Tuesday, 14 June 2022

The Importance of Segmenting Infrastructure

Kafka for Logging

I was recently poking around in the source code of a few technologies that I have been using for a few years when I came across KafkaLog4jAppender. It enables you to use Kafka as a place to capture application logs. The thing that caught my eye was the latest commit associated with that particular class, "KafkaLog4jAppender deadlocks when idempotence is enabled".

In the context of Kafka, idempotence is intended to enable the system to avoid producing duplicate records when a producer may need to retry sending events due to some - hopefully - intermittent connectivity problem between the producer and the receiving broker.

The unfortunate situation that arises here is that the Kafka client code itself uses Log4j, so it can result in the application being blocked from sending its logs via a Kafka topic because the Kafka client Producer gets deadlocked waiting on transaction state.

Kafka For Metrics - But Not For Kafka Metrics

This reminded me of a similar scenario where an organisation might choose to use Kafka as their mechanism for sending out notifications of metrics for their microservices and associated infrastructure. If Kafka happens to be part of the infrastructure that you are interested in being able to monitor, then you need to keep those resources isolated from the metrics Kafka - otherwise you run the risk of an incident impacting Kafka which prevents the metrics from being transmitted.

Keeping Things Separated

A real world example of keeping infrastructure isolated from itself can be seen in the way Confluent Cloud handles audit logs. I found it a little confusing at first, as the organisation that I was working for at the time only had Kafka clusters in a single region, but the audit logs were on completely separate infrastructure in another region and even another cloud provider.

Sometimes You're Using A Service Indirectly

A slightly different - but no less significant - example of the need for isolating resources can arise when a particular type of infrastructure is being used for different types of workload. Rather than having a "big bang" release of changes to all of the systems, a phased rollout approach can be taken. One of my earliest involvements with using AWS came shortly after their 2015 DynamoDB outage, which had a ripple out impact for a range of other AWS services because behind the scenes those other services were themselves utilising DynamoDB.

It's my understanding that AWS subsequently moved to isolating their internal services' DynamoDB resource from general consumers' DynamoDB infrastructure - but don't quote me on that.

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