El contenido no se encuentra disponible en el idioma seleccionado. Estamos trabajando continuamente para agregar más idiomas. Gracias por su apoyo.

Compute
Elastic Cloud Server
Huawei Cloud Flexus
Bare Metal Server
Auto Scaling
Image Management Service
Dedicated Host
FunctionGraph
Cloud Phone Host
Huawei Cloud EulerOS
Networking
Virtual Private Cloud
Elastic IP
Elastic Load Balance
NAT Gateway
Direct Connect
Virtual Private Network
VPC Endpoint
Cloud Connect
Enterprise Router
Enterprise Switch
Global Accelerator
Management & Governance
Cloud Eye
Identity and Access Management
Cloud Trace Service
Resource Formation Service
Tag Management Service
Log Tank Service
Config
OneAccess
Resource Access Manager
Simple Message Notification
Application Performance Management
Application Operations Management
Organizations
Optimization Advisor
IAM Identity Center
Cloud Operations Center
Resource Governance Center
Migration
Server Migration Service
Object Storage Migration Service
Cloud Data Migration
Migration Center
Cloud Ecosystem
KooGallery
Partner Center
User Support
My Account
Billing Center
Cost Center
Resource Center
Enterprise Management
Service Tickets
HUAWEI CLOUD (International) FAQs
ICP Filing
Support Plans
My Credentials
Customer Operation Capabilities
Partner Support Plans
Professional Services
Analytics
MapReduce Service
Data Lake Insight
CloudTable Service
Cloud Search Service
Data Lake Visualization
Data Ingestion Service
GaussDB(DWS)
DataArts Studio
Data Lake Factory
DataArts Lake Formation
IoT
IoT Device Access
Others
Product Pricing Details
System Permissions
Console Quick Start
Common FAQs
Instructions for Associating with a HUAWEI CLOUD Partner
Message Center
Security & Compliance
Security Technologies and Applications
Web Application Firewall
Host Security Service
Cloud Firewall
SecMaster
Anti-DDoS Service
Data Encryption Workshop
Database Security Service
Cloud Bastion Host
Data Security Center
Cloud Certificate Manager
Edge Security
Situation Awareness
Managed Threat Detection
Blockchain
Blockchain Service
Web3 Node Engine Service
Media Services
Media Processing Center
Video On Demand
Live
SparkRTC
MetaStudio
Storage
Object Storage Service
Elastic Volume Service
Cloud Backup and Recovery
Storage Disaster Recovery Service
Scalable File Service Turbo
Scalable File Service
Volume Backup Service
Cloud Server Backup Service
Data Express Service
Dedicated Distributed Storage Service
Containers
Cloud Container Engine
Software Repository for Container
Application Service Mesh
Ubiquitous Cloud Native Service
Cloud Container Instance
Databases
Relational Database Service
Document Database Service
Data Admin Service
Data Replication Service
GeminiDB
GaussDB
Distributed Database Middleware
Database and Application Migration UGO
TaurusDB
Middleware
Distributed Cache Service
API Gateway
Distributed Message Service for Kafka
Distributed Message Service for RabbitMQ
Distributed Message Service for RocketMQ
Cloud Service Engine
Multi-Site High Availability Service
EventGrid
Dedicated Cloud
Dedicated Computing Cluster
Business Applications
Workspace
ROMA Connect
Message & SMS
Domain Name Service
Edge Data Center Management
Meeting
AI
Face Recognition Service
Graph Engine Service
Content Moderation
Image Recognition
Optical Character Recognition
ModelArts
ImageSearch
Conversational Bot Service
Speech Interaction Service
Huawei HiLens
Video Intelligent Analysis Service
Developer Tools
SDK Developer Guide
API Request Signing Guide
Terraform
Koo Command Line Interface
Content Delivery & Edge Computing
Content Delivery Network
Intelligent EdgeFabric
CloudPond
Intelligent EdgeCloud
Solutions
SAP Cloud
High Performance Computing
Developer Services
ServiceStage
CodeArts
CodeArts PerfTest
CodeArts Req
CodeArts Pipeline
CodeArts Build
CodeArts Deploy
CodeArts Artifact
CodeArts TestPlan
CodeArts Check
CodeArts Repo
Cloud Application Engine
MacroVerse aPaaS
KooMessage
KooPhone
KooDrive
Help Center/ Distributed Message Service for Kafka/ Best Practices/ Improving Kafka Message Processing Efficiency

Improving Kafka Message Processing Efficiency

Updated on 2024-10-16 GMT+08:00

During message sending and consumption, Distributed Message Service (DMS) for Kafka, producers, and consumers collaborate to ensure service reliability. In addition, efficiency and accuracy of message sending and consumption improves when developers make proper use of DMS for Kafka topics.

Best practices for message producers and consumers are as follows:

Acknowledging Message Production and Consumption

Message Production

The producer decides whether to re-send a message based on the DMS for Kafka response.

The producer waits for the sending result or asynchronous callback function to determine if the message is successfully sent. If an exception occurs when sending the message, the producer will not receive a success response and must decide whether to re-send the message. If a success response is received, it indicates that the message has been stored in DMS for Kafka.

Message consumption

The consumer acknowledges successful message consumption.

The produced messages are sequentially stored in DMS for Kafka. During consumption, messages stored in DMS for Kafka are obtained in sequence. Consumers obtain messages, consume them, and record the status (successful or failed). The status is then submitted to DMS for Kafka.

During this process, the message consumption status may not be successfully submitted due to an exception. In this case, the corresponding messages will be re-obtained by the consumer in the next message consumption request.

Idempotent Transferring of Message Production and Consumption

To guarantee lossless messaging, DMS for Kafka implements a series of reliability measures. For example, the message synchronization storage mechanism is used to prevent the system and server from being restarted or powered off. The ACK mechanism is used to deal with exceptions that occur during message transmission.

Considering extreme conditions such as network exceptions, you can use DMS for Kafka to design idempotent message transferring in addition to acknowledging message production and consumption.

  • If message sending cannot be acknowledged, the producer needs to re-send the message.
  • After obtaining a message that has been processed, the consumer needs to notify DMS for Kafka that consumption is successful and ensure that the message is not processed repeatedly.

Producing and Consuming Messages in Batches

It is recommended that messages be sent and consumed in batches to improve efficiency.

Figure 1 Messages being produced and consumed in batches
Figure 2 Messages being produced and consumed one by one

When consuming messages in batches, consumers need to process and acknowledge messages in the sequence of receiving messages. Therefore, when a message in the batch fails to be consumed, the consumer does not need to consume the remaining messages, and can directly submit consumption acknowledgment of the successfully consumed messages.

Using Consumer Groups to Facilitate O&M

You can use DMS for Kafka as a message management system. Reading message content from topics is helpful to fault locating and service debugging.

When problems occur during message production and consumption, you can create different consumer groups to locate and analyze problems or debug services for interconnecting with other services. To ensure that other services can continue to process messages in topics, you can create a new consumer group to consume and analyze the messages.

Utilizamos cookies para mejorar nuestro sitio y tu experiencia. Al continuar navegando en nuestro sitio, tú aceptas nuestra política de cookies. Descubre más

Feedback

Feedback

Feedback

0/500

Selected Content

Submit selected content with the feedback