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- Instance Management
- Creating Instance
- Naming with Consecutive Numeric Suffixes or Designated Pattern String
- Viewing Instance
- Upgrading Instance
- Downgrading Instance Configuration
- Terminating/Returning Instances
- Change from Pay-as-You-Go to Monthly Subscription
- Upgrading Instance Version
- Adding Routing Policy
- Public Network Bandwidth Management
- Connecting to Prometheus
- AZ Migration
- Setting Maintenance Time
- Setting Message Size
- Topic Management
- Consumer Group
- Monitoring and Alarms
- Smart Ops
- Permission Management
- Tag Management
- Querying Message
- Event Center
- Migration to Cloud
- Data Compression
- Instance Management
- CKafka Connector
- Practical Tutorial
- Practical Tutorial of CKafka Client
- Connector Practical Tutorial
- Connecting Flink to CKafka
- Connecting Schema Registry to CKafka
- Connecting Spark Streaming to CKafka
- Connecting Flume to CKafka
- Connecting Kafka Connect to CKafka
- Connecting Storm to CKafka
- Connecting Logstash to CKafka
- Connecting Filebeat to CKafka
- Multi-AZ Deployment
- Log Access
- Replacing Supportive Route (Old)
- Practice Tutorial for Cluster Bandwidth in High CPU Utilization Scenarios
- Practice Tutorial for Cluster Capacity Planning
- Troubleshooting
- API Documentation
- History
- Introduction
- API Category
- Making API Requests
- DataHub APIs
- ACL APIs
- Topic APIs
- DescribeTopicProduceConnection
- BatchModifyGroupOffsets
- BatchModifyTopicAttributes
- CreateConsumer
- CreateDatahubTopic
- CreatePartition
- CreateTopic
- CreateTopicIpWhiteList
- DeleteTopic
- DeleteTopicIpWhiteList
- DescribeDatahubTopic
- DescribeTopic
- DescribeTopicAttributes
- DescribeTopicDetail
- DescribeTopicSubscribeGroup
- FetchMessageByOffset
- FetchMessageListByOffset
- ModifyDatahubTopic
- ModifyTopicAttributes
- DescribeTopicSyncReplica
- Instance APIs
- Route APIs
- Other APIs
- Data Types
- Error Codes
- SDK Documentation
- General References
- Conducting Production and Consumption Pressure Testing on CKafka
- Configuration Guide for Common Parameters in CKafka
- Connecting to Legacy Self-Built Kafka
- Suggestions for CKafka Version Selection
- CKafka Data Reliability Description
- Connector
- FAQs
- Service Level Agreement
- Contact Us
- Glossary
Practice Tutorial for Cluster Bandwidth in High CPU Utilization Scenarios
Last updated: 2024-11-07 15:08:17
Applicable Scenarios
CKafka Pro Edition.
The cluster bandwidth specification is higher than 3,000 MB.
In certain special business scenarios, the bandwidth utilization is low but the CPU utilization is high. Therefore, the cluster bandwidth needs to be scaled out based on the CPU utilization.
Scale-out Policies
It is recommended that the CPU utilization of a single node does not exceed 60%. If it exceeds 60%, linear scaling needs to be conducted based on the CPU utilization of the current cluster.
Examples
Current Cluster Status
The cluster bandwidth specification is 3,000 MB, the average CPU utilization is 80%, and the bandwidth utilization is 50%.
For more information on the node-level CPU utilization, see Querying Advanced Monitoring (Pro Edition).
Scale-out Objectives
Business traffic increased by 50%.
CPU utilization decreased to 60%.
Scale-out Solutions
Scale-out coefficient = 80%/60% x (1 + 50%) = 2
Reserved buffer = 30%
Specification after scale-out = 3,000 MB x Scale-out coefficient x Reserved buffer = 3,000 MB x 2 x (1 + 30%) = 7,800 MB
Yes
No
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