The Saga pattern is a sequence of transactions that updates each microservice and publishes events to trigger the next transaction. If any step fails, a compensating transaction is executed.
The two types of Saga implementations are:
- **Choreography**: Each service listens for events and decides the next action.
- **Orchestration**: A centralized orchestrator manages the Saga flow.
CQRS (Command Query Responsibility Segregation) separates read and write operations, improving performance, scalability, and security by using different models for querying and updating data.
Event Sourcing is a pattern where the state of an application is stored as a sequence of events instead of the current state. The state is rebuilt by replaying these events.
Spring Boot supports Saga through frameworks like **Axon Framework**, **Camunda**, and **Spring Cloud Data Flow**, which help in orchestrating and managing distributed transactions.
- Improves system scalability by separating read and write models.
- Optimizes query performance.
- Allows using different databases for read and write operations.
- Increased complexity in managing events.
- Eventual consistency issues.
- Higher storage requirements due to event persistence.
The Outbox Pattern ensures reliable event publishing by storing events in an "outbox" table within the same database transaction as the main update, avoiding inconsistent states.
A compensating transaction reverses the effects of a previous transaction if a failure occurs, ensuring data consistency across microservices.
Kafka enables event-driven CQRS by storing events in topics, allowing different services to consume and process them separately for read and write models.
Spring Cloud Stream provides message-driven communication using brokers like Kafka and RabbitMQ, enabling event-based coordination in Saga workflows.
An Event Store is a database or system that persists events in an append-only manner, allowing microservices to retrieve and replay events to reconstruct system state.
Axon Framework provides built-in support for Event Sourcing by handling event storage, replay, and dispatching, making it easier to implement CQRS and Saga.
- **Command Side**: Handles data modification operations.
- **Query Side**: Optimized for read operations.
- **Event Bus**: Transfers events between components.
In a Choreography-based Saga, microservices listen for events and react to them independently, eliminating the need for a central orchestrator.
- Difficult to track and debug due to distributed nature.
- Increases event complexity as the number of services grows.
- Harder to maintain consistency across services.
In an Orchestration-based Saga, a central service (Saga Orchestrator) coordinates transaction steps, calling each microservice and handling compensations if needed.
- Centralized control improves tracking and debugging.
- Clear transaction flow.
- Easier to implement compensating transactions.
Spring Boot can use Axon Framework, Kafka, or RabbitMQ to separate command and query models, leveraging event sourcing to persist changes in an event log.
- Increased complexity due to separate models.
- Eventual consistency issues.
- Requires additional infrastructure for event storage.
Kafka acts as an event store, where each event is stored as a message in a topic. Consumers can replay events to reconstruct application state.
A traditional database stores the latest state of an entity, while an Event Store keeps a log of all changes as immutable events.
- Define command and query models separately.
- Use Axon Framework for event handling.
- Store events in an event store like Axon Server.
An Event Handler processes stored events and applies changes to the read model, ensuring eventual consistency.
A snapshot is a periodic checkpoint of an entity’s state to improve performance by avoiding replaying all events from the beginning.
A microservice listens to a message broker like Kafka or RabbitMQ, processes the event, and emits new events or executes a compensating transaction.
An Event Bus is responsible for delivering events to various services, ensuring message-driven architecture.
- Use retries with exponential backoff.
- Implement dead-letter queues.
- Use compensating transactions for failed operations.
Idempotency ensures that reprocessing the same request multiple times does not change the system state incorrectly.
- Use event replay for recovery.
- Implement circuit breakers and retries.
- Maintain separate read and write databases for scalability.
Spring Boot integrates with frameworks like Axon and Spring Cloud Stream to coordinate Saga transactions using event-driven mechanisms.
- **Command-side**: Stores transactional data and enforces business rules.
- **Query-side**: Optimized for fast read operations, often using denormalized views.
By designing event consumers to process events asynchronously and retry failed events until successful.
Kafka acts as a distributed event log, allowing services to publish, consume, and replay events in an ordered and durable manner.
Compensating transactions are rollback actions triggered when a step in a distributed transaction fails, ensuring consistency.
Spring Boot provides Axon starters that enable event storage, command handling, and event publishing out-of-the-box.
By creating a separate read model that listens to domain events and updates a database optimized for queries.
A DLQ is a special queue where failed messages are sent for later inspection and retrying.
- Encrypt sensitive event data.
- Use authentication and authorization for message consumers.
- Implement message signing to prevent tampering.
Idempotency ensures that reprocessing the same event multiple times does not cause unintended side effects.
By breaking down distributed transactions into a sequence of smaller, independent transactions with compensation mechanisms.
- **Choreography-based Saga**: Uses events for communication.
- **Orchestration-based Saga**: Uses a central coordinator to manage transactions.
- Implement retries with exponential backoff.
- Use event versioning to ensure data consistency.
- Maintain an audit log for debugging.
An Aggregate is a group of domain objects that should be treated as a single unit, ensuring consistency within its boundary.
Kafka partitions messages based on keys, ensuring events related to the same entity are processed in order.
An Event Store persists all changes as a sequence of immutable events, allowing reconstruction of the system state.
- Use event-driven architecture.
- Implement compensating transactions in Saga.
- Utilize message queues for asynchronous processing.
- Simplifies Event Sourcing implementation.
- Provides built-in support for CQRS.
- Manages event handlers and message routing efficiently.
- Use caching for read-side queries.
- Implement read replicas for scalability.
- Use denormalized views for faster retrieval.
A Compensation Action reverses the effect of a failed step to maintain system consistency.
It ensures that events are stored in a database table as part of a transaction, then reliably published to an event broker.
A Sidecar is an auxiliary service deployed alongside a microservice to handle cross-cutting concerns like logging and security.
- Increased storage requirements.
- Complexity in event versioning.
- Need for event replay mechanisms.
By using message queues like RabbitMQ or Kafka with exponential backoff and dead-letter queues.
A Command Bus routes commands to the appropriate handlers, ensuring decoupling between senders and receivers.
- Introduce versioned events.
- Implement upcasters to transform old events.
- Use backward-compatible changes.
- **Process Manager** tracks business workflows and state.
- **Saga Orchestrator** manages distributed transactions and compensations.
- Use Correlation IDs in events.
- Store audit logs.
- Implement distributed tracing with tools like OpenTelemetry.
By separating read and write models, optimizing queries, and reducing transactional load.
Snapshots store the latest aggregate state to speed up event replay.
An Event Handler listens for domain events and updates read models or triggers other actions.
Kafka persists messages to disk and replicates them across brokers for fault tolerance.
A Command Model handles write operations and enforces business rules before persisting data.
An API Gateway acts as a single entry point, handling authentication, routing, and aggregation of microservice responses.
Consumers that can process the same message multiple times without side effects, ensuring consistency.
By configuring a DLX (Dead Letter Exchange) that routes failed messages to a separate queue for later processing.
A model where data consistency is achieved over time through asynchronous messaging and retries.
Axon provides built-in support for event storage, CQRS, and message handling, reducing boilerplate code.
- Use durable messaging systems (Kafka, RabbitMQ).
- Implement retries and acknowledgments.
- Store events persistently before publishing.
A Projection transforms and stores event data in a read-optimized format for queries.
Each microservice listens for rollback events and triggers a compensating action to undo changes.
- Increased complexity in managing service dependencies.
- Potential single point of failure.
- Limited scalability.
- Enable Kafka Idempotence.
- Use transactional producers and consumers.
- Implement deduplication strategies.
An Aggregate is a cluster of related objects treated as a single unit for consistency.
By separating read and write concerns, access to critical data can be restricted at different levels.
A pattern where an event contains the latest state of an entity, reducing the need for queries.
By implementing snapshots that store the latest aggregate state periodically.
A read-optimized data store that holds denormalized data for fast queries.
By preventing repeated failures and allowing the system to recover before retrying requests.
By using event-driven mechanisms like Kafka or RabbitMQ to propagate updates.
- Use unique transaction IDs.
- Store processed event IDs to avoid duplicate execution.
- Implement compensating transactions where applicable.
- Decouples microservices.
- Ensures reliable event delivery.
- Supports scalability and fault tolerance.
Axon provides built-in support for event sourcing, command handling, and Saga lifecycle management.
An Event Store is a database designed to persist events, allowing event replay and reconstruction of entity state.
- Choreography: Decentralized event-driven flow where each service reacts to events.
- Orchestration: Centralized coordinator manages workflow execution.
- Use event upcasters to transform older events to newer versions.
- Maintain backward compatibility in event schema.
- Implement versioning strategies at the event payload level.
By separating read and write concerns, allowing optimized databases for queries and transactions.
A Projection is a read model derived from stored events, optimized for query performance.
- Provides durable, scalable, and distributed event storage.
- Supports event replay for rebuilding state.
- Enables real-time event processing.
A Snapshot stores the latest state of an entity, reducing the need to replay all past events for state reconstruction.
Since all state changes are recorded as events, it provides a full history of actions for auditing.
- Increased storage and retrieval complexity.
- Requires efficient event versioning.
- Querying data can be complex.
- Use snapshots to store recent state.
- Implement caching strategies.
- Use parallel event processing.
It processes incoming commands, validates business rules, and updates the write model.
- Command: A request to perform an action that may change the state.
- Event: A fact that represents a state change and is immutable.
By ensuring that all microservices react to events asynchronously and retry failed operations.
It ensures atomicity by storing events in a database table before publishing them to a message broker.
- Use idempotent consumers.
- Store event processing logs.
- Enable Kafka transactional messaging.
- Improves scalability and decoupling.
- Enhances real-time data processing.
- Supports eventual consistency in distributed systems.
- Axon Framework.
- Apache Kafka.
- Spring Cloud Stream.