Sizing Guideline
This is a guideline for the compute resources needed for the different components in a deployment.
Small, medium, and large sizing estimates are provided. The small option starts with the minimum recommended resources and, generally, each subsequent size doubles the number of CPU cores and available RAM. Not all components experience the same increase in load, so the estimates may not increase at the same rate for all components.
Many factors influence the number of Apps and Data Streams a deployment can effectively run. These factors include:
the number of data streams,
how frequently the streams process data,
the size of the data payload,
the number of recommendations to be monitored,
the number of apps and event boards being served,
the complexity of apps and event boards (the number of elements and integration points),
and the number of concurrent users accessing the apps and event boards.
As a rough guide, an example workload for a Medium-sized deployment would be:
~200 Data Streams running across
~15 Stream Hosts,
serving data and triggering recommendations for ~10 Apps
On-Premise
Subscription Manager (SM) 1
2 CPU
8GB RAM
2 CPU
8GB RAM
4 CPU 16GB RAM
Application Designer (AD)
2 CPU
8GB RAM
4 CPU 16GB RAM
8 CPU
32GB RAM
Data Stream Designer (DS)
2 CPU
8GB RAM
4 CPU 16GB RAM
8 CPU
32GB RAM
Stream Host Server (SH) 2,3
2 CPU
8GB RAM
4 CPU 16GB RAM
8 CPU
32GB RAM
SQL Database Server
(Combined for SM, AD, DS) 4
2 CPU
8GB RAM
4 CPU
16GB RAM
8 CPU
32GB RAM
Footnotes
1 High volumes of concurrent users may require additional compute.
2 Multiple Stream Hosts can be deployed to the Stream Host Server.
3 If the Stream Host needs more resources, consider increasing the RAM before adding additional CPU cores as Stream Hosts perform in-memory processing of events.
4 High volumes of recommendations may require additional compute and storage.
Azure
Estimates for Azure target the Premium v3 service plan for applications, and Azure SQL Database for the databases.
Azure SQL database estimates are based on the General-Purpose service tier and use the DTU-based purchasing model (a blended measure of compute, storage, and IO resources).
Subscription Manager (SM) App Service Plan 1
P1v3
P1v3
P2v3
Application Designer (AD) App Service Plan
P1v3
P2v3
P3v3
Data Stream Designer (DS) App Service Plan
P1v3
P1v3
P2v3
Stream Host Server (SH) App Service Plan 2,3
P1v3
P2v3
P3v3
Azure SQL Database
(For each of SM, AD, DS) 4
Standard – 20 DTUs
Standard – 50 DTUs
Standard – 100 DTUs
Footnotes
1 High volumes of concurrent users may require additional compute.
2 Multiple Stream Hosts can be deployed to the Stream Host App Service Plan.
3 If the Stream Host needs more resources, consider increasing the RAM before adding additional CPU cores as Stream Hosts perform in-memory processing of events.
4 High volumes of recommendations may require additional compute and storage.
For additional details please see Azure App Service Pricing and Azure SQL Database Pricing.
AWS
Estimates for AWS target Amazon EC2 T3 instances for applications, and an Amazon RDS T3 instance for the databases.
Subscription Manager (SM) EC2 Instance 1
t3.large
t3.large
t3.xlarge
Application Designer (AD) EC2 Instance
t3.large
t3.xlarge
t3.2xlarge
Data Stream Designer (DS) EC2 Instance
t3.large
t3.large
t3.xlarge
Stream Host Server (SH) EC2 Instance 2,3
t3.large
t3.xlarge
t3.2xlarge
Amazon RDS for SQL
(Combined for SM, AD, DS) 4
t3.large
t3.xlarge
t3.2xlarge
Footnotes
1 High volumes of concurrent users may require additional compute.
2 Multiple Stream Hosts can be deployed to the Stream Host Server.
3 If the Stream Host needs more resources, consider increasing the RAM before adding additional CPU cores as Stream Hosts perform in-memory processing of events.
4 High volumes of recommendations may require additional compute and storage.
For additional details please see AWS EC2 and RDS instance types.
Meta tags: XMPro Sizing; Sizing Guidelines; XMPro Guidelines.
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