Added: aurora/site/source/documentation/0.19.1/features/webhooks.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.19.1/features/webhooks.md?rev=1828294&view=auto ============================================================================== --- aurora/site/source/documentation/0.19.1/features/webhooks.md (added) +++ aurora/site/source/documentation/0.19.1/features/webhooks.md Tue Apr 3 23:54:44 2018 @@ -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/0.19.1/getting-started/overview.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.19.1/getting-started/overview.md?rev=1828294&view=auto ============================================================================== --- aurora/site/source/documentation/0.19.1/getting-started/overview.md (added) +++ aurora/site/source/documentation/0.19.1/getting-started/overview.md Tue Apr 3 23:54:44 2018 @@ -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/0.19.1/getting-started/tutorial.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.19.1/getting-started/tutorial.md?rev=1828294&view=auto ============================================================================== --- aurora/site/source/documentation/0.19.1/getting-started/tutorial.md (added) +++ aurora/site/source/documentation/0.19.1/getting-started/tutorial.md Tue Apr 3 23:54:44 2018 @@ -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. Added: aurora/site/source/documentation/0.19.1/getting-started/vagrant.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.19.1/getting-started/vagrant.md?rev=1828294&view=auto ============================================================================== --- aurora/site/source/documentation/0.19.1/getting-started/vagrant.md (added) +++ aurora/site/source/documentation/0.19.1/getting-started/vagrant.md Tue Apr 3 23:54:44 2018 @@ -0,0 +1,154 @@ +A local Cluster with Vagrant +============================ + +This document shows you how to configure a complete cluster using a virtual machine. This setup +replicates a real cluster in your development machine as closely as possible. After you complete +the steps outlined here, you will be ready to create and run your first Aurora job. + +The following sections describe these steps in detail: + +1. [Overview](#overview) +1. [Install VirtualBox and Vagrant](#install-virtualbox-and-vagrant) +1. [Clone the Aurora repository](#clone-the-aurora-repository) +1. [Start the local cluster](#start-the-local-cluster) +1. [Log onto the VM](#log-onto-the-vm) +1. [Run your first job](#run-your-first-job) +1. [Rebuild components](#rebuild-components) +1. [Shut down or delete your local cluster](#shut-down-or-delete-your-local-cluster) +1. [Troubleshooting](#troubleshooting) + + +Overview +-------- + +The Aurora distribution includes a set of scripts that enable you to create a local cluster in +your development machine. These scripts use [Vagrant](https://www.vagrantup.com/) and +[VirtualBox](https://www.virtualbox.org/) to run and configure a virtual machine. Once the +virtual machine is running, the scripts install and initialize Aurora and any required components +to create the local cluster. + + +Install VirtualBox and Vagrant +------------------------------ + +First, download and install [VirtualBox](https://www.virtualbox.org/) on your development machine. + +Then download and install [Vagrant](https://www.vagrantup.com/). To verify that the installation +was successful, open a terminal window and type the `vagrant` command. You should see a list of +common commands for this tool. + + +Clone the Aurora repository +--------------------------- + +To obtain the Aurora source distribution, clone its Git repository using the following command: + + git clone git://git.apache.org/aurora.git + + +Start the local cluster +----------------------- + +Now change into the `aurora/` directory, which contains the Aurora source code and +other scripts and tools: + + cd aurora/ + +To start the local cluster, type the following command: + + vagrant up + +This command uses the configuration scripts in the Aurora distribution to: + +* Download a Linux system image. +* Start a virtual machine (VM) and configure it. +* Install the required build tools on the VM. +* Install Aurora's requirements (like [Mesos](http://mesos.apache.org/) and +[Zookeeper](http://zookeeper.apache.org/)) on the VM. +* Build and install Aurora from source on the VM. +* Start Aurora's services on the VM. + +This process takes several minutes to complete. + +You may notice a warning that guest additions in the VM don't match your version of VirtualBox. +This should generally be harmless, but you may wish to install a vagrant plugin to take care of +mismatches like this for you: + + vagrant plugin install vagrant-vbguest + +With this plugin installed, whenever you `vagrant up` the plugin will upgrade the guest additions +for you when a version mis-match is detected. You can read more about the plugin +[here](https://github.com/dotless-de/vagrant-vbguest). + +To verify that Aurora is running on the cluster, visit the following URLs: + +* Scheduler - http://192.168.33.7:8081 +* Observer - http://192.168.33.7:1338 +* Mesos Master - http://192.168.33.7:5050 +* Mesos Agent - http://192.168.33.7:5051 + + +Log onto the VM +--------------- + +To SSH into the VM, run the following command in your development machine: + + vagrant ssh + +To verify that Aurora is installed in the VM, type the `aurora` command. You should see a list +of arguments and possible commands. + +The `/vagrant` directory on the VM is mapped to the `aurora/` local directory +from which you started the cluster. You can edit files inside this directory in your development +machine and access them from the VM under `/vagrant`. + +A pre-installed `clusters.json` file refers to your local cluster as `devcluster`, which you +will use in client commands. + + +Run your first job +------------------ + +Now that your cluster is up and running, you are ready to define and run your first job in Aurora. +For more information, see the [Aurora Tutorial](../tutorial/). + + +Rebuild components +------------------ + +If you are changing Aurora code and would like to rebuild a component, you can use the `aurorabuild` +command on the VM to build and restart a component. This is considerably faster than destroying +and rebuilding your VM. + +`aurorabuild` accepts a list of components to build and update. To get a list of supported +components, invoke the `aurorabuild` command with no arguments: + + vagrant ssh -c 'aurorabuild client' + + +Shut down or delete your local cluster +-------------------------------------- + +To shut down your local cluster, run the `vagrant halt` command in your development machine. To +start it again, run the `vagrant up` command. + +Once you are finished with your local cluster, or if you would otherwise like to start from scratch, +you can use the command `vagrant destroy` to turn off and delete the virtual file system. + + +Troubleshooting +--------------- + +Most of the Vagrant related problems can be fixed by the following steps: + +* Destroying the vagrant environment with `vagrant destroy` +* Killing any orphaned VMs (see AURORA-499) with `virtualbox` UI or `VBoxManage` command line tool +* Cleaning the repository of build artifacts and other intermediate output with `git clean -fdx` +* Bringing up the vagrant environment with `vagrant up` + +If that still doesn't solve your problem, make sure to inspect the log files: + +* Scheduler: `/var/log/upstart/aurora-scheduler.log` +* Observer: `/var/log/upstart/aurora-thermos-observer.log` +* Mesos Master: `/var/log/mesos/mesos-master.INFO` (also see `.WARNING` and `.ERROR`) +* Mesos Agent: `/var/log/mesos/mesos-slave.INFO` (also see `.WARNING` and `.ERROR`) Added: aurora/site/source/documentation/0.19.1/images/CPUavailability.png URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.19.1/images/CPUavailability.png?rev=1828294&view=auto ============================================================================== Binary file - no diff available. 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Propchange: aurora/site/source/documentation/0.19.1/images/storage_hierarchy.png ------------------------------------------------------------------------------ svn:mime-type = application/octet-stream Added: aurora/site/source/documentation/0.19.1/index.html.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.19.1/index.html.md?rev=1828294&view=auto ============================================================================== --- aurora/site/source/documentation/0.19.1/index.html.md (added) +++ aurora/site/source/documentation/0.19.1/index.html.md Tue Apr 3 23:54:44 2018 @@ -0,0 +1,79 @@ +## Introduction + +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. + +We encourage you to ask questions on the [Aurora user list](http://aurora.apache.org/community/) or +the `#aurora` IRC channel on `irc.freenode.net`. + + +## Getting Started +Information for everyone new to Apache Aurora. + + * [Aurora System Overview](getting-started/overview/) + * [Hello World Tutorial](getting-started/tutorial/) + * [Local cluster with Vagrant](getting-started/vagrant/) + +## Features +Description of important Aurora features. + + * [Containers](features/containers/) + * [Cron Jobs](features/cron-jobs/) + * [Custom Executors](features/custom-executors/) + * [Job Updates](features/job-updates/) + * [Multitenancy](features/multitenancy/) + * [Resource Isolation](features/resource-isolation/) + * [Scheduling Constraints](features/constraints/) + * [Services](features/services/) + * [Service Discovery](features/service-discovery/) + * [SLA Metrics](features/sla-metrics/) + * [Webhooks](features/webhooks/) + +## Operators +For those that wish to manage and fine-tune an Aurora cluster. + + * [Installation](operations/installation/) + * [Configuration](operations/configuration/) + * [Upgrades](operations/upgrades/) + * [Troubleshooting](operations/troubleshooting/) + * [Monitoring](operations/monitoring/) + * [Security](operations/security/) + * [Storage](operations/storage/) + * [Backup](operations/backup-restore/) + +## Reference +The complete reference of commands, configuration options, and scheduler internals. + + * [Task lifecycle](reference/task-lifecycle/) + * Configuration (`.aurora` files) + - [Configuration Reference](reference/configuration/) + - [Configuration Tutorial](reference/configuration-tutorial/) + - [Configuration Best Practices](reference/configuration-best-practices/) + - [Configuration Templating](reference/configuration-templating/) + * Aurora Client + - [Client Commands](reference/client-commands/) + - [Client Hooks](reference/client-hooks/) + - [Client Cluster Configuration](reference/client-cluster-configuration/) + * [Scheduler Configuration](reference/scheduler-configuration/) + * [Observer Configuration](reference/observer-configuration/) + * [Endpoints](reference/scheduler-endpoints/) + +## Additional Resources + * [Tools integrating with Aurora](additional-resources/tools/) + * [Presentation videos and slides](additional-resources/presentations/) + +## Developers +All the information you need to start modifying Aurora and contributing back to the project. + + * [Contributing to the project](contributing/) + * [Committer's Guide](development/committers-guide/) + * [Design Documents](development/design-documents/) + * Developing the Aurora components: + - [Client](development/client/) + - [Scheduler](development/scheduler/) + - [Scheduler UI](development/ui/) + - [Thermos](development/thermos/) + - [Thrift structures](development/thrift/) + + Added: aurora/site/source/documentation/0.19.1/operations/backup-restore.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.19.1/operations/backup-restore.md?rev=1828294&view=auto ============================================================================== --- aurora/site/source/documentation/0.19.1/operations/backup-restore.md (added) +++ aurora/site/source/documentation/0.19.1/operations/backup-restore.md Tue Apr 3 23:54:44 2018 @@ -0,0 +1,91 @@ +# Recovering from a Scheduler Backup + +**Be sure to read the entire page before attempting to restore from a backup, as it may have +unintended consequences.** + +## Summary + +The restoration procedure replaces the existing (possibly corrupted) Mesos replicated log with an +earlier, backed up, version and requires all schedulers to be taken down temporarily while +restoring. Once completed, the scheduler state resets to what it was when the backup was created. +This means any jobs/tasks created or updated after the backup are unknown to the scheduler and will +be killed shortly after the cluster restarts. All other tasks continue operating as normal. + +Usually, it is a bad idea to restore a backup that is not extremely recent (i.e. older than a few +hours). This is because the scheduler will expect the cluster to look exactly as the backup does, +so any tasks that have been rescheduled since the backup was taken will be killed. + +Instructions below have been verified in [Vagrant environment](../../getting-started/vagrant/) and with minor +syntax/path changes should be applicable to any Aurora cluster. + +## Preparation + +Follow these steps to prepare the cluster for restoring from a backup: + +* Stop all scheduler instances + +* Consider blocking external traffic on a port defined in `-http_port` for all schedulers to +prevent users from interacting with the scheduler during the restoration process. This will help +troubleshooting by reducing the scheduler log noise and prevent users from making changes that will +be erased after the backup snapshot is restored. + +* Configure `aurora_admin` access to run all commands listed in + [Restore from backup](#restore-from-backup) section locally on the leading scheduler: + * Make sure the [clusters.json](../../reference/client-cluster-configuration/) file configured to + access scheduler directly. Set `scheduler_uri` setting and remove `zk`. Since leader can get + re-elected during the restore steps, consider doing it on all scheduler replicas. + * Depending on your particular security approach you will need to either turn off scheduler + authorization by removing scheduler `-http_authentication_mechanism` flag or make sure the + direct scheduler access is properly authorized. E.g.: in case of Kerberos you will need to make + a `/etc/hosts` file change to match your local IP to the scheduler URL configured in keytabs: + + <local_ip> <scheduler_domain_in_keytabs> + +* Next steps are required to put scheduler into a partially disabled state where it would still be +able to accept storage recovery requests but unable to schedule or change task states. This may be +accomplished by updating the following scheduler configuration options: + * Set `-mesos_master_address` to a non-existent zk address. This will prevent scheduler from + registering with Mesos. E.g.: `-mesos_master_address=zk://localhost:1111/mesos/master` + * `-max_registration_delay` - set to sufficiently long interval to prevent registration timeout + and as a result scheduler suicide. E.g: `-max_registration_delay=360mins` + * Make sure `-reconciliation_initial_delay` option is set high enough (e.g.: `365days`) to + prevent accidental task GC. This is important as scheduler will attempt to reconcile the cluster + state and will kill all tasks when restarted with an empty Mesos replicated log. + +* Restart all schedulers + +## Cleanup and re-initialize Mesos replicated log + +Get rid of the corrupted files and re-initialize Mesos replicated log: + +* Stop schedulers +* Delete all files under `-native_log_file_path` on all schedulers +* Initialize Mesos replica's log file: `sudo mesos-log initialize --path=<-native_log_file_path>` +* Start schedulers + +## Restore from backup + +At this point the scheduler is ready to rehydrate from the backup: + +* Identify the leading scheduler by: + * examining the `scheduler_lifecycle_LEADER_AWAITING_REGISTRATION` metric at the scheduler + `/vars` endpoint. Leader will have 1. All other replicas - 0. + * examining scheduler logs + * or examining Zookeeper registration under the path defined by `-zk_endpoints` + and `-serverset_path` + +* Locate the desired backup file, copy it to the leading scheduler's `-backup_dir` folder and stage +recovery by running the following command on a leader +`aurora_admin scheduler_stage_recovery --bypass-leader-redirect <cluster> scheduler-backup-<yyyy-MM-dd-HH-mm>` + +* At this point, the recovery snapshot is staged and available for manual verification/modification +via `aurora_admin scheduler_print_recovery_tasks --bypass-leader-redirect` and +`scheduler_delete_recovery_tasks --bypass-leader-redirect` commands. +See `aurora_admin help <command>` for usage details. + +* Commit recovery. This instructs the scheduler to overwrite the existing Mesos replicated log with +the provided backup snapshot and initiate a mandatory failover +`aurora_admin scheduler_commit_recovery --bypass-leader-redirect <cluster>` + +## Cleanup +Undo any modification done during [Preparation](#preparation) sequence. Added: aurora/site/source/documentation/0.19.1/operations/configuration.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.19.1/operations/configuration.md?rev=1828294&view=auto ============================================================================== --- aurora/site/source/documentation/0.19.1/operations/configuration.md (added) +++ aurora/site/source/documentation/0.19.1/operations/configuration.md Tue Apr 3 23:54:44 2018 @@ -0,0 +1,339 @@ +# Scheduler Configuration + +The Aurora scheduler can take a variety of configuration options through command-line arguments. +Examples are available under `examples/scheduler/`. For a list of available Aurora flags and their +documentation, see [Scheduler Configuration Reference](../../reference/scheduler-configuration/). + + +## A Note on Configuration +Like Mesos, Aurora uses command-line flags for runtime configuration. As such the Aurora +"configuration file" is typically a `scheduler.sh` shell script of the form. + + #!/bin/bash + AURORA_HOME=/usr/local/aurora-scheduler + + # Flags controlling the JVM. + JAVA_OPTS=( + -Xmx2g + -Xms2g + # GC tuning, etc. + ) + + # Flags controlling the scheduler. + AURORA_FLAGS=( + # Port for client RPCs and the web UI + -http_port=8081 + # Log configuration, etc. + ) + + # Environment variables controlling libmesos + export JAVA_HOME=... + export GLOG_v=1 + export LIBPROCESS_PORT=8083 + export LIBPROCESS_IP=192.168.33.7 + + JAVA_OPTS="${JAVA_OPTS[*]}" exec "$AURORA_HOME/bin/aurora-scheduler" "${AURORA_FLAGS[@]}" + +That way Aurora's current flags are visible in `ps` and in the `/vars` admin endpoint. + + +## JVM Configuration + +JVM settings are dependent on your environment and cluster size. They might require +custom tuning. As a starting point, we recommend: + +* Ensure the initial (`-Xms`) and maximum (`-Xmx`) heap size are idential to prevent heap resizing + at runtime. +* Either `-XX:+UseConcMarkSweepGC` or `-XX:+UseG1GC -XX:+UseStringDeduplication` are + sane defaults for the garbage collector. +* `-Djava.net.preferIPv4Stack=true` makes sense in most cases as well. + + +## Network Configuration + +By default, Aurora binds to all interfaces and auto-discovers its hostname. To reduce ambiguity +it helps to hardcode them though: + + -http_port=8081 + -ip=192.168.33.7 + -hostname="aurora1.us-east1.example.org" + +Two environment variables control the ip and port for the communication with the Mesos master +and for the replicated log used by Aurora: + + export LIBPROCESS_PORT=8083 + export LIBPROCESS_IP=192.168.33.7 + +It is important that those can be reached from all Mesos master and Aurora scheduler instances. + + +## Replicated Log Configuration + +Aurora schedulers use ZooKeeper to discover log replicas and elect a leader. Only one scheduler is +leader at a given time - the other schedulers follow log writes and prepare to take over as leader +but do not communicate with the Mesos master. Either 3 or 5 schedulers are recommended in a +production deployment depending on failure tolerance and they must have persistent storage. + +Below is a summary of scheduler storage configuration flags that either don't have default values +or require attention before deploying in a production environment. + +### `-native_log_quorum_size` +Defines the Mesos replicated log quorum size. In a cluster with `N` schedulers, the flag +`-native_log_quorum_size` should be set to `floor(N/2) + 1`. So in a cluster with 1 scheduler +it should be set to `1`, in a cluster with 3 it should be set to `2`, and in a cluster of 5 it +should be set to `3`. + + Number of schedulers (N) | ```-native_log_quorum_size``` setting (```floor(N/2) + 1```) + ------------------------ | ------------------------------------------------------------- + 1 | 1 + 3 | 2 + 5 | 3 + 7 | 4 + +*Incorrectly setting this flag will cause data corruption to occur!* + +### `-native_log_file_path` +Location of the Mesos replicated log files. For optimal and consistent performance, consider +allocating a dedicated disk (preferably SSD) for the replicated log. Ensure that this disk is not +used by anything else (e.g. no process logging) and in particular that it is a real disk +and not just a partition. + +Even when a dedicated disk is used, switching from `CFQ` to `deadline` I/O scheduler of Linux kernel +can furthermore help with storage performance in Aurora ([see this ticket for details](https://issues.apache.org/jira/browse/AURORA-1211)). + +### `-native_log_zk_group_path` +ZooKeeper path used for Mesos replicated log quorum discovery. + +See [code](https://github.com/apache/aurora/blob/rel/0.19.1/src/main/java/org/apache/aurora/scheduler/log/mesos/MesosLogStreamModule.java) for +other available Mesos replicated log configuration options and default values. + +### Changing the Quorum Size +Special care needs to be taken when changing the size of the Aurora scheduler quorum. +Since Aurora uses a Mesos replicated log, similar steps need to be followed as when +[changing the Mesos quorum size](http://mesos.apache.org/documentation/latest/operational-guide). + +As a preparation, increase `-native_log_quorum_size` on each existing scheduler and restart them. +When updating from 3 to 5 schedulers, the quorum size would grow from 2 to 3. + +When starting the new schedulers, use the `-native_log_quorum_size` set to the new value. Failing to +first increase the quorum size on running schedulers can in some cases result in corruption +or truncating of the replicated log used by Aurora. In that case, see the documentation on +[recovering from backup](../backup-restore/). + + +## Backup Configuration + +Configuration options for the Aurora scheduler backup manager. + +* `-backup_interval`: The interval on which the scheduler writes local storage backups. + The default is every hour. +* `-backup_dir`: Directory to write backups to. As stated above, this should not be co-located on the + same disk as the replicated log. +* `-max_saved_backups`: Maximum number of backups to retain before deleting the oldest backup(s). + + +## Resource Isolation + +For proper CPU, memory, and disk isolation as mentioned in our [enduser documentation](../../features/resource-isolation/), +we recommend to add the following isolators to the `--isolation` flag of the Mesos agent: + +* `cgroups/cpu` +* `cgroups/mem` +* `disk/du` + +In addition, we recommend to set the following [agent flags](http://mesos.apache.org/documentation/latest/configuration/): + +* `--cgroups_limit_swap` to enable memory limits on both memory and swap instead of just memory. + Alternatively, you could disable swap on your agent hosts. +* `--cgroups_enable_cfs` to enable hard limits on CPU resources via the CFS bandwidth limiting + feature. +* `--enforce_container_disk_quota` to enable disk quota enforcement for containers. + +To enable the optional GPU support in Mesos, please see the GPU related flags in the +[Mesos configuration](http://mesos.apache.org/documentation/latest/configuration/). +To enable the corresponding feature in Aurora, you have to start the scheduler with the +flag + + -allow_gpu_resource=true + +If you want to use revocable resources, first follow the +[Mesos oversubscription documentation](http://mesos.apache.org/documentation/latest/oversubscription/) +and then set set this Aurora scheduler flag to allow receiving revocable Mesos offers: + + -receive_revocable_resources=true + +Both CPUs and RAM are supported as revocable resources. The former is enabled by the default, +the latter needs to be enabled via: + + -enable_revocable_ram=true + +Unless you want to use the [default](https://github.com/apache/aurora/blob/rel/0.19.1/src/main/resources/org/apache/aurora/scheduler/tiers.json) +tier configuration, you will also have to specify a file path: + + -tier_config=path/to/tiers/config.json + + +## Multi-Framework Setup + +Aurora holds onto Mesos offers in order to provide efficient scheduling and +[preemption](../../features/multitenancy/#preemption). This is problematic in multi-framework +environments as Aurora might starve other frameworks. + +With a downside of increased scheduling latency, Aurora can be configured to be more cooperative: + +* Lowering `-min_offer_hold_time` (e.g. to `1mins`) can ensure unused offers are returned back to + Mesos more frequently. +* Increasing `-offer_filter_duration` (e.g to `30secs`) will instruct Mesos + not to re-offer rejected resources for the given duration. + +Setting a [minimum amount of resources](http://mesos.apache.org/documentation/latest/quota/) for +each Mesos role can furthermore help to ensure no framework is starved entirely. + + +## Containers + +Both the Mesos and Docker containerizers require configuration of the Mesos agent. + +### Mesos Containerizer + +The minimal agent configuration requires to enable Docker and Appc image support for the Mesos +containerizer: + + --containerizers=mesos + --image_providers=appc,docker + --isolation=filesystem/linux,docker/runtime # as an addition to your other isolators + +Further details can be found in the corresponding [Mesos documentation](http://mesos.apache.org/documentation/latest/container-image/). + +### Docker Containerizer + +The [Docker containerizer](http://mesos.apache.org/documentation/latest/docker-containerizer/) +requires the Docker engine is installed on each agent host. In addition, it must be enabled on the +Mesos agents by launching them with the option: + + --containerizers=mesos,docker + +If you would like to run a container with a read-only filesystem, it may also be necessary to use +the scheduler flag `-thermos_home_in_sandbox` in order to set HOME to the sandbox +before the executor runs. This will make sure that the executor/runner PEX extractions happens +inside of the sandbox instead of the container filesystem root. + +If you would like to supply your own parameters to `docker run` when launching jobs in docker +containers, you may use the following flags: + + -allow_docker_parameters + -default_docker_parameters + +`-allow_docker_parameters` controls whether or not users may pass their own configuration parameters +through the job configuration files. If set to `false` (the default), the scheduler will reject +jobs with custom parameters. *NOTE*: this setting should be used with caution as it allows any job +owner to specify any parameters they wish, including those that may introduce security concerns +(`privileged=true`, for example). + +`-default_docker_parameters` allows a cluster operator to specify a universal set of parameters that +should be used for every container that does not have parameters explicitly configured at the job +level. The argument accepts a multimap format: + + -default_docker_parameters="read-only=true,tmpfs=/tmp,tmpfs=/run" + +### Common Options + +The following Aurora options work for both containerizers. + +A scheduler flag, `-global_container_mounts` allows mounting paths from the host (i.e the agent machine) +into all containers on that host. The format is a comma separated list of host_path:container_path[:mode] +tuples. For example `-global_container_mounts=/opt/secret_keys_dir:/mnt/secret_keys_dir:ro` mounts +`/opt/secret_keys_dir` from the agents into all launched containers. Valid modes are `ro` and `rw`. + + +## Thermos Process Logs + +### Log destination +By default, Thermos will write process stdout/stderr to log files in the sandbox. Process object +configuration allows specifying alternate log file destinations like streamed stdout/stderr or +suppression of all log output. Default behavior can be configured for the entire cluster with the +following flag (through the `-thermos_executor_flags` argument to the Aurora scheduler): + + --runner-logger-destination=both + +`both` configuration will send logs to files and stream to parent stdout/stderr outputs. + +See [Configuration Reference](../../reference/configuration/#logger) for all destination options. + +### Log rotation +By default, Thermos will not rotate the stdout/stderr logs from child processes and they will grow +without bound. An individual user may change this behavior via configuration on the Process object, +but it may also be desirable to change the default configuration for the entire cluster. +In order to enable rotation by default, the following flags can be applied to Thermos (through the +`-thermos_executor_flags` argument to the Aurora scheduler): + + --runner-logger-mode=rotate + --runner-rotate-log-size-mb=100 + --runner-rotate-log-backups=10 + +In the above example, each instance of the Thermos runner will rotate stderr/stdout logs once they +reach 100 MiB in size and keep a maximum of 10 backups. If a user has provided a custom setting for +their process, it will override these default settings. + + +## Thermos Executor Wrapper + +If you need to do computation before starting the Thermos executor (for example, setting a different +`--announcer-hostname` parameter for every executor), then the Thermos executor should be invoked +inside a wrapper script. In such a case, the aurora scheduler should be started with +`-thermos_executor_path` pointing to the wrapper script and `-thermos_executor_resources` set to a +comma separated string of all the resources that should be copied into the sandbox (including the +original Thermos executor). Ensure the wrapper script does not access resources outside of the +sandbox, as when the script is run from within a Docker container those resources may not exist. + +For example, to wrap the executor inside a simple wrapper, the scheduler will be started like this +`-thermos_executor_path=/path/to/wrapper.sh -thermos_executor_resources=/usr/share/aurora/bin/thermos_executor.pex` + +## Custom Executors + +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. + +For more information on this feature please see the custom executors [documentation](../../features/custom-executors/). + +## A note on increasing executor overhead + +Increasing executor overhead on an existing cluster, whether it be for custom executors or for Thermos, +will result in degraded preemption performance until all task which began life with the previous +executor configuration with less overhead are preempted/restarted. + +## Controlling MTTA via Update Affinity + +When there is high resource contention in your cluster you may experience noticably elevated job update +times, as well as high task churn across the cluster. This is due to Aurora's first-fit scheduling +algorithm. To alleviate this, you can enable update affinity where the Scheduler will make a best-effort +attempt to reuse the same agent for the updated task (so long as the resources for the job are not being +increased). + +To enable this in the Scheduler, you can set the following options: + + --enable_update_affinity=true + --update_affinity_reservation_hold_time=3mins + +You will need to tune the hold time to match the behavior you see in your cluster. If you have extremely +high update throughput, you might have to extend it as processing updates could easily add significant +delays between scheduling attempts. You may also have to tune scheduling parameters to achieve the +throughput you need in your cluster. Some relevant settings (with defaults) are: + + --max_schedule_attempts_per_sec=40 + --initial_schedule_penalty=1secs + --max_schedule_penalty=1mins + --scheduling_max_batch_size=3 + --max_tasks_per_schedule_attempt=5 + +There are metrics exposed by the Scheduler which can provide guidance on where the bottleneck is. +Example metrics to look at: + + - schedule_attempts_blocks (if this number is greater than 0, then task throughput is hitting + limits controlled by --max_scheduler_attempts_per_sec) + - scheduled_task_penalty_* (metrics around scheduling penalties for tasks, if the numbers here are high + then you could have high contention for resources) + +Most likely you'll run into limits with the number of update instances that can be processed per minute +before you run into any other limits. So if your total work done per minute starts to exceed 2k instances, +you may need to extend the update_affinity_reservation_hold_time. Added: aurora/site/source/documentation/0.19.1/operations/installation.md URL: http://svn.apache.org/viewvc/aurora/site/source/documentation/0.19.1/operations/installation.md?rev=1828294&view=auto ============================================================================== --- aurora/site/source/documentation/0.19.1/operations/installation.md (added) +++ aurora/site/source/documentation/0.19.1/operations/installation.md Tue Apr 3 23:54:44 2018 @@ -0,0 +1,256 @@ +# Installing Aurora + +Source and binary distributions can be found on our +[downloads](https://aurora.apache.org/downloads/) page. Installing from binary packages is +recommended for most. + +- [Installing the scheduler](#installing-the-scheduler) +- [Installing worker components](#installing-worker-components) +- [Installing the client](#installing-the-client) +- [Installing Mesos](#installing-mesos) +- [Troubleshooting](#troubleshooting) + +If our binay packages don't suite you, our package build toolchain makes it easy to build your +own packages. See the [instructions](https://github.com/apache/aurora-packaging) to learn how. + + +## Machine profiles + +Given that many of these components communicate over the network, there are numerous ways you could +assemble them to create an Aurora cluster. The simplest way is to think in terms of three machine +profiles: + +### Coordinator +**Components**: ZooKeeper, Aurora scheduler, Mesos master + +A small number of machines (typically 3 or 5) responsible for cluster orchestration. In most cases +it is fine to co-locate these components in anything but very large clusters (> 1000 machines). +Beyond that point, operators will likely want to manage these services on separate machines. +In particular, you will want to use separate ZooKeeper ensembles for leader election and +service discovery. Otherwise a service discovery error or outage can take down the entire cluster. + +In practice, 5 coordinators have been shown to reliably manage clusters with tens of thousands of +machines. + +### Worker +**Components**: Aurora executor, Aurora observer, Mesos agent + +The bulk of the cluster, where services will actually run. + +### Client +**Components**: Aurora client, Aurora admin client + +Any machines that users submit jobs from. + + +## Installing the scheduler +### Ubuntu Trusty + +1. Install Mesos + Skip down to [install mesos](#mesos-on-ubuntu-trusty), then run: + + sudo start mesos-master + +2. Install ZooKeeper + + sudo apt-get install -y zookeeperd + +3. Install the Aurora scheduler + + sudo add-apt-repository -y ppa:openjdk-r/ppa + sudo apt-get update + sudo apt-get install -y openjdk-8-jre-headless wget + + sudo update-alternatives --set java /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java + + wget -c https://apache.bintray.com/aurora/ubuntu-trusty/aurora-scheduler_0.17.0_amd64.deb + sudo dpkg -i aurora-scheduler_0.17.0_amd64.deb + +### CentOS 7 + +1. Install Mesos + Skip down to [install mesos](#mesos-on-centos-7), then run: + + sudo systemctl start mesos-master + +2. Install ZooKeeper + + sudo rpm -Uvh https://archive.cloudera.com/cdh4/one-click-install/redhat/6/x86_64/cloudera-cdh-4-0.x86_64.rpm + sudo yum install -y java-1.8.0-openjdk-headless zookeeper-server + + sudo service zookeeper-server init + sudo systemctl start zookeeper-server + +3. Install the Aurora scheduler + + sudo yum install -y wget + + wget -c https://apache.bintray.com/aurora/centos-7/aurora-scheduler-0.17.0-1.el7.centos.aurora.x86_64.rpm + sudo yum install -y aurora-scheduler-0.17.0-1.el7.centos.aurora.x86_64.rpm + +### Finalizing +By default, the scheduler will start in an uninitialized mode. This is because external +coordination is necessary to be certain operator error does not result in a quorum of schedulers +starting up and believing their databases are empty when in fact they should be re-joining a +cluster. + +Because of this, a fresh install of the scheduler will need intervention to start up. First, +stop the scheduler service. +Ubuntu: `sudo stop aurora-scheduler` +CentOS: `sudo systemctl stop aurora` + +Now initialize the database: + + sudo -u aurora mkdir -p /var/lib/aurora/scheduler/db + sudo -u aurora mesos-log initialize --path=/var/lib/aurora/scheduler/db + +Now you can start the scheduler back up. +Ubuntu: `sudo start aurora-scheduler` +CentOS: `sudo systemctl start aurora` + + +## Installing worker components +### Ubuntu Trusty + +1. Install Mesos + Skip down to [install mesos](#mesos-on-ubuntu-trusty), then run: + + start mesos-slave + +2. Install Aurora executor and observer + + sudo apt-get install -y python2.7 wget + + # NOTE: This appears to be a missing dependency of the mesos deb package and is needed + # for the python mesos native bindings. + sudo apt-get -y install libcurl4-nss-dev + + wget -c https://apache.bintray.com/aurora/ubuntu-trusty/aurora-executor_0.17.0_amd64.deb + sudo dpkg -i aurora-executor_0.17.0_amd64.deb + +### CentOS 7 + +1. Install Mesos + Skip down to [install mesos](#mesos-on-centos-7), then run: + + sudo systemctl start mesos-slave + +2. Install Aurora executor and observer + + sudo yum install -y python2 wget + + wget -c https://apache.bintray.com/aurora/centos-7/aurora-executor-0.17.0-1.el7.centos.aurora.x86_64.rpm + sudo yum install -y aurora-executor-0.17.0-1.el7.centos.aurora.x86_64.rpm + +### Worker Configuration +The executor typically does not require configuration. Command line arguments can +be passed to the executor using a command line argument on the scheduler. + +The observer needs to be configured to look at the correct mesos directory in order to find task +sandboxes. You should 1st find the Mesos working directory by looking for the Mesos agent +`--work_dir` flag. You should see something like: + + ps -eocmd | grep "mesos-slave" | grep -v grep | tr ' ' '\n' | grep "\--work_dir" + --work_dir=/var/lib/mesos + +If the flag is not set, you can view the default value like so: + + mesos-slave --help + Usage: mesos-slave [options] + + ... + --work_dir=VALUE Directory path to place framework work directories + (default: /tmp/mesos) + ... + +The value you find for `--work_dir`, `/var/lib/mesos` in this example, should match the Aurora +observer value for `--mesos-root`. You can look for that setting in a similar way on a worker +node by grepping for `thermos_observer` and `--mesos-root`. If the flag is not set, you can view +the default value like so: + + thermos_observer -h + Options: + ... + --mesos-root=MESOS_ROOT + The mesos root directory to search for Thermos + executor sandboxes [default: /var/lib/mesos] + ... + +In this case the default is `/var/lib/mesos` and we have a match. If there is no match, you can +either adjust the mesos-master start script(s) and restart the master(s) or else adjust the +Aurora observer start scripts and restart the observers. To adjust the Aurora observer: + +#### Ubuntu Trusty + + sudo sh -c 'echo "MESOS_ROOT=/tmp/mesos" >> /etc/default/thermos' + +#### CentOS 7 + +Make an edit to add the `--mesos-root` flag resulting in something like: + + grep -A5 OBSERVER_ARGS /etc/sysconfig/thermos + OBSERVER_ARGS=( + --port=1338 + --mesos-root=/tmp/mesos + --log_to_disk=NONE + --log_to_stderr=google:INFO + ) + + +## Installing the client +### Ubuntu Trusty + + sudo apt-get install -y python2.7 wget + + wget -c https://apache.bintray.com/aurora/ubuntu-trusty/aurora-tools_0.17.0_amd64.deb + sudo dpkg -i aurora-tools_0.17.0_amd64.deb + +### CentOS 7 + + sudo yum install -y python2 wget + + wget -c https://apache.bintray.com/aurora/centos-7/aurora-tools-0.17.0-1.el7.centos.aurora.x86_64.rpm + sudo yum install -y aurora-tools-0.17.0-1.el7.centos.aurora.x86_64.rpm + +### Mac OS X + + brew upgrade + brew install aurora-cli + +### Client Configuration +Client configuration lives in a json file that describes the clusters available and how to reach +them. By default this file is at `/etc/aurora/clusters.json`. + +Jobs may be submitted to the scheduler using the client, and are described with +[job configurations](../../reference/configuration/) expressed in `.aurora` files. Typically you will +maintain a single job configuration file to describe one or more deployment environments (e.g. +dev, test, prod) for a production job. + + +## Installing Mesos +Mesos uses a single package for the Mesos master and agent. As a result, the package dependencies +are identical for both. + +### Mesos on Ubuntu Trusty + + sudo apt-key adv --keyserver keyserver.ubuntu.com --recv E56151BF + DISTRO=$(lsb_release -is | tr '[:upper:]' '[:lower:]') + CODENAME=$(lsb_release -cs) + + echo "deb http://repos.mesosphere.io/${DISTRO} ${CODENAME} main" | \ + sudo tee /etc/apt/sources.list.d/mesosphere.list + sudo apt-get -y update + + # Use `apt-cache showpkg mesos | grep [version]` to find the exact version. + sudo apt-get -y install mesos=1.1.0-2.0.107.ubuntu1404_amd64.deb + +### Mesos on CentOS 7 + + sudo rpm -Uvh https://repos.mesosphere.io/el/7/noarch/RPMS/mesosphere-el-repo-7-1.noarch.rpm + sudo yum -y install mesos-1.1.0 + + +## Troubleshooting + +So you've started your first cluster and are running into some issues? We've collected some common +stumbling blocks and solutions in our [Troubleshooting guide](../troubleshooting/) to help get you moving.
