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Wix.com – 5 Event Driven Architecture Pitfalls!

Wix.com migrated from a request-reply RPC style system to an event driven architecture and, not surprisingly, ran into a few issues. One of the developers wrote a blog post outlining five event driven architecture pitfalls they experienced. Here’s my review of that post, and hopefully sheds more light on their problems and solutions.

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Reliable Publishing

When using an event driven architecture, you’ll be publishing events as a communication mechanism to other parts of your system. You’re telling other parts of your system that something occurred. That “something” is generally that some state change or side effect has occurred.

Other parts of your system can then become dependent on events being published when certain things occur, mainly if various parts of your system are used in a workflow or business process driven by events.

For example, when a payment is processed in the Payment Service, a PaymentCompleted event is published to Kafka. The Inventory Service consumes the PaymentCompleted event and decreases inventory levels based on the Order.

What happens if you make a state change to MySQL, but fail to publish an event to Kafka?

In their example, they process a payment and persist it in MySQL, but it fails to publish the PaymentCompleted event. This means that now the inventory is inconsistent with paid Orders.

One solution to this is using the Outbox Pattern. I’ve covered it in another blog post, but the gist is that you persist your events with your business state in the same transaction into your primary database. Then separately, often in another process or thread, you publish the event. If the event is published successfully, you then delete that event from your primary database.

Another option they chose is to have separate durable storage for the events in case of a failure to publish to Kafka. Then you would publish the events from that fallback durable storage. It’s a similar concept, except it’s not guaranteed since saving state and your event to separate durable storage isn’t atomic (no distributed transaction).

Event Sourcing

One widespread misconception is that Event Sourcing involves using the events as a mechanism for state and for communicating with other service boundaries. Conflating these two ideas can cause a whole lot of complexity.

Event Sourcing is about using events as a way to persist state. Using events that represent state transitions. This has nothing to do with publishing these events as a mechanism for communication with other services.

Events in Event Sourcing are implementation details within a single service boundary. They are internal.

Event Driven Architecture Pitfalls

This means you can choose to use event sourcing and not publish events for other services to consume.

You could also choose not to use event sourcing for any service and publish events for other service boundaries to decouple.

Don’t conflate the two concepts of state and communication.

Distributed Tracing

Another challenge, which is getting better over recent years, especially with OpenTelemery, is a visualization of a workflow when in an even driven architecture.

It isn’t easy to understand all the different services involved when you’re decoupled through publish/subscribe. The entire point is decoupling, which makes it difficult to see the causation and correlation. You have services consuming events and publishing events.

When event choreography is involved, it can be challenging to see the start and end of a workflow. What if something failed mid-way through? How do you know some business process isn’t completed or is in a “hanging” state? You need visibility. Check out my post on Distributed Tracing using OpenTelemery and Zipkin.

Claim Check

Large messages aren’t good. They can be a problem because they can overwhelm your broker or event log, such as Kafka. Meaning you don’t want to have to transfer large message payloads over the wire for every consumer from the broker. Generally, you want to keep event/message payloads small, but how would you do that if you have a message that contains a large image?

The Claim Check Pattern solves this by having the message/event reference where the full contents are.

As an example, a large image may be persisted in blob storage. The event/message will contain an identifier that the consumer will use to know where to locate the file in blob storage. This way, the consumer can retrieve the large payload (image) from blob storage rather than from the message itself.

Check out my post on the Claim Check Pattern for more.

Idempotent Consumers

Duplicate events will occur. This means that consumers need to be prepared that might consume the same event more than once. There are various reasons for this happening, including a different event with the same payload published. Another reason can be the Outbox Pattern mentioned above.

Using my outbox pattern example, if the PaymentCompleted event is consumed by the Inventory service more than once, it will deplete the inventory levels more than they should.

You want your consumers to be idempotent. You want to handle the same event without having a negative side effect.

How you implement this greatly depends on the types of events you publish. If you’re publishing Change Data Capture (CDC) or “Entity Changed” events, you’d want to have a versionId on each event that indicates which version of the entity was when the event was published. This way, consumers can keep track of which version they have and only process the event if it’s newer than their current version.

I generally try to avoid these style events and focus more on domain events involved in workflow. A unique ID associated with every event can be tracked to know if you’re processing an event more than once.

Check out my post on creating Idempotent Consumers for more.

Event Driven Architecture Pitfalls

While Event Driven Architecture is a great way to build a robust system that is decoupled, it has a lot of gotchas and pitfalls that you need to be aware of. Hopefully, this post provides some more insights so you don’t have to figure it out all on your own! All of the problems you’ll run into have solutions/patterns have are well-established and have been around for a long time.

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Stop using trivial Guard Clauses! Try this instead

Guard clauses, on the surface, sound like a great idea. They can reduce conditional complexity by exiting a method or function early. However, I find guard clauses used in the real world to be of little value. Often polluting application-level code for trivial preconditions. I will refactor some code to push those preconditions forcing the edge of your application so your domain focuses on real business concerns.

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Null Checks

The most common case of guard clauses is doing null checks. To illustrate this, I looked at the eShopOnWeb sample application and will use it as an example.

In the example above, two guard clauses do null checks on the anonymousId and userName. While this is incredibly common, I’d rather not have to deal with these preconditions mainly because this method is in the BasketService and a part of the core application code.

Often these types of preconditions are very inconsistent. Since the userName is likely passed around through various layers, does each method that accepts the username have this guard clause? Likely not.

As an example, the above code creates a new instance of the Basket passing the userName to the constrictor. Here’s the constructor.

Sure the TransferBasketAsync method was doing the null check, but does this Basket class get created anywhere else? Is it doing a null check? As you can imagine, if you did this everywhere, you’d have a ton of repetitive trivial code for these null check preconditions.

Forcing Valid Values

I don’t want to litter my core application code with trivial guard clauses, such as null checks, as my example. Instead, I want my core code always to accept valid values so that it doesn’t need to concern itself with doing these guard clauses.

In doing so, you’re pushing the responsibility to the outer edge of your application to produce valid values. If you think about a web application, there is some translation from an HTTP request into your application code. You want to force that translation at the edge, which is your web endpoint, into valid domain values.

One way to accomplish this, as in my example with the userName is to define a type (a record struct) that, during creation, forces the value not to be null.

Then we can use this type wherever we accept the userName as a parameter. Instead of accepting a string for the anonymousId and username in the TransferBasketAsync method, we can move this to the Username type.

To call this TransferBasketAsync in the BasketService, you must construct a Username type. In this example, this is done on a Razor page.

In the above example, the userName comes from the HTTP request, which will be a string. We then construct a Username type passing in that string value. We’re pushing the validation to be at the edge of the application.

Guard Clauses

Our core application defines the Username type, but its usage/creation is at the edge. Nothing can get into the core application without being in a valid state.

Tests

Because of guard clauses, such as null checks, there tend to be tests associated with them. This was the case with this eShopOnWeb sample, where there were tests to confirm those null checks were done. But since we’ve moved to a type that forces it to be valid, these tests still passed because it was throwing. However, these tests are useless now, and we can remove them. We can now remove any tests that were related to doing a null check against the string username.

Instead, we have a few tests to confirm the behavior of our new Username type.

Primitive Obsession

As some others commented on the YouTube video, you might be thinking that I’ve introduced a value object to combat primitive obsession. While true, that’s not the seeing the root cause. The root cause isn’t primitive obsession. The issue is allowing your domain to accept invalid arguments that you need to guard against. This can apply to primitives that are null, as my example illustrates, but it can also be explicit invariants for generic examples, Money, or Date/times with Timezones. However, this can include very domain-specific values.

Guard Clauses

I’m not suggesting guard clauses are not helpful. They are. Exiting early within a method when preconditions aren’t met simplifies logic. However, littering trivial guard clauses all over your codebase is not helpful. Force the outer edge of your application to construct valid values that are passed into your core domain code.

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Do you want to use Kafka? Or do you need a Queue?

Do you want to use Kafka? Or do you need a message broker and queues? While they can seem similar, they have different purposes. I’m going to explain the differences, so you don’t try to brute force patterns and concepts in Kafka that are better used with a message broker.

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Partitioned Log

Kafka is a log. Specifically a partitioned log. I’ll discuss the partition part later in this post and how that affects ordered processing and concurrency.

Kafka Log

When a producer publishes new messages, generally events, to a log (a topic with Kafka), it appends them.

Kafka Log

Events aren’t removed from a topic unless defined by the retention period. You could keep all events forever or purge them after a period of time. This is an important aspect to note in comparison to a queue.

With an event-driven architecture, you can have one service publish events and have many different services consuming those events. It’s about decoupling. The publishing service doesn’t know who is consuming, or if anyone is consuming, the events it’s publishing.

Consumers

In this example, we have a topic with three events. Each consumer works independently, processing messages from the topic.

Consumers

Because events are not removed from the topic, a new consumer could start consuming the first event on the topic. Kafka maintains an offset per topic, per consumer group, and partition. I’ll get to consumer groups and partitions shortly. This allows consumers to process new events that are appended to the topic. However, this also allows existing consumers to re-process existing messages by changing the offset.

Just because a consumer processes an event from a topic does not mean that they cannot process it again or that another consumer can’t consume it. The event is not removed from the topic when it’s consumed.

Commands & Events

A lot of the trouble I see with using Kafka revolves around applying various patterns or semantics typical with queues or a message broker and trying to force it with Kafka. An example of this is Commands.

There are two kinds of messages. Commands and Events. Some will say Queries are also messages, but I disagree in the context of asynchronous messaging.

Commands are about invoking behavior. There can be many producers of a command. There is a required single consumer of a command. The consumer will be within the logical boundary that owns the definition/schema of the command.

Sending Commands

Events are about notifying other parts of your system that something has occurred. There is only a single publisher of an event. The logical boundary that publishes an event owns the schema/definition. There may be many consumers of an event or none.

Publishing Events

Commands and events have different semantics. They have very different purposes, and how that also pertains to coupling.

Commands vs Events

By this definition, how can you publish a command to a Kafka topic and guarantee that only a single consumer will process it? You can’t.

Queue

This is where a queue and a message broker differ.

When you send a command to a queue, there’s going to be a single consumer that will process that message.

Queue Consumer

When the consumer finishes processing the message, it will acknowledge back to the broker.

Queue Consumer Ack

At this point, the broker will remove the message from the queue.

Queue Consumer Ack Remove Message

The message is gone. The consumer cannot consume it again, nor can any other consumer.

Consumer Groups & Partitions

Earlier I mentioned consumer groups and partitions. A consumer group is a way to have multiple consumers consume from the same topic. This is a way to concurrently scale and process more messages from a topic called the competing consumer pattern.

A topic is divided into partitions. Events are appended to a partition within a topic. There can only be one consumer within a consumer group that processes messages from a partition.

Meaning you will process messages from a partition sequentially. This allows for ordered processing.

As an example of the competing consumers pattern, let’s say we have two partitions in a topic. Each partition right now is a single event in each. We have two consumers in a single consumer group. We’ve defined that the top consumer will consume from the top partition, and the bottom consumer will consume from the bottom partition.

Kafka Partition

This means that each consumer within our consumer group can process each message concurrently.

Kafka Partition

If we publish another message to the top partition, this means the top consumer again is the one responsible for consuming it. If it was busy processing another message, the bottom consumer, even if it’s available, will not consume it. Only the top consumer is associated with the top partition.

This allows you to consume messages in order, so as long as you associate them to the same partition.

In contrast, the competing consumers’ pattern with a queue works slightly differently as we don’t have partitions.

If we have two messages in a topic, and we have two consumers within a single consumer group.

Competing Consumers

Messages are consumed by any free/available consumer. Because there are two free consumers, both messages will be consumed concurrently.

Competing Consumers

Even though messages are distributed FIFO (First-in-First-Out), that doesn’t mean we will process them in order.

Why does this matter? With Kafka partitions, you can process messages in order. Because there is only a single consumer within a consumer group associated with a partition, you’ll process them one by one. This isn’t possible with queues. The downside is that if you publish messages to a partition faster than you can consume them, you can end up in a backlog disaster.

Kafka or Message Broker Queues & Topics?

Hopefully, this post (and video) illustrated some of the differences. The primary issue I’ve come across is people using Kafka but trying to apply patterns and concepts (commands, competing consumers, dead letter queues) that are typical with a message broker using queues and topics, but it just doesn’t fit.

Typically when you’re creating an asyncronous workflow, you’re consuming events and sending commands. While you technically can create a topic for commands, you can’t guarantee there won’t be more than a single consumer. Is this a big deal? To me, semantics matter. If you’re already using Kafka and don’t want to introduce another piece of infrastructure like a queue/message broker, then I understand the reasoning for doing so.

Understanding the differences in how the competing consumers pattern works. If you’re not configured correctly and are publishing to a single partition, then you can’t increase throughput by adding another consumer to a consumer group.

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