Quay Performance Tests

Description

The purpose of this repository is to provide a set of scalability and performance tests for Red Hat Quay and Project Quay. These tests are intended to necessarily push Quay to its limits and collect metrics on various operations. These metrics are used to determine how changes to Quay affects its performance.

Quickstart

The test suite is designed to run on openshift platform using a simple configuration file. We just have to set the required parameters and trigger it. More details will be shared below.

Prerequisites

• Deploy a Openshift environment. The tests will run within the cluster.
• Deploy Quay, itself.
• In Quay, as a superuser (important), create an organization for testing purposes. Within that organization, create an application for testing purposes. Within that application, create an OAuth Token with all permissions (checkboxes) granted. Hold on to this token as it will be used later.
• Once after the quay application is deployed. Do a pg_dump in the quay postgres pod to capture the initial snapshot into a sql file and keep it copied at assets/quaydb.sql.
• An elasticsearch cluster is needed to store results. You can spin one using the Operator

Running tests

Building

From the main directory, the podman image can be built and published to your remote repository (if required) using the below commands:

podman build . -t quay-load; 
podman tag localhost/quay-load:latest quay.io/<path-to-yourrepo>:latest; 
podman push quay.io/<path-to-yourrepo>:latest

Running tests on openshift platform

Before running the tests, we need to make sure we are running assets/quay_init.sh to reset the postgres database with the initial snapshot and assets/quay_redis_init.sh to clear the redis cache.

NOTE: We need to have a file at assets/quaydb.sql in order to run assets/quay_init.sh which is the snapshot captured when deployed quay initially.

Now once we have the system ready, Deploy deploy/test.job.yaml on your openshift cluster. It takes below ENVS as arguments.

Envs

• QUAY_HOST - Sting. Indicating quay host url to perform testing.
• QUAY_OAUTH_TOKEN - String. Application oauth token created in the prerequisites step.
• QUAY_ORG- String. Specifies the test organization.
• ES_HOST - String. Elastic search host url.
• ES_PORT - String. Elastic search port number.
• ES_INDEX - String. Elastic search index to store the results.
• PUSH_PULL_IMAGE - Image which contains source code and used in push/pull jobs for testing purposes. Same image that is used for load testing i.e quay-load in our case.
• PUSH_PUSH_ES_INDEX - ES index to store quay push/pull results. It is separate as it follows different document structure.
• PUSH_PULL_NUMBERS - The amount of images to do push/pull operations on.
• TARGET_HIT_SIZE - String. Indicates the total amount of requests to hit the system with.
• CONCURRENCY - String. Indicates the rate(concurrency) at which the requests hits must happen in parallel.
• TEST_NAMESPACE - String. Namespace in which testing needs to be done.
• TEST_PHASES - String. Comma separated string containing list of phases. Valid phases are LOAD, RUN and DELETE. Example: LOAD,DELETE

This should spin up a redis pod and a test orchestrator pod in your desired namespace and start running the tests. Tail the pod logs for more info.

More about tests

The tests use Vegeta to trigger the load and index the results to specified elastic search instance. The list of apis involved in each phase as as below:

LOAD PHASE

NOTE: n is number of objects/requests here

APIs with O(n) operations

POST /api/v1/superuser/users # create_users method
PUT /api/v1/superuser/users # update_passwords method
POST /api/v1/repository # create_repositories method
PUT /api/v1/repository/test # update_repositories method
PUT /api/v1/organization/test/team # create_teams method

APIs with O(n^2) operations

PUT /api/v1/organization/test/team/team_1/members/member_1 # add_team_members method
PUT /api/v1/repository/test/repo_1/permissions/team/team_1 # add_teams_to_organization_repos
PUT /api/v1/repository/test/repo_1/permissions/user/user_1 # add_users_to_organization_repos

RUN PHASE

NOTE: n is number of objects/requests here

APIs with O(n) operations

GET /api/v1/superuser/users/user_1 # get_users method
GET /api/v1/repository/test/repo_1 # get_repositories method
GET /api/v1/organization/test/team/team_1/permissions # list_team_permissions method
GET /api/v1/organization/test/team/team_1/members # list_team_members method
GET /api/v1/repository/test/repo_1/permissions/team/ #list_teams_of_organization_repos
GET /api/v1/repository/test/repo_1/permissions/user/ # list_users_of_organization_repos
GET /v2/user_1/repo_1/tags/list # list_tags method
GET /api/v1/superuser/users/ # list_users method
GET /v2/_catalog # get_catalog method

APIs with O(n^2) operations

GET /api/v1/repository/test/repo_1/permissions/team/team_1 # get_teams_of_organizations_repos method
GET /api/v1/repository/test/repo_1/permissions/user/user_1 # get_users_of_organizations_repos method

Image push/pulls

Unfortunately we don’t have any APIs to hit at this moment. So those are tested using podman commands. For n objects we will be creating n jobs representing n users who will be uploading images in parallel with python multiprocessing implemented. Each job uploads and downloads specified number of images (i.e PUSH_PULL_NUMBERS which has default value of 50).

DELETE PHASE

NOTE: n is number of objects/requests here

APIs with O(n) operations

DELETE /api/v1/organization/test/team/team_1 # delete_teams method
DELETE /api/v1/repository/test/repo_1 # delete_repositories method
DELETE /api/v1/superuser/users/user_1 # delete_users method

APIs with O(n^2) operations

DELETE /api/v1/organization/test/team/team_1/members/member_1 # delete_team_members method
DELETE /api/v1/repository/test/repo_1/permissions/team/team_1 # delete_teams_of_organizations_repos method
DELETE /api/v1/repository/test/repo_1/permissions/user/user_1 # delete_users_of_organizations_repos method
DELETE /api/v1/repository/test/repo_1/tag/tag_1 # delete_repository_tags method

Profiling

Application Level Profiling

Inorder to perform application level profiling we use pyroscope. To install pyroscope onto your cluster, deploy assets/pyroscope-server.yaml. Now wait until the pods are up and running in the pyroscope namespace. For other installation methods please refer this.
Once pyroscope is ready, we should be able to logon to the pyroscope route in the pyroscope namespace and view the application level profiling data using the tags applied as filters. Please refer to this tutorial and live demo for more details on integration.

System Level Profiling

Inorder to perform system level profiling we use parca. To install parca onto your cluster, deploy assets/parca-server.yaml and assets/parca-agent.yaml in sequence. Now wait until the pods are up and running in the parca namespace. For other installation method please refer this.
Once parca is ready, we should be able to logon to the parca route in the parca namespace and view the system level profiling data.

PostgresDB Profiling

In order to perform low overhead database profiling we will be using pgBadger. Below are the steps to do profiling on postgresDB.

• Step 1: Login to the postgres DB and modify the postgres.conf in /var/lib/pgsql/data/userdata/ with below flags (or can be modified according to our own use case).

log_checkpoints = on
log_connections = on
log_disconnections = on
log_lock_waits = on
log_temp_files = 0
log_autovacuum_min_duration = 0
log_error_verbosity = default
log_destination = 'stderr'
logging_collector = on
log_rotation_age = 1d
log_rotation_size = 0
log_truncate_on_rotation = on
log_min_duration_statement = 0
log_min_messages = debug1

• Step 2: Once after modifying the config restart the db using the below command

pg_ctl restart -D /var/lib/pgsql/data/userdata

• Step 3: Now we should be able to find the logs in /var/lib/pgsql/data/userdata/log/. Copy those logs to the location where pgBadger binary is installed.

• Step 4: Then execute the below command to process those logs and get an html report. For more details on usage of pgBadger, refer here.

pgbadger -j 8 ~/output/postgresql-2023-06-18_141703.log -o /home/vchalla/output/output.html --format html

NOTE: It is suggested to disable logs collector once we do the profiling to avoid overhead created by logs files getting accumulating in the DB.