MBU products perf&scale library

OPL library

Contains various helpers for not only RHCloud performance testing.

Generic command-line tools

There is couple of tools used mostly to integrate with CPT and comp.:

• cluster_read.py - Library (and command-line tool) to get data in a ways defined in a config file. It can execute command, read from Prometheus or Grafana. It is used by status_data.py to enritch status data files with facts (as defined in per-test config file). Usually it is info about the cluster (using oc ... | jq ... commands) or monitoring data from Prometheus. E.g. see opl/cluster_read_example.yaml.
• pass_or_fail.py - Tool which uses simple statistic in an attempt to decide if latest test result is PASS or FAIL when compared to historical results. Example of the config file is in opl/investigator/sample_config.yaml.
• junit_cli.py - Script to manipulate JUnit XML file from CLI. It is used by Satellite CPT.
• status_data.py - Library (and command-line tool) to manipulate status data files.
• status_data_report.py - Tool to render Jinja2 template with data from status data file. It is used by tests to provide final summary without need to write per-test tool which would do it - Jinja2 template seems to provide enough logick for what is needed in the output. Each test have its own template - e.g. see opl/status_data_report.txt.
• status_data_diff.py - Convenience tool to show differences in two status data files.
• Deprecated data_investigator.py - Tool which consumes input status data files and loads data from similar status data documents in ElasticSearch and decides if result in provided input status data file is PASS or FAIL. It counts some averages and so to do that. Currently it is used by Satellite CPT.

These tools are only used by tests:

• script-skip-to-end.py - Script to skip to the end of Kafka queues for given consumer group (it should be trivial given python-kafka docs, but simplest way did not worked for me)
• script-manage-db.py - Helper script to manipulate with utility DB. Consumes config with list of tables relevant for given test and SQL needed to create them. E.g. see opl/sample-tables.yaml

Installation

Install with:

python -m venv venv
source venv/bin/activate
python -m pip install git+https://github.com/redhat-performance/opl.git

If you have cloned the git and want to develop locally, replace last step with:

python -m pip install --editable .

Running unit tests

source venv/bin/activate
python -m pytest