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- EMR on TKE Operation Guide
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- Practical Tutorial
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- Contact Us
- Release Notes and Announcements
- Product Introduction
- Purchase Guide
- Getting Started
- EMR on CVM Operation Guide
- Planning Cluster
- Configuring Cluster
- Managing Cluster
- Instance Information
- Node Specification Management
- Checking and Updating Public IP
- Logging in to Clusters
- Cluster Scale-Out
- Cluster Scale-in
- Releasing Now
- Auto Scaling
- Graceful Scale-In
- Changing Configurations
- Automatic Replacement
- Exporting Software Configuration
- Cluster Scripts
- Cluster Termination
- Operation Logs
- Task Center
- Disk Management
- Managing Service
- Monitoring and Alarms
- Cluster Overview
- Node Status
- Service Status
- Cluster Event
- Log
- Application Analysis
- Cluster Inspection
- Java Analysis
- Monitoring Metrics
- Node Monitoring Metrics
- HDFS Monitoring Metrics
- YARN Monitoring Metrics
- ZooKeeper Monitoring Metrics
- HBase Monitoring Metrics
- Hive Monitoring Metrics
- Spark Monitoring Metrics
- Presto Monitoring Metrics
- Trino Monitoring Metrics
- ClickHouse Monitoring Metrics
- Druid Monitoring Metrics
- Kudu Monitoring Metrics
- Alluxio Monitoring Metrics
- PrestoSQL Monitoring Metrics
- Impala Monitoring Metrics
- Ranger Monitoring Metrics
- COSRanger Monitoring Metrics
- Doris Monitoring Metrics
- Kylin Monitoring Metrics
- Zeppelin Monitoring Metrics
- Oozie Monitoring Metrics
- Storm Monitoring Metrics
- Livy Monitoring Metrics
- Kyuubi Monitoring Metrics
- StarRocks Monitoring Metrics
- Kafka Monitoring Metrics
- Alarm Configurations
- Alarm Records
- EMR on TKE Operation Guide
- EMR Serverless HBase Operation Guide
- EMR Development Guide
- Hadoop Development Guide
- HDFS Common Operations
- HDFS Federation Management Development Guide
- HDFS Federation Management
- Submitting MapReduce Tasks
- Automatically Adding Task Nodes Without Assigning ApplicationMasters
- YARN Task Queue Management
- Practices on YARN Label Scheduling
- Hadoop Practical Tutorial
- Using API to Analyze Data in HDFS and COS
- Dumping YARN Job Logs to COS
- Spark Development Guide
- Hbase Development Guide
- Phoenix on Hbase Development Guide
- Hive Development Guide
- Presto Development Guide
- Sqoop Development Guide
- Hue Development Guide
- Oozie Development Guide
- Flume Development Guide
- Kerberos Development Guide
- Knox Development Guide
- Alluxio Development Guide
- Kylin Development Guide
- Livy Development Guide
- Kyuubi Development Guide
- Zeppelin Development Guide
- Hudi Development Guide
- Superset Development Guide
- Impala Development Guide
- Druid Development Guide
- TensorFlow Development Guide
- Kudu Development Guide
- Ranger Development Guide
- Kafka Development Guide
- Iceberg Development Guide
- StarRocks Development Guide
- Flink Development Guide
- Hadoop Development Guide
- Practical Tutorial
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Scenario Description
Data in Kafka can be collected through Flume and stored in HBase.
Preparations for Development
As this job requires access to CKafka, you need to create a CKafka instance first. For more information, please see CKafka.
Confirm that you have activated Tencent Cloud and created an EMR cluster. When creating the EMR cluster, you need to select the Flume component on the software configuration page.
Using the Kafka Toolkit in the EMR Cluster
First, you need to check the private IP and port number of CKafka. Log in to the CKafka Console, select the CKafka instance you want to use, and view its private IP as
$kafkaIP in the basic information section, and the port number is generally 9092 by default. Create a topic named kafka_test on the topic management page.Configuring Flume
1. Create the Flume configuration file
hbase_kafka.properties.vim hbase_kafka.propertiesagent.sources = kafka_sourceagent.channels = mem_channelagent.sinks = hbase_sink# The following code is used to configure the sourceagent.sources.kafka_source.type = org.apache.flume.source.kafka.KafkaSourceagent.sources.kafka_source.channels = mem_channelagent.sources.kafka_source.batchSize = 5000agent.sources.kafka_source.kafka.bootstrap.servers = $kafkaIP:9092agent.sources.kafka_source.kafka.topics = kafka_test# The following code is used to configure the sinkagent.sinks.hbase_sink.channel = mem_channelagent.sinks.hbase_sink.table = foo_tableagent.sinks.hbase_sink.columnFamily = cfagent.sinks.hbase_sink.serializer = org.apache.flume.sink.hbase.RegexHbaseEventSerializer# The following code is used to configure the channelagent.channels.mem_channel.type = memoryagent.channels.mem_channel.capacity = 100000agent.channels.mem_channel.transactionCapacity = 10000
2. Create an HBase table.
hbase shellcreate 'foo_table','cf'
3. Run Flume.
./bin/flume-ng agent --conf ./conf/ -f hbase_kafka.properties -n agent -Dflume.root.logger=INFO,console
4. Run the Kafka producer.
[hadoop@172 kafka]$ ./bin/kafka-console-producer.sh --broker-list $kafkaIP:9092 --topic kafka_testhellohbase_test
Test
Enter information on the client of the Kafka producer and press Enter.
Check whether there is corresponding data in the HBase table.
Yes
No
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