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What event-driven microservices are—and are not
In an event-driven architecture, a service publishes a message when something relevant happens, and one or more consumers handle it. For example, an Order service might publish OrderPlaced; a fulfillment service can reserve stock while a notification service prepares a confirmation. The producer need not call each consumer directly.
An event records a fact; a command requests work
An event describes something that has happened, such as PaymentAuthorized. A command asks a particular service to do something, such as AuthorizePayment. Naming and modeling that distinction helps consumers understand whether they are reacting to a fact or being asked to perform an action. Business rules and authoritative data should remain with the service responsible for them; consumers should not need to query the producer’s database directly.
Asynchronous communication changes coupling; it does not erase it
A broker can separate a producer from the immediate availability of its consumers, and several consumers can react independently. But the services still depend on shared meanings, payloads, delivery behavior, and operational processes. Microsoft’s Azure Architecture Center describes event-driven architectures as involving patterns such as competing consumers, stream processing, choreography, saga orchestration, and observability across decoupled components.
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Which communication pattern fits the interaction?
Choose based on whether a caller needs an immediate answer, how work should be distributed, and whether consumers need retained history. A broker is not automatically the right choice for every service interaction.
| Pattern | Best fit | Key design consideration |
|---|---|---|
| Synchronous request and response | A caller needs an immediate result to continue, such as a validation or lookup. | The caller depends on the responding service’s availability and response time. Use timeouts and failure isolation for these remaining direct calls. |
| Event notification | Consumers need to know that a change occurred and can obtain any further details through an agreed interface. | Keep the event’s meaning clear; avoid making consumers depend on private producer data. |
| Event-carried state | Consumers need enough information in the event to act independently. | Design a stable payload that represents a business contract rather than exposing an internal database model. |
| Competing-consumer queue | A work item should be handled by one worker in a pool. | Confirm the platform’s delivery, ordering, and retry behavior; duplicate handling remains important. |
| Retained event stream | Multiple independent consumers or stream-processing applications need to read events, potentially at different rates or from retained history. | Retention, replay, consumer progress, ordering scope, and storage semantics depend on the platform and its configuration. |
These labels describe different patterns, not guarantees supplied by every broker. Before relying on replay, ordering, fan-out, or delivery semantics, verify how the selected platform implements them.
How to handle a business workflow across services
When each service owns a separate data store, do not assume one ACID transaction can cover the whole distributed workflow. Instead, each participant commits a local transaction and the workflow coordinates what happens next. Microsoft Learn’s Azure Architecture Center defines a saga as managing data consistency across microservices with independent data stores.
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Model the saga as local work and business compensation
- Define the business outcome and the services that own each step and its data.
- For each step, specify the local transaction, the event or message that advances the workflow, and the failure conditions.
- Decide what should happen when a later step cannot complete. Design a compensating action for earlier effects where appropriate.
- Test successful paths, failures at each step, duplicate messages, and recovery or replay behavior.
Compensation is business logic, not necessarily a literal rollback. If an email has already been sent, for example, the remedy may be a correction rather than erasing the original message. The workflow must make that outcome explicit.
Choose choreography or orchestration deliberately
In choreography, participants react to events and publish further events without a central coordinator deciding every transition. This can keep participants independent, but a long workflow may become difficult to follow. In orchestration, a coordinator directs the sequence of actions, making the control flow easier to inspect but introducing a central workflow component. Azure’s saga guidance describes local transactions and compensating actions; AWS Prescriptive Guidance treats choreography and orchestration as distinct saga approaches. The choice depends on workflow complexity, visibility, ownership, and failure handling—not a universal rule that one style is better.
How to avoid losing a message between a database and a broker
A producer can commit a database change and then fail before publishing the corresponding message. The business state now says one thing while downstream consumers have not been told about it. A transactional outbox addresses this gap by recording both the business update and the event to publish in the same local database transaction.
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What the outbox does—and what it does not
A separate relay reads outbox records and publishes them to the broker. Because the relay may publish a record more than once, an outbox does not eliminate duplicate delivery or the need for duplicate-safe consumers. It is a reliability pattern, not a broker-specific guarantee. The service team also needs ownership for relay monitoring, recovery, outbox retention and cleanup, and event-schema evolution.
How to make event processing reliable
Design for the actual delivery and processing behavior of the broker and application. Do not promise end-to-end “exactly once” as a blanket property: a consumer can receive an event again, and failures can happen between processing and acknowledging work.
Make handlers idempotent
A handler is idempotent when processing the same event repeatedly does not repeat a non-idempotent business effect. Use stable event IDs and, where appropriate, durable deduplication records or idempotency keys. Microsoft’s event-sourcing guidance notes that consumer delivery is typically at least once, so duplicates are possible. Deduplication state must be durable enough for the relevant retry and replay window.
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Set retry, dead-letter, and replay policies
- Retry transient failures with bounded policies and backoff; unbounded or aggressive retries can create retry storms.
- Define what happens when retries are exhausted, including where failed messages can be inspected and who owns that work.
- Make replay a supported procedure with controls, not an improvised action. Confirm whether replay can repeat side effects or change ordering.
- Document which failures are safe to retry and which require correction or human intervention.
Microsoft’s event-driven architecture guidance discusses competing consumers and observability; its microservices design-pattern guidance covers failure-handling patterns. The precise retry and dead-letter mechanisms remain platform-specific.
Scope ordering to the business requirement
Do not assume that all events arrive or are processed in one global order. If a workflow needs per-entity ordering, define the entity key—for example, an order ID—and verify that the chosen broker’s partitioning and delivery behavior preserve the required order for that key. Retries, recovery, and resubmission can still affect processing order.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Event streaming is not event sourcing
Event streaming distributes events to consumers and can support stream processing or retained reads. Event sourcing is a persistence model: it stores state changes as an append-only sequence from which current state or projections can be derived. A system can use a stream or broker without making that broker its authoritative event store.
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Event sourcing may support audit and replay requirements, but it also makes schema evolution, projection rebuilding, retention, privacy, and recovery part of the design. Microsoft’s Azure Architecture Center notes that Kafka can distribute events to projections and external consumers, but is not a substitute for an event store when per-entity stream queries and optimistic concurrency are required. Adopt event sourcing only when durable event history and replay are actual requirements.
What to monitor across asynchronous boundaries
A request trace may no longer show the full path once work crosses a broker and is handled later. Standardize correlation identifiers and propagate trace context where supported so an event can be connected to its originating action and downstream work. Monitor logs, metrics, traces, consumer lag, failures, retry activity, and replay behavior across service boundaries.
AWS’s 2023 Architecture Blog guidance recommends decentralized ownership of event-driven components alongside centrally standardized logging and observability practices. This pairing gives service teams responsibility for their components without making cross-service diagnosis depend on incompatible telemetry conventions.
Design questions to answer before adopting the pattern
- Interaction: Does the caller need an immediate answer, or can the result arrive asynchronously?
- Consistency: Is a local transaction sufficient, or does the business workflow cross service-owned data stores and require a saga?
- Delivery and replay: Are duplicates safe? Must events be retained or replayed? Is the need work distribution or stream processing?
- Ordering and latency: Which entity requires ordered processing, and how much delay is acceptable?
- Operations: Can the team operate the broker, event schemas, retries, dead letters, consumer lag, and distributed tracing?
- Ownership and evolution: Who owns each event contract, data boundary, compatibility policy, and process for discovering and changing consumers?
- Security and lifecycle: What access controls, sensitive-data handling, retention, and replay policies apply in this deployment?
These questions reflect concerns addressed in Microsoft’s Azure Architecture Center guidance on event-driven architecture, sagas, event sourcing, and microservices readiness, as well as AWS architecture guidance. Specific regulatory obligations and platform guarantees depend on the deployment context.
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