ECS instance type selection

Enterprise-level instance families

Heterogeneous computing instance families

For CPU compute-intensive scenarios, such as general-purpose applications, gaming applications, and live streaming, we recommend that you select an instance type with a balanced CPU-to-memory ratio, such as 1:2, and use ESSDs as system disks and data disks. For scenarios that require higher network performance such as on-screen video comments, select a high-specification instance type to improve packet forwarding rates.

In big data scenarios, such as Hadoop, Spark, and Kafka, each node has different performance requirements. You must balance the performance of all nodes, including the computing performance, storage throughput, and network performance.

• Management nodes: Select an instance family in the same manner that you select an instance family in general-purpose scenarios. We recommend that you select g series instance families.

• Compute nodes: Select an instance family in the same manner that you select an instance family in general-purpose scenarios. We recommend that you select g series instance families. Select instance types based on the cluster size. For example, you can select ecs.g7.4xlarge for a cluster that consists of less than 100 nodes and ecs.g7.8xlarge for a cluster that consists of 100 or more nodes.

• Cache nodes: used to store hot data or deploy Really Simple Syndication (RSS). These nodes require high disk and network I/O performance. We recommend that you select the i4g or i2g instance family.

• Compute and cache nodes: used to compute and cache data. When you select an instance family, consider the computing performance, I/O performance, and disk capacity. We recommend that you select the i4, i4r, or d3c instance family.

  Note

  You can use spot instances as compute nodes to improve cost-effectiveness. For more information, see What is spot instance?

• Data nodes: require high storage throughput, high network throughput, and balanced CPU-to-memory ratios. We recommend that you use d series big data instance families. For example, you can select ecs.d2s.5xlarge and ecs.d3s.4xlarge for MapReduce and Hive and ecs.d2s.10xlarge for Spark and MLib.

In most cases, the databases, caches, and search scenarios require CPU-to-memory ratios higher than 1:4. Specific software in the scenarios is sensitive to latency and storage I/O capabilities. We recommend that you select instance families that provide cost-effective memory.

This section uses databases as an example. In normal cases, business systems directly connect to OLTP databases, and data redundancy is implemented by RAIDs. However, you can use Alibaba Cloud ECS to flexibly deploy lightly and heavily loaded databases.

• For lightly loaded databases, use i4r and i4g instance families with cloud disks, which are more cost-effective.

• Heavily loaded databases require high storage IOPS and low read/write latency. We recommend that you use i series instance families equipped with local SSDs. The local SSDs are high I/O Non-Volatile Memory Express (NVMe) SSDs that can meet the requirements of large heavily loaded databases.

If you select the instance type to create an instance and find that the CPU utilization of the instance is low, we recommend that you check whether the memory usage of the instance is high by using one of the following methods:

• View monitoring data in the ECS console

• View the monitoring data of a cloud disk

If the memory usage of the instance is high, you can change the instance to an instance type that has a more suitable CPU-to-memory ratio. For more information, see the following topics:

• Configuration change overview

• Pre-checks on instance type change