In my previous post I used Blockade, Python and some Bash scripts to test a RabbitMQ cluster under various failure conditions such as failed nodes, network partitions, packet loss and a slow network. The aim was to find out how and when a RabbitMQ cluster loses messages. In this post we’ll do exactly the same but with a Kafka cluster. We’ll use our knowledge of the inside workings of Kafka and Zookeeper to produce various failure modes that produce message loss. Please read my post on Kafka fault tolerance as this post assumes you understand the basics of the acknowledgements and replication protocol.
In my RabbitMQ vs Kafka series Part 5 post I covered the theory of RabbitMQ clustering and some of the gotchas. In this post we'll demonstrate the message loss scenarios described in that post using Docker and Blockade. I recommend you read that post first as this post assumes understanding of the topics covered.
Blockade is a really easy way to test out how distributed systems cope with network partitions, flaky networks and slow networks. It was inspired by the Jepson series. In this post we'll either be killing off nodes, partitioning the cluster, introducing packet loss or slowing down the network. So with Blockade, some bash and python scripts we’ll test out some failure scenarios.
In the last post we took a look at the RabbitMQ clustering feature for fault tolerance and high availability. In this post we'll dig deep into Apache Kafka and its offering.
With Kafka the unit of replication is the partition. Each topic has one or more partitions and each partition has a leader and zero or more followers. When you create a topic you specify the number of partitions and the replication factor. A replication factor of three is common, this equates to one leader and two followers. Both leaders and followers can be referred to as replicas.
Fault tolerance and High Availability are big subjects and so we'll tackle RabbitMQ and Kafka in separate posts. In this post we'll look at RabbitMQ and in Part 6 we'll look at Kafka while making comparisons to RabbitMQ. This is a long post, even though we only look at RabbitMQ, so get comfortable.
In this post we'll look at the strategies for fault tolerance, consistency and high availability (HA) and the trade-offs each strategy makes. RabbitMQ can operate as a cluster of nodes and as such can be classed as a distributed system. When it comes to distributed data systems we often speak about consistency and availability.
We talk about consistency and availability with distributed systems because they describe how the system behaves under failure. A network link fails, a server fails, a hard disk fails, a server is temporarily unavailable due to GC or a network link is lossy or slow. All these things can cause outages, data loss or data conflicts. It turns out that it is generally not possible to provide a system that is ultimately consistent (no data loss, no data divergence) and available (will accept reads and writes) under all failure modes.
We'll see that consistency and availability are at two ends of a spectrum and you'll need to choose which of those you'll optimize for. The good news is that with RabbitMQ this is a choice that you can make. It gives you the nerd knobs required to tune it for greater consistency or greater availability.
In this post we'll be paying close attention to what configurations produce data loss of acknowledged writes. There is a chain of responsibility between producers, brokers and consumers. Once a message has been handed off to a broker, it is the broker's job not to lose that message. When the broker acknowledges receipt of a message to the publisher, we don't expect that message to be lost. But we'll see that this indeed can happen depending on your broker and publisher configuration.
Fact: Hackers would love to get hold of your username and password or access key and secret access key and run up a big bill running a crypto mining operation on EC2. In this post we'll look at two ways to protect yourself when running commands from AWS CLI or automation tools from your personal computer.
With RabbitMQ we can scale-out our consumers by simply adding more, but we can also scale-out our queues. There are a few reasons why scaling out our queues might be preferential to simply adding more consumers to a single queue (competing consumers), one of those reasons is when using the work queue pattern.
This is the first post in a series that will look at bringing Kafka features to RabbitMQ. In this post we'll look at how we can partition a RabbitMQ queue into multiple queues, perform automatic queue assignment to consumers and perform automatic rebalancing as the number of queues and consumers change.
I am writing my first AWS Lambda function. It maintains some state in a static variable and I wanted to know whether that static variable sticks around if an uncontrolled exception occurs.
In a console application for example, an uncontrolled exception results in the ending of that process. Any state is lost. In an ASP.NET application, only the request dies, but the application continues. So not knowing the details of the hosting environment of a function, I wasn't sure what would happen.
In the previous post we looked at relative event ordering and the decoupling of publishers and consumers among other things. In this post we'll take those concepts and look at an example architecture. We'll look at the various modelling possibilities we have with RabbitMQ representing a queue based system, and Kafka representing a log based system.