Basics

Listing Available Tests

otava list-groups

Lists all available test groups - high-level categories of tests.

otava list-tests [group name]

Lists all tests or the tests within a given group, if the group name is provided.

Listing Available Metrics for Tests

To list all available metrics defined for the test:

otava list-metrics <test>

Example

[!TIP] See otava.yaml for the full example configuration.

$ otava list-metrics local.sample
metric1
metric2

Finding Change Points

otava analyze <test>...
otava analyze <group>...

This command prints interesting results of all runs of the test and a list of change-points. A change-point is a moment when a metric value starts to differ significantly from the values of the earlier runs and when the difference is persistent and statistically significant that it is unlikely to happen by chance. Otava calculates the probability (P-value) that the change point was caused by chance - the closer to zero, the more “sure” it is about the regression or performance improvement. The smaller is the actual magnitude of the change, the more data points are needed to confirm the change, therefore Otava may not notice the regression immediately after the first run that regressed. However, it will eventually identify the specific commit that caused the regression, as it analyzes the history of changes rather than just the HEAD of a branch.

The analyze command accepts multiple tests or test groups. The results are simply concatenated.

Example

[!TIP] See otava-local.yaml for the full example configuration and local_sample.csv for the data.

$ otava analyze local.sample --since=2026-01-01T00:00:00Z
INFO: Computing change points for test local.sample...
time                       commit      metric1    metric2
-------------------------  --------  ---------  ---------
2026-01-01 02:00:00 +0000  aaa0         154023      10.43
2026-01-02 02:00:00 +0000  aaa1         138455      10.23
2026-01-03 02:00:00 +0000  aaa2         143112      10.29
2026-01-04 02:00:00 +0000  aaa3         149190      10.91
2026-01-05 02:00:00 +0000  aaa4         132098      10.34
2026-01-06 02:00:00 +0000  aaa5         151344      10.69
                                                ·········
                                                   -12.9%
                                                ·········
2026-01-07 02:00:00 +0000  aaa6         155145       9.23
2026-01-08 02:00:00 +0000  aaa7         148889       9.11
2026-01-09 02:00:00 +0000  aaa8         149466       9.13
2026-01-10 02:00:00 +0000  aaa9         148209       9.03

Avoiding test definition duplication

You may find that your test definitions are very similar to each other, e.g. they all have the same metrics. Instead of copy-pasting the definitions you can use templating capability built-in otava to define the common bits of configs separately.

First, extract the common pieces to the templates section:

templates:
  common-metrics:
    throughput:
      suffix: client.throughput
    response-time:
      suffix: client.p50
      direction: -1    # lower is better
    cpu-load:
      suffix: server.cpu
      direction: -1    # lower is better

Next you can recall a template in the inherit property of the test:

my-product.test-1:
  type: graphite
  tags: [perf-test, daily, my-product, test-1]
  prefix: performance-tests.daily.my-product.test-1
  inherit: common-metrics
my-product.test-2:
  type: graphite
  tags: [perf-test, daily, my-product, test-2]
  prefix: performance-tests.daily.my-product.test-2
  inherit: common-metrics

You can inherit more than one template.

Validating Performance of a Feature Branch

When developing a feature, you may want to analyze performance test results from a specific branch to detect any regressions introduced by your changes. The --branch option allows you to run change-point analysis on branch-specific data.

Configuration

To support branch-based analysis, use the %{BRANCH} placeholder in your test configuration. This placeholder will be replaced with the branch name specified via --branch:

tests:
  my-product.test:
    type: graphite
    prefix: performance-tests.%{BRANCH}.my-product
    tags: [perf-test, my-product]
    metrics:
      throughput:
        suffix: client.throughput
        direction: 1
      response_time:
        suffix: client.p50
        direction: -1

For PostgreSQL or BigQuery tests, use %{BRANCH} in your SQL query:

tests:
  my-product.db-test:
    type: postgres
    time_column: commit_ts
    attributes: [experiment_id, commit]
    query: |
      SELECT commit, commit_ts, throughput, response_time
      FROM results
      WHERE branch = %{BRANCH}
      ORDER BY commit_ts ASC
    metrics:
      throughput:
        direction: 1
      response_time:
        direction: -1

For CSV data sources, the branching is done by looking at the branch column in the CSV file and filtering rows based on the specified branch value.

Usage

Run the analysis with the --branch option:

otava analyze my-product.test --branch feature-xyz

This will:

Fetch data from the branch-specific location.
Run change-point detection on the branch's performance data

Example

$ otava analyze my-product.test --branch feature-new-cache --since=-30d
INFO: Computing change points for test my-product.test...
my-product.test:
time                         throughput    response_time
-------------------------  ------------  ---------------
2024-01-15 10:00:00 +0000        125000            45.2
2024-01-16 10:00:00 +0000        124500            44.8
2024-01-17 10:00:00 +0000        126200            45.1
                               ········
                                 +15.2%
                               ········
2024-01-18 10:00:00 +0000        145000            38.5
2024-01-19 10:00:00 +0000        144200            39.1
2024-01-20 10:00:00 +0000        146100            38.2

The --branch option can also be set via the BRANCH environment variable:

BRANCH=feature-xyz otava analyze my-product.test