For example, a service can be both a control plane (such as occasionally-called CRUD APIs on long-living resources) and a data plane (high throughput business-super-critical APIs). We would want the data plane APIs to continue to operate even if the control plane APIs are having trouble talking to their dependencies. • Any unanticipated failure mode—Sometimes servers fail in such a way that they return errors that they identify error as the client’s instead of theirs .
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Servers may slow down instead of failing, or they may respond faster than their peers, which is a sign that they’re returning false responses to their callers. Anomaly detection is an incredible catchall for unanticipated failure modes.
- • Configure the work producer to perform liveness and local health checks.
- Implementations around Amazon range from a simple semaphore in Java to the more complex analysis of trends in CPU utilization.
- Servers also fail for correlated reasons that cause many or all servers in a fleet fail together.
- Servers are taken out of service automatically by the load balancer only if they have some problem that is definitively local to that server, such as a bad disk.
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More information about how Stanford Health Check handles your personal data can be found at the Health Check FAQ. When zombie servers come back to life they can be significantly out of sync with the rest of the fleet, which can cause serious problems.
For example, if a zombie server is running a much older, incompatible software version, it can cause failures when it tries to interact with a database with different schema or it can use the wrong configuration. Servers can become disconnected from the network for periods of time but remain running, or they can power off for extended periods and later be rebooted. This deployment-per-Availability Zone alignment is convenient because services are already designed to be able to keep operating if there are problems with a single Availability Zone. Even though we separate functionality into different services, each service likely serves multiple APIs. If one API is impacted, we prefer for the service to continue serving the other APIs.
Software issues, such as deadlocks or bugs in connection pools, can also hinder network communication. However, when a server doesn’t see an update for a while, it doesn’t know whether the update mechanism is broken or the central update system stopped publishing updates to all servers. • Inability to write to or read from disk—It may be tempting to believe that a stateless service doesn’t require a writable disk. However, at Amazon our services tend to use their disks for things like monitoring, logging, and publishing asynchronous metering data.