browbeat/doc/source/usage.rst

Usage

Run Overcloud checks

$ ansible-playbook -i hosts check/site.yml

Your Overcloud check output is located in results/bug_report.log

NOTE: It is strongly advised to not run the ansible playbooks in a venv.

Run performance stress tests through Browbeat on the undercloud:

$ ssh undercloud-root
[root@ospd ~]# su - stack
[stack@ospd ~]$ screen -S browbeat
[stack@ospd ~]$ . browbeat-venv/bin/activate
(browbeat-venv)[stack@ospd ~]$ cd browbeat/
(browbeat-venv)[stack@ospd browbeat]$ vi browbeat-config.yaml # Edit browbeat-config.yaml to control how many stress tests are run.
(browbeat-venv)[stack@ospd browbeat]$ ./browbeat.py <workload> #perfkit, rally, shaker or "all"

Run performance stress tests through Browbeat

[stack@ospd ansible]$ . ../../browbeat-venv/bin/activate
(browbeat-venv)[stack@ospd ansible]$ cd ..
(browbeat-venv)[stack@ospd browbeat]$ vi browbeat-config.yaml # Edit browbeat.cfg to control how many stress tests are run.
(browbeat-venv)[stack@ospd browbeat]$ ./browbeat.py <workload> #perfkit, rally, shaker or "all"

Running PerfKitBenchmarker

Work is on-going to utilize PerfKitBenchmarker as a workload provider to Browbeat. Many benchmarks work out of the box with Browbeat. You must ensure that your network is setup correctly to run those benchmarks and you will need to configure the settings in ansible/install/group_vars/all.yml for Browbeat public/private networks. Currently tested benchmarks include: aerospike, bonnie++, cluster_boot, copy_throughput(cp,dd,scp), fio, iperf, mesh_network, mongodb_ycsb, netperf, object_storage_service, ping, scimark2, and sysbench_oltp.

To run Browbeat's PerfKit Benchmarks, you can start by viewing the tested benchmark's configuration in conf/browbeat-perfkit-complete.yaml. You must add them to your specific Browbeat config yaml file or enable/disable the benchmarks you wish to run in the default config file (browbeat-config.yaml). There are many flags exposed in the configuration files to tune how those benchmarks run. Additional flags are exposed in the source code of PerfKitBenchmarker available on the Google Cloud Github.

Example running only PerfKitBenchmarker benchmarks with Browbeat from browbeat-config.yaml:

(browbeat-venv)[stack@ospd browbeat]$ ./browbeat.py  perfkit -s browbeat-config.yaml

Running Shaker

Running Shaker requires the shaker image to be built, which in turn requires instances to be able to access the internet. The playbooks for this installation have been described in the installation documentation but for the sake of convenience they are being mentioned here as well.

$ ansible-playbook -i hosts install/browbeat_network.yml
$ ansible-playbook -i hosts install/shaker_build.yml

Note

The playbook to setup networking is provided as an example only and might not work for you based on your underlay/overlay network setup. In such cases, the exercise of setting up networking for instances to be able to access the internet is left to the user.

Once the shaker image is built, you can run Shaker via Browbeat by filling in a few configuration options in the configuration file. The meaning of each option is summarized below:

shaker:

enabled

Boolean true or false, enable shaker or not

server

IP address of the shaker-server for agent to talk to (undercloud IP by default)

port

Port to connect to the shaker-server (undercloud port 5555 by default)

flavor

OpenStack instance flavor you want to use

join_timeout

Timeout in seconds for agents to join

sleep_before

Time in seconds to sleep before executing a scenario

sleep_after

Time in seconds to sleep after executing a scenario

venv

venv to execute shaker commands in, /home/stack/shaker-venv by default

shaker_region

OpenStack region you want to use

external_host

IP of a server for external tests (should have browbeat/util/shaker-external.sh executed on it previously and have iptables/firewalld/selinux allowing connections on the ports used by network testing tools netperf and iperf)

scenarios: List of scenarios you want to run

- name

Name for the scenario. It is used to create directories/files accordingly

enabled

Boolean true or false depending on whether or not you want to execute the scenario

density

Number of instances

compute

Number of compute nodes across which to spawn instances

placement

single_room would mean one instance per compute node and double_room would give you two instances per compute node

progression

null means all agents are involved, linear means execution starts with one agent and increases linearly, quadratic would result in quadratic growth in number of agents participating in the test concurrently

time

Time in seconds you want each test in the scenario file to run

file

The base shaker scenario file to use to override options (this would depend on whether you want to run L2, L3 E-W or L3 N-S tests and also on the class of tool you want to use such as flent or iperf3)

To analyze results sent to Elasticsearch (you must have Elasticsearch enabled and the IP of the Elasticsearch host provided in the browbeat configuration file), you can use the following playbook to setup some prebuilt dashboards for you:

$ ansible-playbook -i hosts install/kibana-visuals.yml

Alternatively you can create your own visualizations of specific shaker runs using some simple searches such as:

shaker_uuid: 97092334-34e8-446c-87d6-6a0f361b9aa8 AND record.concurrency: 1 AND result.result_type: bandwidth
shaker_uuid: c918a263-3b0b-409b-8cf8-22dfaeeaf33e AND record.concurrency:1 AND record.test:Bi-Directional

Running YODA

YODA (Yet Openstack Deployment tool, Another) is a workload integrated into Browbeat for benchmarking TripleO deployment. This includes importing baremetal nodes, running introspections and overcloud deployements of various kinds. Note that YODA assumes it is on the undercloud of a TripleO instance post undercloud installation and introspection.

Configuration

For examples of the configuration see browbeat-complete.yaml in the repo root directory. Additional configuration documentation can be found below for each subworkload of YODA.

Overcloud

For overcloud workloads, note that the nodes dictionary is dynamic, so you don't have to define types you aren't using, this is done in the demonstration configurations for the sake of completeness. Furthermore the node name is taken from the name of the field, meaning custom role names should work fine there.

The step parameter decides how many nodes can be distributed between the various types to get from start scale to end scale, if these are the same it won't matter. But if they are different up to that many nodes will be distributed to the different node types (in no particular order) before the next deploy is performed. The step rule is violated if and only if it is required to keep the deployment viable, for example if the step dictates that 2 control nodes be deployed it will skip to 3 even if it violates step.

YODA has basic support for custom templates and more advanced roles, configure the templates: paramater in the overcloud benchmark section with a string for template paths.

templates: "-e /usr/share/openstack-tripleo-heat-templates/environments/network-isolation.yaml"

Note that --templates is passed to the overcloud deploy command before this, then nodes sizes, ntp server and timeout are passed after, so your templates will override the defaults, but not scale, timeout, or ntp settings from the YODA config. If you want to use scheduling hints for your overcloud deploy you will need to pip install [ostag](https://github.com/jkilpatr/ostag) and set node_pinning: True in your config file. Ostag will be used before every deploy to clean all tags and tag the appropriate nodes. If you set node_pinning: False tags will be cleaned before the deploy. If you need more advanced features view the ostag readme for how to tag based on node properties. If you don't want YODA to edit your node properties, don't define node_pinning in your configuration.

Introspection

Introspection workloads have two modes, batch and individual, the batch workload follows the documentation exactly, nodes are imported, then bulk introspection is run. Individual introspection has it's own custom batch size and handles failures more gracefully (individual instead of group retries). Both have a timeout configured in seconds and record the amount of time required for each node to pxe and the number of failures.

timeout is how long we wait for the node to come back from introspection this is hardware variable. Although the default 900 seconds has been shown to be the 99th percentile for success across at least two stes of hardware. Adjust as required.

Note that batch_size can not produce a batch of unintrospected ndoes if none exist so the last batch may be below the maximum size. When nodes in a batch fail the failure_count is incremented and the nodes are returned to the pool. So it's possible that same node will fail again in another batch. There is a saftey mechanism that will kill Yoda if a node exceeds 10 retries as that's pretty much garunteed to be misconfigured. For bulk introspection all nodes are tried once and what you get is what you get.

If you wish to change the introspection workload failure threshold of 10% you can set max_fail_amnt to any floating point value you desire.

I would suggest bulk introspection for testing documented TripleO workflows and individual introspection to test the performance of introspection itself.

Interpreting Browbeat Results

By default results for each test will be placed in a timestamped folder results/ inside your Browbeat folder. Each run folder will contain output files from the various workloads and benchmarks that ran during that Browbeat run, as well as a report card that summarizes the results of the tests.

Browbeat for the most part tries to restrict itself to running tests, it will only exit with a nonzero return code if a workload failed to run. If, for example, Rally where to run but not be able to boot any instances on your cloud Browbeat would return with RC 0 without any complaints, only by looking into the Rally results for that Browbeat run would you determine that your cloud had a problem that made benchmarking it impossible.

Likewise if Rally manages to run at a snails pace, Browbeat will still exit without complaint. Be aware of this when running Browbeat and take the time to either view the contents of the results folder after a run. Or setup Elasticsearch and Kibana to view them more easily.

Working with Multiple Clouds

If you are running playbooks from your local machine you can run against more than one cloud at the same time. To do this, you should create a directory per-cloud and clone Browbeat into that specific directory:

[browbeat@laptop ~]$ mkdir cloud01; cd cloud01
[browbeat@laptop cloud01]$ git clone git@github.com:openstack/browbeat.git
...
[browbeat@laptop cloud01]$ cd browbeat/ansible
[browbeat@laptop ansible]$ ./generate_tripleo_hostfile.sh -t <cloud01-ip-address>
[browbeat@laptop ansible]$ ansible-playbook -i hosts (Your playbook you wish to run...)
[browbeat@laptop ansible]$ ssh -F ssh-config overcloud-controller-0  # Takes you to first controller

Repeat the above steps for as many clouds as you have to run playbooks against your clouds.

Compare software-metadata from two different runs

Browbeat's metadata is great to help build visuals in Kibana by querying on specific metadata fields, but sometimes we need to see what the difference between two builds might be. Kibana doesn't have a good way to show this, so we added an option to Browbeat CLI to query ElasticSearch.

To use :

$ python browbeat.py --compare software-metadata --uuid "browbeat-uuid-1" "browbeat-uuid-2"

Real world use-case, we had two builds in our CI that used the exact same DLRN hash, however the later build had a 10x performance hit for two Neutron operations, router-create and add-interface-to-router. Given we had exactly the same DLRN hash, the only difference could be how things were configured. Using this new code, we could quickly identify the difference -- TripleO enabled l3_ha.

[rocketship:browbeat] jtaleric:browbeat$ python browbeat.py --compare software-metadata --uuid "3fc2f149-7091-4e16-855a-60738849af17" "6738eed7-c8dd-4747-abde-47c996975a57"
2017-05-25 02:34:47,230 - browbeat.Tools -    INFO - Validating the configuration file passed by the user
2017-05-25 02:34:47,311 - browbeat.Tools -    INFO - Validation successful
2017-05-25 02:34:47,311 - browbeat.Elastic -    INFO - Querying Elastic : index [_all] : role [controller] : uuid [3fc2f149-7091-4e16-855a-60738849af17]
2017-05-25 02:34:55,684 - browbeat.Elastic -    INFO - Querying Elastic : index [_all] : role [controller] : uuid [6738eed7-c8dd-4747-abde-47c996975a57]
2017-05-25 02:35:01,165 - browbeat.Elastic -    INFO - Difference found : Host [overcloud-controller-2] Service [neutron] l3_ha [False]
2017-05-25 02:35:01,168 - browbeat.Elastic -    INFO - Difference found : Host [overcloud-controller-1] Service [neutron] l3_ha [False]
2017-05-25 02:35:01,172 - browbeat.Elastic -    INFO - Difference found : Host [overcloud-controller-0] Service [neutron] l3_ha [False]