How is Apache Kafka different from traditional message brokers?

Vaibhav Kothia May 26, 2023 0 Comments

Traditional message brokers are designed to deliver messages from one application to another. They provide a point-to-point communication pattern, where each message is sent to a single consumer.

Apache Kafka is a distributed streaming platform that provides a publish-subscribe messaging system. It provides a distributed, partitioned, and replicated log service, which is used to store and process streams of data records. Kafka is designed to scale out horizontally and handle large volumes of data in real-time. It is highly available and fault-tolerant, allowing for message delivery even when some of the nodes fail.

For example, a traditional message broker might be used to send a message from a web application to a mobile application. The web application would send the message to the broker, which would then deliver it to the mobile application.

With Apache Kafka, the web application would publish the message to a Kafka topic. The mobile application would then subscribe to that topic and receive the message. The message would be replicated across multiple Kafka nodes, providing fault tolerance and scalability.

How does Apache Kafka handle data replication?

Vaibhav Kothia May 26, 2023 0 Comments

Apache Kafka handles data replication by replicating messages from a leader to one or more followers. The leader is responsible for managing the message replication process, while the followers passively replicate the leader.

For example, let’s say there is a Kafka cluster with three nodes, A, B, and C. Node A is the leader and nodes B and C are the followers. When a message is published to the cluster, it is first written to the leader (node A). The leader then replicates the message to the followers (nodes B and C). If the leader fails, one of the followers (node B or C) will be elected as the new leader and will continue to replicate messages to the other followers.

What is the difference between Apache Kafka and Apache Storm?

Vaibhav Kothia May 26, 2023 0 Comments

Apache Kafka and Apache Storm are two different technologies used for different purposes.

Apache Kafka is an open-source messaging system used for building real-time data pipelines and streaming applications. It is used to ingest large amounts of data into a system and then process it in real-time. For example, Kafka can be used to create a real-time data pipeline that ingests data from various sources and then streams it to downstream applications for further processing.

Apache Storm is a distributed, real-time processing system used for streaming data. It is used to process large amounts of data quickly and efficiently. For example, Storm can be used to process a continuous stream of data from a website and then perform analytics on it in real-time.

What is the purpose of Apache Kafka Connect?

Vaibhav Kothia May 26, 2023 0 Comments

Apache Kafka Connect is a tool for streaming data between Apache Kafka and other systems. It is a framework for connecting Kafka with external systems such as databases, key-value stores, search indexes, and file systems, using so-called Connectors.

For example, a Connector can be used to stream data from a database like MySQL into a Kafka topic. This enables Kafka to act as a real-time data pipeline, ingesting data from multiple sources and making it available for consumption by other systems.

How does Apache Kafka handle message delivery?

Vaibhav Kothia May 26, 2023 0 Comments

Apache Kafka handles message delivery by using a pull-based, consumer-driven approach. This means that consumers must request messages from Kafka in order to receive them.

For example, let’s say a consumer wants to receive messages from a Kafka topic. First, the consumer calls the Kafka consumer API and subscribes to the topic. Then, the consumer sends a pull request to the Kafka server. The Kafka server then sends the messages to the consumer. The consumer can then process the messages and send an acknowledgement back to the Kafka server. The Kafka server then removes the messages from the topic. This process is repeated until the consumer has received all the messages from the topic.

What are topics and partitions in Apache Kafka?

Vaibhav Kothia May 26, 2023 0 Comments

Topics: A topic is a category or feed name to which records are published. Each record consists of a key, a value, and a timestamp. Examples of topics include “user-signups”, “page-views”, and “error-logs”.

Partitions: A partition is a unit of parallelism in Kafka. It is an ordered, immutable sequence of records that is continually appended to. A partition is identified by its topic and partition number. For example, the topic “page-views” may have four partitions labelled 0, 1, 2, and 3. Each partition can be stored on a different machine to allow for multiple consumers to read from a topic in parallel.

What are the main components of Apache Kafka?

Vaibhav Kothia May 26, 2023 0 Comments

1. Brokers: A Kafka cluster consists of one or more servers (Kafka brokers) running Kafka. Each broker is identified by its id, and it contains certain topic partitions. For example, a broker with id 1 may contain topic partitions 0 and 1.

2. Topics: A topic is a category or feed name to which messages are published. For example, a topic can be a user activity log or a financial transaction log.

3. Producers: Producers are processes that publish data to topics. For example, a producer may publish a user purchase event to a topic called “user_purchases”.

4. Consumers: Consumers are processes that subscribe to topics and process the published messages. For example, a consumer may subscribe to the “user_purchases” topic and process each message to update the user’s profile in the database.

5. Zookeeper: Apache Zookeeper is a distributed coordination service that helps maintain configuration information and provide synchronization across the cluster. It is used by Kafka to manage the cluster.

What is Apache Kafka?

Vaibhav Kothia May 26, 2023 0 Comments

Apache Kafka is an open-source distributed streaming platform that enables you to build real-time streaming data pipelines and applications. It is a high-throughput, low-latency platform that can handle hundreds of megabytes of reads and writes per second from thousands of clients.

For example, a company may use Apache Kafka to build a real-time data pipeline to collect and analyze customer data from multiple sources. The data can then be used to create personalized recommendations, trigger automated actions, or power a dashboard.