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What are the core technologies of big data Kafka? I believe many inexperienced people don't know what to do about it. Therefore, this paper summarizes the causes and solutions of the problems. Through this article, I hope you can solve this problem.

What is kafka?

Kafka is a distributed streaming platform for publishing and subscribing to record streams. Kafka can be used for fault-tolerant storage. Kafka copies topic log partitions to multiple servers. Kafka is designed so that your application can be processed immediately after the record is generated. The processing speed of Kafka is very fast and IO is used efficiently by batch processing and compressing records. Kafka decouples the data flow. Kafka is used to stream data to data lakes, applications, and real-time flow analysis systems. Kafka is mainly used for real-time information flow big data collection or real-time analysis (or both). Kafka can not only provide services for memory microservices, but also be used to feedback events to complex event flow systems and IoT/IFTTT automation systems.

Currently, 1/3 of the world's top 500 companies use kafka, and there are several reasons why it is so popular:

First, the speed of kafka is fast.

Based on the principle of zero copy, Kafka deeply relies on the operating system kernel to move data quickly, and can process the data records in batches. These batches of data can be used end-to-end from the producer to the file system (Kafka topic log) to the consumer. Batch processing can achieve more efficient data compression and reduce I / O latency. Kafka writes immutable commit logs to continuous disks, thus avoiding the problems of random disk access and slow disk seek. Kafka supports adding partitions for scale-out. It divides the topic log into hundreds (possibly thousands) of partitions to thousands of servers. This approach allows Kafka to carry a large load.

Second, Kafka supports multiple languages

Kafka communication between the client and the server uses the TCP-based line protocol, which is versioned and documented. Kafka is committed to maintaining backward compatibility with older clients and supports a variety of languages, including Category, Java, and Pythonology, Ruby, and other languages. The Kafka ecosystem also provides REST proxies that can be easily integrated through HTTP and JSON. Kafka also supports Avro mode through Kafka's converged mode registration (ConfluentSchema Registry). Avro and schema registration allow customers to make and read complex records in multiple programming languages and to allow changes in records.

Third, kafka is widely used.

Kafka supports building real-time streaming data pipelines, supporting memory microservices (such as actors,Akka,Baratine.io,QBit,reactors,reactive,Vert.x,RxJava,SpringReactor), building real-time streaming applications, performing real-time data analysis, transformation, response, aggregating, joining real-time data streams, and executing CEP.

Fourth, Kafka scalable message storage

Kafka is a good record or information storage system. Kafka is like a high-speed file system for submitting logs, storing and replicating. These characteristics make Kafka suitable for various applications. Records written to Kafka topics are persisted to disk and copied to other servers for fault tolerance. This approach is very useful because the disk is now fast and quite large. The Kafka producer can wait for an acknowledgment, so the message is persistent because the producer does not complete the write operation until the replication is complete. The Kafka disk structure can be well extended. Disks have very high throughput when streaming in large quantities. In addition, Kafka clients and consumers can control the read location (offset), which allows use cases such as log replay when important errors occur (that is, fix errors and replay). Moreover, because the offset is tracked by each consumer group, consumers can replay the log very flexibly.

Kafka allows the right data to appear in the right place in the right form. Kafka provides message queues, allowing producers to add data to the end of the queue, allowing multiple consumers to read data from the queue in turn and then process it on their own. Such a convenient mode is bound to strengthen the application of kafka in various fields.

In the DT era, the application of kafka will continue to deepen. In the future, not only the world's top 500 enterprises will use kafka, but any enterprise will use this convenient tool to realize the layout of big data. Technology is always constantly updated and developed, and kafka is also constantly iterating in more detail. I believe that the layout of big data of enterprises in the future will be more convenient because of kafka.

After reading the above, have you mastered the core technology of big data Kafka? If you want to learn more skills or want to know more about it, you are welcome to follow the industry information channel, thank you for reading!

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