- Release Notes and Announcements
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- Naming with Consecutive Numeric Suffixes or Designated Pattern String
- Viewing Instance
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- 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
- Release Notes and Announcements
- Product Introduction
- Purchase Guide
- Getting Started
- Operation Guide
- 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)
- Troubleshooting
- API Documentation
- History
- Introduction
- API Category
- Making API Requests
- DataHub APIs
- ACL APIs
- Topic APIs
- BatchModifyGroupOffsets
- BatchModifyTopicAttributes
- CreateConsumer
- CreateDatahubTopic
- CreatePartition
- CreateTopic
- CreateTopicIpWhiteList
- DeleteTopic
- DeleteTopicIpWhiteList
- DescribeDatahubTopic
- DescribeTopic
- DescribeTopicAttributes
- DescribeTopicDetail
- DescribeTopicProduceConnection
- 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
What Happens to Data that Fails to Be Processed due to Format Issues During Data Processing?
During data processing, data with processing failure due to format issues or does not meet filter rules will be directed to the Failure Handling policy.
Failure Handling policies include:
1. The Discard policy means discarding raw data that failed to process.
2. The Retain policy means retaining the raw data that failed to process and encapsulating the raw data and failure information into a new message body, and delivering it to the target topic.
3. The Dead Letter Queue policy means retaining the raw data that failed to process and encapsulating the raw data and failure information into a new message body, and delivering it to the Dead Letter Queue.
How to Identify Performance Bottlenecks in Data Processing?
The process of data processing consists of three phases:
1. Consuming data from the source Topic;
2. Processing the raw data;
3. Delivering the processed data to the data target Topic.
We can monitor the performance of phase 1 by tracking the message backlog and consumption speed of the consumer, and for phase 3, by monitoring the production speed of the target Topic.
Generally, we can increase the number of partitions in the source Topic to improve the maximum concurrency of task scalability, thereby enhancing the overall task performance.
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