Kafka

1. Kafka
2. Why use a Message Queue like Kafka
   1. 1. Asynchronous
   2. 2. Decoupling
   4. 3. Peak clipping
3. Basic Concept
   1. 1. Client
   2. 2. Consumer Group
      1. 3. Server-side Broker
      2. 4. Topic
4. Producer   Workflow
   1. 1. Serialize
   2. 2. Partition
   3. 3. Compression
   4. 4. Record accumulator
   5. 5. Sender 
   6. 6. ACK
      1. Acks=0
      2. Acks=all or -1
      3. Acks = 1
   7. 7. Producer message idempotence
   8. 
      8. Producer message transaction
5. Cluster MetaData
   1. Controller
   2. Leader

Kafka

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.

Why use a Message Queue like Kafka

1. Asynchronous

The program can continue execution without waiting for I/O to complete, increasing throughput.

2. Decoupling

It refers to reducing the dependencies between different parts of the system so that each component of the system can be developed, maintained, and evolved relatively independently. The main goal of decoupling is to reduce tight coupling between components to improve system flexibility, maintainability, and scalability.

3. Peak clipping

Peak clipping is essential to delay user requests more, filter user access needs layer by layer, and follow the principle of "the number of requests that ultimately land on the database is as small as possible".

Basic Concept

1. Client

Including producer and consumer.

2. Consumer Group

Each consumer can specify the consumer group to which he belongs. Each message will be consumed by multiple interested consumer groups.

But within each consumer group, a message means that it will be consumed by the consumer once.

3. Server-side Broker

A Kafka Server is a broker.

4. Topic

In a logical concept, a topic can be viewed as a set of business information that clients can produce or consume by binding specific topics of interest.

Producer   Workflow

1. Serialize

The serialization mechanism is a very important optimization mechanism in high-concurrency scenarios. Efficient serialization implementation can greatly improve distributed systems

Systematic network transmission and data disk capabilities.

2. Partition

According to the specified key, the message is allocated to a specific partition through a specific algorithm.

3. Compression

In this step, the producer record is compressed before it’s written to the record accumulator.

4. Record accumulator

The message to be sent by KafkaProducer will be in

Cached in ReocrdAccumulator and then sent to Kafka Broker in batches

5. Sender 

The sender is an independent thread in KafkaProducer used to send messages. As you can see here, each KafkaProducer object corresponds to a sender thread. He will be responsible for sending messages from the RecordAccumulator to Kafka.

The Sender does not send all the messages cached in the RecordAccumulator at once, but only takes out a part of the messages at a time. He only obtains the ProducerBatch messages whose cache content in the RecordAccumulator reaches the BATCH_SIZE_CONFIG size.

Of course, if there are relatively few messages, the message size in ProducerBatch cannot reach BATCH_SIZE_CONFIG for a long time, and the Sender will not wait forever. The maximum waiting time is LINGER_MS_CONFIG. Then the messages in ProducerBatch will be read.

6. ACK

Determine whether the message is successfully sent to the Broker.

Acks=0

The producer does not care whether the Broker writes the message to the Partition, it just sends the message and then forgets it. Highest throughput, but lowest data security.

Acks=all or -1

The producer needs to wait for all Partitions (Leader Partition and its corresponding FollowerPartition) on the Broker side to be written before getting the return result. This method is the most secure for data, but it will take longer to send messages each time. swallow

Throughput is the lowest.

Acks = 1

Is a relatively neutral strategy. After the Leader Partition writes its own message, it returns the result to the producer.

7. Producer message idempotence

Kafka ensures that no matter what the Producer sends to the Broker, No matter how many times the data is repeated, the Broker only retains one message.

8. Producer message transaction

This transaction mechanism ensures that this batch of messages can successfully maintain idempotence at the same time. Or this batch of messages fails at the same time so that the producer can start retrying as a whole and the messages will not be repeated.

Cluster MetaData

The main metadata of the Kafka cluster is stored in Zookeeper. There are two main states.

Controller

Among multiple Brokers, one Broker needs to be elected to serve as

Controller role. The Controller role manages the partition and replica status of the entire cluster.

Leader

In multiple Partitions under the same Topic, a Leader role needs to be elected. The Partition of the Leader role is responsible for data interaction with the client.