Your
Trusted
Get Fully AWS Funded
Cloud Migration
Cloud Migration
AWS CloudWatch Synthetics runs configurable canary scripts on a schedule to monitor endpoints, APIs and defined application journeys. The scripts can validate responses and produce availability and latency signals for investigation and alerting.
Synthetic monitoring tests a defined path from a chosen perspective. Application metrics, logs and traces answer different questions about what happened inside the system. Use the signals together so a failed journey can be investigated with the evidence needed to act.
Terraform makes the check configuration, packaging inputs, schedules and alarms part of a version-controlled infrastructure workflow. That gives teams a review point for operational changes and a repeatable way to apply the intended configuration across environments.
Prioritise journeys where failure blocks a customer or a critical business process. Sign-in, account creation, checkout, public API calls, document upload and integration handoffs are common candidates. Choose the route based on actual service dependency and impact, then assign clear ownership for the response.
“A service can look healthy in dashboards while a customer-facing path is already broken. A login flow may fail after an identity change, a checkout may return an error after a dependency update, or a critical API may respond slowly enough to interrupt an automated process.”
These failures need a signal that follows the path a user or integration actually takes.
CloudWatch monitoring can provide that signal when synthetic checks are treated as an owned part of the platform. The check, its schedule, its alert and its response path should live with the systems they protect, so teams can review changes before they reach production and understand who acts when the signal changes.
An uptime check is an operational contract. It states which journey matters, from which perspective it is tested, how often it runs and which condition should trigger attention. Leaving those decisions in a console makes them easy to create and hard to review later. The check becomes detached from the application change, the alerting rule and the team that owns the response.
A small set of well-chosen checks gives leadership and engineering teams a shared view of whether essential customer paths are working. It also makes the limits visible: one passing HTTP request does not confirm that authentication, permissions, browser execution and downstream dependencies are working together. Each check should have a purpose and a defined next step when it fails.
CloudWatch Synthetics canaries run configurable scripts on a schedule against endpoints and APIs. A canary can follow a route through an application, make requests, validate responses and report availability or latency signals. The module’s default CloudWatchSynthetics/Failed alarm detects failed runs. For slow responses, add a duration alarm or an explicit timing check with a threshold; a successful but slow path otherwise needs its own signal. This makes it useful for checking an API endpoint, a sign-in journey, a transaction step or another route that represents real service value.
The value comes from choosing the journey carefully. Start with flows where failure blocks a customer, a partner or an internal operator: authentication, account creation, payment confirmation, a document upload, a public API call or a critical integration handoff. Keep the script focused on the evidence the team needs, then make its expected result explicit.
Canaries cover one perspective on service behaviour. Metrics, logs and traces supply the context needed to investigate a failure, while application and infrastructure ownership determine who coordinates the response. Treating these signals as connected operating inputs helps avoid an alert that only says something is wrong.
Terraform turns the canary configuration into a reviewable change. Source-controlled configuration can show the script location, runtime settings, schedule, tags, alarm threshold and ownership context in the same workflow as other infrastructure changes. That reduces the chance that a production check becomes a console-only exception with no clear history.
Code also makes environment differences easier to reason about. A team can use the same module pattern across development, staging and production while supplying environment-specific endpoints, schedules, credentials references and alert routing. The goal is consistent intent with explicit differences, rather than identical checks copied manually between accounts.
Das Meta’s monitoring module added a generic CloudWatch Synthetics capability in its v1.23.0 release. It is designed for teams that keep private Python canary code in their own repository and want Terraform to package the source, upload the artifact and create the supporting AWS resources as part of the infrastructure workflow.
The CloudWatch Synthetics module accepts a map of ZIP destination paths to source files relative to the Terraform root. Terraform uses archive_file to build the ZIP, uploads it to a private S3 artifact bucket, then creates the canary, its IAM role and a CloudWatchSynthetics/Failed alarm. Each source map must include python/canary.py; the module fixes the handler to canary.handler and the runtime to syn-python-selenium-11.1. Keep the canary source close to the service or platform code it exercises, and keep secrets out of checked-in configuration and Terraform state. Protect canary outputs as well: logs, screenshots, HAR files and other artifacts can contain sensitive information. Restrict access to their S3 and CloudWatch Logs locations, and use Synthetics redaction controls for sensitive URL parameters and headers.
The v1.23.1 release added support for optional explicit canary names. Set canary_name at initial creation when a stable external identity is required for alerts or surrounding operations. Adding it after apply, or changing it on an existing canary, replaces that canary; omitting it retains the module-generated name without replacement.
A canary is only one part of an effective response. Define the owner for the journey, the team that receives the alarm, the severity rule, the first diagnostic action and the escalation path. Capture the route to related dashboards, logs, traces and runbooks where responders can see them without reconstructing the system under pressure.
Alert quality matters as much as coverage. In a Grafana Labs survey of 1,363 engineers, SREs and technology leaders across 76 countries, 30% cited alert fatigue as the biggest obstacle to faster incident response. Use failure thresholds that match the journey’s importance and the check schedule. A single failed run can be useful evidence for a high-risk flow, while a less critical route may need repeated failures before it should page someone. The right policy is one that prompts timely action without teaching the team to ignore its alerts.
Synthetic checks should evolve with the application. Review them when customer journeys, dependencies, authentication models or deployment patterns change. Retire checks that no longer represent a useful path, and add new ones when a new capability becomes operationally important.
Start small enough to create an operating habit, then widen coverage from evidence rather than from a generic checklist.