Elastic Cloud Hardware

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Use this information to learn how Elastic Cloud instance configurations such as gcp.kibana.1 or aws.data.highio.i3 relate to the underlying cloud platform hardware that we use when you create an Elastic Cloud deployments.

Instance Configurations

Deployments use a range of virtualized hardware resources from a cloud provider, such as Amazon EC2 (AWS) or Google Compute Platform (GCP). We call these virtualized hardware resources instance configurations on Elastic Cloud, but you might recognize the underlying hardware they build on as instance types on AWS or machine types on GCP.

Instance configurations enable instances—the products and features of the Elastic Stack—to run on suitable virtualized hardware resources that support their intended purpose. For example: If you have a logging use case that benefits from Elasticsearch data nodes with large amounts of slower but more cost-efficient storage space, your instance configuration will use large spindle drives rather than more expensive SSD storage. Each instance configuration provides a specific combination of CPU resources, memory, and storage, all of which you can scale from small to very large.

How to read an Elastic Cloud instance configuration name, such as aws.data.highio.i3:

CLOUD_PLATFORM.ELASTIC_CLOUD_INSTANCE_TYPE.HARDWARE_PROFILE.HARDWARE_TYPE

The cloud platform that the underlying hardware belongs to, such as Amazon EC2 (AWS) or Google Compute Platform (GCP).

The products or features of the Elastic Stack that can be hosted on the instance configuration, including:

  • Elasticsearch master, ingest, and data nodes
  • Kibana instances
  • Machine learning (ML) nodes

Optional: The hardware resource characteristics of the instance configuration, such as optimization for I/O, storage, CPU (compute), or memory.

The short name for an AWS instance type or a number for a custom GCP machine type on which Elastic Cloud hosts an instance configuration. Host machines are shared between deployments, but containerization and guaranteed resource assignment for each deployment prevent a noisy neighbor effect.

Tip

A word about terminology: Elastic Cloud instance types are not the same AWS instance types. Elastic Cloud instances types refer to the deployable products or features of the Elastic Stack that we support as part of a deployment template, such as Elasticsearch and Kibana. You’ll sometimes also see them referred to as instances, which just means that they are running in a deployment. When you resize or change the configuration of Elasticsearch, for example, you are working with the associated instances, in this case Elasticsearch nodes.

Amazon EC2 (AWS)

Instance configurations map to instance types and AWS EC2 instance types as follows:

Instance configuration Instance types AWS EC2 instance type Memory Sizes1

aws.data.highio.i3

Data nodes (co-located master and ingest nodes)

i3.8xl

4, 8, 15, 29, 58, 116, 232 …

aws.data.highstorage.d2

Data nodes (co-located master and ingest nodes)

d2.4xl

4, 8, 15, 29, 58, 116, 232 …

aws.data.highcpu.m4

Data nodes (co-located master and ingest nodes)

m4.16xl2

4, 8, 15, 30, 60, 120, 240 …

aws.data.highcpu.m5

Data nodes (co-located master and ingest nodes)

m5.12xl3

4, 8, 15, 30, 60, 120, 240 …

aws.data.highmem.r4

Data nodes (co-located master and ingest nodes)

r4.8xl2

4, 8, 15, 29, 58, 116, 232 …

aws.master.r4

Master nodes

r4.8xl2

1, 2, 4, 8, 15, 29, 58, 116, 232 …

aws.coordinator.r4

Coordinator3

r4.8xl2

1, 2, 4, 8, 15, 29, 58, 116, 232 …

aws.ingest.m4

Ingest nodes

m4.16xl2

1, 2, 4, 8, 15, 30, 60, 120, 240 …

aws.ingest.m5

Ingest nodes

m5.12xl3

1, 2, 4, 8, 15, 30, 60, 120, 240 …

aws.ml.m5

Data nodes

m5.12xl3

1, 2, 4, 8, 15, 30, 60, 120, 240 …

aws.kibana.r4

Kibana2

r4.8xl

1, 2, 4, 8, 15, 29, 58, 116, 232 …

aws.apm.r4

APM

r4.8xl2

1, 2, 4, 8, 15, 29, 58, 116, 232 …

1 Memory sizes ensure efficient hardware utilization and might not scale to the power of two (n2)

2 EBS GP2 storage 1024GB (memory:storage ratio of 1:4)

3 EBS GP2 storage 1024GB (memory:storage ratio of 1:4). Used instead of m4 in available regions.

To learn more about AWS EC2 hardware instance types, see Amazon EC2 Instance Types.

Google Cloud Platform (GCP)

Instance configurations map to instance types and GCP machine types as follows. Note that Elastic Cloud uses custom GCP machine types.

Instance configuration Instance types GCP machine type Memory Sizes1

gcp.data.highio.1

Data (co-located master and ingest nodes)

12 cores, 78 GB memory, 3000 GB storage

4, 8, 16, 32, 64, 128, 256 …

gcp.data.highstorage.1

Data nodes (co-located master and ingest nodes)

22 cores, 140 GB memory, 12800 GB storage2

4, 8, 16, 32, 64, 128, 256 …

gcp.data.highcpu.1

Data nodes (co-located master and ingest nodes)

64 cores, 270 GB memory, 1024 GB storage3

4, 8, 16, 32, 64, 128, 256 …

gcp.data.highmem.1

Data nodes (co-located master and ingest nodes)

22 cores, 140 GB memory, 1024 GB storage3

4, 8, 16, 32, 64, 128, 256 …

gcp.master.1

Master nodes

22 cores, 140 GB memory, 1024 GB storage3

1, 2, 4, 8, 16, 32, 64, 128, 256 …

gcp.coordinator.1

Coordinator3

22 cores, 140 GB memory, 1024 GB storage3

1, 2, 4, 8, 16, 32, 64, 128, 256 …

gcp.ingest.1

Ingest nodes

64 cores, 270 GB memory, 1024 GB storage3

1, 2, 4, 8, 16, 32, 64, 128, 256 …

gcp.ml.1

ML

64 cores, 270 GB memory, 1024 GB storage3

1, 2, 4, 8, 16, 32, 64, 128, 256 …

gcp.kibana.1

Kibana

22 cores, 140 GB memory, 1024 GB storage3

1, 2, 4, 8, 16, 32, 64, 128, 256 …

gcp.apm.1

APM

22 cores, 140 GB memory, 1024 GB storage3

1, 2, 4, 8, 16, 32, 64, 128, 256 …

1 Memory sizes ensure efficient hardware utilization and might not scale to the power of two (n2)

2 Using SSD persistent volumes, no RAID

3 Using SSD persistent volumes, no RAID (memory:storage ratio of 1:4)

To learn more about GCP hardware machine types, see Machine Types.