Skip to main content

Event/Messaging Antipatterns

(Compiled from AWS, Azure, Google and and other community sources)

Queue-Based Antipatterns​

The following are antipatterns to avoid:

  • Unbounded Queues:
    • Having queues that are not bounded can lead to excessive growth in queue size and cause the system to become unresponsive. Always use bounded queues to limit queue growth and ensure system responsiveness.
  • Single Point of Failure:
    • A single point of failure in a queue-based architecture can result in the loss of messages and downtime. Ensure that the system is designed with redundancy to eliminate single points of failure.
  • Inadequate Monitoring and Logging:
    • Inadequate monitoring and logging can lead to undetected issues in the system. Use monitoring and logging tools to proactively detect and troubleshoot issues.
  • Uncontrolled Consumer Load:
    • Uncontrolled consumer load can result in a backlog of messages and cause the system to become unresponsive. Use load balancing techniques to distribute the load evenly among consumers.
  • Unbounded Retries:
    • Unbounded retries can cause an infinite loop of message processing, leading to system failures. Always use a finite number of retries and use a dead-letter queue to handle messages that fail to process after the maximum number of retries.
  • Inadequate Security:
    • Inadequate security measures can lead to unauthorized access to the queue and result in the loss or corruption of messages. Use encryption, access control, and other security measures to protect the queue from unauthorized access.
  • Inadequate Redundancy:
    • Inadequate redundancy can lead to message loss and downtime in the event of a failure. Use redundant queues, brokers, and servers to ensure that the system can survive failures and maintain message delivery.
  • Overuse of Queues:
    • Overusing queues can lead to unnecessary complexity and inefficiency in the system. Use queues only when they are necessary and use other mechanisms, such as direct messaging or streaming, when appropriate.
  • Mixed Message Types:
    • Mixing different types of messages in a single queue can lead to processing errors and reduce system performance. Use separate queues for different message types to ensure clean processing and high performance.
  • Uncontrolled Queue Growth:
    • Uncontrolled queue growth can lead to an excessive amount of storage and result in higher costs. Use automatic queue cleanup and archive mechanisms to ensure that the queue stays within its boundaries and operates efficiently.
  • Oversized messages:
    • Sending large messages that exceed the queue's maximum message size can result in message loss or increased processing times. Instead, messages should be kept as small as possible and larger payloads should be sent via a separate mechanism, such as object storage.
  • Over-reliance on a single queue:
    • Depending on a single queue for all message traffic creates a single point of failure and can lead to degraded performance. Instead, consider using multiple queues and load-balancing message traffic across them.
  • Lack of message ordering:
    • Some message processing scenarios require messages to be processed in a specific order. Failing to ensure message ordering can lead to incorrect processing results. Instead, use message sequencing techniques to ensure messages are processed in the correct order.
  • Ignoring queue limits and quotas:
    • Ignoring queue limits and quotas can lead to increased costs, degraded performance, and message loss. Instead, monitor and adjust queue limits and quotas as needed to ensure optimal performance.
  • Not monitoring queue performance:
    • Failing to monitor queue performance can lead to increased latency, decreased throughput, and message loss. Instead, regularly monitor queue performance metrics, such as message throughput, latency, and error rates, and use this information to optimize the queue configuration and usage.

Message issues​

  • Long-lived messages:
    • Messages that remain in the queue for a long time can result in stale data and increased processing times. Instead, messages should be processed as soon as possible or have a TTL (Time To Live) to ensure they are removed from the queue after a specified period.
  • Bloated messages:
    • Large messages can cause increased latency and reduced throughput. Instead, messages should be kept as small as possible, and larger payloads should be sent via a separate mechanism, such as object storage.
  • Inappropriate message encoding:
    • Using an inappropriate encoding for messages can result in increased processing times and message loss. Instead, use a standard encoding format such as JSON or XML, and avoid proprietary encoding methods.
  • Not handling message duplicates:
    • Duplicate messages can result in incorrect processing results and other issues. Instead, implement duplicate detection and removal mechanisms.
  • Ignoring message priority:
    • Ignoring message priority can result in messages being processed in the wrong order or being delayed unnecessarily. Instead, use message prioritization techniques to ensure high-priority messages are processed first.
  • Lack of message validation:
    • Failing to validate messages can result in incorrect processing results and other issues. Instead, implement message validation techniques, such as schema validation or checksum verification, to ensure message integrity.
  • Not monitoring message latency:
    • Failing to monitor message latency can result in increased processing times and decreased throughput. Instead, monitor message latency metrics and use this information to optimize message processing.
  • Too many message retries:
    • Retry attempts can cause increased processing times and message loss. Instead, implement a reasonable number of retries and an exponential backoff strategy to prevent overload.
  • Inappropriate queue configuration:
    • Inappropriate queue configurations can cause increased latency, reduced throughput, and message loss. Instead, configure the queue parameters, such as message size limits, retention period, and throughput limits, according to the specific use case.
  • Lack of message compression:
    • Failing to compress messages can result in increased message size and processing times. Instead, use message compression techniques to reduce message size and improve processing times.

Event-driven Architecture Antipatterns​

The following are antipatterns to avoid:

  • Overusing synchronous requests:
    • Event-driven architecture is designed to handle asynchronous requests. Overusing synchronous requests can defeat the purpose of the architecture and cause a bottleneck.
  • Uncontrolled event volume:
    • In an event-driven architecture, events can be produced and consumed at a high volume. Without proper controls, this can overwhelm the system and cause performance issues.
  • Poorly defined events:
    • Events should be well-defined and include all the necessary information. Poorly defined events can lead to confusion, errors, and additional processing time.
  • Overcomplicated event routing:
    • Overcomplicated routing can make it difficult to manage events and add unnecessary complexity to the system.
  • Hard-coded business logic:
    • Hard-coding business logic into the event-driven architecture can lead to inflexibility and make it difficult to update or modify the system.
  • Overly complex event data:
    • The data carried by events should be kept as simple as possible. Overly complex event data can lead to issues with storage, processing, and readability.
  • Failure to handle event processing errors:
    • In an event-driven architecture, errors can occur during event processing. It's important to have a mechanism in place to handle and log errors to help with troubleshooting and prevent issues from affecting the entire system.
  • Inconsistent event format:
    • Consistency is critical for event-driven architecture. Inconsistent event formats can lead to issues with compatibility, processing, and data integrity.
  • Too many events:
    • Too many events can make the system difficult to manage and lead to performance issues. It's important to design the system to manage the volume of events it will receive.
  • Incomplete event processing:
    • Events should be fully processed before being marked as complete. Incomplete event processing can cause issues with data integrity and system stability.
  • Tight coupling of services:
    • If services in an event-driven architecture are tightly coupled, it can lead to cascading failures and result in a system that is brittle and difficult to maintain.
  • Excessive event processing:
    • If too many events are processed, it can lead to performance degradation and potential scalability issues.
  • Monolithic event handlers:
    • If a monolithic event handler is used to process all events, it can lead to a single point of failure, slow processing times, and a lack of flexibility in the system.
  • Inadequate testing:
    • If event-driven architecture is not properly tested, it can lead to a lack of confidence in the system and increase the risk of issues in production.
  • Inconsistent event schemas:
    • If event schemas are inconsistent, it can lead to issues with data integrity, data quality, and cause confusion for developers and operations teams.
  • Inadequate error handling:
    • If error handling is not implemented effectively, it can lead to data loss, incomplete processing of events, and an unreliable system.
  • Lack of proper monitoring:
    • If proper monitoring is not implemented, it can be difficult to detect issues in the system, leading to longer resolution times and a higher risk of production issues.

Kafka antipatterns​

  • Overuse of topics:
    • Creating too many topics can lead to increased complexity and overhead in the Kafka cluster, which can impact performance and increase operational costs.
  • Use of single-partition topics:
    • Using single-partition topics can limit the throughput of a Kafka cluster, as only a single broker can handle a particular partition.
  • Use of very large messages:
    • Using very large messages can cause issues with disk I/O and memory usage, as well as lead to slow processing times and increased network traffic.
  • Lack of proper monitoring:
    • Without proper monitoring, it can be difficult to detect issues in a Kafka cluster, leading to longer resolution times and a higher risk of production issues.
  • Incorrect use of Kafka consumer groups:
    • Improper use of Kafka consumer groups can lead to issues such as duplicate message processing, message loss, and decreased performance.
  • Poor partitioning strategy:
    • Inadequate partitioning can cause imbalanced data distribution, leading to issues with data processing and decreased performance.
  • Insufficient resource allocation:
    • Insufficient resource allocation, such as not enough memory or disk space, can lead to performance issues and decreased throughput in the Kafka cluster.
  • Not using the latest Kafka version:
    • Running an outdated Kafka version can lead to security vulnerabilities, performance issues, and potential compatibility issues with third-party tools.
  • No disaster recovery plan:
    • Without a disaster recovery plan in place, it can be difficult to recover from data loss, cluster failures, or other unexpected issues.
  • Improper message format:
    • Using an improper message format can make it difficult for producers and consumers to understand the data, leading to data processing issues.
  • Inadequate topic partition replication:
    • Inadequate replication can lead to data loss in the event of a broker or partition failure, as well as decreased availability and increased recovery time.
  • Overuse of synchronous communication:
    • Using synchronous communication for Kafka can decrease performance and lead to blocking issues, as well as increase latency for downstream services.

AWS Eventbridge antipatterns​

  • Poorly defined event schemas:
    • Not having clear and consistent event schemas can lead to difficulty in processing events downstream and make it challenging for consumers to handle the events.
  • Overusing EventBridge rules:
    • Overusing EventBridge rules can create unnecessary complexity and make it harder to manage events and rules over time.
  • Excessive event processing:
    • Excessive processing of events can lead to high costs and decreased performance. It is important to monitor and optimize event processing to ensure that it is efficient and cost-effective.
  • Incomplete event filtering:
    • Incomplete event filtering can result in unnecessary processing of events, which can lead to increased costs and decreased performance. It is important to set up event filtering carefully to ensure that it is accurate and effective.
  • Not monitoring EventBridge:
    • Failing to monitor EventBridge can lead to missed events, issues with rule processing, and other problems. It is important to have adequate monitoring in place to ensure that the system is functioning as expected.
  • Overly complex event routing:
    • Overly complex event routing can create unnecessary complexity and make it harder to manage events over time. It is important to simplify event routing as much as possible to make it more manageable and less error-prone.
  • Lack of testing and validation:
    • Not testing and validating events can lead to unexpected behavior and errors downstream. It is important to have adequate testing and validation in place to ensure that events are being processed correctly and without issue.