Tezt: Long Tests and Performance Regression Test Framework

Tezt can also be used for long tests. Here are the differences with regular Tezt tests:

  • long tests are not run in the CI but on dedicated machines with stable, predictable performance, and with no global timeout like in the CI;

  • long tests are declared in tezt/long_tests/main.ml instead of tezt/tests/main.ml;

  • long tests are registered with Long_test.register instead of Test.register;

  • long tests have easy access to a Performance Regression Test framework which provides these features:

    • Persist measurement samples (in an InfluxDB database) such as how long it takes to do something. These samples will be used to prevent regressions of performance.

    • Provide easy access to a Grafana instance that can be updated to create graphs displaying the samples sent to InfluxDB;

    • Call Slack webhooks to send alerts when a performance regression has been detected.

Adding a Long Test

To add a long test, you need to register it with Long_test.register (respectively Long_test.register_with_protocol) instead of Test.register (respectively Protocol.register_test). The test must be registered in tezt/long_tests/main.ml instead of tezt/tests/main.ml. Long_test.register is very similar to Test.register. The main difference is that you have to declare a timeout (that should be significantly overestimated, see the documentation of the timeout type in tezt/lib_performance_regression/long_test.mli) and that Long_test.register will handle InfluxDB, Grafana and Slack alerts. Just like regular Tezt tests, your test should be implemented in the same file with other thematically-related tests, with only a single line in tezt/long_tests/main.ml.

If you declare a test in tezt/long_tests/main.ml and merge it into master, it will be automatically run on dedicated machines that regularly pull the latest version of master and run long tests. If your test takes a significant time to run (days), you should however ask that a new dedicated machine is created to run your test. Please ask on the #tests Slack channel of tezos-dev before merging.

Performance Regression Test framework: Time Series, Alerts and Graphs

Long tests can use functions from the Long_test module (tezt/lib_performance_regression/long_test.mli) to send data points to InfluxDB, which is a time-series database. A time-series database stores values annotated with a timestamp. In the particular case of InfluxDB, data points are composed of:

  • a timestamp (usually the time the measure was taken);

  • a measurement name (such as client load time);

  • optionally, some tags that can be used to refine the measurement name (such as a tag mode equal to client, mockup or proxy);

  • fields, which are composed of field names and field values (such as a field duration equal to how long it took for the client to load).

The Long_test module allows you to send any data point using the add_data_point function. They will be sent at the end of the test execution. You can query statistics about data points from previous tests using function get_previous_stats to compare them with your new data points and send an alert on Slack with function alert if you detect that something is wrong. Note that function check_regression, which is essentially a combination of get_previous_stats and alert, can be used in most cases. And if all you want to do is measure the time it takes for a chunk of code to run though, you can use the time function instead, which does everything for you: measure time, send data points, compare them with previous runs, and send an alert if the difference is too large.

The Long_test module also provides function update_grafana_dashboard called in tezt/long_tests/main.ml with a specification to create/overwrite a dashboard in Nomadic Labs’ Grafana. Default is named Long Tezts but you can add additional dashboards using the Long_test.update_grafana_dashboard function. To add a dashboard for your tests, define it next to your test (in the same file), and declare it in the call to update_grafana_dashboard in tezt/long_tests/main.ml.

As always in Tezt, the above functions try to provide flexibility. The time function in particular is parameterized by settings like the number of times the test should be repeated, how many previous data points should be fetched from InfluxDB when comparing with new measurements, how much of a difference to tolerate before alerting, etc. time itself being a combination of other lower-level functions that are also provided and which you can combine to fit your needs. And of course you can contribute to improve them.

Automated long tezts logs are available in Nomadic Labs’ public S3 bucket browser. In case of InfluxDB issues, you can inspect metrics in the InfluxDB dashboard. InfluxDB itself is private and direct access via CLI is restricted to administrators.

Example

See tezt/long_tests/prt_client.ml, which is a very simple test that measures how long it takes for tezos-client to load. It uses Long_test.time_lwt to measure how long it takes for Client.version to run and to emit alerts if this time is significantly higher than usual. It also defines a graph of this time. This test and its graph are registered in tezt/long_tests/main.ml.

One-Shot Tests

You may be interested in running some long tests using this framework on your own branch instead of master.

Check the documentation of the PRT one-shot repository

Providing Large Data

Your test may require data that is too large to commit in tezos/tezos. For example, a benchmark in which measurement is dependent on some block’s context would need to load the same data directory on each execution.

There is an Amazon S3 bucket where you can upload your data which will be made available for your test. Data will be synchronized with the server your tests will be running on.

For security reasons, this storage has its access limited to authorized people. If you want to upload data, please contact Jérémie Goldberg (@jgonlabs) or anyone with admin access on the Tezos AWS account to allow you to do so.

Please note that the S3 storage root folder is mounted in /s3data/. E.g. if your file is under /myfolder/myfile in the Amazon bucket, your tests will find it under /s3data/myfolder/myfile.

Testing Your Benchmarks Locally

When developing a benchmark depending on the Performance Regression Test framework, it can be useful to test it using development backends so that your tests does not impact production ones.

The Performance Regression Test framework now contains a setup that can automatically provision and configure InfuxDB and Grafana instances using Docker Compose.

Provisioning InfluxDB and Grafana

The following steps assume that you already installed Docker as well as docker-compose and correctly configured it. For more information on this subject, please refer to:

From the root folder of tezos run the following commands from a terminal to start the Docker containers in background:

docker-compose -f tezt/lib_performance_regression/local-sandbox/docker-compose.yml up -d

After containers have been started, you can test that InfluxDB is properly started and that the performance_regression database has been automatically created:

curl --get http://localhost:8086/query\?pretty\=true --data-urlencode "q=show databases"

The command should display the following:

{
    "results": [
        {
            "statement_id": 0,
            "series": [
                {
                    "name": "databases",
                    "columns": [
                        "name"
                    ],
                    "values": [
                        [
                            "performance_regression"
                        ],
                        [
                            "_internal"
                        ]
                    ]
                }
            ]
        }
    ]
}

Also, you should be able to connect to the Grafana web UI by connecting to http://localhost:3000 on your browser. By going to the Datasources menu in the webapp configuration, you can see that an InfluxDB datasource has been pre-configured and is connected to the performance_regression.

Note that as security does not really matter for tests, it has been disable for ease. This is why you can connect to the Graphana web app with full privileges or send requests to InfluxDB without having to authenticate.

To stop the container, simply run:

docker-compose -f tezt/lib_performance_regression/local-sandbox/docker-compose.yml down

The created containers use persistent Docker volumes, so that data stored in the database and created dashboards will be preserved between container runs. To permanently remove these docker volumes, run the command docker volume rm local-sandbox_influxdb local-sandbox_grafana.

Configuring and Running Tezt Long Tests

For more information about how to use the configuration file, please refer to the Long test module API.

A predefined configuration has already been shiped in tezt/lib_performance_regression/local-sandbox/tezt_config.json. It allows to use the InfluxDB and Grafana instances set up by the Docker compose file presented in the previous section.

All content related to Grafana and InfluxDB has already been set and can be used as is.

Other aspects of the configuration (for example the test_data_path) should be updated to match the needs of your local machine.

To run Tezt long tests, run the following command:

TEZT_CONFIG=tezt/lib_performance_regression/local-sandbox/tezt_config.json dune exec tezt/long_tests/main.exe