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- EMR on TKE Operation Guide
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- Spark Development Guide
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- 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 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
- API Documentation
- FAQs
- Service Level Agreement
- Contact Us
Hive supports using MapReduce, Tez, and Spark as execution engines, with MapReduce as the default in EMR. This document will show you how to configure the execution engine in Hive.
It is generally recommended to use the Tez execution engine instead of MapReduce for improved computation efficiency.
Development Preparation
Make sure you have activated Tencent Cloud and created an EMR cluster. For more details, see Creating a Cluster.
When an EMR cluster is created, select the Hive component in the software configuration interface. If you need to switch to the Tez or Spark execution engine, check the corresponding component when creating the cluster.
Configuring Engines
Log in to the Hive client, following the directions in Hive Connection Method. The execution engine for Hive is configured using the hive.execution.engine parameter, with the default engine being mr. You can use the following command to check the current execution engine:
hive> set hive.execution.engine;hive.execution.engine=mr
Switching Execution Engine in the Command Line
Use the following command to modify the execution engine for the current client session:
Modify the execution engine to Tez:
hive> set hive.execution.engine=tez;
Modify the execution engine to Spark:
hive> set hive.execution.engine=spark;
After execution, you can enter the following command to verify:
hive> set hive.execution.engine;
If the returned information matches the execution engine you set, it indicates that the configuration is successful.
Note:
The above execution engine switch operation is only effective for the current client session; the default execution engine will be used when accessing by other clients or after disconnecting and reconnecting.
Switching Execution Engine in the Configuration File
You can globally switch the default execution engine through the EMR console by navigating to Cluster Services > HIVE > Configuration Management. Locate the hive-site.xml configuration file and set the following parameter value:
Modify the execution engine to Tez:
hive.execution.engine = tez
Modify the execution engine to Spark:
hive.execution.engine = sparkP
Perform Save Configuration > Save and Deploy, and Restart HiveServer2. After the restart is completed, the default execution engine of Hive will be modified.
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