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[ML] AIOps: Stabilize performance journey for log rate analysis. (#180468)

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## Summary

Changes the flow of the performance journey for log rate analysis a bit:
Previously we prepopulated the URL so the analysis would run right away.
This update reduces the options stored in the URL to just load the page
with the correct time range. The second step then clicks on the spike in
the chart to intentionally start the analysis.

### Checklist

- [x] [Unit or functional
tests](https://www.elastic.co/guide/en/kibana/master/development-tests.html)
were updated or added to match the most common scenarios
- [x] This was checked for breaking API changes and was [labeled
appropriately](https://www.elastic.co/guide/en/kibana/master/contributing.html#kibana-release-notes-process)
This commit is contained in:
Walter Rafelsberger 2024-04-11 12:47:09 +02:00 • committed by GitHub
parent 13c3da1804
commit 619fa44280
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1 changed files with 14 additions and 6 deletions
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20
x-pack/performance/journeys_e2e/aiops_log_rate_analysis.ts
View file

@ -14,19 +14,27 @@ export const journey = new Journey({
kbnArchives: ['x-pack/performance/kbn_archives/large_arrays_data_view'],
esArchives: ['x-pack/test/functional/es_archives/large_arrays'],
})
.step('Go to AIOps Log Rate Analysis', async ({ page, kbnUrl }) => {
// Navigate to Log Rate Analysis with a prepopulated url state that runs
// the analysis directly on page load without any necessary user interactions.
.step('Go to AIOps Log Rate Analysis', async ({ page, kbnUrl, kibanaPage }) => {
// Navigate to Log Rate Analysis with just a prepopulated time range.
await page.goto(
kbnUrl.get(
`app/ml/aiops/log_rate_analysis?index=${dataView.id}&_g=%28refreshInterval%3A%28pause%3A%21t%2Cvalue%3A60000%29%2Ctime%3A%28from%3A%272019-07-01T15%3A35%3A38.700Z%27%2Cto%3A%272019-07-05T15%3A35%3A38.700Z%27%29%29&_a=%28logRateAnalysis%3A%28filters%3A%21%28%29%2CsearchQuery%3A%28match_all%3A%28%29%29%2CsearchQueryLanguage%3Akuery%2CsearchString%3A%27%27%2Cwp%3A%28bMax%3A1562198400000%2CbMin%3A1562097600000%2CdMax%3A1562270400000%2CdMin%3A1562234400000%29%29%29`
`app/ml/aiops/log_rate_analysis?index=${dataView.id}&_g=%28refreshInterval%3A%28pause%3A%21t%2Cvalue%3A60000%29%2Ctime%3A%28from%3A%272019-07-01T15%3A35%3A38.700Z%27%2Cto%3A%272019-07-05T15%3A35%3A38.700Z%27%29%29&_a=%28logRateAnalysis%3A%28filters%3A%21%28%29%2CsearchQuery%3A%28match_all%3A%28%29%29%2CsearchQueryLanguage%3Akuery%2CsearchString%3A%27%27%29%29`
)
);
// Wait for the AIOps Log Rate Analysis page wrapper to load
await page.waitForSelector(subj('aiopsLogRateAnalysisPage'));
await page.waitForSelector(subj('aiopsDocumentCountChart'));
// Wait for the histogram chart to load
await kibanaPage.waitForCharts({
parentLocator: subj('aiopsDocumentCountChart'),
count: 1,
});
await page.waitForSelector(subj('aiopsNoWindowParametersEmptyPrompt'));
})
.step('Run AIOps Log Rate Analysis', async ({ page, kbnUrl }) => {
// Wait for the analysis to complete with extended timeout, this one tracks a known issue with slow frequent_item_sets performance.
.step('Run AIOps Log Rate Analysis', async ({ page }) => {
// Select the chart and click in the area where the spike is located to trigger log rate analysis.
const chart = await page.locator(subj('aiopsDocumentCountChart'));
await chart.click({ position: { x: 710, y: 50 } });
await page.waitForSelector(subj('aiopsAnalysisComplete'), { timeout: 120000 });
});