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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
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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
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Cluster Resource Management APIs
Cluster Services APIs
User Management APIs
Data Inquiry APIs
Scaling APIs
Configuration APIs
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Serverless HBase APIs
YARN Resource Scheduling APIs
Making API Requests
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EMR on CVM
Service Level Agreement
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Features

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Last updated: 2023-12-26 17:51:47
Tencent Cloud Elastic MapReduce (EMR) is integrated with open-source frameworks and projects, such as Apache Hadoop, Apache Hive, Apache Spark, and Apache Storm. The cloud-based Hadoop services allow you to process vast amount of data securely, cost-efficiently, and elastically at scale. It has the following advantages:

Auto Scaling

Creating a cluster in minutes You can create a secure, stable, cloud-managed Hadoop cluster in just a few minutes in the console.
Scaling in minutes Your EMR cluster can be smoothly scaled up or down just a few minutes as your computing needs change.
API support EMR clusters can be easily created, scaled, and terminated in programs through APIs.

Separation of Storage and Computation

Intra-cluster separation of storage and computation At the cluster level, cloud-based Hadoop clusters can be planned in a manner where storage nodes and compute nodes are separated, so that you can scale the compute nodes as needed to lower the hardware costs.
COS-based separation of storage and computation Massive amounts of data to be analyzed can be stored in COS. While the storage costs is reduced through COS, different versions of EMR clusters can be created to analyze the same data, which brings out extreme architectural flexibility.

OPS Support

Monitoring and multi-channel alarming A comprehensive monitoring and OPS system is provided, which can detect exceptions in components such as Spark, Hive, and Presto and jobs within seconds after they occur to ensure the robust operations of big data clusters.
Technical support In addition to comprehensive technical documentation, Tencent Cloud also has a technical service system where complete technical support is provided through various channels such as ticket.

Security

EMR uses security groups to control inbound and outbound traffic to your CVM instances. Components’ web UIs can only be accessed through one specified instance assigned with public IP, and the access requests must be authenticated by username and password. In addition, the security group of this instance only allows SSH ports and proxy access ports.
Caution
Changing the project will cause the CVM instance to lose its security group.

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