I met Kafka
What are the features of Kafka?
- High throughput and low latency: Kafka can process hundreds of thousands of messages per second with latency as low as a few milliseconds. Each topic can be divided into partitions, and the Consumer group consumes the partitions.
- Scalability: Kafka clusters support hot scaling
- Persistence, reliability: Messages are persisted to local disks and data backup is supported to prevent data loss
- Fault tolerance: Allow nodes in the cluster to fail (n-1 nodes are allowed to fail if the number of replicas is N)
- High concurrency: Thousands of clients can read and write data simultaneously
Should we learn Kafka?
Messaging, storage, and streaming are simple, but how they fit together is elegant, and Kafka does it.
Compared with HDFS, although it supports efficient storage and batch processing of data, it only supports processing historical data in the past.
Compared to a normal messaging system, it does not store historical data, although it can process data from now to the future.
Kafka brings together the best of the best and enables the system to meet all needs in one word: “perfect”.
Ali architect ten years of development, Kafka insights PDF for you
This PDF will take you through the following aspects:
- I met Kafka
- Install the Kafka
- Kafka producer one – writes data to Kafka
- A Kafka consumer reads data from Kafka
- Kafka deeply
- Reliable data delivery
- Building a data pipeline
- Cross-cluster data mirroring
- Management of Kafka
- Monitor the Kafka
- Flow processing
For those of you who need this Kafka tip:
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Kafka result
Kafka result
Kafka result
Kafka result
The chapter begins with an explanation of streaming, a canonical definition of the streaming paradigm, some of its common attributes, and comparisons with other programming paradigms.
It then lists three applications developed based on Kafka Streams to explain some very important streaming processing concepts.
After detailing these examples, we gave an architectural overview of Kafka Streams and explained its internals.
We conclude the book by providing some usage scenarios for streaming processing and some suggestions for comparing streaming frameworks.
For those of you who need this Kafka tip:
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