How does Spring Kafka work? (2023)

How does Spring Kafka work?

The Spring for Apache Kafka (spring-kafka) project applies core Spring concepts to the development of Kafka-based messaging solutions. It provides a "template" as a high-level abstraction for sending messages. It also provides support for Message-driven POJOs with @KafkaListener annotations and a "listener container".

This exactly once process works by committing the offsets by producers instead of consumer. i.e., the produce of result to kafka and committing the consumed messages all are done by kafka producer (instead of separate kafka consumer and producer) which brings the exactly once.

At-Least-Once Delivery in Apache Kafka

At-least-once delivery requires the producer to maintain an extra state about message status and to resend failed messages. This means that at-least-once delivery sacrifices some performance in exchange for the guarantee that all messages will be delivered.

In general, concurrency is the ability to perform parallel processing with no affect on the end result. In Kafka, the parallel consumption of messages is achieved through consumer groups where individual consumers read from a given topic/partitions in parallel.

Kafka blends together concepts seen in traditional messaging systems, Big Data infrastructure, and traditional databases and Confluent expands on this with an online platform with better scalability, infinite storage, and event streaming features such as data lineage, schemas, and advanced security.

In a nutshell, Kafka Streams lets you read data in real time from a topic, process that data (such as by filtering, grouping, or aggregating it) and then write the resulting data into another topic or to other systems of record.

Kafka is real-time!

Kafka provides capabilities to process trillions of events per day. Each Kafka broker (= server) can process tens of thousands of messages per second. End-to-end latency from producer to consumer can be as low as ~10ms if the hardware and network setup are good enough.

Apache Kafka is used for both real-time and batch data processing, and is the chosen event log technology for Amadeus microservice-based streaming applications. Kafka is also used for operational use cases such as application logs collection.

Replication means that the same copies of your data are located on several servers (Kafka brokers) within the cluster. Because the data is saved on disk, the data is still there even if the Kafka cluster becomes inactive for some period of time.

How many messages per second can Kafka handle?

How many messages can Apache Kafka® process per second? At Honeycomb, it's easily over one million messages.

By default, Kafka provides at-least-once delivery guarantees. Users may implement at-most-once and exactly once semantics by customizing offset location recording. In any case, Kafka offers you the option for choosing which delivery semantics you strive to achieve.

A consumer can be assigned to consume multiple partitions. So the rule in Kafka is only one consumer in a consumer group can be assigned to consume messages from a partition in a topic and hence multiple Kafka consumers from a consumer group can not read the same message from a partition.

A replication factor is the number of copies of data over multiple brokers. The replication factor value should be greater than 1 always (between 2 or 3). This helps to store a replica of the data in another broker from where the user can access it.

Here is the anatomy of an application that uses the Kafka Streams API. It provides a logical view of a Kafka Streams application that contains multiple stream threads, that each contain multiple stream tasks.

Kafka is primarily used to build real-time streaming data pipelines and applications that adapt to the data streams. It combines messaging, storage, and stream processing to allow storage and analysis of both historical and real-time data.

The purpose of APIs is to essentially provide a way to communicate between different services, development sides, microservices, etc. The REST API is one of the most popular API architectures out there. But when you need to build an event streaming platform, you use the Kafka API.

Kafka uses a binary protocol over TCP. The protocol defines all APIs as request response message pairs.

Kafka is widely used for the asynchronous processing of events/messages. By default, the Kafka client uses a blocking call to push the messages to the Kafka broker. We can use the non-blocking call if application requirements permit.

Use 'systemctl status kafka' to check the status.

Is Kafka FIFO or LIFO?

Kafka supports a publish-subscribe model that handles multiple message streams. These message streams are stored as a first-in-first-out (FIFO) queue in a fault-tolerant manner.

Apache Kafka

Kafka takes on the burden of handling all the problems related to distributed computing: node failures, replication or ensuring data integrity. It makes Kafka a great candidate for the fundamental piece of architecture, a central log that can serve as a source of truth for other services.

It enables applications to publish or subscribe to data or event streams. It stores data records accurately and is highly fault-tolerant. It is capable of real-time, high-volume data processing. It is able to take in and process trillions of data records per day, without any performance issues.

It is more fault-tolerant than databases. Unlike databases, it is possible to scale Kafka's storage independent of memory and CPU. Thus, it is a long-term storage solution due to its higher flexibility than databases.

A websocket server receives the request and sends the data to Kafka. Even as data is being sent to Kafka we have a stream processing layer in the form of ksqlDB which processes each of these requests and sends back a response to a Kafka topic.

However, analytical and transactional workloads across all industries use Kafka. It is the de-facto standard for event streaming everywhere. Hence, Kafka is often combined with other technologies and platforms.

These are the best Apache Kafka online training courses from Udemy, Coursera, Pluralsight to learn Kafka in 2022.

How do you avoid message loss in Kafka?

For making sure that you process a Kafka message only once and also make sure no data is lost, try custom offset management by saving the offset to a system like Zookeeper or HBase.

Specifically, if Apache Kafka invokes a message handler more than once for the same message, it detects and discards any duplicate messages produced by the handler. The message handler will still execute the database transaction repeatedly.

Kafka has a default limit of 1MB per message in the topic. This is because very large messages are considered inefficient and an anti-pattern in Apache Kafka.

Why is Kafka fast? Kafka achieves low latency message delivery through Sequential I/O and Zero Copy Principle. The same techniques are commonly used in many other messaging/streaming platforms.

Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.

You can use the Kafka-console-consumer to view your messages. It provides a command line utility, bin/kafka-console-consumer.sh, that sends messages from a topic to an output file. To display a maximum number of messages by using: --from-beginning and --max-messages ${NUM_MESSAGES}.

If the consumer fails after writing the data to the database but before saving the offsets back to Kafka, it will reprocess the same records next time it runs and save them to the database once more.

Because Kafka consumers pull data from the topic, different consumers can consume the messages at different pace. Kafka also supports different consumption models. You can have one consumer processing the messages at real-time and another consumer processing the messages in batch mode.

Can two consumers read from same topic in Kafka?

Yes, a single consumer or consumer group can read from multiple Kafka topics. We recommend using different consumer IDs and consumer groups per topic.

However, you can install and run Kafka without Zookeeper. In this case, instead of storing all the metadata inside Zookeeper, all the Kafka configuration data will be stored as a separate partition within Kafka itself.

Even a lightly used Kafka cluster deployed for production purposes requires three to six brokers and three to five ZooKeeper nodes. The components should be spread across multiple availability zones for redundancy.

For most implementations you want to follow the rule of thumb of 10 partitions per topic, and 10,000 partitions per Kafka cluster.

Stateful operations in Kafka Streams are backed by state stores. The default is a persistent state store implemented in RocksDB, but you can also use in-memory stores. State stores are backed up by a changelog topic, making state in Kafka Streams fault-tolerant.

CPUs. Most Kafka deployments tend to be rather light on CPU requirements.

As a technology that enables stream processing on a global scale, Kafka has emerged as the de facto standard for streaming architecture.

Spring IoC Container is the core of Spring Framework. It creates the objects, configures and assembles their dependencies, manages their entire life cycle. The Container uses Dependency Injection(DI) to manage the components that make up the application.

Spring Cloud Stream is a framework for building highly scalable, event-driven microservices connected with shared messaging systems. Spring Cloud Stream provides components that abstract the communication with many message brokers away from the code.

Spring Repository annotation is a specialization of @Component annotation, so Spring Repository classes are autodetected by spring framework through classpath scanning. Spring Repository is very close to DAO pattern where DAO classes are responsible for providing CRUD operations on database tables.

How does Spring inject work?

Dependency Injection is a fundamental aspect of the Spring framework, through which the Spring container “injects” objects into other objects or “dependencies”. Simply put, this allows for loose coupling of components and moves the responsibility of managing components onto the container.

Spark streaming is better at processing groups of rows(groups,by,ml,window functions, etc.) Kafka streams provide true a-record-at-a-time processing capabilities. it's better for functions like row parsing, data cleansing, etc.

Spring Cloud Netflix provides Netflix OSS integrations for Spring Boot apps through autoconfiguration and binding to the Spring Environment and other Spring programming model idioms.

Spring Cloud provides tools for developers to quickly build some of the common patterns in distributed systems (e.g. configuration management, service discovery, circuit breakers, intelligent routing, micro-proxy, control bus, one-time tokens, global locks, leadership election, distributed sessions, cluster state).

Spring @Autowired annotation is used for automatic dependency injection. Spring framework is built on dependency injection and we inject the class dependencies through spring bean configuration file.

@Service annotates classes at the service layer. @Repository annotates classes at the persistence layer, which will act as a database repository.

Spring boot framework provides us repository which is responsible to perform various operations on the object. To make any class repository in spring boot we have to use the repository annotation on that class, this annotation is provided by the spring framework itself.

What is difference between @inject and @autowired?

@Inject and @Autowired both annotations are used for autowiring in your application. @Inject annotation is part of Java CDI which was introduced in Java 6, whereas @Autowire annotation is part of spring framework. Both annotations fulfill same purpose therefore, anything of these we can use in our application. Sr.

Autowiring happens by placing an instance of one bean into the desired field in an instance of another bean. Both classes should be beans, i.e. they should be defined to live in the application context. What is "living" in the application context? This means that the context instantiates the objects, not you.

The reasons why field injection is frowned upon are as follows: You cannot create immutable objects, as you can with constructor injection. Your classes have tight coupling with your DI container and cannot be used outside of it. Your classes cannot be instantiated (for example in unit tests) without reflection.