tencent cloud

Elastic MapReduce

Release Notes and Announcements
Release Notes
Announcements
Security Announcements
Product Introduction
Overview
Strengths
Architecture
Features
Use Cases
Constraints and Limits
Technical Support Scope
Product release
Purchase Guide
EMR on CVM Billing Instructions
EMR on TKE Billing Instructions
EMR Serverless HBase Billing Instructions
Getting Started
EMR on CVM Quick Start
EMR on TKE Quick Start
EMR on CVM Operation Guide
Planning Cluster
Administrative rights
Configuring Cluster
Managing Cluster
Managing Service
Monitoring and Alarms
TCInsight
EMR on TKE Operation Guide
Introduction to EMR on TKE
Configuring Cluster
Cluster Management
Service Management
Monitoring and Ops
Application Analysis
EMR Serverless HBase Operation Guide
EMR Serverless HBase Product Introduction
Quotas and Limits
Planning an Instance
Managing an Instance
Monitoring and Alarms
Development Guide
EMR Development Guide
Hadoop Development Guide
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
JupyterLab Development Guide
MLflow Development Guide
Practical Tutorial
Practice of EMR on CVM Ops
Data Migration
Practical Tutorial on Custom Scaling
API Documentation
History
Introduction
API Category
Cluster Resource Management APIs
Cluster Services APIs
User Management APIs
Data Inquiry APIs
Scaling APIs
Configuration APIs
Other APIs
Serverless HBase APIs
YARN Resource Scheduling APIs
Making API Requests
Data Types
Error Codes
FAQs
EMR on CVM
Service Level Agreement
Contact Us

Graceful Scale-In

PDF
Focus Mode
Font Size
Last updated: 2025-07-03 11:35:04

Overview

After graceful scale-in is enabled, if the scale-in action is triggered when a node is executing tasks, the node will not be released immediately. Instead, it will be released after completing the tasks. Graceful scale-in is available for both automatic scaling and manual scale-in.
Note:
YARN, HBase, and Presto (renamed Trino in EMR v2.7.0 and v3.40 or later) support graceful scale-in. However, Presto cannot be scaled in gracefully in Ranger, Kerberos, and OpenLDAP integration scenarios.

Directions

Auto-scaling

Auto-scaling allows you to enable or disable graceful scale-in for all scale-in rules. This feature is called global graceful scale-in and is disabled by default. When you add or edit a single scale-in rule, graceful scale-in is enabled by default, and the default duration is 60 seconds (valid range: 60–86400 seconds).
Note:
When both graceful scale-in and a single scale-in rule are enabled, the graceful scale-in takes effect.
1. Log in to the EMR console, click the ID/name of the target cluster to enter the cluster details page, and click Auto-scaling.
2. In the Scaling rule management section on the Auto-scaling page, click Add rule and add a scale-in rule.

Manual scale-in

When you try to manually remove a node, graceful scale-in is disabled by default. When you enable this feature, the default duration is 60 seconds (valid range: 60–86400 seconds).
1. Click the ID/name of the target cluster to enter the cluster details page. Then, select Cluster resources > Resources.
2. Select the node to remove and click Scale in. In this case, graceful scale-in is disabled by default. You can enable it and set a duration.
3. After finishing your settings, click Next, confirm the node information, and click Start termination.

Help and Support

Was this page helpful?

Help us improve! Rate your documentation experience in 5 mins.

Feedback