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Sep 7, 2022 · Azure Event Hubs acts like a “front door” for an event pipeline, often called an event ingestor. An event ingestor is a component or service that sits between event publishers and...
Nov 7, 2023 · Azure Event Hubs is a big data streaming platform and event ingestion service. It can receive and process millions of events per second. It represents the “front door” for an event pipeline,...
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What is the behavior of the Azure Functions event hubs trigger?
How does Azure Functions consume event hub events?
What is Azure Event Hub?
Why should I integrate event hubs with Azure Data Explorer?
- Overview
- Challenges of event streams in distributed systems
- How Azure Functions consumes Event Hubs events
- Handling exceptions
- Non-exception errors
- Stop and restart execution
- Resources
- Next steps
Event processing is one of the most common scenarios associated with serverless architecture. This article describes how to create a reliable message processor with Azure Functions to avoid losing messages.
Consider a system that sends events at a constant rate of 100 events per second. At this rate, within minutes multiple parallel Functions instances can consume the incoming 100 events every second.
However, any of the following less-optimal conditions are possible:
•What if the event publisher sends a corrupt event?
•What if your Functions instance encounters unhandled exceptions?
•What if a downstream system goes offline?
How do you handle these situations while preserving the throughput of your application?
Azure Functions consumes Event Hub events while cycling through the following steps:
1.A pointer is created and persisted in Azure Storage for each partition of the event hub.
2.When new messages are received (in a batch by default), the host attempts to trigger the function with the batch of messages.
3.If the function completes execution (with or without exception) the pointer advances and a checkpoint is saved to the storage account.
4.If conditions prevent the function execution from completing, the host fails to progress the pointer. If the pointer isn't advanced, then later checks end up processing the same messages.
5.Repeat steps 2–4
Retry mechanisms and policies
Some exceptions are transient in nature and don't reappear when an operation is attempted again moments later. This is why the first step is always to retry the operation. You can leverage the function app retry policies or author retry logic within the function execution. Introducing fault-handling behaviors to your functions allow you to define both basic and advanced retry policies. For instance, you could implement a policy that follows a workflow illustrated by the following rules: •Try to insert a message three times (potentially with a delay between retries). •If the eventual outcome of all retries is a failure, then add a message to a queue so processing can continue on the stream. •Corrupt or unprocessed messages are then handled later.
Some issues arise even when an error is not present. For example, consider a failure that occurs in the middle of an execution. In this case, if a function doesn’t complete execution, the offset pointer is never progressed. If the pointer doesn't advance, then any instance that runs after a failed execution continues to read the same messages. This situation provides an "at-least-once" guarantee.
The assurance that every message is processed at least one time implies that some messages may be processed more than once. Your function apps need to be aware of this possibility and must be built around the principles of idempotency.
While a few errors may be acceptable, what if your app experiences significant failures? You may want to stop triggering on events until the system reaches a healthy state. Having the opportunity to pause processing is often achieved with a circuit breaker pattern. The circuit breaker pattern allows your app to "break the circuit" of the event process and resume at a later time.
There are two pieces required to implement a circuit breaker in an event process:
•Shared state across all instances to track and monitor health of the circuit
•Master process that can manage the circuit state (open or closed)
Implementation details may vary, but to share state among instances you need a storage mechanism. You may choose to store state in Azure Storage, a Redis cache, or any other account that is accessible by a collection of functions.
Azure Logic Apps or durable functions are a natural fit to manage the workflow and circuit state. Other services may work just as well, but logic apps are used for this example. Using logic apps, you can pause and restart a function's execution giving you the control required to implement the circuit breaker pattern.
•Reliable event processing samples
•Azure Durable Entity Circuit Breaker
For more information, see the following resources:
•Azure Functions error handling
•Automate resizing uploaded images using Event Grid
•Create a function that integrates with Azure Logic Apps
Mar 15, 2018 · Event processing is one of the most common scenarios in serverless and Azure Functions. A few weeks ago I wrote about how you can process events in order with functions, and for this blog I...
- Jeff Hollan
4 days ago · Azure Event Hubs is the preferred event ingestion layer of any event streaming solution that you build on top of Azure. It seamlessly integrates with data and analytics services inside and outside Azure to build your complete data streaming pipeline to serve following use cases.
Mar 8, 2021 · An event ingestor is a component or service that sits between event publishers and event consumers to decouple the production of an event stream from the consumption of those events. Event Hubs provides a unified streaming platform with time retention buffer, decoupling event producers from event consumers.
An event is a notification or state change that is represented as a fact that happened in the past. Events are immutable and persisted in an event hub, also referred to as a topic in Kafka. An event hub is comprised of one or more partitions. Partitions.
