Lava Dispatcher Design¶

This is the developer documentation for the new V2 dispatcher design. See Advanced Use Cases for information for lab administrators and users of the new design.

The refactoring takes place alongside the V1 dispatcher and existing JSON jobs are unaffected. A migration will take place where individual devices are configured for pipeline support and individual jobs are then re-written using the pipeline_schema. The administrator of each instance will be able to manage their own migration and at some point after validation.linaro.org has completed the migration of all devices to pipeline support, the support for the current dispatcher will be removed.

validation.linaro.org supports LAVA V2 pipeline submissions as of the 2016.2 release and the V2 support will continue to expand in subsequent releases.

The LAVA developers use a staging instance for testing of the current master branch and release candidates for the next production release.

Pipeline Architecture¶

Principal changes¶

Database isolation - Only the master daemon has a connection to the database. This simplifies the architecture and avoids the use of fault-intolerant database connections to remote workers.
Drop use of SSHFS between workers and master - this was awkward to configure and problematic over external connections.
Move configuration onto the master - The worker becomes a simple worker which receives all configuration and tasks from the master.

Objectives¶

The new dispatcher design is intended to make it easier to adapt the dispatcher flow to new boards, new mechanisms and new deployments. It also shifts support to do less work on the dispatcher, make fewer assumptions about the test in the dispatcher configuration and put more flexibility into the hands of the test writer.

Note

The new code is still developing, some areas are absent, some areas will change substantially before the migration completes. There may be changes to the submission formats but these will be announced on the lava-announce mailing list.

From 2015.8 onwards the sample jobs supporting the unit tests conform to the LAVA schema.

Design¶

Start with a Job which is broken up into a Deployment, a Boot and a Test class. Results are transmitted live during any part of the job.


Deployment
	DeployAction
		DownloadAction
		ChecksumAction
		MountAction
		CustomiseAction
		TestDefAction
		UnmountAction
	BootAction
	TestAction

The Job manages the Actions using a Pipeline structure. Actions can specialize actions by using internal pipelines and an Action can include support for retries and other logical functions:

DownloadAction
	HttpDownloadAction
	FileDownloadAction

If a Job includes one or more Test definitions, the Deployment can then extend the Deployment to overlay the LAVA test scripts without needing to mount the image twice:

DeployAction
	OverlayAction
		MultinodeOverlayAction
		LMPOverlayAction

The TestDefinitionAction has a similar structure with specialist tasks being handed off to cope with particular tools:

TestDefinitionAction
	RepoAction
		GitRepoAction
		UrlRepoAction

Following the code flow¶

Filename	Role
lava_dispatcher/actions/commands.py	Command line arguments, call to YAML parser
lava_dispatcher/device.py	YAML Parser to create the Device object
lava_dispatcher/parser.py	YAML Parser to create the Job object
….actions/deploy/	Handlers for different deployment strategies
….actions/boot/	Handlers for different boot strategies
….actions/test/	Handlers for different LavaTestShell strategies
….actions/deploy/image.py	DeployImages strategy creates DeployImagesAction
….actions/deploy/image.py	DeployImagesAction.populate adds deployment actions to the Job pipeline
*repeat for each strategy*	each `populate` function adds more Actions
….action.py	`Pipeline.run_actions()` to start

The deployment is determined from the device_type specified in the Job (or the device_type of the specified target) by reading the list of support methods from the device_types YAML configuration.

Each Action can define an internal pipeline and add sub-actions in the Action.populate function.

Particular Logic Actions (like RetryAction) require an internal pipeline so that all actions added to that pipeline can be retried in the same order. (Remember that actions must be idempotent.) Actions which fail with a JobError or InfrastructureError can trigger Diagnostic actions. See Logical actions.

actions:
  deploy:
    allow:
      - image
  boot:
    allow:
      - image

This then matches the python class structure:

actions/
   deploy/
       image.py

The class defines the list of Action classes needed to implement this deployment. See also Dispatcher Action Reference.

Pipeline construction and flow¶

The pipeline is a FIFO and has branches which are handled as a tree walk. The top level object is the job, based on the YAML definition supplied by the lava-master. The definition is processed by the scheduler and the submission interface with information specific to the actual device. The processed definition is parsed to generate the top level pipeline and strategy classes. Each strategy class adds a top level action to the top level pipeline. The top level action then populates branches containing more actions.

Actions are populated, validated and executed in strict order. The next action in any branch waits until all branches of the preceding action have completed. Populating an action in a pipeline creates a level string, e.g. all actions in level 1.2.1, including all actions in sublevel 1.2.1.2 are executed before the pipeline moves on to processing level 1.3 or 2:

Deploy (1)
   |
   \___ 1.1
   |
   \ __ 1.2
   |     |
   |     \_ 1.2.1
   |     |   |
   |     |   \_ 1.2.1.1
   |     |   |
   |     |   \_ 1.2.1.2
   |     |         |
   |     |         \__ 1.2.1.2.1
   |     |
   |     \__1.2.2
   |
   \____1.3
   |
  Boot (2)
   |
   \_ 2.1
   |
   \_ 2.2

One device per job. One top level pipeline per job
- loads only the configuration required for this one job.
A NewDevice is built from the target specified (commands.py)
A Job is generated from the YAML by the parser.
The top level Pipeline is constructed by the parser.
Strategy classes are initialized by the parser
1. Strategy classes add the top level Action for that strategy to the top level pipeline.
2. Top level pipeline calls populate() on each top level Action added.
  1. Each Action.populate() function may construct one internal pipeline, based on parameters.
  2. internal pipelines call populate() on each Action added.
  3. A sublevel is set for each action in the internal pipeline. Level 1 creates 1.1 and level 2.3.2 creates 2.3.2.1.
Parser waits while each Strategy completes branch population.
Parser adds the FinalizeAction to the top-level pipeline
Loghandlers are set up
Job validates the completed pipeline
1. Dynamic data can be added to the context
If --validate not specified, the job runs.
1. Each run() function can add dynamic data to the context and/or results to the pipeline.
2. Pipeline walks along the branches, executing actions.
Job ends, check for errors
Completed pipeline is available.

Using strategy classes¶

Strategies are ways of meeting the requirements of the submitted job within the limits of available devices and code support.

If an internal pipeline would need to allow for optional actions, those actions still need to be idempotent. Therefore, the pipeline can include all actions, with each action being responsible for checking whether anything actually needs to be done. The populate function should avoid using conditionals. An explicit select function can be used instead.

Whenever there is a need for a particular job to use a different Action based solely on job parameters or device configuration, that decision should occur in the Strategy selection using classmethod support.

Where a class is used in lots of different strategies, identify whether there is a match between particular strategies always needing particular options within the class. At this point, the class can be split and particular strategies use a specialized class implementing the optional behavior and calling down to the base class for the rest.

If there is no clear match, for example in testdef.py where any particular job could use a different VCS or URL without actually being a different strategy, a select function is preferable. A select handler allows the pipeline to contain only classes supporting git repositories when only git repositories are in use for that job.

The list of available strategies can be determined in the codebase from the module imports in the strategies.py file for each action type.

This results in more classes but a cleaner (and more predictable) pipeline construction.

Lava test shell scripts¶

Note

See LAVA review criteria - it is a mistake to think of the LAVA test support scripts as an overlay - the scripts are an extension to the test. Wherever possible, current deployments are being changed to supply the extensions alongside the deployment instead of overlaying, and thereby altering, the deployment.

The LAVA scripts are a standard addition to a LAVA test and are handled as a single unit. Using idempotent actions, the test script extension can support LMP or MultiNode or other custom requirements without requiring this support to be added to all tests. The extensions are created during the deploy strategy and specific deployments can override the ApplyExtensionAction to unpack the extension tarball alongside the test during the deployment phase and then mount the extension inside the image. The tarball itself remains in the output directory and becomes part of the test records. The checksum of the overlay is added to the test job log.

Pipeline error handling¶

RuntimeError Exception¶

Runtime errors include:

Parser fails to handle device configuration
Parser fails to handle submission YAML
Parser fails to locate a Strategy class for the Job.
Code errors in Action classes cause Pipeline to fail.
Errors in YAML cause errors upon pipeline validation.

Each runtime error is a bug in the code - wherever possible, implement a unit test to prevent regressions.

InfrastructureError Exception¶

Infrastructure errors include:

Missing dependencies on the dispatcher
Device configuration errors

JobError Exception¶

Job errors include:

Failed to find the specified URL.
Failed in an operation to create the necessary extensions.

TestError Exception¶

Test errors include:

Failed to handle a signal generated by the device
Failed to parse a test case

Result bundle identifiers¶

Old style result bundles are assigned a text based UUID during submission. This has several issues:

The UUID is not sequential or predictable, so finding this one, the next one or the previous one requires a database lookup for each. The new dispatcher model will not have a persistent database connection.
The UUID is not available to the dispatcher while running the job, so cannot be cross-referenced to logs inside the job.
The UUID makes the final URL of individual test results overly long, unmemorable and complex, especially as the test run is also given a separate UUID in the old dispatcher model.

The new dispatcher creates a pipeline where every action within the pipeline is guaranteed to have a unique level string which is strictly sequential, related directly to the type of action and shorter than a UUID. To make a pipeline result unique on a per instance basis, the only requirement is that the result includes the JobID which is a sequential number, passed to the job in the submission YAML. This could also have been a UUID but the JobID is already a unique ID for this instance.

When bundles are downloaded, the database query will need to assign a UUID to that downloaded file but the file will also include the job number and the query can also insert the source of the bundle in a comment in the YAML.

Secondary media¶

With the migration from master images on an SD card to dynamic master images over NFS, other possibilities arise from the refactoring.

Deploy a ramdisk, boot and deploy an entire image to a USB key, boot and direct bootloader at USB filesystem, including kernel and initrd.
Deploy an NFS system, boot and bootstrap an image to SATA, boot and direct bootloader at SATA filesystem, including kernel and initrd.
Deploy using a script written by the test author (e.g. debootstrap) which is installed in the initial deployment. Parameters for the script need to be contained within the test image.

Device configuration design¶

Device configuration, as received by lava_dispatch has moved to YAML and the database device configuration has moved to Jinja2 templates. This method has a much larger scope of possible methods, related to the pipeline strategies as well as allowing simple overrides and reuse of common device configuration stanzas.

There is no need for the device configuration to include the hostname in the YAML as there is nothing on the dispatcher to check against - the dispatcher uses the command line arguments and the supplied device configuration. The configuration includes all the data the dispatcher needs to be able to run the job on the device attached to the specified ports.

The device type configuration on the dispatcher is replaced by a device type template on the server which is used to generate the YAML device configuration sent to the dispatcher.

Device Dictionary¶

The normal admin flow for individual devices will be to make changes to the device dictionary of that device. In time, an editable interface will exist within the admin interface. Initially, changes to the dictionary are made from the command line with details being available in a read-only view in the admin interface.

The device dictionary acts as a set of variables inside the template, in a very similar manner to how Django handles HTML templates. In turn, a device type template will extend a base template.

It is a bug in the template if a missing value causes a broken device configuration to be generated. Values which are not included in the specified template will be ignored.

Once the device dictionary has been populated, the scheduler can be told that the device is a pipeline device in the admin interface.

Note

Several parts of this process still need helpers and tools or may give unexpected errors - there is a lot of ongoing work in this area.

Exporting an existing device dictionary¶

If the local instance has a working pipeline device called mypanda, the device dictionary can be exported as a Jinja2 child template which extends a device type jinja template:

$ sudo lava-server manage device-dictionary --hostname mypanda --export
{% extends 'panda.jinja2' %}
{% set power_off_command = '/usr/bin/pduclient --daemon tweetypie --hostname pdu --command off --port 08' %}
{% set hard_reset_command = '/usr/bin/pduclient --daemon tweetypie --hostname pdu --command reboot --port 08' %}
{% set connection_list = [‘uart0’] %}
{% set connection_commands = {‘uart0’: ‘telnet dispatcher01 7001’} %}
{% set connection_tags = {‘uart0’: [‘primary’, 'telnet']} %}
{% set power_on_command = '/usr/bin/pduclient --daemon tweetypie --hostname pdu --command on --port 08' %}

This dictionary declares that the device inherits the rest of the device configuration from the panda device type. Settings specific to this one device are then specified.

Reviewing an existing device dictionary¶

To populate the full configuration using the device dictionary and the associated templates, use the review option:

$ sudo lava-server manage device-dictionary --hostname mypanda --review

Example device configuration review¶

device_type: beaglebone-black
commands:
  connect: telnet localhost 6000
  hard_reset: /usr/bin/pduclient --daemon localhost --hostname pdu --command reboot --port 08
  power_off: /usr/bin/pduclient --daemon localhost --hostname pdu --command off --port 08
  power_on: /usr/bin/pduclient --daemon localhost --hostname pdu --command on --port 08

parameters:
 bootm:
  kernel: '0x80200000'
  ramdisk: '0x81600000'
  dtb: '0x815f0000'
 bootz:
  kernel: '0x81000000'
  ramdisk: '0x82000000'
  dtb: '0x81f00000'

actions:
 deploy:
   # list of deployment methods which this device supports
   methods:
     # - image # not ready yet
     - tftp

 boot:
   # list of boot methods which this device supports.
   methods:
     - u-boot:
         parameters:
           bootloader_prompt: U-Boot
           boot_message: Booting Linux
           # interrupt: # character needed to interrupt u-boot, single whitespace by default
         # method specific stanza
         oe:
           commands:
           - setenv initrd_high '0xffffffff'
           - setenv fdt_high '0xffffffff'
           - setenv bootcmd 'fatload mmc 0:3 0x80200000 uImage; fatload mmc 0:3 0x815f0000 board.dtb;
             bootm 0x80200000 - 0x815f0000'
           - setenv bootargs 'console=ttyO0,115200n8 root=/dev/mmcblk0p5 rootwait ro'
           - boot
         nfs:
           commands:
           - setenv autoload no
           - setenv initrd_high '0xffffffff'
           - setenv fdt_high '0xffffffff'
           - setenv kernel_addr_r '{KERNEL_ADDR}'
           - setenv initrd_addr_r '{RAMDISK_ADDR}'
           - setenv fdt_addr_r '{DTB_ADDR}'
           - setenv loadkernel 'tftp ${kernel_addr_r} {KERNEL}'
           - setenv loadinitrd 'tftp ${initrd_addr_r} {RAMDISK}; setenv initrd_size ${filesize}'
           - setenv loadfdt 'tftp ${fdt_addr_r} {DTB}'
           # this could be a pycharm bug or a YAML problem with colons. Use &#58; for now.
           # alternatively, construct the nfsroot argument from values.
           - setenv nfsargs 'setenv bootargs console=ttyO0,115200n8 root=/dev/nfs rw nfsroot={SERVER_IP}&#58;{NFSROOTFS},tcp,hard,intr ip=dhcp'
           - setenv bootcmd 'dhcp; setenv serverip {SERVER_IP}; run loadkernel; run loadinitrd; run loadfdt; run nfsargs; {BOOTX}'
           - boot
         ramdisk:
           commands:
           - setenv autoload no
           - setenv initrd_high '0xffffffff'
           - setenv fdt_high '0xffffffff'
           - setenv kernel_addr_r '{KERNEL_ADDR}'
           - setenv initrd_addr_r '{RAMDISK_ADDR}'
           - setenv fdt_addr_r '{DTB_ADDR}'
           - setenv loadkernel 'tftp ${kernel_addr_r} {KERNEL}'
           - setenv loadinitrd 'tftp ${initrd_addr_r} {RAMDISK}; setenv initrd_size ${filesize}'
           - setenv loadfdt 'tftp ${fdt_addr_r} {DTB}'
           - setenv bootargs 'console=ttyO0,115200n8 root=/dev/ram0 ip=dhcp'
           - setenv bootcmd 'dhcp; setenv serverip {SERVER_IP}; run loadkernel; run loadinitrd; run loadfdt; {BOOTX}'
           - boot

Importing configuration using a known template¶

To add or update the device dictionary, a file using the same syntax as the export content can be imported into the database:

$ sudo lava-server manage device-dictionary --hostname mypanda --import mypanda.yaml

(The file extension is unnecessary and the content is not actually YAML but will be rendered as YAML when the templates are used.)

Creating a new template¶

Start with the base.yaml template and use the structure of that template to ensure that your template remains valid YAML.

Start with a complete device configuration (in YAML) which works on the lava-dispatch command line, then iterate over changes in the template to produce the same output.

Note

A helper is being planned for this step.

Running lava-run directly¶

lava-run can be used to execute test jobs locally on the worker without submitting the job or waiting for the scheduler. This is used during device integration and triage of infrastructure problems.

Caution

Ensure that the device is offline (health state Bad from a failed health check or manually set to health Maintenance) so that the scheduler does not try to start another test job whilst you are running a test job locally.

lava-run accepts a YAML file containing the device configuration which can be accessed from the download link on the device dictionary page. The absolute or relative path to the YAML file must be specified to the --device option. --output-dir must also be specified. Additional, a fake --job-id should be specified to satisfy lava-run:

$ sudo lava-run --job-id 0 --device devices/fred.yaml panda-ramdisk.yaml --output-dir=/tmp/test