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From s...@apache.org
Subject [3/7] aurora git commit: Reorganize Documentation
Date Mon, 28 Mar 2016 20:55:44 GMT
http://git-wip-us.apache.org/repos/asf/aurora/blob/f28f41a7/docs/presentations.md
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-# Apache Aurora Presentations
-Video and slides from presentations and panel discussions about Apache Aurora.
-
-_(Listed in date descending order)_
-
-<table>
-
-	<tr>
-		<td><img src="images/presentations/10_08_2015_mesos_aurora_on_a_small_scale_thumb.png" alt="Mesos and Aurora on a Small Scale Thumbnail" /></td>
-		<td><strong><a href="https://www.youtube.com/watch?v=q5iIqhaCJ_o">Mesos &amp; Aurora on a Small Scale (Video)</a></strong>
-		<p>Presented by Florian Pfeiffer</p>
-		<p>October 8, 2015 at <a href="http://events.linuxfoundation.org/events/archive/2015/mesoscon-europe">#MesosCon Europe 2015</a></p></td>
-	</tr>
-	<tr>
-		<td><img src="images/presentations/10_08_2015_sla_aware_maintenance_for_operators_thumb.png" alt="SLA Aware Maintenance for Operators Thumbnail" /></td>
-		<td><strong><a href="https://www.youtube.com/watch?v=tZ0-SISvCis">SLA Aware Maintenance for Operators (Video)</a></strong>
-		<p>Presented by Joe Smith</p>
-		<p>October 8, 2015 at <a href="http://events.linuxfoundation.org/events/archive/2015/mesoscon-europe">#MesosCon Europe 2015</a></p></td>
-	</tr>
-	<tr>
-		<td><img src="images/presentations/09_20_2015_shipping_code_with_aurora_thumb.png" alt="Shipping Code with Aurora Thumbnail" /></td>
-		<td><strong><a href="https://www.youtube.com/watch?v=y1hi7K1lPkk">Shipping Code with Aurora (Video)</a></strong>
-		<p>Presented by Bill Farner</p>
-		<p>August 20, 2015 at <a href="http://events.linuxfoundation.org/events/archive/2015/mesoscon">#MesosCon 2015</a></p></td>
-	</tr>
-	<tr>
-		<td><img src="images/presentations/09_20_2015_twitter_production_scale_thumb.png" alt="Twitter Production Scale Thumbnail" /></td>
-		<td><strong><a href="https://www.youtube.com/watch?v=nNrh-gdu9m4">Twitter’s Production Scale: Mesos and Aurora Operations (Video)</a></strong>
-		<p>Presented by Joe Smith</p>
-		<p>August 20, 2015 at <a href="http://events.linuxfoundation.org/events/archive/2015/mesoscon">#MesosCon 2015</a></p></td>
-	</tr>
-	<tr>
-		<td><img src="images/presentations/04_30_2015_monolith_to_microservices_thumb.png" alt="From Monolith to Microservices with Aurora Video Thumbnail" /></td>
-		<td><strong><a href="https://www.youtube.com/watch?v=yXkOgnyK4Hw">From Monolith to Microservices w/ Aurora (Video)</a></strong>
-		<p>Presented by Thanos Baskous, Tony Dong, Dobromir Montauk</p>
-		<p>April 30, 2015 at <a href="http://www.meetup.com/Bay-Area-Apache-Aurora-Users-Group/events/221219480/">Bay Area Apache Aurora Users Group</a></p></td>
-	</tr>
-	<tr>
-		<td><img src="images/presentations/03_07_2015_aurora_mesos_in_practice_at_twitter_thumb.png" alt="Aurora + Mesos in Practice at Twitter Thumbnail" /></td>
-		<td><strong><a href="https://www.youtube.com/watch?v=1XYJGX_qZVU">Aurora + Mesos in Practice at Twitter (Video)</a></strong>
-		<p>Presented by Bill Farner</p>
-		<p>March 07, 2015 at <a href="http://www.bigeng.io/aurora-mesos-in-practice-at-twitter">Bigcommerce TechTalk</a></p></td>
-	</tr>
-	<tr>
-		<td><img src="images/presentations/02_28_2015_apache_aurora_thumb.png" alt="Apache Auroraの始めかた Slideshow Thumbnail" /></td>
-		<td><strong><a href="http://www.slideshare.net/zembutsu/apache-aurora-introduction-and-tutorial-osc15tk">Apache Auroraの始めかた (Slides)</a></strong>
-		<p>Presented by Masahito Zembutsu</p>
-		<p>February 28, 2015 at <a href="http://www.ospn.jp/osc2015-spring/">Open Source Conference 2015 Tokyo Spring</a></p></td>
-	</tr>
-	<tr>
-		<td><img src="images/presentations/02_19_2015_aurora_adopters_panel_thumb.png" alt="Apache Aurora Adopters Panel Video Thumbnail" /></td>
-		<td><strong><a href="https://www.youtube.com/watch?v=2Jsj0zFdRlg">Apache Aurora Adopters Panel (Video)</a></strong>
-		<p>Panelists Ben Staffin, Josh Adams, Bill Farner, Berk Demir</p>
-		<p>February 19, 2015 at <a href="http://www.meetup.com/Bay-Area-Mesos-User-Group/events/220279080/">Bay Area Mesos Users Group</a></p></td>
-	</tr>
-	<tr>
-		<td><img src="images/presentations/02_19_2015_aurora_at_twitter_thumb.png" alt="Operating Apache Aurora and Mesos at Twitter Video Thumbnail" /></td>
-		<td><strong><a href="https://www.youtube.com/watch?v=E4lxX6epM_U">Operating Apache Aurora and Mesos at Twitter (Video)</a></strong>
-		<p>Presented by Joe Smith</p>
-		<p>February 19, 2015 at <a href="http://www.meetup.com/Bay-Area-Mesos-User-Group/events/220279080/">Bay Area Mesos Users Group</a></p></td>
-	</tr>
-	<tr>
-		<td><img src="images/presentations/02_19_2015_aurora_at_tellapart_thumb.png" alt="Apache Aurora and Mesos at TellApart" /></td>
-		<td><strong><a href="https://www.youtube.com/watch?v=ZZXtXLvTXAE">Apache Aurora and Mesos at TellApart (Video)</a></strong>
-		<p>Presented by Steve Niemitz</p>
-		<p>February 19, 2015 at <a href="http://www.meetup.com/Bay-Area-Mesos-User-Group/events/220279080/">Bay Area Mesos Users Group</a></p></td>
-	</tr>
-	<tr>
-		<td><img src="images/presentations/08_21_2014_past_present_future_thumb.png" alt="Past, Present, and Future of the Aurora Scheduler Video Thumbnail" /></td>
-		<td><strong><a href="https://www.youtube.com/watch?v=Dsc5CPhKs4o">Past, Present, and Future of the Aurora Scheduler (Video)</a></strong>
-		<p>Presented by Bill Farner</p>
-		<p>August 21, 2014 at <a href="http://events.linuxfoundation.org/events/archive/2014/mesoscon">#MesosCon 2014</a></p></td>
-	</tr>
-	<tr>
-		<td><img src="images/presentations/03_25_2014_introduction_to_aurora_thumb.png" alt="Introduction to Apache Aurora Video Thumbnail" /></td>
-		<td><strong><a href="https://www.youtube.com/watch?v=asd_h6VzaJc">Introduction to Apache Aurora (Video)</a></strong>
-		<p>Presented by Bill Farner</p>
-		<p>March 25, 2014 at <a href="https://www.eventbrite.com/e/aurora-and-mesosframeworksmeetup-tickets-10850994617">Aurora and Mesos Frameworks Meetup</a></p></td>
-	</tr>
-</table>

http://git-wip-us.apache.org/repos/asf/aurora/blob/f28f41a7/docs/reference/client-cluster-configuration.md
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+# Client Cluster Configuration
+
+A cluster configuration file is used by the Aurora client to describe the Aurora clusters with
+which it can communicate. Ultimately this allows client users to reference clusters with short names
+like us-east and eu.
+
+A cluster configuration is formatted as JSON.  The simplest cluster configuration is one that
+communicates with a single (non-leader-elected) scheduler.  For example:
+
+    [{
+      "name": "example",
+      "scheduler_uri": "http://localhost:55555",
+    }]
+
+
+A configuration for a leader-elected scheduler would contain something like:
+
+    [{
+      "name": "example",
+      "zk": "192.168.33.7",
+      "scheduler_zk_path": "/aurora/scheduler"
+    }]
+
+
+The following properties may be set:
+
+  **Property**             | **Type** | **Description**
+  :------------------------| :------- | :--------------
+   **name**                | String   | Cluster name (Required)
+   **slave_root**          | String   | Path to mesos slave work dir (Required)
+   **slave_run_directory** | String   | Name of mesos slave run dir (Required)
+   **zk**                  | String   | Hostname of ZooKeeper instance used to resolve Aurora schedulers.
+   **zk_port**             | Integer  | Port of ZooKeeper instance used to locate Aurora schedulers (Default: 2181)
+   **scheduler_zk_path**   | String   | ZooKeeper path under which scheduler instances are registered.
+   **scheduler_uri**       | String   | URI of Aurora scheduler instance.
+   **proxy_url**           | String   | Used by the client to format URLs for display.
+   **auth_mechanism**      | String   | The authentication mechanism to use when communicating with the scheduler. (Default: UNAUTHENTICATED)
+
+
+## Details
+
+### `name`
+
+The name of the Aurora cluster represented by this entry. This name will be the `cluster` portion of
+any job keys identifying jobs running within the cluster.
+
+### `slave_root`
+
+The path on the mesos slaves where executing tasks can be found. It is used in combination with the
+`slave_run_directory` property by `aurora task run` and `aurora task ssh` to change into the sandbox
+directory after connecting to the host. This value should match the value passed to `mesos-slave`
+as `-work_dir`.
+
+### `slave_run_directory`
+
+The name of the directory where the task run can be found. This is used in combination with the
+`slave_root` property by `aurora task run` and `aurora task ssh` to change into the sandbox
+directory after connecting to the host. This should almost always be set to `latest`.
+
+### `zk`
+
+The hostname of the ZooKeeper instance used to resolve the Aurora scheduler. Aurora uses ZooKeeper
+to elect a leader. The client will connect to this ZooKeeper instance to determine the current
+leader. This host should match the host passed to the scheduler as `-zk_endpoints`.
+
+### `zk_port`
+
+The port on which the ZooKeeper instance is running. If not set this will default to the standard
+ZooKeeper port of 2181. This port should match the port in the host passed to the scheduler as
+`-zk_endpoints`.
+
+### `scheduler_zk_path`
+
+The path on the ZooKeeper instance under which the Aurora serverset is registered. This value should
+match the value passed to the scheduler as `-serverset_path`.
+
+### `scheduler_uri`
+
+The URI of the scheduler. This would be used in place of the ZooKeeper related configuration above
+in circumstances where direct communication with a single scheduler is needed (e.g. testing
+environments). It is strongly advised to **never** use this property for production deploys.
+
+### `proxy_url`
+
+Instead of using the hostname of the leading scheduler as the base url, if `proxy_url` is set, its
+value will be used instead. In that scenario the value for `proxy_url` would be, for example, the
+URL of your VIP in a loadbalancer or a roundrobin DNS name.
+
+### `auth_mechanism`
+
+The identifier of an authentication mechanism that the client should use when communicating with the
+scheduler. Support for values other than `UNAUTHENTICATED` requires a matching scheduler-side
+[security configuration](../operations/security.md).

http://git-wip-us.apache.org/repos/asf/aurora/blob/f28f41a7/docs/reference/client-commands.md
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+Aurora Client Commands
+======================
+
+- [Introduction](#introduction)
+- [Cluster Configuration](#cluster-configuration)
+- [Job Keys](#job-keys)
+- [Modifying Aurora Client Commands](#modifying-aurora-client-commands)
+- [Regular Jobs](#regular-jobs)
+    - [Creating and Running a Job](#creating-and-running-a-job)
+    - [Running a Command On a Running Job](#running-a-command-on-a-running-job)
+    - [Killing a Job](#killing-a-job)
+    - [Adding Instances](#adding-instances)
+    - [Updating a Job](#updating-a-job)
+        - [Coordinated job updates](#user-content-coordinated-job-updates)
+    - [Renaming a Job](#renaming-a-job)
+    - [Restarting Jobs](#restarting-jobs)
+- [Cron Jobs](#cron-jobs)
+- [Comparing Jobs](#comparing-jobs)
+- [Viewing/Examining Jobs](#viewingexamining-jobs)
+    - [Listing Jobs](#listing-jobs)
+    - [Inspecting a Job](#inspecting-a-job)
+    - [Versions](#versions)
+    - [Checking Your Quota](#checking-your-quota)
+    - [Finding a Job on Web UI](#finding-a-job-on-web-ui)
+    - [Getting Job Status](#getting-job-status)
+    - [Opening the Web UI](#opening-the-web-ui)
+    - [SSHing to a Specific Task Machine](#sshing-to-a-specific-task-machine)
+    - [Templating Command Arguments](#templating-command-arguments)
+
+Introduction
+------------
+
+Once you have written an `.aurora` configuration file that describes
+your Job and its parameters and functionality, you interact with Aurora
+using Aurora Client commands. This document describes all of these commands
+and how and when to use them. All Aurora Client commands start with
+`aurora`, followed by the name of the specific command and its
+arguments.
+
+*Job keys* are a very common argument to Aurora commands, as well as the
+gateway to useful information about a Job. Before using Aurora, you
+should read the next section which describes them in detail. The section
+after that briefly describes how you can modify the behavior of certain
+Aurora Client commands, linking to a detailed document about how to do
+that.
+
+This is followed by the Regular Jobs section, which describes the basic
+Client commands for creating, running, and manipulating Aurora Jobs.
+After that are sections on Comparing Jobs and Viewing/Examining Jobs. In
+other words, various commands for getting information and metadata about
+Aurora Jobs.
+
+Cluster Configuration
+---------------------
+
+The client must be able to find a configuration file that specifies available clusters. This file
+declares shorthand names for clusters, which are in turn referenced by job configuration files
+and client commands.
+
+The client will load at most two configuration files, making both of their defined clusters
+available. The first is intended to be a system-installed cluster, using the path specified in
+the environment variable `AURORA_CONFIG_ROOT`, defaulting to `/etc/aurora/clusters.json` if the
+environment variable is not set. The second is a user-installed file, located at
+`~/.aurora/clusters.json`.
+
+For more details on cluster configuration see the
+[Client Cluster Configuration](client-cluster-configuration.md) documentation.
+
+Job Keys
+--------
+
+A job key is a unique system-wide identifier for an Aurora-managed
+Job, for example `cluster1/web-team/test/experiment204`. It is a 4-tuple
+consisting of, in order, *cluster*, *role*, *environment*, and
+*jobname*, separated by /s. Cluster is the name of an Aurora
+cluster. Role is the Unix service account under which the Job
+runs. Environment is a namespace component like `devel`, `test`,
+`prod`, or `stagingN.` Jobname is the Job's name.
+
+The combination of all four values uniquely specifies the Job. If any
+one value is different from that of another job key, the two job keys
+refer to different Jobs. For example, job key
+`cluster1/tyg/prod/workhorse` is different from
+`cluster1/tyg/prod/workcamel` is different from
+`cluster2/tyg/prod/workhorse` is different from
+`cluster2/foo/prod/workhorse` is different from
+`cluster1/tyg/test/workhorse.`
+
+Role names are user accounts existing on the slave machines. If you don't know what accounts
+are available, contact your sysadmin.
+
+Environment names are namespaces; you can count on `prod`, `devel` and `test` existing.
+
+Modifying Aurora Client Commands
+--------------------------------
+
+For certain Aurora Client commands, you can define hook methods that run
+either before or after an action that takes place during the command's
+execution, as well as based on whether the action finished successfully or failed
+during execution. Basically, a hook is code that lets you extend the
+command's actions. The hook executes on the client side, specifically on
+the machine executing Aurora commands.
+
+Hooks can be associated with these Aurora Client commands.
+
+  - `job create`
+  - `job kill`
+  - `job restart`
+
+The process for writing and activating them is complex enough
+that we explain it in a devoted document, [Hooks for Aurora Client API](client-hooks.md).
+
+Regular Jobs
+------------
+
+This section covers Aurora commands related to running, killing,
+renaming, updating, and restarting a basic Aurora Job.
+
+### Creating and Running a Job
+
+    aurora job create <job key> <configuration file>
+
+Creates and then runs a Job with the specified job key based on a `.aurora` configuration file.
+The configuration file may also contain and activate hook definitions.
+
+### Running a Command On a Running Job
+
+    aurora task run CLUSTER/ROLE/ENV/NAME[/INSTANCES] <cmd>
+
+Runs a shell command on all machines currently hosting shards of a
+single Job.
+
+`run` supports the same command line wildcards used to populate a Job's
+commands; i.e. anything in the `{{mesos.*}}` and `{{thermos.*}}`
+namespaces.
+
+### Killing a Job
+
+    aurora job killall CLUSTER/ROLE/ENV/NAME
+
+Kills all Tasks associated with the specified Job, blocking until all
+are terminated. Defaults to killing all instances in the Job.
+
+The `<configuration file>` argument for `kill` is optional. Use it only
+if it contains hook definitions and activations that affect the
+kill command.
+
+### Adding Instances
+
+    aurora job add CLUSTER/ROLE/ENV/NAME/INSTANCE <count>
+
+Adds `<count>` instances to the existing job. The configuration of the new instances is derived from
+an active job instance pointed by the `/INSTANCE` part of the job specification. This command is
+a simpler way to scale out an existing job when an instance with desired task configuration
+already exists. Use `aurora update start` to add instances with a new (updated) configuration.
+
+### Updating a Job
+
+You can manage job updates using the `aurora update` command.  Please see
+[the Job Update documentation](../features/job-updates.md) for more details.
+
+
+### Renaming a Job
+
+Renaming is a tricky operation as downstream clients must be informed of
+the new name. A conservative approach
+to renaming suitable for production services is:
+
+1.  Modify the Aurora configuration file to change the role,
+    environment, and/or name as appropriate to the standardized naming
+    scheme.
+2.  Check that only these naming components have changed
+    with `aurora diff`.
+
+        aurora job diff CLUSTER/ROLE/ENV/NAME <job_configuration>
+
+3.  Create the (identical) job at the new key. You may need to request a
+    temporary quota increase.
+
+        aurora job create CLUSTER/ROLE/ENV/NEW_NAME <job_configuration>
+
+4.  Migrate all clients over to the new job key. Update all links and
+    dashboards. Ensure that both job keys run identical versions of the
+    code while in this state.
+5.  After verifying that all clients have successfully moved over, kill
+    the old job.
+
+        aurora job killall CLUSTER/ROLE/ENV/NAME
+
+6.  If you received a temporary quota increase, be sure to let the
+    powers that be know you no longer need the additional capacity.
+
+### Restarting Jobs
+
+`restart` restarts all of a job key identified Job's shards:
+
+    aurora job restart CLUSTER/ROLE/ENV/NAME[/INSTANCES]
+
+Restarts are controlled on the client side, so aborting
+the `job restart` command halts the restart operation.
+
+**Note**: `job restart` only applies its command line arguments and does not
+use or is affected by `update.config`. Restarting
+does ***not*** involve a configuration change. To update the
+configuration, use `update.config`.
+
+The `--config` argument for restart is optional. Use it only
+if it contains hook definitions and activations that affect the
+`job restart` command.
+
+Cron Jobs
+---------
+
+You can manage cron jobs using the `aurora cron` command.  Please see
+[the Cron Jobs Feature](../features/cron-jobs.md) for more details.
+
+Comparing Jobs
+--------------
+
+    aurora job diff CLUSTER/ROLE/ENV/NAME <job configuration>
+
+Compares a job configuration against a running job. By default the diff
+is determined using `diff`, though you may choose an alternate
+ diff program by specifying the `DIFF_VIEWER` environment variable.
+
+Viewing/Examining Jobs
+----------------------
+
+Above we discussed creating, killing, and updating Jobs. Here we discuss
+how to view and examine Jobs.
+
+### Listing Jobs
+
+    aurora config list <job configuration>
+
+Lists all Jobs registered with the Aurora scheduler in the named cluster for the named role.
+
+### Inspecting a Job
+
+    aurora job inspect CLUSTER/ROLE/ENV/NAME <job configuration>
+
+`inspect` verifies that its specified job can be parsed from a
+configuration file, and displays the parsed configuration.
+
+### Checking Your Quota
+
+    aurora quota get CLUSTER/ROLE
+
+Prints the production quota allocated to the role's value at the given
+cluster. Only non-[dedicated](../features/constraints.md#dedicated-attribute)
+[production](configuration.md#job-objects) jobs consume quota.
+
+### Finding a Job on Web UI
+
+When you create a job, part of the output response contains a URL that goes
+to the job's scheduler UI page. For example:
+
+    vagrant@precise64:~$ aurora job create devcluster/www-data/prod/hello /vagrant/examples/jobs/hello_world.aurora
+    INFO] Creating job hello
+    INFO] Response from scheduler: OK (message: 1 new tasks pending for job www-data/prod/hello)
+    INFO] Job url: http://precise64:8081/scheduler/www-data/prod/hello
+
+You can go to the scheduler UI page for this job via `http://precise64:8081/scheduler/www-data/prod/hello`
+You can go to the overall scheduler UI page by going to the part of that URL that ends at `scheduler`; `http://precise64:8081/scheduler`
+
+Once you click through to a role page, you see Jobs arranged
+separately by pending jobs, active jobs and finished jobs.
+Jobs are arranged by role, typically a service account for
+production jobs and user accounts for test or development jobs.
+
+### Getting Job Status
+
+    aurora job status <job_key>
+
+Returns the status of recent tasks associated with the
+`job_key` specified Job in its supplied cluster. Typically this includes
+a mix of active tasks (running or assigned) and inactive tasks
+(successful, failed, and lost.)
+
+### Opening the Web UI
+
+Use the Job's web UI scheduler URL or the `aurora status` command to find out on which
+machines individual tasks are scheduled. You can open the web UI via the
+`open` command line command if invoked from your machine:
+
+    aurora job open [<cluster>[/<role>[/<env>/<job_name>]]]
+
+If only the cluster is specified, it goes directly to that cluster's
+scheduler main page. If the role is specified, it goes to the top-level
+role page. If the full job key is specified, it goes directly to the job
+page where you can inspect individual tasks.
+
+### SSHing to a Specific Task Machine
+
+    aurora task ssh <job_key> <shard number>
+
+You can have the Aurora client ssh directly to the machine that has been
+assigned a particular Job/shard number. This may be useful for quickly
+diagnosing issues such as performance issues or abnormal behavior on a
+particular machine.
+
+### Templating Command Arguments
+
+    aurora task run [-e] [-t THREADS] <job_key> -- <<command-line>>
+
+Given a job specification, run the supplied command on all hosts and
+return the output. You may use the standard Mustache templating rules:
+
+- `{{thermos.ports[name]}}` substitutes the specific named port of the
+  task assigned to this machine
+- `{{mesos.instance}}` substitutes the shard id of the job's task
+  assigned to this machine
+- `{{thermos.task_id}}` substitutes the task id of the job's task
+  assigned to this machine
+
+For example, the following type of pattern can be a powerful diagnostic
+tool:
+
+    aurora task run -t5 cluster1/tyg/devel/seizure -- \
+      'curl -s -m1 localhost:{{thermos.ports[http]}}/vars | grep uptime'
+
+By default, the command runs in the Task's sandbox. The `-e` option can
+run the command in the executor's sandbox. This is mostly useful for
+Aurora administrators.
+
+You can parallelize the runs by using the `-t` option.

http://git-wip-us.apache.org/repos/asf/aurora/blob/f28f41a7/docs/reference/client-hooks.md
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+# Hooks for Aurora Client API
+
+You can execute hook methods around Aurora API Client methods when they are called by the Aurora Command Line commands.
+
+Explaining how hooks work is a bit tricky because of some indirection about what they apply to. Basically, a hook is code that executes when a particular Aurora Client API method runs, letting you extend the method's actions. The hook executes on the client side, specifically on the machine executing Aurora commands.
+
+The catch is that hooks are associated with Aurora Client API methods, which users don't directly call. Instead, users call Aurora Command Line commands, which call Client API methods during their execution. Since which hooks run depend on which Client API methods get called, you will need to know which Command Line commands call which API methods. Later on, there is a table showing the various associations.
+
+**Terminology Note**: From now on, "method(s)" refer to Client API methods, and "command(s)" refer to Command Line commands.
+
+- [Hook Types](#hook-types)
+- [Execution Order](#execution-order)
+- [Hookable Methods](#hookable-methods)
+- [Activating and Using Hooks](#activating-and-using-hooks)
+- [.aurora Config File Settings](#aurora-config-file-settings)
+- [Command Line](#command-line)
+- [Hooks Protocol](#hooks-protocol)
+  - [pre_ Methods](#pre_-methods)
+  - [err_ Methods](#err_-methods)
+  - [post_ Methods](#post_-methods)
+- [Generic Hooks](#generic-hooks)
+- [Hooks Process Checklist](#hooks-process-checklist)
+
+
+## Hook Types
+
+Hooks have three basic types, differing by when they run with respect to their associated method.
+
+`pre_<method_name>`: When its associated method is called, the `pre_` hook executes first, then the called method. If the `pre_` hook fails, the method never runs. Later code that expected the method to succeed may be affected by this, and result in terminating the Aurora client.
+
+Note that a `pre_` hook can error-trap internally so it does not
+return `False`. Designers/contributors of new `pre_` hooks should
+consider whether or not to error-trap them. You can error trap at the
+highest level very generally and always pass the `pre_` hook by
+returning `True`. For example:
+
+    def pre_create(...):
+      do_something()  # if do_something fails with an exception, the create_job is not attempted!
+      return True
+
+    # However...
+    def pre_create(...):
+      try:
+        do_something()  # may cause exception
+      except Exception:  # generic error trap will catch it
+        pass  # and ignore the exception
+      return True  # create_job will run in any case!
+
+`post_<method_name>`: A `post_` hook executes after its associated method successfully finishes running. If it fails, the already executed method is unaffected. A `post_` hook's error is trapped, and any later operations are unaffected.
+
+`err_<method_name>`: Executes only when its associated method returns a status other than OK or throws an exception. If an `err_` hook fails, the already executed method is unaffected. An `err_` hook's error is trapped, and any later operations are unaffected.
+
+## Execution Order
+
+A command with `pre_`, `post_`, and `err_` hooks defined and activated for its called method executes in the following order when the method successfully executes:
+
+1. Command called
+2. Command code executes
+3. Method Called
+4. `pre_` method hook runs
+5. Method runs and successfully finishes
+6. `post_` method hook runs
+7. Command code executes
+8. Command execution ends
+
+The following is what happens when, for the same command and hooks, the method associated with the command suffers an error and does not successfully finish executing:
+
+1. Command called
+2. Command code executes
+3. Method Called
+4. `pre_` method hook runs
+5. Method runs and fails
+6. `err_` method hook runs
+7. Command Code executes (if `err_` method does not end the command execution)
+8. Command execution ends
+
+Note that the `post_` and `err_` hooks for the same method can never both run for a single execution of that method.
+
+## Hookable Methods
+
+You can associate `pre_`, `post_`, and `err_` hooks with the following methods. Since you do not directly interact with the methods, but rather the Aurora Command Line commands that call them, for each method we also list the command(s) that can call the method. Note that a different method or methods may be called by a command depending on how the command's other code executes. Similarly, multiple commands can call the same method. We also list the methods' argument signatures, which are used by their associated hooks. <a name="Chart"></a>
+
+  Aurora Client API Method | Client API Method Argument Signature | Aurora Command Line Command
+  -------------------------| ------------------------------------- | ---------------------------
+  ```create_job``` | ```self```, ```config``` | ```job create```, <code>runtask
+  ```restart``` | ```self```, ```job_key```, ```shards```, ```update_config```, ```health_check_interval_seconds``` | ```job restart```
+  ```kill_job``` | ```self```, ```job_key```, ```shards=None``` |  ```job kill```
+  ```start_cronjob``` | ```self```, ```job_key``` | ```cron start```
+  ```start_job_update``` | ```self```, ```config```, ```instances=None``` | ```update start```
+
+Some specific examples:
+
+* `pre_create_job` executes when a `create_job` method is called, and before the `create_job` method itself executes.
+
+* `post_cancel_update` executes after a `cancel_update` method has successfully finished running.
+
+* `err_kill_job` executes when the `kill_job` method is called, but doesn't successfully finish running.
+
+## Activating and Using Hooks
+
+By default, hooks are inactive. If you do not want to use hooks, you do not need to make any changes to your code. If you do want to use hooks, you will need to alter your `.aurora` config file to activate them both for the configuration as a whole as well as for individual `Job`s. And, of course, you will need to define in your config file what happens when a particular hook executes.
+
+## .aurora Config File Settings
+
+You can define a top-level `hooks` variable in any `.aurora` config file. `hooks` is a list of all objects that define hooks used by `Job`s defined in that config file. If you do not want to define any hooks for a configuration, `hooks` is optional.
+
+    hooks = [Object_with_defined_hooks1, Object_with_defined_hooks2]
+
+Be careful when assembling a config file using `include` on multiple smaller config files. If there are multiple files that assign a value to `hooks`, only the last assignment made will stick. For example, if `x.aurora` has `hooks = [a, b, c]` and `y.aurora` has `hooks = [d, e, f]` and `z.aurora` has, in this order, `include x.aurora` and `include y.aurora`, the `hooks` value will be `[d, e, f]`.
+
+Also, for any `Job` that you want to use hooks with, its `Job` definition in the `.aurora` config file must set an `enable_hooks` flag to `True` (it defaults to `False`). By default, hooks are disabled and you must enable them for `Job`s of your choice.
+
+To summarize, to use hooks for a particular job, you must both activate hooks for your config file as a whole, and for that job. Activating hooks only for individual jobs won't work, nor will only activating hooks for your config file as a whole. You must also specify the hooks' defining object in the `hooks` variable.
+
+Recall that `.aurora` config files are written in Pystachio. So the following turns on hooks for production jobs at cluster1 and cluster2, but leaves them off for similar jobs with a defined user role. Of course, you also need to list the objects that define the hooks in your config file's `hooks` variable.
+
+    jobs = [
+            Job(enable_hooks = True, cluster = c, env = 'prod') for c in ('cluster1', 'cluster2')
+           ]
+    jobs.extend(
+       Job(cluster = c, env = 'prod', role = getpass.getuser()) for c in ('cluster1', 'cluster2'))
+       # Hooks disabled for these jobs
+
+## Command Line
+
+All Aurora Command Line commands now accept an `.aurora` config file as an optional parameter (some, of course, accept it as a required parameter). Whenever a command has a `.aurora` file parameter, any hooks specified and activated in the `.aurora` file can be used. For example:
+
+    aurora job restart cluster1/role/env/app myapp.aurora
+
+The command activates any hooks specified and activated in `myapp.aurora`. For the `restart` command, that is the only thing the `myapp.aurora` parameter does. So, if the command was the following, since there is no `.aurora` config file to specify any hooks, no hooks on the `restart` command can run.
+
+    aurora job restart cluster1/role/env/app
+
+## Hooks Protocol
+
+Any object defined in the `.aurora` config file can define hook methods. You should define your hook methods within a class, and then use the class name as a value in the `hooks` list in your config file.
+
+Note that you can define other methods in the class that its hook methods can call; all the logic of a hook does not have to be in its definition.
+
+The following example defines a class containing a `pre_kill_job` hook definition that calls another method defined in the class.
+
+    # Defines a method pre_kill_job
+    class KillConfirmer(object):
+      def confirm(self, msg):
+        return raw_input(msg).lower() == 'yes'
+
+      def pre_kill_job(self, job_key, shards=None):
+        shards = ('shards %s' % shards) if shards is not None else 'all shards'
+        return self.confirm('Are you sure you want to kill %s (%s)? (yes/no): '
+                            % (job_key, shards))
+
+### pre_ Methods
+
+`pre_` methods have the signature:
+
+    pre_<API method name>(self, <associated method's signature>)
+
+`pre_` methods have the same signature as their associated method, with the addition of `self` as the first parameter. See the [chart](#Chart) above for the mapping of parameters to methods. When writing `pre_` methods, you can use the `*` and `**` syntax to designate that all unspecified parameters are passed in a list to the `*`ed variable and all named parameters with values are passed as name/value pairs to the `**`ed variable.
+
+If this method returns False, the API command call aborts.
+
+### err_ Methods
+
+`err_` methods have the signature:
+
+    err_<API method name>(self, exc, <associated method's signature>)
+
+`err_` methods have the same signature as their associated method, with the addition of a first parameter `self` and a second parameter `exc`. `exc` is either a result with responseCode other than `ResponseCode.OK` or an `Exception`. See the [chart](#Chart) above for the mapping of parameters to methods. When writing `err`_ methods, you can use the `*` and `**` syntax to designate that all unspecified parameters are passed in a list to the `*`ed variable and all named parameters with values are passed as name/value pairs to the `**`ed variable.
+
+`err_` method return codes are ignored.
+
+### post_ Methods
+
+`post_` methods have the signature:
+
+    post_<API method name>(self, result, <associated method signature>)
+
+`post_` method parameters are `self`, then `result`, followed by the same parameter signature as their associated method. `result` is the result of the associated method call. See the [chart](#chart) above for the mapping of parameters to methods. When writing `post_` methods, you can use the `*` and `**` syntax to designate that all unspecified arguments are passed in a list to the `*`ed parameter and all unspecified named arguments with values are passed as name/value pairs to the `**`ed parameter.
+
+`post_` method return codes are ignored.
+
+## Generic Hooks
+
+There are seven Aurora API Methods which any of the three hook types can attach to. Thus, there are 21 possible hook/method combinations for a single `.aurora` config file. Say that you define `pre_` and `post_` hooks for the `restart` method. That leaves 19 undefined hook/method combinations; `err_restart` and the 3 `pre_`, `post_`, and `err_` hooks for each of the other 6 hookable methods. You can define what happens when any of these otherwise undefined 19 hooks execute via a generic hook, whose signature is:
+
+    generic_hook(self, hook_config, event, method_name, result_or_err, args*, kw**)
+
+where:
+
+* `hook_config` is a named tuple of `config` (the Pystashio `config` object) and `job_key`.
+
+* `event` is one of `pre`, `err`, or `post`, indicating which type of hook the genetic hook is standing in for. For example, assume no specific hooks were defined for the `restart` API command. If `generic_hook` is defined and activated, and `restart` is called, `generic_hook` will effectively run as `pre_restart`, `post_restart`, and `err_restart`. You can use a selection statement on this value so that `generic_hook` will act differently based on whether it is standing in for a `pre_`, `post_`, or `err_` hook.
+
+* `method_name` is the Client API method name whose execution is causing this execution of the `generic_hook`.
+
+* `args*`, `kw**` are the API method arguments and keyword arguments respectively.
+* `result_or_err` is a tri-state parameter taking one of these three values:
+  1. None for `pre_`hooks
+  2. `result` for `post_` nooks
+  3. `exc` for `err_` hooks
+
+Example:
+
+    # Overrides the standard do-nothing generic_hook by adding a log writing operation.
+    from twitter.common import log
+      class Logger(object):
+        '''Adds to the log every time a hookable API method is called'''
+        def generic_hook(self, hook_config, event, method_name, result_or_err, *args, **kw)
+          log.info('%s: %s_%s of %s'
+                   % (self.__class__.__name__, event, method_name, hook_config.job_key))
+
+## Hooks Process Checklist
+
+1. In your `.aurora` config file, add a `hooks` variable. Note that you may want to define a `.aurora` file only for hook definitions and then include this file in multiple other config files that you want to use the same hooks.
+
+    hooks = []
+
+2. In the `hooks` variable, list all objects that define hooks used by `Job`s defined in this config:
+
+    hooks = [Object_hook_definer1, Object_hook_definer2]
+
+3. For each job that uses hooks in this config file, add `enable_hooks = True` to the `Job` definition. Note that this is necessary even if you only want to use the generic hook.
+
+4. Write your `pre_`, `post_`, and `err_` hook definitions as part of an object definition in your `.aurora` config file.
+
+5. If desired, write your `generic_hook` definition as part of an object definition in your `.aurora` config file. Remember, the object must be listed as a member of `hooks`.
+
+6. If your Aurora command line command does not otherwise take an `.aurora` config file argument, add the appropriate `.aurora` file as an argument in order to define and activate the configuration's hooks.

http://git-wip-us.apache.org/repos/asf/aurora/blob/f28f41a7/docs/reference/configuration-best-practices.md
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+Aurora Configuration Best Practices
+===================================
+
+Use As Few .aurora Files As Possible
+------------------------------------
+
+When creating your `.aurora` configuration, try to keep all versions of
+a particular job within the same `.aurora` file. For example, if you
+have separate jobs for `cluster1`, `cluster1` staging, `cluster1`
+testing, and`cluster2`, keep them as close together as possible.
+
+Constructs shared across multiple jobs owned by your team (e.g.
+team-level defaults or structural templates) can be split into separate
+`.aurora`files and included via the `include` directive.
+
+
+Avoid Boilerplate
+------------------
+
+If you see repetition or find yourself copy and pasting any parts of
+your configuration, it's likely an opportunity for templating. Take the
+example below:
+
+`redundant.aurora` contains:
+
+    download = Process(
+      name = 'download',
+      cmdline = 'wget http://www.python.org/ftp/python/2.7.3/Python-2.7.3.tar.bz2',
+      max_failures = 5,
+      min_duration = 1)
+
+    unpack = Process(
+      name = 'unpack',
+      cmdline = 'rm -rf Python-2.7.3 && tar xzf Python-2.7.3.tar.bz2',
+      max_failures = 5,
+      min_duration = 1)
+
+    build = Process(
+      name = 'build',
+      cmdline = 'pushd Python-2.7.3 && ./configure && make && popd',
+      max_failures = 1)
+
+    email = Process(
+      name = 'email',
+      cmdline = 'echo Success | mail feynman@tmc.com',
+      max_failures = 5,
+      min_duration = 1)
+
+    build_python = Task(
+      name = 'build_python',
+      processes = [download, unpack, build, email],
+      constraints = [Constraint(order = ['download', 'unpack', 'build', 'email'])])
+
+As you'll notice, there's a lot of repetition in the `Process`
+definitions. For example, almost every process sets a `max_failures`
+limit to 5 and a `min_duration` to 1. This is an opportunity for factoring
+into a common process template.
+
+Furthermore, the Python version is repeated everywhere. This can be
+bound via structural templating as described in the [Advanced Binding](configuration-templating.md#AdvancedBinding)
+section.
+
+`less_redundant.aurora` contains:
+
+    class Python(Struct):
+      version = Required(String)
+      base = Default(String, 'Python-{{version}}')
+      package = Default(String, '{{base}}.tar.bz2')
+
+    ReliableProcess = Process(
+      max_failures = 5,
+      min_duration = 1)
+
+    download = ReliableProcess(
+      name = 'download',
+      cmdline = 'wget http://www.python.org/ftp/python/{{python.version}}/{{python.package}}')
+
+    unpack = ReliableProcess(
+      name = 'unpack',
+      cmdline = 'rm -rf {{python.base}} && tar xzf {{python.package}}')
+
+    build = ReliableProcess(
+      name = 'build',
+      cmdline = 'pushd {{python.base}} && ./configure && make && popd',
+      max_failures = 1)
+
+    email = ReliableProcess(
+      name = 'email',
+      cmdline = 'echo Success | mail {{role}}@foocorp.com')
+
+    build_python = SequentialTask(
+      name = 'build_python',
+      processes = [download, unpack, build, email]).bind(python = Python(version = "2.7.3"))
+
+
+Thermos Uses bash, But Thermos Is Not bash
+-------------------------------------------
+
+#### Bad
+
+Many tiny Processes makes for harder to manage configurations.
+
+    copy = Process(
+      name = 'copy',
+      cmdline = 'rcp user@my_machine:my_application .'
+     )
+
+     unpack = Process(
+       name = 'unpack',
+       cmdline = 'unzip app.zip'
+     )
+
+     remove = Process(
+       name = 'remove',
+       cmdline = 'rm -f app.zip'
+     )
+
+     run = Process(
+       name = 'app',
+       cmdline = 'java -jar app.jar'
+     )
+
+     run_task = Task(
+       processes = [copy, unpack, remove, run],
+       constraints = order(copy, unpack, remove, run)
+     )
+
+#### Good
+
+Each `cmdline` runs in a bash subshell, so you have the full power of
+bash. Chaining commands with `&&` or `||` is almost always the right
+thing to do.
+
+Also for Tasks that are simply a list of processes that run one after
+another, consider using the `SequentialTask` helper which applies a
+linear ordering constraint for you.
+
+    stage = Process(
+      name = 'stage',
+      cmdline = 'rcp user@my_machine:my_application . && unzip app.zip && rm -f app.zip')
+
+    run = Process(name = 'app', cmdline = 'java -jar app.jar')
+
+    run_task = SequentialTask(processes = [stage, run])
+
+
+Rarely Use Functions In Your Configurations
+-------------------------------------------
+
+90% of the time you define a function in a `.aurora` file, you're
+probably Doing It Wrong(TM).
+
+#### Bad
+
+    def get_my_task(name, user, cpu, ram, disk):
+      return Task(
+        name = name,
+        user = user,
+        processes = [STAGE_PROCESS, RUN_PROCESS],
+        constraints = order(STAGE_PROCESS, RUN_PROCESS),
+        resources = Resources(cpu = cpu, ram = ram, disk = disk)
+     )
+
+     task_one = get_my_task('task_one', 'feynman', 1.0, 32*MB, 1*GB)
+     task_two = get_my_task('task_two', 'feynman', 2.0, 64*MB, 1*GB)
+
+#### Good
+
+This one is more idiomatic. Forced keyword arguments prevents accidents,
+e.g. constructing a task with "32*MB" when you mean 32MB of ram and not
+disk. Less proliferation of task-construction techniques means
+easier-to-read, quicker-to-understand, and a more composable
+configuration.
+
+    TASK_TEMPLATE = SequentialTask(
+      user = 'wickman',
+      processes = [STAGE_PROCESS, RUN_PROCESS],
+    )
+
+    task_one = TASK_TEMPLATE(
+      name = 'task_one',
+      resources = Resources(cpu = 1.0, ram = 32*MB, disk = 1*GB) )
+
+    task_two = TASK_TEMPLATE(
+      name = 'task_two',
+      resources = Resources(cpu = 2.0, ram = 64*MB, disk = 1*GB)
+    )

http://git-wip-us.apache.org/repos/asf/aurora/blob/f28f41a7/docs/reference/configuration-templating.md
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+Aurora Configuration Templating
+===============================
+
+The `.aurora` file format is just Python. However, `Job`, `Task`,
+`Process`, and other classes are defined by a templating library called
+*Pystachio*, a powerful tool for configuration specification and reuse.
+
+[Aurora Configuration Reference](configuration.md)
+has a full reference of all Aurora/Thermos defined Pystachio objects.
+
+When writing your `.aurora` file, you may use any Pystachio datatypes, as
+well as any objects shown in the *Aurora+Thermos Configuration
+Reference* without `import` statements - the Aurora config loader
+injects them automatically. Other than that the `.aurora` format
+works like any other Python script.
+
+
+Templating 1: Binding in Pystachio
+----------------------------------
+
+Pystachio uses the visually distinctive {{}} to indicate template
+variables. These are often called "mustache variables" after the
+similarly appearing variables in the Mustache templating system and
+because the curly braces resemble mustaches.
+
+If you are familiar with the Mustache system, templates in Pystachio
+have significant differences. They have no nesting, joining, or
+inheritance semantics. On the other hand, when evaluated, templates
+are evaluated iteratively, so this affords some level of indirection.
+
+Let's start with the simplest template; text with one
+variable, in this case `name`;
+
+    Hello {{name}}
+
+If we evaluate this as is, we'd get back:
+
+    Hello
+
+If a template variable doesn't have a value, when evaluated it's
+replaced with nothing. If we add a binding to give it a value:
+
+    { "name" : "Tom" }
+
+We'd get back:
+
+    Hello Tom
+
+Every Pystachio object has an associated `.bind` method that can bind
+values to {{}} variables. Bindings are not immediately evaluated.
+Instead, they are evaluated only when the interpolated value of the
+object is necessary, e.g. for performing equality or serializing a
+message over the wire.
+
+Objects with and without mustache templated variables behave
+differently:
+
+    >>> Float(1.5)
+    Float(1.5)
+
+    >>> Float('{{x}}.5')
+    Float({{x}}.5)
+
+    >>> Float('{{x}}.5').bind(x = 1)
+    Float(1.5)
+
+    >>> Float('{{x}}.5').bind(x = 1) == Float(1.5)
+    True
+
+    >>> contextual_object = String('{{metavar{{number}}}}').bind(
+    ... metavar1 = "first", metavar2 = "second")
+
+    >>> contextual_object
+    String({{metavar{{number}}}})
+
+    >>> contextual_object.bind(number = 1)
+    String(first)
+
+    >>> contextual_object.bind(number = 2)
+    String(second)
+
+You usually bind simple key to value pairs, but you can also bind three
+other objects: lists, dictionaries, and structurals. These will be
+described in detail later.
+
+
+### Structurals in Pystachio / Aurora
+
+Most Aurora/Thermos users don't ever (knowingly) interact with `String`,
+`Float`, or `Integer` Pystashio objects directly. Instead they interact
+with derived structural (`Struct`) objects that are collections of
+fundamental and structural objects. The structural object components are
+called *attributes*. Aurora's most used structural objects are `Job`,
+`Task`, and `Process`:
+
+    class Process(Struct):
+      cmdline = Required(String)
+      name = Required(String)
+      max_failures = Default(Integer, 1)
+      daemon = Default(Boolean, False)
+      ephemeral = Default(Boolean, False)
+      min_duration = Default(Integer, 5)
+      final = Default(Boolean, False)
+
+Construct default objects by following the object's type with (). If you
+want an attribute to have a value different from its default, include
+the attribute name and value inside the parentheses.
+
+    >>> Process()
+    Process(daemon=False, max_failures=1, ephemeral=False,
+      min_duration=5, final=False)
+
+Attribute values can be template variables, which then receive specific
+values when creating the object.
+
+    >>> Process(cmdline = 'echo {{message}}')
+    Process(daemon=False, max_failures=1, ephemeral=False, min_duration=5,
+            cmdline=echo {{message}}, final=False)
+
+    >>> Process(cmdline = 'echo {{message}}').bind(message = 'hello world')
+    Process(daemon=False, max_failures=1, ephemeral=False, min_duration=5,
+            cmdline=echo hello world, final=False)
+
+A powerful binding property is that all of an object's children inherit its
+bindings:
+
+    >>> List(Process)([
+    ... Process(name = '{{prefix}}_one'),
+    ... Process(name = '{{prefix}}_two')
+    ... ]).bind(prefix = 'hello')
+    ProcessList(
+      Process(daemon=False, name=hello_one, max_failures=1, ephemeral=False, min_duration=5, final=False),
+      Process(daemon=False, name=hello_two, max_failures=1, ephemeral=False, min_duration=5, final=False)
+      )
+
+Remember that an Aurora Job contains Tasks which contain Processes. A
+Job level binding is inherited by its Tasks and all their Processes.
+Similarly a Task level binding is available to that Task and its
+Processes but is *not* visible at the Job level (inheritance is a
+one-way street.)
+
+#### Mustaches Within Structurals
+
+When you define a `Struct` schema, one powerful, but confusing, feature
+is that all of that structure's attributes are Mustache variables within
+the enclosing scope *once they have been populated*.
+
+For example, when `Process` is defined above, all its attributes such as
+{{`name`}}, {{`cmdline`}}, {{`max_failures`}} etc., are all immediately
+defined as Mustache variables, implicitly bound into the `Process`, and
+inherit all child objects once they are defined.
+
+Thus, you can do the following:
+
+    >>> Process(name = "installer", cmdline = "echo {{name}} is running")
+    Process(daemon=False, name=installer, max_failures=1, ephemeral=False, min_duration=5,
+            cmdline=echo installer is running, final=False)
+
+WARNING: This binding only takes place in one direction. For example,
+the following does NOT work and does not set the `Process` `name`
+attribute's value.
+
+    >>> Process().bind(name = "installer")
+    Process(daemon=False, max_failures=1, ephemeral=False, min_duration=5, final=False)
+
+The following is also not possible and results in an infinite loop that
+attempts to resolve `Process.name`.
+
+    >>> Process(name = '{{name}}').bind(name = 'installer')
+
+Do not confuse Structural attributes with bound Mustache variables.
+Attributes are implicitly converted to Mustache variables but not vice
+versa.
+
+### Templating 2: Structurals Are Factories
+
+#### A Second Way of Templating
+
+A second templating method is both as powerful as the aforementioned and
+often confused with it. This method is due to automatic conversion of
+Struct attributes to Mustache variables as described above.
+
+Suppose you create a Process object:
+
+    >>> p = Process(name = "process_one", cmdline = "echo hello world")
+
+    >>> p
+    Process(daemon=False, name=process_one, max_failures=1, ephemeral=False, min_duration=5,
+            cmdline=echo hello world, final=False)
+
+This `Process` object, "`p`", can be used wherever a `Process` object is
+needed. It can also be reused by changing the value(s) of its
+attribute(s). Here we change its `name` attribute from `process_one` to
+`process_two`.
+
+    >>> p(name = "process_two")
+    Process(daemon=False, name=process_two, max_failures=1, ephemeral=False, min_duration=5,
+            cmdline=echo hello world, final=False)
+
+Template creation is a common use for this technique:
+
+    >>> Daemon = Process(daemon = True)
+    >>> logrotate = Daemon(name = 'logrotate', cmdline = './logrotate conf/logrotate.conf')
+    >>> mysql = Daemon(name = 'mysql', cmdline = 'bin/mysqld --safe-mode')
+
+### Advanced Binding
+
+As described above, `.bind()` binds simple strings or numbers to
+Mustache variables. In addition to Structural types formed by combining
+atomic types, Pystachio has two container types; `List` and `Map` which
+can also be bound via `.bind()`.
+
+#### Bind Syntax
+
+The `bind()` function can take Python dictionaries or `kwargs`
+interchangeably (when "`kwargs`" is in a function definition, `kwargs`
+receives a Python dictionary containing all keyword arguments after the
+formal parameter list).
+
+    >>> String('{{foo}}').bind(foo = 'bar') == String('{{foo}}').bind({'foo': 'bar'})
+    True
+
+Bindings done "closer" to the object in question take precedence:
+
+    >>> p = Process(name = '{{context}}_process')
+    >>> t = Task().bind(context = 'global')
+    >>> t(processes = [p, p.bind(context = 'local')])
+    Task(processes=ProcessList(
+      Process(daemon=False, name=global_process, max_failures=1, ephemeral=False, final=False,
+              min_duration=5),
+      Process(daemon=False, name=local_process, max_failures=1, ephemeral=False, final=False,
+              min_duration=5)
+    ))
+
+#### Binding Complex Objects
+
+##### Lists
+
+    >>> fibonacci = List(Integer)([1, 1, 2, 3, 5, 8, 13])
+    >>> String('{{fib[4]}}').bind(fib = fibonacci)
+    String(5)
+
+##### Maps
+
+    >>> first_names = Map(String, String)({'Kent': 'Clark', 'Wayne': 'Bruce', 'Prince': 'Diana'})
+    >>> String('{{first[Kent]}}').bind(first = first_names)
+    String(Clark)
+
+##### Structurals
+
+    >>> String('{{p.cmdline}}').bind(p = Process(cmdline = "echo hello world"))
+    String(echo hello world)
+
+### Structural Binding
+
+Use structural templates when binding more than two or three individual
+values at the Job or Task level. For fewer than two or three, standard
+key to string binding is sufficient.
+
+Structural binding is a very powerful pattern and is most useful in
+Aurora/Thermos for doing Structural configuration. For example, you can
+define a job profile. The following profile uses `HDFS`, the Hadoop
+Distributed File System, to designate a file's location. `HDFS` does
+not come with Aurora, so you'll need to either install it separately
+or change the way the dataset is designated.
+
+    class Profile(Struct):
+      version = Required(String)
+      environment = Required(String)
+      dataset = Default(String, hdfs://home/aurora/data/{{environment}}')
+
+    PRODUCTION = Profile(version = 'live', environment = 'prod')
+    DEVEL = Profile(version = 'latest',
+                    environment = 'devel',
+                    dataset = 'hdfs://home/aurora/data/test')
+    TEST = Profile(version = 'latest', environment = 'test')
+
+    JOB_TEMPLATE = Job(
+      name = 'application',
+      role = 'myteam',
+      cluster = 'cluster1',
+      environment = '{{profile.environment}}',
+      task = SequentialTask(
+        name = 'task',
+        resources = Resources(cpu = 2, ram = 4*GB, disk = 8*GB),
+        processes = [
+      Process(name = 'main', cmdline = 'java -jar application.jar -hdfsPath
+                 {{profile.dataset}}')
+        ]
+       )
+     )
+
+    jobs = [
+      JOB_TEMPLATE(instances = 100).bind(profile = PRODUCTION),
+      JOB_TEMPLATE.bind(profile = DEVEL),
+      JOB_TEMPLATE.bind(profile = TEST),
+     ]
+
+In this case, a custom structural "Profile" is created to self-document
+the configuration to some degree. This also allows some schema
+"type-checking", and for default self-substitution, e.g. in
+`Profile.dataset` above.
+
+So rather than a `.bind()` with a half-dozen substituted variables, you
+can bind a single object that has sensible defaults stored in a single
+place.

http://git-wip-us.apache.org/repos/asf/aurora/blob/f28f41a7/docs/reference/configuration-tutorial.md
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+Aurora Configuration Tutorial
+=============================
+
+How to write Aurora configuration files, including feature descriptions
+and best practices. When writing a configuration file, make use of
+`aurora job inspect`. It takes the same job key and configuration file
+arguments as `aurora job create` or `aurora update start`. It first ensures the
+configuration parses, then outputs it in human-readable form.
+
+You should read this after going through the general [Aurora Tutorial](../getting-started/tutorial.md).
+
+- [The Basics](#user-content-the-basics)
+	- [Use Bottom-To-Top Object Ordering](#user-content-use-bottom-to-top-object-ordering)
+- [An Example Configuration File](#user-content-an-example-configuration-file)
+- [Defining Process Objects](#user-content-defining-process-objects)
+- [Getting Your Code Into The Sandbox](#user-content-getting-your-code-into-the-sandbox)
+- [Defining Task Objects](#user-content-defining-task-objects)
+	- [SequentialTask: Running Processes in Parallel or Sequentially](#user-content-sequentialtask-running-processes-in-parallel-or-sequentially)
+	- [SimpleTask](#user-content-simpletask)
+	- [Combining tasks](#user-content-combining-tasks)
+- [Defining Job Objects](#user-content-defining-job-objects)
+- [The jobs List](#user-content-the-jobs-list)
+- [Basic Examples](#basic-examples)
+
+
+The Basics
+----------
+
+To run a job on Aurora, you must specify a configuration file that tells
+Aurora what it needs to know to schedule the job, what Mesos needs to
+run the tasks the job is made up of, and what Thermos needs to run the
+processes that make up the tasks. This file must have
+a`.aurora` suffix.
+
+A configuration file defines a collection of objects, along with parameter
+values for their attributes. An Aurora configuration file contains the
+following three types of objects:
+
+- Job
+- Task
+- Process
+
+A configuration also specifies a list of `Job` objects assigned
+to the variable `jobs`.
+
+- jobs (list of defined Jobs to run)
+
+The `.aurora` file format is just Python. However, `Job`, `Task`,
+`Process`, and other classes are defined by a type-checked dictionary
+templating library called *Pystachio*, a powerful tool for
+configuration specification and reuse. Pystachio objects are tailored
+via {{}} surrounded templates.
+
+When writing your `.aurora` file, you may use any Pystachio datatypes, as
+well as any objects shown in the [*Aurora+Thermos Configuration
+Reference*](configuration-reference.md), without `import` statements - the
+Aurora config loader injects them automatically. Other than that, an `.aurora`
+file works like any other Python script.
+
+[*Aurora Configuration Reference*](configuration.md)
+has a full reference of all Aurora/Thermos defined Pystachio objects.
+
+### Use Bottom-To-Top Object Ordering
+
+A well-structured configuration starts with structural templates (if
+any). Structural templates encapsulate in their attributes all the
+differences between Jobs in the configuration that are not directly
+manipulated at the `Job` level, but typically at the `Process` or `Task`
+level. For example, if certain processes are invoked with slightly
+different settings or input.
+
+After structural templates, define, in order, `Process`es, `Task`s, and
+`Job`s.
+
+Structural template names should be *UpperCamelCased* and their
+instantiations are typically *UPPER\_SNAKE\_CASED*. `Process`, `Task`,
+and `Job` names are typically *lower\_snake\_cased*. Indentation is typically 2
+spaces.
+
+An Example Configuration File
+-----------------------------
+
+The following is a typical configuration file. Don't worry if there are
+parts you don't understand yet, but you may want to refer back to this
+as you read about its individual parts. Note that names surrounded by
+curly braces {{}} are template variables, which the system replaces with
+bound values for the variables.
+
+    # --- templates here ---
+	class Profile(Struct):
+	  package_version = Default(String, 'live')
+	  java_binary = Default(String, '/usr/lib/jvm/java-1.7.0-openjdk/bin/java')
+	  extra_jvm_options = Default(String, '')
+	  parent_environment = Default(String, 'prod')
+	  parent_serverset = Default(String,
+                                 '/foocorp/service/bird/{{parent_environment}}/bird')
+
+	# --- processes here ---
+	main = Process(
+	  name = 'application',
+	  cmdline = '{{profile.java_binary}} -server -Xmx1792m '
+	            '{{profile.extra_jvm_options}} '
+	            '-jar application.jar '
+	            '-upstreamService {{profile.parent_serverset}}'
+	)
+
+	# --- tasks ---
+	base_task = SequentialTask(
+	  name = 'application',
+	  processes = [
+	    Process(
+	      name = 'fetch',
+	      cmdline = 'curl -O
+                  https://packages.foocorp.com/{{profile.package_version}}/application.jar'),
+	  ]
+	)
+
+        # not always necessary but often useful to have separate task
+        # resource classes
+        staging_task = base_task(resources =
+                         Resources(cpu = 1.0,
+                                   ram = 2048*MB,
+                                   disk = 1*GB))
+	production_task = base_task(resources =
+                            Resources(cpu = 4.0,
+                                      ram = 2560*MB,
+                                      disk = 10*GB))
+
+	# --- job template ---
+	job_template = Job(
+	  name = 'application',
+	  role = 'myteam',
+	  contact = 'myteam-team@foocorp.com',
+	  instances = 20,
+	  service = True,
+	  task = production_task
+	)
+
+	# -- profile instantiations (if any) ---
+	PRODUCTION = Profile()
+	STAGING = Profile(
+	  extra_jvm_options = '-Xloggc:gc.log',
+	  parent_environment = 'staging'
+	)
+
+	# -- job instantiations --
+	jobs = [
+          job_template(cluster = 'cluster1', environment = 'prod')
+	               .bind(profile = PRODUCTION),
+
+          job_template(cluster = 'cluster2', environment = 'prod')
+	                .bind(profile = PRODUCTION),
+
+          job_template(cluster = 'cluster1',
+                        environment = 'staging',
+			service = False,
+			task = staging_task,
+			instances = 2)
+			.bind(profile = STAGING),
+	]
+
+## Defining Process Objects
+
+Processes are handled by the Thermos system. A process is a single
+executable step run as a part of an Aurora task, which consists of a
+bash-executable statement.
+
+The key (and required) `Process` attributes are:
+
+-   `name`: Any string which is a valid Unix filename (no slashes,
+    NULLs, or leading periods). The `name` value must be unique relative
+    to other Processes in a `Task`.
+-   `cmdline`: A command line run in a bash subshell, so you can use
+    bash scripts. Nothing is supplied for command-line arguments,
+    so `$*` is unspecified.
+
+Many tiny processes make managing configurations more difficult. For
+example, the following is a bad way to define processes.
+
+    copy = Process(
+      name = 'copy',
+      cmdline = 'curl -O https://packages.foocorp.com/app.zip'
+    )
+    unpack = Process(
+      name = 'unpack',
+      cmdline = 'unzip app.zip'
+    )
+    remove = Process(
+      name = 'remove',
+      cmdline = 'rm -f app.zip'
+    )
+    run = Process(
+      name = 'app',
+      cmdline = 'java -jar app.jar'
+    )
+    run_task = Task(
+      processes = [copy, unpack, remove, run],
+      constraints = order(copy, unpack, remove, run)
+    )
+
+Since `cmdline` runs in a bash subshell, you can chain commands
+with `&&` or `||`.
+
+When defining a `Task` that is just a list of Processes run in a
+particular order, use `SequentialTask`, as described in the [*Defining*
+`Task` *Objects*](#Task) section. The following simplifies and combines the
+above multiple `Process` definitions into just two.
+
+    stage = Process(
+      name = 'stage',
+      cmdline = 'curl -O https://packages.foocorp.com/app.zip && '
+                'unzip app.zip && rm -f app.zip')
+
+    run = Process(name = 'app', cmdline = 'java -jar app.jar')
+
+    run_task = SequentialTask(processes = [stage, run])
+
+`Process` also has optional attributes to customize its behaviour. Details can be found in the [Aurora Configuration Reference](configuration.md#process-objects).
+
+
+## Getting Your Code Into The Sandbox
+
+When using Aurora, you need to get your executable code into its "sandbox", specifically
+the Task sandbox where the code executes for the Processes that make up that Task.
+
+Each Task has a sandbox created when the Task starts and garbage
+collected when it finishes. All of a Task's processes run in its
+sandbox, so processes can share state by using a shared current
+working directory.
+
+Typically, you save this code somewhere. You then need to define a Process
+in your `.aurora` configuration file that fetches the code from that somewhere
+to where the slave can see it. For a public cloud, that can be anywhere public on
+the Internet, such as S3. For a private cloud internal storage, you need to put in
+on an accessible HDFS cluster or similar storage.
+
+The template for this Process is:
+
+    <name> = Process(
+      name = '<name>'
+      cmdline = '<command to copy and extract code archive into current working directory>'
+    )
+
+Note: Be sure the extracted code archive has an executable.
+
+## Defining Task Objects
+
+Tasks are handled by Mesos. A task is a collection of processes that
+runs in a shared sandbox. It's the fundamental unit Aurora uses to
+schedule the datacenter; essentially what Aurora does is find places
+in the cluster to run tasks.
+
+The key (and required) parts of a Task are:
+
+-   `name`: A string giving the Task's name. By default, if a Task is
+    not given a name, it inherits the first name in its Process list.
+
+-   `processes`: An unordered list of Process objects bound to the Task.
+    The value of the optional `constraints` attribute affects the
+    contents as a whole. Currently, the only constraint, `order`, determines if
+    the processes run in parallel or sequentially.
+
+-   `resources`: A `Resource` object defining the Task's resource
+        footprint. A `Resource` object has three attributes:
+        -   `cpu`: A Float, the fractional number of cores the Task
+        requires.
+        -   `ram`: An Integer, RAM bytes the Task requires.
+        -   `disk`: An integer, disk bytes the Task requires.
+
+A basic Task definition looks like:
+
+    Task(
+        name="hello_world",
+        processes=[Process(name = "hello_world", cmdline = "echo hello world")],
+        resources=Resources(cpu = 1.0,
+                            ram = 1*GB,
+                            disk = 1*GB))
+
+A Task has optional attributes to customize its behaviour. Details can be found in the [Aurora Configuration Reference](configuration.md#task-object)
+
+
+### SequentialTask: Running Processes in Parallel or Sequentially
+
+By default, a Task with several Processes runs them in parallel. There
+are two ways to run Processes sequentially:
+
+-   Include an `order` constraint in the Task definition's `constraints`
+    attribute whose arguments specify the processes' run order:
+
+        Task( ... processes=[process1, process2, process3],
+	          constraints = order(process1, process2, process3), ...)
+
+-   Use `SequentialTask` instead of `Task`; it automatically runs
+    processes in the order specified in the `processes` attribute. No
+    `constraint` parameter is needed:
+
+        SequentialTask( ... processes=[process1, process2, process3] ...)
+
+### SimpleTask
+
+For quickly creating simple tasks, use the `SimpleTask` helper. It
+creates a basic task from a provided name and command line using a
+default set of resources. For example, in a .`aurora` configuration
+file:
+
+    SimpleTask(name="hello_world", command="echo hello world")
+
+is equivalent to
+
+    Task(name="hello_world",
+         processes=[Process(name = "hello_world", cmdline = "echo hello world")],
+         resources=Resources(cpu = 1.0,
+                             ram = 1*GB,
+                             disk = 1*GB))
+
+The simplest idiomatic Job configuration thus becomes:
+
+    import os
+    hello_world_job = Job(
+      task=SimpleTask(name="hello_world", command="echo hello world"),
+      role=os.getenv('USER'),
+      cluster="cluster1")
+
+When written to `hello_world.aurora`, you invoke it with a simple
+`aurora job create cluster1/$USER/test/hello_world hello_world.aurora`.
+
+### Combining tasks
+
+`Tasks.concat`(synonym,`concat_tasks`) and
+`Tasks.combine`(synonym,`combine_tasks`) merge multiple Task definitions
+into a single Task. It may be easier to define complex Jobs
+as smaller constituent Tasks. But since a Job only includes a single
+Task, the subtasks must be combined before using them in a Job.
+Smaller Tasks can also be reused between Jobs, instead of having to
+repeat their definition for multiple Jobs.
+
+With both methods, the merged Task takes the first Task's name. The
+difference between the two is the result Task's process ordering.
+
+-   `Tasks.combine` runs its subtasks' processes in no particular order.
+    The new Task's resource consumption is the sum of all its subtasks'
+    consumption.
+
+-   `Tasks.concat` runs its subtasks in the order supplied, with each
+    subtask's processes run serially between tasks. It is analogous to
+    the `order` constraint helper, except at the Task level instead of
+    the Process level. The new Task's resource consumption is the
+    maximum value specified by any subtask for each Resource attribute
+    (cpu, ram and disk).
+
+For example, given the following:
+
+    setup_task = Task(
+      ...
+      processes=[download_interpreter, update_zookeeper],
+      # It is important to note that {{Tasks.concat}} has
+      # no effect on the ordering of the processes within a task;
+      # hence the necessity of the {{order}} statement below
+      # (otherwise, the order in which {{download_interpreter}}
+      # and {{update_zookeeper}} run will be non-deterministic)
+      constraints=order(download_interpreter, update_zookeeper),
+      ...
+    )
+
+    run_task = SequentialTask(
+      ...
+      processes=[download_application, start_application],
+      ...
+    )
+
+    combined_task = Tasks.concat(setup_task, run_task)
+
+The `Tasks.concat` command merges the two Tasks into a single Task and
+ensures all processes in `setup_task` run before the processes
+in `run_task`. Conceptually, the task is reduced to:
+
+    task = Task(
+      ...
+      processes=[download_interpreter, update_zookeeper,
+                 download_application, start_application],
+      constraints=order(download_interpreter, update_zookeeper,
+                        download_application, start_application),
+      ...
+    )
+
+In the case of `Tasks.combine`, the two schedules run in parallel:
+
+    task = Task(
+      ...
+      processes=[download_interpreter, update_zookeeper,
+                 download_application, start_application],
+      constraints=order(download_interpreter, update_zookeeper) +
+                        order(download_application, start_application),
+      ...
+    )
+
+In the latter case, each of the two sequences may operate in parallel.
+Of course, this may not be the intended behavior (for example, if
+the `start_application` Process implicitly relies
+upon `download_interpreter`). Make sure you understand the difference
+between using one or the other.
+
+## Defining Job Objects
+
+A job is a group of identical tasks that Aurora can run in a Mesos cluster.
+
+A `Job` object is defined by the values of several attributes, some
+required and some optional. The required attributes are:
+
+-   `task`: Task object to bind to this job. Note that a Job can
+    only take a single Task.
+
+-   `role`: Job's role account; in other words, the user account to run
+    the job as on a Mesos cluster machine. A common value is
+    `os.getenv('USER')`; using a Python command to get the user who
+    submits the job request. The other common value is the service
+    account that runs the job, e.g. `www-data`.
+
+-   `environment`: Job's environment, typical values
+    are `devel`, `test`, or `prod`.
+
+-   `cluster`: Aurora cluster to schedule the job in, defined in
+    `/etc/aurora/clusters.json` or `~/.clusters.json`. You can specify
+    jobs where the only difference is the `cluster`, then at run time
+    only run the Job whose job key includes your desired cluster's name.
+
+You usually see a `name` parameter. By default, `name` inherits its
+value from the Job's associated Task object, but you can override this
+default. For these four parameters, a Job definition might look like:
+
+    foo_job = Job( name = 'foo', cluster = 'cluster1',
+              role = os.getenv('USER'), environment = 'prod',
+              task = foo_task)
+
+In addition to the required attributes, there are several optional
+attributes. Details can be found in the [Aurora Configuration Reference](configuration.md#job-objects).
+
+
+## The jobs List
+
+At the end of your `.aurora` file, you need to specify a list of the
+file's defined Jobs. For example, the following exports the jobs `job1`,
+`job2`, and `job3`.
+
+    jobs = [job1, job2, job3]
+
+This allows the aurora client to invoke commands on those jobs, such as
+starting, updating, or killing them.
+
+
+
+Basic Examples
+==============
+
+These are provided to give a basic understanding of simple Aurora jobs.
+
+### hello_world.aurora
+
+Put the following in a file named `hello_world.aurora`, substituting your own values
+for values such as `cluster`s.
+
+    import os
+    hello_world_process = Process(name = 'hello_world', cmdline = 'echo hello world')
+
+    hello_world_task = Task(
+      resources = Resources(cpu = 0.1, ram = 16 * MB, disk = 16 * MB),
+      processes = [hello_world_process])
+
+    hello_world_job = Job(
+      cluster = 'cluster1',
+      role = os.getenv('USER'),
+      task = hello_world_task)
+
+    jobs = [hello_world_job]
+
+Then issue the following commands to create and kill the job, using your own values for the job key.
+
+    aurora job create cluster1/$USER/test/hello_world hello_world.aurora
+
+    aurora job kill cluster1/$USER/test/hello_world
+
+### Environment Tailoring
+
+Put the following in a file named `hello_world_productionized.aurora`, substituting your own values
+for values such as `cluster`s.
+
+    include('hello_world.aurora')
+
+    production_resources = Resources(cpu = 1.0, ram = 512 * MB, disk = 2 * GB)
+    staging_resources = Resources(cpu = 0.1, ram = 32 * MB, disk = 512 * MB)
+    hello_world_template = hello_world(
+        name = "hello_world-{{cluster}}"
+        task = hello_world(resources=production_resources))
+
+    jobs = [
+      # production jobs
+      hello_world_template(cluster = 'cluster1', instances = 25),
+      hello_world_template(cluster = 'cluster2', instances = 15),
+
+      # staging jobs
+      hello_world_template(
+        cluster = 'local',
+        instances = 1,
+        task = hello_world(resources=staging_resources)),
+    ]
+
+Then issue the following commands to create and kill the job, using your own values for the job key
+
+    aurora job create cluster1/$USER/test/hello_world-cluster1 hello_world_productionized.aurora
+
+    aurora job kill cluster1/$USER/test/hello_world-cluster1


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