Apache Kafka Explained: Introduction to Event-Driven Applications with Kafka
What exactly is Kafka?
Apache Kafka is an open-source platform for streaming events that LinkedIn created in 2011. The initial purpose was to push (lots of) messages into LinkedIn’s channels, and it has since been release to the public. Here we are.
What is it that it can be employee to do?
A wide range of applications built around events can benefit from using Apache Kafka as the messaging and eventing backbone. Particular examples include:
- Log aggregation
- SEDA pipelines
- Log shipping
- Event-Sourcing CQRS
What are the most important elements of Kafka?
Apache Kafka is a well distributed system which includes several key components:
Broker Nodes are responsible for the major portion of I/O operations and the long-lasting persistence inside the cluster. Brokers can host append-only log files, which comprise the topic partitions hosted by the cluster. Partitions can be replicate across several brokers to provide the horizontal scale and greater durability — they are referred to as replicas. A broker node can be the leader of specific replicas while also being an observer for other replicas. A single broker node can also be chosen for the role of cluster controller who is accountable for managing internal partition states. This includes the arbitrage of leader-follower positions for any particular partition.
- ZooKeeper nodes Under the hood, Kafka needs a way to monitor the general status of controllers in the cluster. If the controller fails to function, there’s an option to choose a new controller from the pool of remaining brokers. The mechanics behind controller selection, heart beating, and other things are generally implement in ZooKeeper. ZooKeeper also functions as a repository for configurations that stores cluster metadata such as leader-follower state and quotas, user details ACLs, and other household-keeping items. Due to the consensus protocol and gossiping, the number of ZooKeeper nodes could be odd.
- Producers Application for the client responsible for adding the record in Kafka topics. Due to Kafka’s log structure Kafka and its ability to share topics between different consumer ecosystems, only producers can alter the information in the log files that are used to create them. Broker nodes do the actual I/O processing for producers’ clients. Various producers can submit to the exact subject choosing the partitions to keep the records.
- Consumers: Client applications that read information from various topics. Many consumers are reading from the same subject. However, based on the configuration and the grouping of consumers, some rules govern the distribution of information between the users. We’ll be revisiting this shortly.
What does it have to do with conventional message broker services?
Traditional “enterprise” message brokers provide a variety of distinct distribution topologies. One, there are message queues. message queue — long-lasting transportation for point-to-point messages with the option of load-balancing for consumers but at the expense of messaging ordering. (Although messages on a queue can be order, the order is broken if more than one consumer pulls out of their queues.) However, pub-sub topics permit broadcast-style messages; however, it does not provide consumer load-balancing or messaging durability.
Kafka expands these ideas into one unifying model; a single source topic could power an array of consumers without creating a duplicate. Parallelism and concurrently are at the center of Kafka’s model, creating partially-ordered events streams that can be distributed across an incredibly scalable consumer ecosystem. Reconfiguring consumers and the groups surrounding them could result in a vastly different distribution of events and processing semantics. Shifting the offset commit point could change the guarantee for delivery from an at-least-once and then to an at-least-once-model.
Think of the Kafka system as a conclusion of opinionated building blocks. You can try them to erect a little beach shack or a skyscraper — they enable you without overly constraining you.
Does Kafka support multi tenancy?
Based on Kafka Maintainers, Kafka is able to support multi-tenancy. But don’t get too excited. Its structure is restrict only to control lists of access (ACLs) to divide subjects and keep quotas in place that create an illusion of separation for users but doesn’t create an illusion of administrative isolation. This is like saying that your fridge is compatible with multi-tenancy as it lets you store food on various shelves. True multi-tenancy solutions could provide multiple different logical clusters inside a larger physical cluster. The logical clusters can be manage independently; an incorrect configuration of an ACL within one logical group such as this one, for example, would in no way affect another logical cluster.
Are there any decent Kafka tools?
When it comes to tooling, Kafka is not a plug-and-play solution. The Kafka tarball includes primitive CLI tools that are insufficient for all but the most basic operations. The majority of practitioners have long already switched to open-source and commercial alternatives.
We’ve barely begun to scratch the surface of incredibly powerful technology. Apache Kafka open and flexible model has a myriad of possibilities. It is possible to create almost every kind of distributed system while ensuring security and performance. It also has its shortcomings, including the poor tools and the difficulty of the configuration options.
If you’re starting, most of the lessons are ahead of you. The foundations are in place and the tools you need to succeed, and there’s plenty of information on the internet. It’s a given that the most effective method to get start is to put on your sleeves and create something. Start by using an existing application and breaking it down into loosely-coupled services. You can also start by creating your own from scratch. Do something you’ve always wanted to do but couldn’t locate the perfect moment. Now it’s your chance.
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