- 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
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- Releasing Now
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- Automatic Replacement
- Exporting Software Configuration
- Cluster Scripts
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- Cluster Overview
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- Cluster Event
- Log
- Application Analysis
- Cluster Inspection
- Java Analysis
- Monitoring Metrics
- Node Monitoring Metrics
- HDFS Monitoring Metrics
- YARN Monitoring Metrics
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- HBase Monitoring Metrics
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- ClickHouse Monitoring Metrics
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- Zeppelin Monitoring Metrics
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- Storm Monitoring Metrics
- Livy Monitoring Metrics
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- Kafka Monitoring Metrics
- Alarm Configurations
- Alarm Records
- EMR on TKE Operation Guide
- EMR Lite 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
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- 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
- API Documentation
- FAQs
- Service Level Agreement
- 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 Lite 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
- API Documentation
- FAQs
- Service Level Agreement
- Contact Us
You can use Hive to map HBase tables. By doing so, you can read data in HBase with Hive and run Hive-SQL statements to perform operations such as query and insertion on HBase tables.
Preparations for Development
Confirm that you have activated Tencent Cloud and created an EMR cluster. When creating the EMR cluster, select the Hive and HBase components on the software configuration page.
Hive and its dependencies are installed under the EMR cluster directory
/usr/local/service/Creating an HBase Table
First, you need to log in to any node (preferably a master one) in the EMR cluster. For more information on how to log in to EMR, please see Logging in to Linux Instances. Here, you can choose to log in with WebShell. Click "Log in" on the right of the desired CVM instance to enter the login page. The default username is
root, and the password is the one you set when creating the EMR cluster. Once the correct credentials are entered, you can enter the command line interface.Run the following command in EMR command-line interface to switch to the Hadoop user and go to the HBase folder to enter HBase Shell:
[root@172 ~]# su hadoop[hadoop@172 ~]# cd /usr/local/service/hbase[hadoop@10hbase]$ bin/hbase shell
Create a table in HBase as shown below:
hbase(main):001:0> create 'test', 'cf'hbase(main):003:0> put 'test', 'row1', 'cf:a', 'value1'hbase(main):004:0> put 'test', 'row1', 'cf:b', 'value2'hbase(main):005:0> put 'test', 'row1', 'cf:c', 'value3'
For more information on HBase operations, please see the Hbase Operation Guide or official documentation.
After the creation is completed, you can use the
list and scan operations to view the newly created table.hbase(main):001:0> list 'test'TABLEtest1 row(s) in 0.0030 seconds=> ["test"]hbase(main):002:0> scan 'test'ROW COLUMN+CELLrow1 column=cf:a, timestamp=1530276759697, value=value1row2 column=cf:b, timestamp=1530276777806, value=value2row3 column=cf:c, timestamp=1530276792839, value=value33 row(s) in 0.2110 seconds
Mapping a Hive Table
Switch to the Hive folder and connect to Hive:
[hadoop@172 hive]$ cd /usr/local/service/hive/[hadoop@172 hive]$ bin/hive
Next, create a Hive external table and map it to the HBase table created in step 2:
hive> CREATE EXTERNAL TABLE hive_test (> rowkey string,> a string,> b string,> c string> ) STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler' WITH> SERDEPROPERTIES("hbase.columns.mapping" = ":key,cf:a,cf:b,cf:c")> TBLPROPERTIES("hbase.table.name" = "test");OKTime taken: 2.086 seconds
Now, a mapping from the Hive table to the HBase is created. You can run the following command to view the elements in the Hive table:
hive> select * from hive_test;OKrow1 value1 value2 value3Time taken: 0.305 seconds, Fetched: 1 row(s)
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