Getting You Started

This section provides an overview for users about how to begin using the Genie harness. We recommend reading this section in order, to help users understand the fundamental building blocks to begin using Genie.

Note

The examples below are runnable. The only additional requirement is for users to have a pyATS testbed file with at least one device.

Set-up

The first step is to create a job file for executing the Genie harness.

# Import Genie run
from genie.harness.main import gRun


def main():

    # Set job file path to current directory
    test_path = os.path.dirname(os.path.abspath(__file__))

    gRun(mapping_datafile=os.path.join(test_path, 'mapping.yaml'))

A pyATS script is derived from the information provided to the Genie Harness with gRun.

An example of the mapping datafile contains:

devices:
    PE1:
     context: cli
     label: uut
     mapping:
        cli: vty
        yang: netconf

The pyATS YAML :testbed:`testbed <http>` file describes the testbed topology and the testbed’s devices. Any given device in the testbed topology can be configured to support various connection types such as: Console, Network Mangement VTY, NETCONF(YANG), and more in the future.

Each of those connections names are not standardize, so Genie cannot know which one to use for which context. The mapping file allows this mapping. It also allows to specify the context (cli, yang, etc) to be specified per device. In the above example, we are mapping the vty connection to be used for cli, and netconf connection for yang. The device name can either be the hostname of the device defined in the testbed file, or the device alias. They must be unique for each device.

Available mapping are : cli, yang, rest. More will be added in the future.

As Genie is event driven, the triggers are performed on a given device, which is known as the uut (Unit Under Test) for Genie. The uut is identified via the label field.

The context drives the management interface that will be used to connect to devices during execution.

To run the example above, create the mapping.yaml file in the same directory as the job file. Copy the code from example above and modify it to fit the requirement of your testbed, alias, and the context.

Note

In the mapping file, the key cli is mandatory and yang is optional.

Note

To have standardized mapping files between users, the first key under devices can either be the device hostname or the device alias given in the testbed file.

We can now execute the example with the following command:

easypy job.py -testbed_file <pyats testbed location>

This job does the following:

• Runs common setup

• Connects to a device

• Executes ‘show running-config’

• No testcase to run yet

• Runs common cleanup

• Executes ‘show running-config’

The first ‘show’ command in the common setup takes a snapshot of the configuration. The second ‘show’ command in the common cleanup takes another snapshot and compares them together to ensure the configuration remained the same.

Device Configuration

Genie supports multiple ways to apply configuration on the devices.

1. Manually applied on the devices before the run starts (Done by user)

The user can connect manually to the devices and apply any configuration wanted.

2. Automatically applied on the devices in the Common Setup/Cleanup with Tftp/Ftp/Scp

With the config_datafile argument, a config.yaml is provided. This file contains what configuration to apply on which device.

3. Automatically applied on the devices in the Common Setup/Cleanup using Jinja2 rendering

Using the jinja2_config argument, the configuration will be rendered with the key/value pairs from jinja2_arguments. The Unicon configure() service will be used to apply the configuration. You can pass additional arguments to the configure service using the configure_arguments key in the config section, e.g. you can enable bulk configure. The configuration is rendered using the load_jinja_template device API.

gRun(mapping_datafile=os.path.join(test_path, 'mapping.yaml'),
     runtime=runtime, config_datafile=os.path.join('configs.yaml'))

The configs.yaml datafile lists all of the configurations files.

devices:
    uut:
        1:
          config: /path/to/my/configuration
          sleep: 3
          invalid: ['overlaps', '(.*inval.*)']
        2:
          config: <full path>
          sleep: 2
          invalid: ['some words']
        3:
          jinja2_config: routing.j2
          jinja2_arguments:
            lstrip_blocks: true
            trim_blocks: true
            bgp_data:
                bgp_as: 100
                neighbor_ips: [
                    '1.1.1.1', '2.2.2.2'
                ]
          configure_arguments:
            bulk: True
            timeout: 180

Important

The configuration is applied in the numerical sequence specified in the YAML file, (1,2,..), as shown above.

Configurations are applied to devices in parallel using multiprocessing.

The configuration file is a typical show running style. After each configuration applied, a wait period is recommended to allow the configuration to stabilize; this is achieved with the key sleep, specified in seconds.

When applying configurations, we may see some error or warning messages which may or may not be safe to ignore. Any error or warning patterns specified in the invalid key will cause the configure subsection to fail if matched. The invalid key supports regex.

A server must be added to copy the configuration on the devices, the file_transfer_protocol sections explain what to add to the testbed datafile.

As an example, let’s create a sample device configuration file, named uut_config1.

interface Ethernet0/1
    no shutdown

interface Ethernet0/2
    no switchport

And modify the previously created configs.yaml file as follows:

devices:
    uut:
        1:
          config: <full/path/to/uut_config1>
          sleep: 3

Note

In case you need multiple configuration files, the number provide the sequence of the configuration.

Note

The configuration section is optional, as it can be prefered to manually configured the devices before the run is started.

3. Custom subsection to apply configuration on the device in any way

The subsections section explains how to add your own subsection and performs any action.

check_config

Once the devices are configured, Genie learns the configured state of the topology via check_config. The check_config subsection runs twice: first, during the common setup and then, during common cleanup.

In the common setup, check_config executes the show running command on all of the devices in the topology to create a snapshot of the configured state of the topology. If devices are specified in subsection yaml file, it will only perform check on these devices. example on sepecifying device for check_config in subsection yaml file:

setup:
    sections:
      check_config:
        method: genie.harness.commons.check_config
        parameters:
            devices:
              uut: None

In the common cleanup, check_config executes the show running command on all devices in the topology once again to create a second snapshot of the configured state of the topology. Afterwards, it compares the two snapshots to ensure that the configuration of the devices remained intact during the execution of the job.

Certain values, such as uptime, age, etc., collected in the check_config snapshots can dynamically change during the execution of the job. Users can add the exclude_config_check argument to the configs.yaml file to ignore comparisons of certain configurations. This argument accepts a String or Regular expression expression as an input and then skips comparison of any configuration matching the expression.

Let’s see an example of how to add the exclude_config_check argument:

devices:
    uut:
        1:
            config: <full path to config file>
            sleep: 5
            invalid : ['(.*ERROR.*)']
exclude_config_check: ['(.*description.*)']

PTS

As a recap, in the previous two sections we connected to our devices and then applied various configurations. We also confirmed that the configurations were applied without error.

Once the devices contain configurations, Genie can learn the state of the topology via PTS. A first round of profile snapshots of each feature are taken during the common setup and then a second round of profile snapshots are taken during the common cleanup. These snapshots are compared to those saved in the common setup to confirm that operational states did not change during the job’s execution.

PTS learns the configurations applied to a device by creating Genie Ops objects or parsed dictionary. These objects are snapshots of the operational state of the devices in the topology. Multiple commands (sent by cli/yang/xml) are executed to collect a feature’s state/operational information.

The user can specify which configured features Genie should learn using the pts_features argument. Each learned feature will create a subsection in the common setup with prefix profile_<feature_name>.

Let’s add the pts_features argument to gRun in our job file:

gRun(mapping_datafile=os.path.join(test_path, 'mapping.yaml'),
     runtime=runtime, config_datafile=os.path.join('configs.yaml'),
     pts_features=['ospf', 'hsrp', 'show ip ospf interface',
                  'show ip ospf interface vrf default'])

By default PTS will only run on the uut. It can be modified in the pts_datafile.yaml file that maps devices to the features listed in the pts_datafile argument as shown below.

Please copy the following code into a new file called pts_datafile.yaml.

extends: "%CALLABLE{genie.libs.sdk.genie_yamls.datafile(pts)}"

ospf:
    devices: ['uut', 'helper']
    exclude:
        - age
        - uptime

hsrp:
    devices: ['uut', 'helper']
    devices_attributes:
        uut:
            exclude:
                - next_hello_time
        helper:
            exclude:
                - next_hello_time
    exclude:
        - date

Note

Be sure to provide devices to each PTS that you would like to execute. If no devices are provided, PTS will not run

Certain values taken in the profile snapshot for each feature can dynamically change during execution of the script, such as uptime, age etc. The user can choose to exclude comparisons of these values by specifying them with the exclude key, as shown above. Each value can be excluded at the device level or the feature level.

Let’s add the pts_datafile argument to gRun in our job file.

gRun(mapping_datafile=os.path.join(test_path, 'mapping.yaml'),
     runtime=runtime, config_datafile=os.path.join('configs.yaml'),
     pts_features=['ospf', 'hsrp'],
     pts_datafile=os.path.join(test_path, 'pts_datafile'))

Note

Show command does not need to be added to the PTS file to run. By default they will use the same exclude keys as their Verification datafile corespondance.

Golden Config

In the previous sections, we configured our devices and learned the state of the topology. However, how can we be certain that the state of the topology is precisely the one we expected?

The pts_golden_config compares the profiles learned by PTS in the current run to a profile that has been verified to be a golden snapshot by your team. After each run, a file named pts is generated and saved to the pyATS archive directory. This file can then be saved to a fixed location. This file can then be provided as an argument to the job file via pts_golden_config argument as shown below.

Let’s add the pts_golden_config argument to gRun in our job file:

gRun(mapping_datafile=os.path.join(test_path, 'mapping.yaml'),
     runtime=runtime, config_datafile=os.path.join('configs.yaml'),
     pts_features=['ospf', 'hsrp'],
     pts_datafile=os.path.join(test_path, 'pts_datafile'),
     pts_golden_config='<path>/golden_pts')

Verifications

A verification is the execution of a command to retrieve the current state of a device. The state can be retrieved by using cli, yang, xml and so on or a mix of them.

There are two types of verifications: Global and Local.

Global verifications are run immediately after the common setup. At this stage, the information retrieved is saved as a snapshots. After each subsequent trigger, the same set of verifications are executed again and their state is compared to the previous snapshot.

Local verifications are independent of the Global verifications. They are run as subsections of a trigger. A first set of snapshots are taken before performing the trigger action. A second set of snapshots are taken after the trigger action and then compared to the first set of snapshots.

The verification_datafile specifies which verifications Genie should run. Let’s create a verification_datafile.

extends: verifications.yaml

Verify_Ospf:
    groups: ['L3']
    devices: ['uut']
    iteration:
        attempt: 3
        interval: 10
    exclude: ['uptime']
    processors:
        # verification with pre processor
        pre:
            extra_sleep:
                pkg: genie.libs.sdk
                method: libs.prepostprocessor.sleep_processor
        # verification with post processor
        post:
            extra_sleep:
                pkg: genie.libs.sdk
                method: libs.prepostprocessor.sleep_processor

Note

Be sure to provide a device for each verification that you want to execute. If no device is specified, the verification will not run

Note

Genie libs contains available ready to use processors, For more information on how to use them, go to harness developer.

Certain values taken in the verification snapshot can dynamically change during execution of the script, such as uptime, age etc. The user can choose to exclude comparison of these values by specifying them with the exclude key as shown in the example above.

At times, configurations on the device require some time to reach a stable state. The iteration key tells Genie to rerun the verification for the number of attempt specified while waiting for interval seconds between each attempt.

Note

Verification failure handling: Whenever a verifications fails due to mismatched key values when comparing snapshots, it will mark the section as a failure. At this stage, Genie saves this new snapshot as the latest snapshot containing the updated key values. It then uses this new snapshot to compare verifications snapshots generated after the failure. This reduces the number of failing verifications in a run.

The Genie SDK is a community driven library containing verifications which can be used by Genie.

Let’s add the verification_datafile argument to gRun in our job file in order to execute Global verifications:

gRun(mapping_datafile=os.path.join(test_path, 'mapping.yaml'),
     runtime=runtime, config_datafile=os.path.join('configs.yaml'),
     pts_features=['ospf', 'hsrp'],
     pts_datafile=os.path.join(test_path, 'pts_datafile'),
     pts_golden_config='<path>/pts',
     verification_datafile=os.path.join(test_path, 'verification_datafile'))

Note

More information on verification in the developer guide.

Note

Genie’s infrastructure includes a set of verifications. These verifications are stored in the extends location.

Triggers

A trigger is a set of actions and verifications that collectively constitute a testcase. These actions can include removal/addition of configuration, flapping protocols/interfaces, perform HA events, and any other actions that users may want to apply to test their devices. Triggers can also include verifications that check whether the above actions were performed correctly on the devices. We call these triggers, local verifications.

The trigger_datafile specifies which triggers Genie should run. Let’s create a trigger_datafile:

extends: "%CALLABLE{genie.libs.sdk.genie_yamls.datafile(trigger)}"

# Simple trigger which will run on the uut and part of the L3 group
TriggerShutNoShutOspf:
    groups: ['L3']
    devices: ['uut']
    processors:
        # trigger with pre processor
        pre:
            extra_sleep:
                pkg: genie.libs.sdk
                method: libs.prepostprocessor.sleep_processor

# A trigger with a local verification
TriggerClearOspf:
    groups: ['L3']
    devices: ['uut']
    verifications:
      Verify_Ospf:
        devices: ['uut']
        devices_attributes:
          uut:
            iteration:
              attempt: 6
              interval: 10
        parameters:
            vrf: default
    processors:
        # trigger with post processor
        post:
            extra_sleep:
                pkg: genie.libs.sdk
                method: libs.prepostprocessor.sleep_processor
order: ['TriggerClearOspf']

Note

Be sure sure you specify a device for each trigger you would like to execute. If no device is specified, the trigger will not run

Note

The local verification name must match a verification which exists in the verification_datafile

Note

The local verification parameters defined here will overwrite existing parameter which exists in the verification_datafile

Note

The order key define the order of execution. If a trigger is not a part of the list, the trigger will still execute but in a arbitrary order as python dictionaries are unordered.

Note

Genie’s infrastructure includes a set of triggers. These triggers are stored in the extends location. For more information, please consult the datafile page.

Note

Genie libs contains available ready to use processors, For more information on how to use them, go to harness developer.

The Genie SDK is a community driven library containing triggers which can be used by Genie.

Let’s add the trigger_datafile argument to gRun in our job file.

gRun(mapping_datafile=os.path.join(test_path, 'mapping.yaml'),
     runtime=runtime, config_datafile=os.path.join('configs.yaml'),
     pts_features=['ospf', 'hsrp'],
     pts_datafile=os.path.join(test_path, 'pts_datafile'),
     pts_golden_config='<path>/pts',
     verification_datafile=os.path.join(test_path, 'verification_datafile'),
     trigger_datafile=os.path.join(test_path, 'trigger_datafile'),
     trigger_uids=['TriggerShutNoShutOspf', 'TriggerClearOspf'])

File Transfer Protocol

The file transfer protocol to be used during common setup copy configuration section or during copy cores/crashdumps in later stages in the run can be set by the user in the job file.

It is an optional argument, if user didn’t provide it in the job file. The protocol will be extracted from the testbed yaml file as shown below.

testbed:
  name: <testbed name>
  servers:
      <File Transfer Protocol>:
          address: <tftp server ip address>
          path: <tftpboot location>

The valid transfer protocols are ‘tftp’, ‘ftp’ and ‘scp’.

Let’s add the filetransfer_protocol argument to gRun in our job file:

gRun(mapping_datafile=os.path.join(test_path, 'mapping.yaml'),
     runtime=runtime, config_datafile=os.path.join('configs.yaml'),
     pts_features=['ospf', 'hsrp'],
     pts_datafile=os.path.join(test_path, 'pts_datafile'),
     pts_golden_config='<path>/pts',
     filetransfer_protocol='tftp')

Connection Pool

Performance! Speed! With a connection pool commands on the same devices are not send one after the other, but in parallel! User can provide pool_size argument in the Mapping datafile and will be able to start a pool of connections during the genie script run. Refer to pyATS :connection-pool:`connection-pool <http>` for more details about connection sharing.