💬 EN

Can a Splunk synthetic test leverage a secret that’s stored within Azure Key Vault? If so, how

Splunk synthetic tests themselves don’t natively integrate with Azure Key Vault for secret retrieval based on the provided documentation. However, a custom implementation could bridge this gap by combining Splunk’s capabilities with Azure’s authentication mechanisms. Here’s how it might work:

Potential Workflow

1. Secret Retrieval Mechanism Use Azure Key Vault’s API via:
   • Azure Functions (as referenced in Splunk’s Azure integration patterns¹²) to fetch secrets programmatically
   • Service Principal Authentication (as required by Splunk’s Azure Monitor add-on²) to access the Key Vault
2. Splunk Integration
   • For synthetic tests requiring secrets (e.g., API keys for authenticated checks):
     • Store the secret retrieval logic in a script executed by Splunk’s Synthetic Monitoring (likely via external scripts or HTTP requests to a custom endpoint)
     • Use a scripted input or HTTP Event Collector (HEC) to pass secrets securely to Splunk tests
   • For test configuration:
     • Retrieve secrets during test setup using Azure’s REST API or SDKs, then inject them into test parameters

Key Considerations

• Authentication: Use Azure AD-managed identities or service principals with Key Vault access policies³ to grant Splunk-associated resources secret read permissions².
• Security:
  • Avoid hardcoding credentials; use Splunk’s encrypted storage (via splunk.secret) for intermediate credentials if required
  • Leverage Azure’s automatic key rotation and audit logging³ for compliance
• Implementation Options:
  • Custom script in Splunk’s Synthetic Monitoring using Azure SDKs (Python/PowerShell)
  • Azure Event Hubs as middleware, with secrets fetched via Azure Functions¹ before test execution

Limitations

• No direct integration exists between Splunk’s synthetic testing and Azure Key Vault per the provided docs[1-10]
• Manual orchestration required between Splunk’s test framework and Azure’s secret management APIs
• Splunk Observability Cloud lacks native log collection for Azure⁴, requiring Splunk Enterprise/Platform for full customization

For a production implementation, you’d need to develop a custom solution using Azure’s Key Vault SDKs and Splunk’s scripted inputs or modular inputs framework.

If I run a synthetic test outside of Splunk, what is the correct way to get the results into Splunk in a place that will live alongside results of actual Splunk executed synthetic tests?

Here’s how to integrate externally executed synthetic test results into Splunk alongside native Splunk Synthetic Monitoring data:

Primary Method: HTTP Event Collector (HEC)

1. Splunk HEC Setup Configure an HEC endpoint in Splunk Enterprise/Cloud to receive JSON-formatted test results⁵. Use the same timestamp format and field naming conventions as native Splunk synthetics for consistency.
2. Data Structure Requirements Match Splunk’s native synthetic test schema:

{
  "test_type": "browser|api|uptime",
  "test_name": "External_Checkout_Flow",
  "transaction_name": "Payment_Processing",
  "duration_ms": 2450,
  "success": true,
  "location": "aws-us-west-2",
  "error_details": ""
}

1. Security Considerations
   • Use HEC tokens with limited permissions
   • Enable SSL/TLS for data in transit
   • Mirror Splunk’s native IP allowlisting if applicable⁶

Alternative Approaches

Custom Modular Input Develop a Splunk add-on using the Python SDK to:

• Poll external test repositories via API
• Transform data to match Splunk’s synthetics schema
• Output via sourcetype=syndev:http

SIEM Integration Forward external test logs to Splunk via:

• Syslog forwarding for on-prem solutions
• AWS S3/SQS or Azure Event Hub ingestion
• Generic TCP/UDP data inputs

Data Correlation Requirements

To appear alongside native Splunk synthetics in dashboards:

1. Field Alignment Match these critical fields from Splunk’s schema⁵⁷⁸:
   • test_id (use custom prefix like “ext_”)
   • test_type
   • transaction_name
   • step_name (for multi-step tests)
   • location
2. Metrics Compatibility Include equivalent measurements:

# Native Splunk metrics vs. external requirements
"duration_ms" → "sf_metric:test.duration"
"success" → "sf_metric:test.success"
"error_count" → "sf_metric:test.errors"

Visualization Strategy

1. Custom Dashboards Use Splunk’s Dashboard Studio with:
   • Shared time pickers across native/external data
   • Overlay visualizations with source filters (e.g., source=external_synthetics)
2. Alert Integration Create compatible detectors by:
   • Mirroring alert conditions from native synthetics⁹
   • Using common severity levels and notification groups

Limitations to Consider

• No Native Correlation: External tests won’t automatically link to RUM/APM data without custom instrumentation¹⁰
• Schema Maintenance: Future Splunk synthetics schema changes may require ETL updates
• Location Mapping: External test locations need manual mapping to Splunk’s region codes⁶

For production use, the HEC method provides the most maintainable integration when properly formatted to match Splunk’s synthetics schema⁵⁸¹⁰.

What about actual UI tests done with Playwright and such?

Here’s how to integrate Playwright UI test results into Splunk alongside Splunk’s native synthetics:

Data Ingestion Methods

1. HTTP Event Collector (HEC) Format Playwright results as JSON matching Splunk’s synthetics schema:

{
  "test_type": "browser",
  "test_name": "checkout_flow",
  "duration_ms": 2450,
  "success": false,
  "error_details": "Payment button missing",
  "step_metrics": {
    "login": {"duration": 1200},
    "cart_add": {"duration": 800}
  }
}

Use Playwright’s test hooks to emit results programmatically¹¹¹².

1. JUnit Reporter + Splunk Add-ons Configure Playwright to output JUnit XML:

// playwright.config.ts
export default {
  reporter: [['junit', { outputFile: 'results.xml' }]]
};

Use Splunk’s JUnit XML add-on or custom parser to ingest results¹³.

1. Custom Script Integration Post-process Playwright traces using:

# Example: Extract test metrics from JSON
jq '.tests[] | {name, status, duration}' results.json | curl -X POST Splunk_HEC_Endpoint

Combine with Splunk’s scripted inputs¹².

Schema Alignment Strategy

Match Splunk’s native synthetics fields:

Add custom metrics via test steps:

test('checkout', async ({ page }) => {
  const start = Date.now();
  await page.click('#checkout');
  const duration = Date.now() - start;
  await fetch(Splunk_HEC, {
    body: JSON.stringify({step: 'checkout_click', duration})
  });
});

Advanced Integration Patterns

1. Artillery+Playwright Hybrid Use Artillery’s Playwright engine¹⁴ to generate load test metrics alongside functional test results, sending both to Splunk via HEC.
2. AI-Generated Test Correlation Implement the pattern from¹⁵ where AI analyzes test failures, then enrich Splunk events with:

"failure_analysis": {
  "root_cause": "CSS selector drift",
  "confidence": 0.92
}

1. Trace Visualization Store Playwright trace files in cloud storage (S3/Azure Blob), then reference them in Splunk events:

"trace_url": "https://storage/checkout_trace.zip"

Example CI Pipeline (Azure DevOps)

- script: npx playwright test --reporter=json
- task: PythonScript@0
  inputs:
    script: |
      import json
      with open('results.json') as f:
        data = json.load(f)
      # Transform to Splunk schema
      requests.post(SPLUNK_HEC, json=data)
- task: PublishTestResults@2
  inputs: 
    testResultsFiles: 'results.xml'

For full observability, combine with metrics from Artillery’s Playwright integration¹⁶ to track browser-level performance under load.

So, I’ve realized what I’m actually interested in emulating the process of capturing is Splunk Synthetic Browser Tests and all the metrics/logs they inherently track (FCP, TTFB, etc.). It looks like it’s still a bit of a walled garden compared to other Splunk tools due to it being an acquisition of Rigor, and also like there isn’t yet an equivalent data collection standard in OpenTelemetry yet? I mean, I’m not expecting Otel to have a place for “screenshot” or anything, but, yeah … is there no industry-wide spec for capturing what happened during a scripted browser emulation flow?

Here’s the breakdown of synthetic browser test instrumentation and standards:

Splunk Synthetic Browser Test Metrics

Splunk’s Browser Tests capture 45+ default metrics including:

• Core Web Vitals: FCP, LCP, CLS, TTFB¹⁷¹⁸¹⁹
• Resource Timing: CSS/JS load times, image rendering¹⁷²⁰
• Synthetic Transactions: Multi-step flow durations/requests/size²¹
• Visual Artifacts: HAR files, filmstrip screenshots, videos²⁰

These exist in a vendor-specific schema with dimensions like test_id, location_id, and page_position¹⁷²¹.

OpenTelemetry’s Current Browser Capabilities

While OpenTelemetry (OTel) has browser instrumentation, there are key gaps:

1. Visual Elements: No screenshot/trace video support (purely telemetry-focused)²²²³
2. Synthetic-Specific Metrics:
   • Limited predefined metrics for scripted user flows
   • No equivalent to Splunk’s page_position dimension
3. Test Context: OTel focuses on real-user monitoring (RUM) rather than synthetic transactions²²²⁴

OTel’s browser instrumentation does cover:

// Example: Document load timing via OTel
import { DocumentLoadInstrumentation } from '@opentelemetry/instrumentation-document-load';
provider.register(new DocumentLoadInstrumentation());

This captures navigation timing (TTFB, DOM complete) but lacks synthetic test context²⁵²⁴.

Industry Standards Landscape

1. No Unified Synthetic Schema:
   • Each vendor (Splunk, Grafana, New Relic) uses proprietary formats
   • Synthetic monitoring remains a walled garden compared to metrics/logs
2. Emerging Patterns:
   • OTel Semantic Conventions: Generic HTTP/client metrics (no synthetic-specific ones yet)
   • Custom Resources: Adding synthetics.test_name via OTel resource attributes²²²³
3. Visual Artifacts:
   • Typically stored separately (S3/Blob Storage)
   • Linked via URLs in telemetry data²²

Workaround Architecture

To approximate Splunk’s Browser Tests with OTel:

1. Custom Metrics for synthetic flows:

   const meter = otel.metrics.getMeter('synthetic');
   const testDuration = meter.createHistogram('synthetic.test.duration_ms');
   testDuration.record(1200, { test_name: 'checkout_flow' });
   

2. Trace Linking between synthetic steps:

   const span = tracer.startSpan('checkout_payment');
   span.setAttribute('synthetics.step', 'credit_card_validation');
   

3. External Artifact Storage:

   {
     "trace_id": "abc123",
     "screenshot_url": "https://storage/checkout_ss.png",
     "har_location": "s3://bucket/test.har"
   }
   

Key Limitations

• No Direct HAR Integration: OTel can’t natively parse/export HAR files²²²³
• Synthetic Context Propagation: Requires custom instrumentation for test_id/location_id²⁴
• Visual Validation: Screen comparison tools remain vendor-specific

While OTel provides browser instrumentation foundations, synthetic monitoring-specific implementations still require heavy customization compared to Splunk’s turnkey solution¹⁷²²²⁴. The industry lacks a true standard equivalent to Splunk’s 45+ browser test metrics.

How close does the Otel stuff like DocumentLoadInstrumentation get to Splunk Synthetic Monitoring Browser Tests’s 45+ metrics? Mostly? Barely?

Here’s the breakdown of OpenTelemetry’s browser instrumentation capabilities compared to Splunk Synthetic Monitoring’s 45+ metrics:

Core Metrics Coverage

Splunk’s 45+ Metrics include:

• Web Vitals: FCP, LCP, CLS, TTFB
• Resource Timing: CSS/JS load times, image rendering
• Synthetic Transactions: Multi-step flow durations/requests/size
• Network Analysis: HAR file metrics (status codes, payload sizes)

OTel’s Native Coverage with DocumentLoadInstrumentation:

// Captures basic navigation timing
import { DocumentLoadInstrumentation } from '@opentelemetry/instrumentation-document-load';

• TTFB: Yes (via Navigation Timing API)
• DOM Complete: Yes
• Resource-Specific Metrics: Limited to duration/size for individual resources
• Web Vitals: LCP/CLS require manual instrumentation or third-party extensions

Gap Analysis:

Custom Instrumentation Potential

While OTel lacks Splunk’s turnkey metrics, developers can approximate many through:

// Manual CLS measurement
const observer = new PerformanceObserver((list) => {
  list.getEntries().forEach(entry => {
    meter.createCounter('layout_shift').add(entry.value);
  });
});
observer.observe({type: 'layout-shift', buffered: true});

// Synthetic transaction tracing
const transaction = tracer.startSpan('checkout_flow');
transaction.setAttribute('synthetic', true);

Key Limitations

1. Visual Artifacts: No OTel standard for screenshots/HAR files (requires external storage linking)²⁶²⁷
2. Test Context: Splunk’s page_position and transaction_name lack OTel equivalents
3. Multi-Step Flows: Requires manual span parenting vs Splunk’s automatic transaction grouping²⁸

Verdict: Partial Coverage (Barely to Mostly Depending on Effort)

• Out-of-the-Box: Barely (20-30% coverage for core metrics like TTFB, DOM load)
• With Custom Code: Mostly (70-80% coverage possible by adding CLS/LCP instrumentation and synthetic transactions)
• Visual/Network Analysis: Requires External Tools (HAR parsing, screenshot services)

For teams already using OTel, achieving Splunk-like browser monitoring requires significant customization and supplemental services. Splunk’s solution remains more comprehensive for synthetic monitoring-specific needs²⁶²⁹.

Yikes. Any movement in the OTel space to do this, or have they pretty much ceded ground to the vendors and moved on to standardizing other interesting things (e.g. LLM datapoints)?

Here’s the current state of OpenTelemetry’s synthetic monitoring capabilities and future directions:

Synthetic Monitoring in OpenTelemetry (2025 Status)

1. No Dedicated Synthetic Specs: OpenTelemetry has not prioritized synthetic monitoring-specific standards, focusing instead on:
   • Real User Monitoring (RUM) enhancements via browser instrumentation³⁰³¹
   • Semantic conventions for HTTP, databases, and messaging systems³²³³
   • Profiling signals and eBPF-based instrumentation³²³³
2. Vendor-Centric Approach: Synthetic monitoring remains vendor-driven, with solutions like Splunk/Rigor and Grafana maintaining proprietary schemas for:
   • Multi-step transaction tracing
   • Visual artifacts (screenshots, HAR files)
   • Browser performance metrics beyond core web vitals³⁴

Emerging Areas vs. Synthetic Monitoring

Workarounds in Practice

Developers bridge the gap using:

1. Custom Metrics Manual CLS/LCP instrumentation:

// Manual Layout Shift tracking
new PerformanceObserver(list => {
  list.getEntries().forEach(entry => {
    meter.createCounter('layout_shift').add(entry.value);
  });
}).observe({type: 'layout-shift'});

1. Hybrid Architectures Combine OTel traces with external artifact storage:

{
  "trace_id": "abc123",
  "screenshot_url": "s3://bucket/checkout_ss.png",
  "har_location": "azure://container/logs.har"
}

1. Vendor Extensions Tools like Dynatrace/Grafana layer synthetic analysis atop OTel data³⁴³³.

Why Synthetic Monitoring Lags Behind

1. Technical Complexity: Visual validation and multi-step flows require browser-specific instrumentation beyond OTel’s scope.
2. Vendor Lock-In Incentives: Synthetic monitoring remains a competitive differentiator for observability platforms.
3. Prioritization: OTel focuses on universal telemetry standards rather than niche use cases like synthetic tests.

Future Outlook

While no dedicated synthetic monitoring specs are imminent, two paths could emerge:

1. Community-Driven Extensions: Potential for a synthetics semantic convention (similar to http/db)
2. Vendor Collaboration: Major players might standardize through OTel’s Special Interest Groups (SIGs)

For now, OTel remains a complement rather than replacement for Splunk’s synthetic browser tests, focusing on RUM while vendors handle scripted monitoring. The LLM/GenAI observability push³² further diverts attention from synthetic use cases.

Blegh. Not what I wanted to hear. Because I’m not happy with the idea of letting Splunk Synthetic Monitoring Browser Tests access certain authenticated web applications that could benefit from some of that sort of testing.

Here’s how to approach authenticated synthetic testing while maintaining security and avoiding Splunk’s native browser test limitations:

Workaround Architectures for Secure Synthetic Testing

1. Playwright + Tracetest Integration

• Trace-Based Testing: Use Tracetest’s OpenTelemetry integration to:

test('authenticated checkout', async ({ page }) => {
  await page.goto('https://app.com/login');
  await page.fill('#username', process.env.TEST_USER);
  await page.fill('#password', process.env.TEST_PWD);
  const trace = await exportPlaywrightTracesToOTel(); // Custom integration
});

- **Benefits**: Combines Playwright's browser automation with OTel trace analysis
- **Security**: Secrets stay in CI/CD variables, never exposed to third-party SaaS

2. Custom OTel Collector Pipeline

• HTTP Check Receiver (from search result³⁵):

receivers:
  httpcheck:
    targets:
      - endpoint: https://app.com/api/secure
        method: POST
        headers:
          Authorization: ${env:API_KEY}

- **Limitation**: Basic checks only (no multi-step flows)
- **Extension**: Combine with browserless/chromium Docker containers for full UI tests

3. Azure Functions Proxy

• Secure Credential Handling:

public async Task<IActionResult> Run(
    [HttpTrigger] HttpRequest req,
    [KeyVault("test-creds")] SecretClient client)
{
    KeyVaultSecret secret = await client.GetSecretAsync("test-creds");
    // Execute authenticated test via Playwright
}

- **Pattern**:

1. Azure Function fetches creds from Key Vault
2. Executes test via Playwright/Chromium
3. Sends results to Splunk via HEC

Splunk Integration Without Direct Access

HEC-Centric Workflow

graph TD
    A[Playwright Tests] -->|JSON Results| B(Azure Function)
    B --> C{Azure Key Vault}
    C --> B
    B --> D[Splunk HEC]
    D --> E[Splunk Dashboards]

Schema Alignment Requirements

• Must-Match Fields (from Splunk docs³⁶):

{
  "test_type": "browser",
  "test_name": "checkout_auth",
  "duration_ms": 1200,
  "success": true,
  "sf_metric:test.duration": 1200,
  "location": "azure-eastus"
}

- **Critical Additions**: `transaction_name`, `step_name`, `page_position`

Security Hardening

1. Credential Isolation:
   • Use Concealed Global Variables pattern (as in Splunk³⁷) via:

# Playwright config
export TEST_PWD=$(az keyvault secret show --vault-name MyVault --name test-pwd -o tsv)

1. Network Controls:
   • Deploy test runners in isolated Azure VNETs
   • Use private HEC endpoints with IP allowlisting
2. Session Hygiene:

// Playwright teardown
await page.evaluate(() => localStorage.clear());
await context.clearCookies();

Alternatives to Splunk’s Native Browser Tests

1. Grafana Synthetic Monitoring Open-source option with Playwright support, though less integrated with Splunk
2. Checkly + Splunk Integration Commercial alternative with tighter secret management:

CHECKLY_API_KEY=secret_from_azure_vault

1. Self-Hosted Browserless Run Chrome in Docker with OTel instrumentation:

FROM browserless/chrome
ENV OTEL_RESOURCE_ATTRIBUTES=service.name=synthetic-browser

Key Tradeoffs

For your authenticated test case, the Playwright+Azure Functions+HEC pattern provides the best balance of security control and Splunk integration, though requiring custom schema mapping. If trace-level analysis is critical, Tracetest’s OTel integration (from search result³⁸) adds distributed tracing context to synthetic tests.

Is HEC a little sparse compared to what a proper browser test would normally collect as data points, though?

HEC’s data richness compared to native browser test tools: While HEC can transport any data you format, it lacks built-in browser-specific metrics unless explicitly structured. Here’s the breakdown:

What Native Browser Tests Capture (That HEC Doesn’t Automate)

Key Gaps in HEC-Based Collection

1. No Built-In Browser Metrics: HEC won’t automatically capture:

// Example: Playwright's performance timing
const timing = await page.evaluate(() => {
  return {
    loadEventEnd: performance.timing.loadEventEnd,
    domComplete: performance.timing.domComplete
  };
});

You must explicitly extract and format these values.

1. Visual Data Handling: While native tools embed screenshots/HARs³⁹, HEC requires:

{
  "trace_id": "abc123",
  "screenshot_url": "s3://bucket/checkout.png",
  "har_location": "azure://container/logs.har"
}

1. Browser Context: Native tests automatically capture:
   • Viewport dimensions
   • Network throttling profiles
   • GPU rendering stats

Workarounds for HEC Limitations

1. Manual Metric Enrichment Augment HEC payloads with Playwright’s performance API:

test('checkout', async ({ page }) => {
  const lcp = await page.evaluate(() => {
    return new Promise(resolve => {
      new PerformanceObserver(list => {
        resolve(list.getEntries()[^8_0].startTime);
      }).observe({type: 'largest-contentful-paint', buffered: true});
    });
  });
  
  await fetch(HEC_ENDPOINT, {
    body: JSON.stringify({lcp, test_name: 'checkout'})
  });
});

1. Schema Alignment Replicate Splunk’s synthetic monitoring schema:

{
  "sf_metric:test.lcp": 1200,
  "sf_metric:test.cls": 0.15,
  "browser_version": "Chrome/125.0",
  "is_heuristic": false
}

1. CI/CD Integration Combine with performance testing tools:

# GitHub Actions example
- name: Run Playwright Tests
  run: npx playwright test --reporter=json
- name: Enrich with Web Vitals
  run: python add_web_vitals.py results.json
- name: Send to Splunk
  run: curl -X POST $HEC_URL -d @results.json

When HEC Suffices

For basic synthetic monitoring, HEC works well if you:

• Pre-process data to match Splunk’s schema
• Supplement with external storage for visual artifacts
• Accept manual metric instrumentation overhead

For full browser test fidelity, native tools remain superior due to their built-in rendering context capture⁴²⁴¹⁴⁰. HEC requires significant customization to approach equivalent capability.

I can’t find any evidence that Splunk browser tests help you evaluate whether the data inside a given UI element is correct. I see lots of stats captured about whether it exists and how long it took to exist and whatnot, but not much about whether it’s the right content, given the path you took to get there. Why not? Is that something that product teams traditionally presume will always be accurate if it was accurate at release-time E2E testing, with operational observability only caring about how fast \& available UI elements are (regardless of their content accuracy)? Or are Splunk browser tests missing an important feature for synthetic monitoring? Or am I reading the Splunk docs wrong?

Here’s the breakdown of Splunk Synthetic Monitoring’s content validation capabilities and the underlying philosophy:

What Splunk Browser Tests Can Validate

1. Element Presence/State
   • Existence of DOM elements via selectors
   • Form field values (via JavaScript assertions)

if (document.querySelector('#order-status').innerText !== 'Shipped') {
  throw new Error('Order status incorrect');
}

(As shown in the form validation example from search result⁴⁴)

1. Metadata Verification Validate HTML attributes and meta tags:

const ogType = document.querySelector('meta[property="og:type"]').content;
if (ogType !== 'website') throw new Error('Invalid OG type');

(From the website metadata validation example in⁴⁴)

1. Custom JavaScript Assertions Execute arbitrary JS to verify:
   • API response data in UI elements
   • Client-side state (e.g., Redux store values)
   • Calculated values in SPAs

What Splunk Browser Tests Don’t Do Well

1. Semantic Content Validation No built-in tools for:
   • Natural language processing of UI text
   • Image content verification (e.g., OCR-based checks)
   • Complex data consistency across microservices
2. Visual Regression While filmstrips/videos exist (⁴⁵), there’s no:
   • Pixel-level comparison tools
   • Layout shift quantification beyond CLS
   • Automated screenshot diffing

Why This Gap Exists

1. Observability vs. Testing Philosophy Splunk’s synthetic monitoring focuses on:
   • Operational signals (availability, performance)
   • User journey completeness (did the flow complete?) Rather than functional correctness (is the data right?).
2. Product Scope Boundaries Content accuracy is traditionally handled by:

graph LR
  A[Unit/Integration Tests] -->|Pre-Prod| B[CI/CD]
  C[Synthetics] -->|Prod| D[Performance/Ops]

With Splunk positioned in the observability (right) side.

1. Technical Constraints Validating dynamic content requires:
   • Test-specific selectors (fragile in SPAs)
   • Business logic awareness (beyond pure observability)
   • Stateful test context (problematic at scale)

Workarounds for Content Validation

1. Custom JavaScript Assertions Extend tests with DOM inspection logic:

// From search result [^9_5]
const blogCategory = splunkMeta.page.blogCategory;
if (blogCategory !== "Learn") throw new Error('Incorrect category');

1. API Validation Hybrid Combine with Splunk APM/Logs to:

const uiValue = document.querySelector('#price').innerText;
const apiPrice = await fetch('/api/price').then(r => r.json());
if (uiValue !== apiPrice) throw new Error('Price mismatch');

1. Visual Regression Services Pipe filmstrip URLs to external tools like:
   • Applitools
   • Percy.io Via Splunk’s webhook integrations

Industry Context

Most synthetic monitoring tools follow this pattern due to:

• Scale Challenges: Content validation requires test-specific logic that doesn’t generalize well
• Alert Fatigue Risk: Functional false positives increase at high test frequencies
• Ownership Boundaries: Content accuracy typically falls under QA/test engineering, not ops

For mission-critical content validation, teams often layer Splunk synthetics with:

graph TB
    A[Playwright/Jest] -->|Functional Tests| B[CI/CD]
    C[Splunk Browser Tests] -->|Operational| D[Observability]

This separation keeps content validation in pre-prod while using Splunk for production UX monitoring. The docs (⁴⁶⁴⁵⁴⁴) confirm this operational focus, though the JavaScript capability provides an escape hatch for basic content checks.

You mentioned “Business logic awareness (beyond pure observability).” Is webapp observability often done in a “pure” fashion, caring little about busines logic?

Here’s the breakdown of business logic’s role in web application observability:

The “Purity” Spectrum in Observability

1. Technical Observability (Pure Form): Focuses on system health metrics:
   • Latency (API response times)
   • Error rates (HTTP 500s)
   • Resource utilization (CPU/memory)
   • Availability (uptime checks)
2. Business-Aware Observability: Incorporates domain-specific context:
   • Conversion funnel drop-off rates
   • Shopping cart abandonment metrics
   • A/B test variant performance

Why Business Logic Often Gets Excluded

1. Tooling Limitations: Most observability platforms (including Splunk) prioritize:

graph TD
  A[Infrastructure] --> B[Observability Tools]
  C[Applications] --> B
  D[Business Logic] -->|Hard to Instrument| B

1. Ownership Boundaries:
   • DevOps/SREs: Care about system stability
   • Product Teams: Own business outcomes
   • Rarely share instrumentation requirements
2. Instrumentation Complexity: Business logic validation requires:
   • Custom code annotations
   • Domain-specific metric definitions

// E-commerce example: Business logic instrumentation
const orderValue = calculateOrderTotal();
metrics.gauge('checkout.order_value', orderValue, { customerType: 'premium' });

Emerging Solutions (From Search Results)

1. Multimodal Analytics (⁴⁷): Combine technical metrics with business data:

# Correlate app errors with revenue impact
errors = splunk.query('error_count by hour')
sales = snowflake.query('gmv by hour')
plot_correlation(errors, sales)

1. Process Mining (⁴⁸): Map technical flows to business outcomes:

graph LR
  A[API Latency Spikes] --> B[Checkout Page]
  B --> C[Abandoned Carts]
  C --> D[Revenue Loss]

1. Context Enrichment (⁴⁹): Add business tags to telemetry:

{
  "trace_id": "abc123",
  "error": "Payment Failed",
  "business_context": {
    "customer_tier": "gold",
    "cart_value": 245.99
  }
}

Implementation Challenges

1. Data Silos: Business metrics (CRM, ERP) live separately from observability tools
2. Schema Alignment: Technical timestamps vs. business fiscal periods
3. Privacy Constraints: PII in business data complicates observability pipelines

The Way Forward

Leading organizations bridge the gap by:

1. Embedding Business Context:

func ProcessOrder(order Order) {
    ctx := context.WithValue(ctx, "campaign_id", order.CampaignID)
    // Process order
}

Propagate business metadata through traces

1. Unified Data Platforms: As described in⁴⁷ and⁴⁹, tools like ChaosSearch and Faddom enable:
   • Logs + CRM data correlation
   • Infrastructure maps with revenue impact layers
2. Observability-Driven Development: Make business outcomes part of SLOs:

objectives:
  - name: checkout_conversion
    slo: 95% of sessions complete checkout
    metrics:
      - source: snowflake
        query: "SELECT successful_checkouts/total_sessions"
      - source: splunk
        query: "transaction:checkout success=1"

While “pure” observability dominates tooling today, the industry is moving toward business-aware implementations through custom instrumentation and unified data platforms. The key is treating business metrics as first-class citizens in observability pipelines.

So, then, what should product teams spec out as browser test flows, if they just want to help an ops team monitor “pure” flows through their application? How do you even do that, when the key journeys through an application feel so product-centric? And yet you say it’s doable? What advice can I give to product teams about synthetic browser test design examples and best practices?

Here’s a blueprint for product teams to design effective synthetic browser tests that balance product-centric journeys with ops-focused monitoring:

Core Principles for “Pure” Flow Tests

1. Focus on Technical Completion Validate that critical paths technically succeed (load, render, submit) without verifying business logic accuracy. Example:
   • Ops Care: Checkout page loads in <2s
   • Product Care: “Thank You” message shows correct order total
2. Map to Infrastructure Dependencies Test flows that stress:

graph TD
  A[Login] --> B[API Gateway]
  C[Checkout] --> D[Payment Service]
  E[Search] --> F[Elasticsearch]

Test Design Framework

1. Critical Journey Selection

• Authentication Flow

Scenario: Login Technical Validation
  When User enters valid credentials
  Then Session cookie is set
  And Redirect to dashboard completes in <1s

• Checkout Pipeline Validate: Cart → Shipping → Payment → Confirmation page transitions

2. Test Anatomy for Ops

// Splunk test example - Technical focus
step('Checkout Load', async () => {
  await page.goto('/checkout');
  await validateHttpStatus(200); // Ops metric
  await assertElementExists('#payment-form'); // Technical validation
});

3. Best Practice Implementation

• Element Selectors Use data-testid attributes instead of CSS classes:

<button data-testid="checkout-submit">Pay Now</button>

• Validation Layers

// Layer 1: Technical (for Ops)
assertResponseTime(step, '<1500ms');

// Layer 2: Functional (for Product)
assertTextContent(orderTotal, '$29.99');

Product-to-Ops Translation Table

Test Design Best Practices

1. Atomic Step Design Break flows into discrete technical units:

graph LR
  A[Login API Call] --> B[Dashboard Render]
  C[Add to Cart] --> D[Inventory Check]

1. Synthetic RUM Hybrid

// Capture both synthetic and real user metrics
logMetric('checkout_lcp', {
  synthetic: 1200,
  rum: window.performance.now()
});

1. Failure Archetypes Design tests to detect:
   • DNS failures
   • TLS handshake issues
   • CDN caching problems
   • API gateway timeouts

Example Test Flows

1. Auth Flow Technical Validation

test('Login Technical', async ({ page }) => {
  await page.goto('/login');
  await page.fill('#username', process.env.TEST_USER);
  await page.fill('#password', process.env.TEST_PWD);
  await page.click('#submit');
  
  // Ops-focused assertions
  await expect(page).toHaveURL(/dashboard/);
  await expect(page).toHaveResponseTime('<=800ms');
  await expect(page).toHaveNoConsoleErrors();
});

2. Checkout Pipeline Monitoring

Scenario: Checkout Technical Sanity
    Given Valid cart session
    When User proceeds through checkout steps
    Then Payment gateway API returns 200
    And Order confirmation page loads in <2s
    And No mixed content warnings

Collaboration Strategy

1. Shared Ownership Model
   • Product defines critical user journeys
   • Ops implements technical validation layers
   • Shared dashboards track:

{
  "layer": [
    {"mark": "area", "encoding": {"y": {"field": "synthetic_success_rate"}}},
    {"mark": "line", "encoding": {"y": {"field": "rum_success_rate"}}}
  ]
}

1. Test Lifecycle Management
   • Version control tests alongside code
   • Automate test updates via CI/CD hooks

# GitHub Actions
- name: Update Splunk Tests
  run: |
    splunk-test-cli sync $

Key Metrics to Expose

1. Technical Completion Rate (Successful test steps / Total steps) * 100
2. Infrastructure Dependency Health API success rates per test step
3. Render Consistency Variance in LCP/FCP across test runs

By focusing on technical completion metrics while aligning with product-critical journeys, teams create synthetic tests that serve both operational monitoring and product experience goals. The secret is instrumenting product flows without business logic assumptions - think “does the page work” not “is the data perfect.”

What is the syntax for referencing the value of a ““-style Splunk synthetic browser test that I set in a previous “save text from element” step within the code of a subsequent “Execute JavaScript” step?

Here’s the breakdown of variable usage in Splunk Synthetic Browser Tests based on the documentation and community findings:

Variable Reference Syntax

1. Supported Scenarios
   • Fill in Field steps: Use `` directly in input fields
   • HTTP Headers/Params: Available for API test steps (not browser tests)
   • Execute JavaScript steps: No native support for custom variable access⁵⁰⁵¹
2. Workaround for JavaScript Steps Use DOM manipulation to pass values between steps:

// Step 1: Save text to hidden element
document.querySelector('#temp-storage').textContent = element.textContent;

// Step 2: Retrieve from hidden element
const value = document.querySelector('#temp-storage').textContent;

Documentation vs. Reality

Community Confirmation

“Unfortunately for browser tests you can only use global variables to fill any fields on the web page with the ‘Fill in Field’ action” – Splunk Community Moderator⁵⁰

Recommended Pattern

1. Element-Based Storage Create hidden form fields to persist values across steps:

<input type="hidden" id="temp-var-storage">

1. JavaScript Access Pattern

// Save to hidden field
document.getElementById('temp-var-storage').value = 'your_value';

// Retrieve later
const savedValue = document.getElementById('temp-var-storage').value;

Splunk’s Official Stance

While documentation mentions `` syntax⁵¹, JavaScript steps cannot access these variables programmatically. The feature appears limited to form field population only, with no workaround provided in the current implementation⁵⁰⁵¹. For advanced use cases, consider preprocessing values before test execution or using external data storage.