler and Mesos master. It is also + used as a vehicle for service discovery, see [Service Discovery](../../features/service-discovery/) + +* **Mesos master** + The master is responsible for tracking worker machines and performing accounting of their + resources. The scheduler interfaces with the master to control the cluster. + +* **Mesos agent** + The agent receives work assigned by the scheduler and executes them. It interfaces with Linux + isolation systems like cgroups, namespaces and Docker to manage the resource consumption of tasks. + When a user task is launched, the agent will launch the executor (in the context of a Linux cgroup + or Docker container depending upon the environment), which will in turn fork user processes. + + In earlier versions of Mesos and Aurora, the Mesos agent was known as the Mesos slave. + + +Jobs, Tasks and Processes +-------------------------- + +Aurora is a Mesos framework used to schedule *jobs* onto Mesos. Mesos +cares about individual *tasks*, but typical jobs consist of dozens or +hundreds of task replicas. Aurora provides a layer on top of Mesos with +its `Job` abstraction. An Aurora `Job` consists of a task template and +instructions for creating near-identical replicas of that task (modulo +things like "instance id" or specific port numbers which may differ from +machine to machine). + +How many tasks make up a Job is complicated. On a basic level, a Job consists of +one task template and instructions for creating near-identical replicas of that task +(otherwise referred to as "instances" or "shards"). + +A task can merely be a single *process* corresponding to a single +command line, such as `python2.7 my_script.py`. However, a task can also +consist of many separate processes, which all run within a single +sandbox. For example, running multiple cooperating agents together, +such as `logrotate`, `installer`, master, or agent processes. This is +where Thermos comes in. While Aurora provides a `Job` abstraction on +top of Mesos `Tasks`, Thermos provides a `Process` abstraction +underneath Mesos `Task`s and serves as part of the Aurora framework's +executor. + +You define `Job`s,` Task`s, and `Process`es in a configuration file. +Configuration files are written in Python, and make use of the +[Pystachio](https://github.com/wickman/pystachio) templating language, +along with specific Aurora, Mesos, and Thermos commands and methods. +The configuration files typically end with a `.aurora` extension. + +Summary: + +* Aurora manages jobs made of tasks. +* Mesos manages tasks made of processes. +* Thermos manages processes. +* All that is defined in `.aurora` configuration files + + + +Each `Task` has a *sandbox* created when the `Task` starts and garbage +collected when it finishes. All of a `Task'`s processes run in its +sandbox, so processes can share state by using a shared current working +directory. + +The sandbox garbage collection policy considers many factors, most +importantly age and size. It makes a best-effort attempt to keep +sandboxes around as long as possible post-task in order for service +owners to inspect data and logs, should the `Task` have completed +abnormally. But you can't design your applications assuming sandboxes +will be around forever, e.g. by building log saving or other +checkpointing mechanisms directly into your application or into your +`Job` description. + Added: aurora/site/source/documentation/0.22.0/getting-started/tutorial.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.22.0/getting-started/tutorial.md?rev=1871319&view=auto ============================================================================== --- aurora/site/source/documentation/0.22.0/getting-started/tutorial.md (added) +++ aurora/site/source/documentation/0.22.0/getting-started/tutorial.md Fri Dec 13 05:37:33 2019 @@ -0,0 +1,258 @@ +# Aurora Tutorial + +This tutorial shows how to use the Aurora scheduler to run (and "`printf-debug`") +a hello world program on Mesos. This is the recommended document for new Aurora users +to start getting up to speed on the system. + +- [Prerequisite](#setup-install-aurora) +- [The Script](#the-script) +- [Aurora Configuration](#aurora-configuration) +- [Creating the Job](#creating-the-job) +- [Watching the Job Run](#watching-the-job-run) +- [Cleanup](#cleanup) +- [Next Steps](#next-steps) + + +## Prerequisite + +This tutorial assumes you are running [Aurora locally using Vagrant](../vagrant/). +However, in general the instructions are also applicable to any other +[Aurora installation](../../operations/installation/). + +Unless otherwise stated, all commands are to be run from the root of the aurora +repository clone. + + +## The Script + +Our "hello world" application is a simple Python script that loops +forever, displaying the time every few seconds. Copy the code below and +put it in a file named `hello_world.py` in the root of your Aurora repository clone +(Note: this directory is the same as `/vagrant` inside the Vagrant VMs). + +The script has an intentional bug, which we will explain later on. + +<!-- NOTE: If you are changing this file, be sure to also update examples/vagrant/test_tutorial.sh. +--> +```python +import time + +def main(): + SLEEP_DELAY = 10 + # Python experts - ignore this blatant bug. + for i in xrang(100): + print("Hello world! The time is now: %s. Sleeping for %d secs" % ( + time.asctime(), SLEEP_DELAY)) + time.sleep(SLEEP_DELAY) + +if __name__ == "__main__": + main() +``` + +## Aurora Configuration + +Once we have our script/program, we need to create a *configuration +file* that tells Aurora how to manage and launch our Job. Save the below +code in the file `hello_world.aurora`. + +<!-- NOTE: If you are changing this file, be sure to also update examples/vagrant/test_tutorial.sh. +--> +```python +pkg_path = '/vagrant/hello_world.py' + +# we use a trick here to make the configuration change with +# the contents of the file, for simplicity. in a normal setting, packages would be +# versioned, and the version number would be changed in the configuration. +import hashlib +with open(pkg_path, 'rb') as f: + pkg_checksum = hashlib.md5(f.read()).hexdigest() + +# copy hello_world.py into the local sandbox +install = Process( + name = 'fetch_package', + cmdline = 'cp %s . && echo %s && chmod +x hello_world.py' % (pkg_path, pkg_checksum)) + +# run the script +hello_world = Process( + name = 'hello_world', + cmdline = 'python -u hello_world.py') + +# describe the task +hello_world_task = SequentialTask( + processes = [install, hello_world], + resources = Resources(cpu = 1, ram = 1*MB, disk=8*MB)) + +jobs = [ + Service(cluster = 'devcluster', + environment = 'devel', + role = 'www-data', + name = 'hello_world', + task = hello_world_task) +] +``` + +There is a lot going on in that configuration file: + +1. From a "big picture" viewpoint, it first defines two +Processes. Then it defines a Task that runs the two Processes in the +order specified in the Task definition, as well as specifying what +computational and memory resources are available for them. Finally, +it defines a Job that will schedule the Task on available and suitable +machines. This Job is the sole member of a list of Jobs; you can +specify more than one Job in a config file. + +2. At the Process level, it specifies how to get your code into the +local sandbox in which it will run. It then specifies how the code is +actually run once the second Process starts. + +For more about Aurora configuration files, see the [Configuration +Tutorial](../../reference/configuration-tutorial/) and the [Configuration +Reference](../../reference/configuration/) (preferably after finishing this +tutorial). + + +## Creating the Job + +We're ready to launch our job! To do so, we use the Aurora Client to +issue a Job creation request to the Aurora scheduler. + +Many Aurora Client commands take a *job key* argument, which uniquely +identifies a Job. A job key consists of four parts, each separated by a +"/". The four parts are `<cluster>/<role>/<environment>/<jobname>` +in that order: + +* Cluster refers to the name of a particular Aurora installation. +* Role names are user accounts existing on the agent machines. If you +don't know what accounts are available, contact your sysadmin. +* Environment names are namespaces; you can count on `test`, `devel`, +`staging` and `prod` existing. +* Jobname is the custom name of your job. + +When comparing two job keys, if any of the four parts is different from +its counterpart in the other key, then the two job keys identify two separate +jobs. If all four values are identical, the job keys identify the same job. + +The `clusters.json` [client configuration](../../reference/client-cluster-configuration/) +for the Aurora scheduler defines the available cluster names. +For Vagrant, from the top-level of your Aurora repository clone, do: + + $ vagrant ssh + +Followed by: + + vagrant@aurora:~$ cat /etc/aurora/clusters.json + +You'll see something like the following. The `name` value shown here, corresponds to a job key's cluster value. + +```javascript +[{ + "name": "devcluster", + "zk": "192.168.33.7", + "scheduler_zk_path": "/aurora/scheduler", + "auth_mechanism": "UNAUTHENTICATED", + "slave_run_directory": "latest", + "slave_root": "/var/lib/mesos" +}] +``` + +The Aurora Client command that actually runs our Job is `aurora job create`. It creates a Job as +specified by its job key and configuration file arguments and runs it. + + aurora job create <cluster>/<role>/<environment>/<jobname> <config_file> + +Or for our example: + + aurora job create devcluster/www-data/devel/hello_world /vagrant/hello_world.aurora + +After entering our virtual machine using `vagrant ssh`, this returns: + + vagrant@aurora:~$ aurora job create devcluster/www-data/devel/hello_world /vagrant/hello_world.aurora + INFO] Creating job hello_world + INFO] Checking status of devcluster/www-data/devel/hello_world + Job create succeeded: job url=http://aurora.local:8081/scheduler/www-data/devel/hello_world + + +## Watching the Job Run + +Now that our job is running, let's see what it's doing. Access the +scheduler web interface at `http://$scheduler_hostname:$scheduler_port/scheduler` +Or when using `vagrant`, `http://192.168.33.7:8081/scheduler` +First we see what Jobs are scheduled: + + + +Click on your user name, which in this case was `www-data`, and we see the Jobs associated +with that role: + + + +If you click on your `hello_world` Job, you'll see: + + + +Oops, looks like our first job didn't quite work! The task is temporarily throttled for +having failed on every attempt of the Aurora scheduler to run it. We have to figure out +what is going wrong. + +On the Completed tasks tab, we see all past attempts of the Aurora scheduler to run our job. + + + +We can navigate to the Task page of a failed run by clicking on the host link. + + + +Once there, we see that the `hello_world` process failed. The Task page +captures the standard error and standard output streams and makes them available. +Clicking through to `stderr` on the failed `hello_world` process, we see what happened. + + + +It looks like we made a typo in our Python script. We wanted `xrange`, +not `xrang`. Edit the `hello_world.py` script to use the correct function +and save it as `hello_world_v2.py`. Then update the `hello_world.aurora` +configuration to the newest version. + +In order to try again, we can now instruct the scheduler to update our job: + + vagrant@aurora:~$ aurora update start devcluster/www-data/devel/hello_world /vagrant/hello_world.aurora + INFO] Starting update for: hello_world + Job update has started. View your update progress at http://aurora.local:8081/scheduler/www-data/devel/hello_world/update/8ef38017-e60f-400d-a2f2-b5a8b724e95b + +This time, the task comes up. + + + +By again clicking on the host, we inspect the Task page, and see that the +`hello_world` process is running. + + + +We then inspect the output by clicking on `stdout` and see our process' +output: + + + +## Cleanup + +Now that we're done, we kill the job using the Aurora client: + + vagrant@aurora:~$ aurora job killall devcluster/www-data/devel/hello_world + INFO] Killing tasks for job: devcluster/www-data/devel/hello_world + INFO] Instances to be killed: [0] + Successfully killed instances [0] + Job killall succeeded + +The job page now shows the `hello_world` tasks as completed. + + + +## Next Steps + +Now that you've finished this Tutorial, you should read or do the following: + +- [The Aurora Configuration Tutorial](../../reference/configuration-tutorial/), which provides more examples + and best practices for writing Aurora configurations. You should also look at + the [Aurora Configuration Reference](../../reference/configuration/). +- Explore the Aurora Client - use `aurora -h`, and read the + [Aurora Client Commands](../../reference/client-commands/) document.
