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What is apache kafka data acquisition? I believe that most people do not understand, today, in order to let you know more about apache kafka data collection, summarize the following content, let's look down together.

Apache Kafka-introduction

Apache Kafka originated from LinkedIn, became an open source Apache project in 2011, and then became a top-notch Apache project in 2012. Kafka is written in Scala and Java. Apache Kafka is a fault-tolerant messaging system based on publish and subscribe. It is a fast, scalable and distributed design.

This tutorial will explore the principles, installation, and operation of Kafka, and then introduce the deployment of Kafka clusters. Finally, we will summarize real-time applications and integration with Big Data Technologies.

Before doing this tutorial, you must have a good understanding of Java,Scala, distributed messaging systems, and Linux environments.

In big data, a large amount of data is used. With regard to data, we have two main challenges. The first challenge is how to collect large amounts of data, and the second challenge is to analyze the collected data. To overcome these challenges, you need a messaging system.

Kafka is designed for distributed high-throughput systems. Kafka tends to work well as an alternative to a more traditional mail broker. Compared with other messaging systems, Kafka has better throughput, built-in partitioning, replication and inherent fault tolerance, which makes it very suitable for large-scale message processing applications.

What is a mail system?

The messaging system is responsible for transferring data from one application to another, so the application can focus on the data without worrying about how to share it. Distributed messaging is based on the concept of reliable message queues. Messages are queued asynchronously between the client application and the messaging system. Two types of messaging modes are available-one is peer-to-peer, and the other is a publish-subscribe (pub-sub) messaging system. Most messaging patterns follow pub-sub.

Point-to-point information system

In a peer-to-peer system, messages remain in the queue. One or more consumers can use messages in the queue, but specific messages can be consumed by at most one consumer. Once the consumer reads the message in the queue, it disappears from the queue. A typical example of this system is an order processing system, where each order will be processed by one order processor, but multiple order processors can work at the same time. The following figure depicts the structure.

Publish and subscribe message system

In a publish / subscribe system, messages remain in the topic. Unlike peer-to-peer systems, consumers can subscribe to one or more topics and consume all messages in that topic. In Publish-Subscribe systems, message generators are called publishers and message consumers are called subscribers. A real example is Dish TV, which publishes different channels, such as sports, movies, music, etc., and anyone can subscribe to their own channels and get their subscription channels.

What is Kafka?

Apache Kafka is a distributed publish / subscribe messaging system and powerful queue that handles large amounts of data and enables you to pass messages from one endpoint to another. Kafka is suitable for offline and online message consumption. Kafka messages are kept on disk and replicated within the cluster to prevent data loss. Kafka is built on top of ZooKeeper synchronization services. It combines perfectly with Apache Storm and Spark for real-time streaming data analysis.

Here are several benefits of Kafka-

Reliability-Kafka is distributed, partitioned, replicated and fault tolerant.

Scalability-the Kafka messaging system can be easily extended without downtime.

Durability-Kafka uses distributed commit logs, which means that messages still exist on disk as soon as possible, so it is durable.

Performance-Kafka has high throughput for both publish and subscribe messages. It maintains stable performance, even though many TB messages are stored.

Kafka is very fast, ensuring zero downtime and zero data loss.

Use case

Kafka can be used for many use cases. Some of them are listed below-

Metrics-Kafka is often used to run monitoring data. This involves aggregating statistics from distributed applications to produce centralized feeds of operational data.

Log aggregation solution-Kafka can be used throughout the organization to collect logs from multiple services and provide them to multiple servers in a standard format.

Streaming-popular frameworks such as Storm and Spark

Streaming) reads data from a topic, processes it, and writes the processed data to a new topic that is available to users and applications. The strong durability of Kafka is also very useful in streaming.

Kafka needs

Kafka is a unified platform for handling all real-time data sources. Kafka supports low-latency message delivery and ensures fault tolerance in the event of a machine failure. It has the ability to deal with a large number of different consumers. Kafka is very fast, performing 2 million writes per second. Kafka keeps all data to disk, which essentially means that all writes go to the page cache of the operating system (RAM). This is very efficient in transferring data from the page cache to the network socket.

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