synthetic

Synthetic Checks for Critical User Journeys

Synthetic checks for critical user journeys enable DevOps engineers and SREs to proactively monitor and validate end-to-end user experiences by simulating real-world interactions before issues impact customers. Unlike traditional uptime checks that only verify single endpoints, these checks…

Opsgenie

— 4 min read
Synthetic Checks for Critical User Journeys

Synthetic Checks for Critical User Journeys

Synthetic checks for critical user journeys enable DevOps engineers and SREs to proactively monitor and validate end-to-end user experiences by simulating real-world interactions before issues impact customers. Unlike traditional uptime checks that only verify single endpoints, these checks replicate complete workflows—such as login, checkout, or report generation—across multiple geographies and environments, detecting latency, failures, and regressions early in the SDLC[1][2][3].

What Are Synthetic Checks for Critical User Journeys?

Synthetic checks for critical user journeys involve scripted simulations that mimic genuine user behavior, measuring key metrics like response time, success rates, and jitter. These checks run continuously or on-demand from global locations, providing a baseline for expected performance and alerting on deviations that could degrade user experience[1][2][5].

Traditional monitoring focuses on infrastructure metrics or API availability, but synthetic checks for critical user journeys go further by validating the full transaction flow, including authentication, data exchanges, and business logic completion. For instance, a simple ping confirms a homepage loads, but a synthetic check simulates a user adding items to a cart, applying a discount, and completing payment—revealing issues like slow database queries or third-party API failures hidden in aggregate metrics[1][3][5].

Key benefits include:

  • Proactive detection of issues before real users encounter them, reducing MTTR[3][4].
  • Global coverage to identify regional performance degradations[2][5].
  • Integration with observability tools for automated diagnostics, correlating failures to logs, traces, and metrics[1][3].

Why DevOps and SRE Teams Need Synthetic Checks for Critical User Journeys

In modern microservices architectures, failures often cascade across services, making end-to-end validation essential. Synthetic checks for critical user journeys shift reliability left, embedding user-centric tests into CI/CD pipelines to catch regressions during deployments[4][6]. This approach prevents "shipping blind" by validating workflows in pre-prod and production environments 24/7[3].

Business-critical paths—like e-commerce checkout or account onboarding—directly tie to revenue and retention. Degradations here, even if individual components are healthy, can lead to cart abandonment or churn. Synthetic checks quantify user satisfaction via scores like Apdex, tracking availability and response times for these journeys worldwide[5].

Compared to Real User Monitoring (RUM), which captures live behavior, synthetics provide controlled, repeatable tests unaffected by traffic volume or user variability. Combining both yields comprehensive insights: synthetics set baselines, RUM validates realism[3].

Identifying Critical User Journeys for Synthetic Checks

Start by collaborating with product teams to map high-impact workflows. Prioritize journeys where failure affects revenue or SLAs, such as:

  1. Login and authentication flows.
  2. Transaction processing (e.g., payments).
  3. Report generation or data sync.
  4. Onboarding new users[1].

Validate these against production telemetry: compare synthetic metrics to RUM data for alignment. Divergences signal modeling gaps, like unaccounted caching or geo-routing[1]. Tools like dependency graphs help trace each step to underlying services, ensuring comprehensive coverage[1].

Implementing Synthetic Checks: Practical Examples

Most observability platforms (e.g., Splunk, Kentik, Elastic) offer no-code builders or scripting for synthetic checks for critical user journeys. Here's how to set them up.

Example 1: HTTP-Based Synthetic Check for Login Journey

For a simple web app login, configure an HTTP transaction test. Set thresholds for latency (<2s) and success rate (100%). Run every 5 minutes from 5 global locations[2][5].

// Pseudocode for a basic HTTP synthetic script (using a tool like Kentik or Dotcom-Monitor)
step1: GET /login - expect 200 OK
step2: POST /api/auth - headers: {Authorization: 'Bearer token'}, body: {username: 'testuser', password: 'testpass'} - expect 200
step3: GET /dashboard - with session cookie - measure full load time
assert: status === 200 && loadTime < 2000ms

This detects auth failures or dashboard load issues early[5].

Example 2: Browser-Based Synthetic Check for E-Commerce Checkout

Use full browser tests to simulate JavaScript-heavy flows. Tools like Splunk Synthetic Monitoring support Selenium-like scripting[3][8].

/* JavaScript example for browser synthetic check */
await page.goto('https://example.com/cart');
await page.click('#add-to-cart');
await page.fill('#promo-code', 'SAVE10');
await page.click('#apply-promo');
await page.click('#checkout');
await expect(page.locator('#order-confirmation')).toBeVisible();
metrics: { cartToConfirm: performance.now() - startTime }

Run from private probes for internal apps, capturing screenshots on failure for quick triage[5].

Embed in CI/CD: Post-build, trigger checks against staging. Use webhooks to block deploys on failure[1][6].

Alerting and Diagnostics

Configure alerts for threshold breaches (e.g., >5% failure rate). Link to traces for root-cause: a failed checkout might pinpoint a slow payment API[1][2].

// Alert policy example (YAML-like)
name: Checkout-Journey-Alert
conditions:
  - latency > 3000ms
  - error_rate > 0.05
actions:
  - notify: slack(#sre-oncall)
  - capture: screenshot, traces, logs

Integrating Synthetic Checks into CI/CD Pipelines

Add synthetic gates after integration tests. In GitHub Actions or Jenkins:

jobs:
  synthetic-checks:
    runs-on: ubuntu-latest
    steps:
      - deploy-to-staging
      - run-synthetics:
          uses: splunk/synthetic-action@v1
          with:
            test-id: checkout-journey
            env: staging
      - if: failure() then halt-deploy

This creates baselines automatically, flagging regressions vs. prior releases. Enable A/B testing by comparing versions[5][6].

Best Practices for Synthetic Checks for Critical User Journeys

  • Keep scripts maintainable: Use data-driven params (e.g., test accounts) and avoid hardcoding[1].
  • Run frequently: Every 1-5 minutes for critical paths[5].
  • Geo-diversify: Test from user-heavy regions[2].
  • Automate everything: Baselines, alerts, rollbacks[1][6].
  • Combine with RUM/APM: Correlate for full observability[3].

Monitor test realism quarterly by aligning with RUM[1].

Measuring Success and ROI

Track MTTR reduction, deploy confidence, and SLA adherence. Teams report 50% faster incident response and fewer P1 incidents post-adoption[3][6]. Monthly reports on Apdex and uptime ensure continuous improvement[5].

By prioritizing synthetic checks for critical user journeys, SREs transform reactive firefighting into predictive reliability, delivering resilient services that scale with user demands.

(Word count: 1028)

Read more