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Introduction to Kafka

Introduce
Kafka It's a distributed distribution - Subscribe to the messaging system . It was originally made by LinkedIn Companies to develop , Then become Apache Part of the project .Kafka It's a distributed one , Divisible , Persistent log service for redundant backups . It is mainly used to process active streaming data .
Similar to JMS Characteristics of , But it's totally different in design and implementation .

JMS
JMS(Java Message Service,Java Message service )API It's a set of messaging standards , Allow application components based on JavaEE Platform creation 、 send out 、 Receive and read messages .
Asynchronous communication between systems , Reduce the coupling between systems .
Supports two message models

  1. Point to point or queue model
  2. Publisher / Subscriber model

Kafka Overall framework
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Basic concepts
Topic:Kafka Seed the message (Feed) Be arranged , Each type of message is called a topic .
Producer: The object that publishes the message is called the subject producer .
Consumer: An object that subscribes to a message and processes the seed of a published message is called a topic consumer .
Broker: Published messages are saved in a set of servers , be called Kafka colony . Each server in the cluster is a proxy (Broker). Consumers can subscribe to one or more topics and from Broker Pull data , To consume these published messages .
Partition:Topic Physical grouping , One topic Can be divided into multiple partition, Every partition It's an ordered queue .partition Each message is assigned an ordered one id(offset).

Workflow
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Topic
For each of these Topic,Kafka The cluster maintains this partition's log, Messages are stored in log files ;
Each partition is sequential 、 Immutable message queue , And you can keep adding . Messages in the partition are assigned a sequence number , Called offset (offset), This offset is unique in each partition ;
Kafka The cluster will keep the message for a period of time ( Configurable ), Whether the message is consumed or not , Delete after expiration , Consumers only hold the offset of the message .
Offset has consumer control , Consumers can reset the offset to an older offset , Reread the message , So one consumer's actions don't affect other consumers log To deal with .
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Partition
Partition is a physical concept , stay Broker In the form of a catalog , Each partition contains multiple segments (Segment), Every Segment Corresponding to a log file , The file is named message's offset.
Topic The partitions are distributed to multiple servers in the cluster , Each server processes the partition it is assigned to .
According to the configuration, multiple backups can be set for each partition (replicas), Copy to other servers as backup fault tolerance .
Each partition has one leader, Zero or more follower.Leader Handle all read and write requests for this partition , and follower Passively copying data . If leader Downtime , The other one follower Will be promoted as new leader.
leader Responsible for tracking all follower state , If follower“ backward ” Too much or failure ,leader Will take it from replicas Delete from the sync list . When all follower Each saved a message successfully , This message is considered to be “committed”, So at this time consumer To consume it .
A server may be a partition at the same time leader, Another partition of follower. This can balance the load , Avoid all requests being processed by only one or several servers .
Producer
The producer goes to some Topic Post message on , At the same time, the producer is also responsible for choosing to publish to Topic Which partition on ( To the partition Leader);
Message sending strategy is divided into synchronization 、 There are two kinds of asynchrony ;
Which message is routed to partition On , By producer The client decides . For example, the client uses Random,Hash And RoundRobin Polling, etc ;
Consumer
Generally speaking , There are two types of message models , Queues and releases - Subscription .Kafka Provides a single consumer abstract model for both models : Consumer group (consumer group), Consumers label themselves with a consumer group name .
One was posted in Topic The message is distributed to a consumer in this consumer group . If all the consumers are in one group , So this becomes queue Model ; If all the consumers are in different groups , So it's all about Publishing - Subscription model .
We can create groups of consumers as logical subscribers . Each group contains a different number of consumers , Multiple consumers within a group can be used to extend performance and fault tolerance .
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Kafka The design of the
High throughput , High availability is the core design goal .
Data disk persistence : The message is not in memory cache, Write directly to disk , Make full use of the disk's sequential read-write performance .
zero-copy: Reduce IO Operation steps ( disk -> kernel -> network card ).
Support bulk data sending and pulling .
Support data compression .
Topic Divided into multiple partition, Improve parallel processing capability .
Horizontal expansion .
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Message queue comparison
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Application scenarios
Applicable scenario :

  1. User behavior data analysis
  2. Operational monitoring
  3. Log collection
  4. The messaging system

Not applicable to the scene :

  1. Need something to support ;
  2. Strict order consumption .

Kafka In risk control
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