Apache Kafka is an event streaming service utilized mainly through more than 30% of the Top 500 companies. Numerous aspects of Kafka contribute to its status as the de-facto benchmark for event streaming platforms. In this blog post, we shall discuss the top five things we believe every Kafka programmer should understand.
Some of the elements in our top five are concerned with performance, while others with the fundamental architectural principles that make Kafka operate. We hope that by the time you’ve finished reading this blog article, you’ll have a better grasp of how Kafka functions.
#1 Processing in the Background and Decoupling
Kafka simulates a number of different mechanisms that queue messages with consumers and producers on opposite ends. This is an example of asynchronous processing in action. Producers are not expected to wait for information to be digested. It would be inconvenient to utilize Kafka in a synchronized environment where producers must block-wait for client answers.
In principle, we might accomplish the same async effect by having producers send an RPC straight to end-users, expecting just an ACK as an answer, or having consumers fetch directly from a producer-exposed endpoint. The advantage of Kafka would be that it detaches consumers and producers, allowing for their development, deployment, and management to be done independently.
Once an agreement on a common message contract is reached, the producers will continue to generate messages and deliver them to Kafka. Consumers who are interested in communications will extract them from Kafka. Producers and customers are not required to be aware of one another’s addresses. They both communicate exclusively through a logically centralized service called Kafka.
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#2 Store of Persistent Messages
Because consumers and producers are no longer in sync, it is simple for producers to create an overwhelming number of signals that consumers cannot comprehend in time. It functions as a persistent cache, buffering unprocessed messages and allowing our system to tolerate bursty traffic or consumer failure. Kafka’s session retention is customizable, which enables it to adapt to a broad variety of requirements.
Additionally, Kafka’s continuous message storage is extremely efficient. It adheres to a log-based architecture and appends messages only at the conclusion of a file. In addition, Kafka uses a standardized binary data structure for transmission and storage, which lowers processing cost and frequently permits instantaneous bytes transmission between both the network and disc through the send file system function.
A desirable side effect of log-based storage is that it maintains the transaction ordering. Consumption status can be represented by a basic offset parameter pointing to the next message to be read. As consumers consume information, they increase the offset and could even reverse it to relive the past.
#3 Routing of messages and load sharding
Kafka enables communication routing based on topics. Both consumers and producers engage with Kafka on certain subjects. Topics segregate and categorize communications logically. Kafka ensures that the appropriate messages are sent to consumers who have subscribed to certain topics. For instance, all user click actions are assigned to one subject, while all system logs are assigned to another. It optimizes our system architecture diagrams by eliminating the requirement for upstream systems to communicate with anything other than a single messaging endpoint. Kafka is responsible for multiplexing the messages and distributing them to the relevant downstream systems.
Moreover, Kafka allows divisions inside a subject. Producers deliver messages directly to the subject divisions for which they are intended. The partition key of a message determines its partition. Messages within the same subject division are kept in the same sequence in which they were sent. Anywhere at one moment, only one consumer entity can consume messages from a single topic division.
It is possible for a consumer instance to receive messages from many topic divisions concurrently. If a consumer object dies, a replacement must be created. This can be accomplished automatically or manually through the use of customer groups. Due to the fact that distinct partitions within a subject function in parallel, the partitioning idea effectively splits the load. Likewise, the subject and partition pairing can be utilized as a shuffling method.
#4 Replication and Adaptability
Thus far, Kafka has been described as a centralized service. However, in order to adequately address the topic, we must first understand the protections Kafka has in place to protect himself in the case of failure. A standard Kafka deployment entails the use of several computers. Clients are configured with numerous Kafka server names as a bootstrap, which enables them to locate all Kafka servers. If a specific server node crashes, users can move to another.
All Kafka servers are capable of providing clients with the most up-to-date information, allowing clients to determine which servers to communicate with for their intended features and content requests. Within Kafka, Zookeeper is used to synchronize controller election and to maintain data like cluster membership, authentication, and topic configurations. Zookeeper is a fault-tolerant distributed system. Of course, we will need to share Zookeeper. The basic solitary Zookeeper server configuration will not survive failures.
Each partition of a topic is duplicated across several Kafka servers. A single server will lead that subject division. Moreover, it can lead to additional subject divisions concurrently. The leader is responsible for all reads and writes to the subject partition. A group of followers replicates the leader’s copy of the subject division passively.
#5 Semantics of Client Fault and Message Delivery
A producer either may fail to submit a message before or after it has been committed. It has no means of knowing other than to retry, which results in repeated messages if the signal has already been committed. To avoid duplication, the producer sends messages with a Kafka-assigned ID and a uniformly rising sequence number.
Kafka denies the transaction if there is already a recorded message with an equivalent or greater sequence number from the same source. Clearly, the producer is responsible for maintaining notes of the ID and serial number. A consumer may fail to handle a message after it has been processed but before the delay has been persistent, in that case, re-attempt reprocesses the signal. If it decides to retain the offset initially, it may fail to interpret the message after preserving the offset, in that case, re-attempt results in a skipped message.
Importantly, Kafka supports transaction semantics when posting to multiple topics, allowing consumers to molecularly store outputs and offsets in multiple destinations Kafka topics.
Author: I am Anita Basa, an enthusiastic Digital Marketer and content writer working at Mindmajix.com. I wrote articles on trending IT-related topics such as Microsoft Dynamics CRM, Oracle, Salesforce, Cloud Technologies, Business Tools, and Softwares. You can reach me on Linkedin: Anita Basa