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The system uses a configuration file to define behavior. The system reads a configuration file at startup to determine how each component behaves, how services are discovered, how retries and timeouts are applied, and how logs are formatted. This configuration file is typically named config.yaml and is loaded from the application root unless an explicit path is provided through the environment variable APP_CONFIG_PATH. Every field in the file has a documented default, so an empty file is technically valid, but production deployments are expected to override at least the service endpoint, the database connection string, and the log level. The loader parses the file into a strongly typed structure, validates each field against its declared type and range, and aborts startup with a non-zero exit code if validation fails. Validation errors are reported together rather than one at a time, so operators can fix multiple mistakes in a single edit. The loader also supports environment variable interpolation using the $ syntax, which lets secrets stay out of version control while keeping the rest of the configuration readable. Interpolation is applied after parsing but before validation, and unresolved variables cause a startup failure rather than silently becoming empty strings. Nested structures are supported to a depth of ten levels, which is more than any realistic deployment needs but avoids arbitrary limits that surprise users. The schema is versioned with a top-level apiVersion field, and the loader rejects files whose version it does not understand instead of guessing at compatibility. When a field is renamed between versions, the loader accepts the old name for one major release, emits a deprecation warning to the log, and records the warning in a metrics counter so operators can track migration progress. The configuration is immutable after startup; components that need to change behavior at runtime must expose their own administrative interface rather than mutating shared state. Hot reload is deliberately not supported, because partial reconfiguration of a running system is a common source of inconsistent state and hard-to-reproduce bugs. Instead, a configuration change is applied by restarting the process, which is simple, predictable, and compatible with rolling deployment strategies. The restart is cheap: startup completes in under two seconds for a typical configuration, and readiness probes prevent traffic from reaching a process that has not finished loading. The file is read exactly once, and the resulting structure is passed by reference to every component that needs it. Components must not retain pointers into the configuration beyond their initialization phase, because doing so couples their lifetime to a structure they do not own. The recommended pattern is to copy the specific values a component needs into its own fields during construction, which makes the dependency explicit and keeps the component testable in isolation. Tests construct the configuration structure directly in code rather than reading files from disk, which keeps them fast and independent of the working directory. A small number of integration tests do read real files, and those files live under testdata/ with names that describe the scenario they exercise, such as minimal.yaml, full.yaml, and invalid-missing-endpoint.yaml. The loader is covered by unit tests for every validation rule, including boundary values, and by property-based tests that generate random valid configurations and assert that parsing then serializing then parsing again yields an identical structure. Serialization is provided for tooling but is not used at runtime; the canonical form of a configuration is the file on disk, and any generated output is clearly marked as derived. Comments in the file are preserved by editors but discarded by the loader, so documentation that must survive belongs in the schema description rather than in inline comments. The schema itself is published as a JSON Schema document, which enables editor autocompletion and inline validation for anyone writing a configuration by hand. The schema is generated from the same source as the runtime validation code, so the two cannot drift apart. Secrets are handled specially: fields marked as sensitive are redacted in logs, excluded from the diagnostic dump, and never included in error messages, even when validation fails on them. A validation failure on a sensitive field reports only the field name and the nature of the problem, never the value. The diagnostic dump is a single command that prints the effective configuration with secrets redacted, and it is the first thing support asks for when a deployment misbehaves. The dump includes the source of each value, distinguishing values that came from the file, from environment variables, and from defaults, which resolves most questions about why a setting has an unexpected value. Defaults are chosen to be safe rather than convenient: timeouts default to values that fail fast, retry counts default to zero, and any feature that could cause data loss defaults to disabled. Enabling such a feature requires an explicit opt-in, and the documentation for each such field explains the risk in plain language. The configuration format was chosen over alternatives such as a database-backed settings service because it keeps deployments self-contained and reviewable; a change to behavior is a change to a file that goes through the same review process as code. This property is valuable enough that the team accepted the trade-off of requiring a restart to change behavior. For multi-tenant deployments, a single configuration can describe multiple tenants, and each tenant entry inherits from a shared defaults section unless it overrides a field. Inheritance is shallow and explicit: a tenant either specifies a field or it does not, and there is no deep merging of nested structures, because deep merge semantics are difficult to explain and easy to get wrong. The effective configuration for a tenant can be printed with the same diagnostic command used for the whole process, scoped to that tenant. Configuration errors are the most common cause of failed deployments, so the error messages are written with particular care. Each message states which file was read, which field failed, what was expected, and what was found, in that order. When the failure is caused by an environment variable interpolation, the message names the variable and notes that it was unset or empty. When the failure is caused by a type mismatch, the message shows the offending value only if it is not sensitive. Messages never include a stack trace, because the location of a validation failure is not useful to the operator and the trace obscures the actual problem.

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