Sizing and scalability considerations

The performance of the product is largely dependent on the amount by which your hardware size meets your business needs. Therefore, before deploying IT Data Analytics, you need to plan the hardware capacity required for both installing and using the product effectively.

Use the following guidelines to identify the appropriate architecture and hardware capacity for deploying the BMC TrueSight IT Data Analytics product.

Sizing and scaling your deployment

Related topics

Where to find more information

Deployment use cases and best practices

Planning worksheets

System requirements

FAQs about performance and scalability

Depending on your business needs you can deploy the product in a single-server environment or a multiple-server environment. Alternatively, you can start with a single-server deployment and as your business needs increase, you can scale your deployment either horizontally or vertically.

The hardware capacity required for the deployment depends on the size and scale of your business.

Scaling is a process of increasing the capacity of the system to process more data or make the data accessible to multiple users without impacting the product performance.

Horizontal scaling involves adding more servers to the environment while vertical scaling involves adding additional resources (such as CPU, RAM, and disk space) to one server. Sometimes vertically scaling might also involve increasing the hardware capacity across more than one server.

You can choose to scale horizontally or vertically based on the resources available.

Recommendation

Horizontal scaling is recommended over vertical scaling.

The following topics provide the recommended hardware sizes for various kinds of deployment.

Standard sizing guidelines

The hardware sizing guidelines are divided into four parts: nonproduction setups, small setups, medium setups, and large setups.

The nonproduction and small setup scenarios are recommended for a single-server deployment, while medium and large setup scenarios are recommended for a multiple-server deployment. You can also deploy the product in a single-server deployment and later vertically scale the product.

The hardware capacity recommendations for sizing a single-server deployment (without scaling), horizontal scaling, and vertical scaling are based on the standard sizing guidelines.

The following table lists the definitions of each size:

Size	Daily Indexing volume	Data retention	Concurrent users
Nonproduction	20 GB per day	7 days	2
Small
Scenario 1	100 GB per day	14 days	8
Scenario 2	50 GB per day	14 days	16
Medium
Scenario 1	400 GB per day	14 days	8
Scenario 2	200 GB per day	14 days	16
Large
Scenario 1	1 TB per day	14 days	8
Scenario 2	500 GB per day	14 days	16

What components to scale?

The IT Data Analytics product helps you perform the following main functions:

Data collection (handled by the Collection Station)
Indexing (handled by the Indexer)
Search (handled by the Search component)

Based on your needs, you can split these functions across multiple servers to handle these functions separately.

Thus, you can consider scaling the Collection Station, Indexer, and Search components.

The following topics provide the recommended deployment scenarios for scaling.

When to scale?

The following two factors are an indication that you might need to scale the Collection Station, Indexer, or Search components:

Product performance is deteriorating
Hardware resources such as the processor, memory, storage and disk I/O start exceeding acceptable limits.

For more information, see Indicators for scaling.

Which variables impact sizing and performance?

The amount by which the capacity of your system meets your business needs plays an important role in determining the performance of the system. This means the overall product performance is largely influenced by the hardware capacity available for supporting the business needs. The accuracy of your hardware sizing estimates therefore acts as a base for ensuring a smooth deployment.

The primary drivers that affect sizing are:

Volume of data indexed per day
Retention period (duration for which you want to store data indexed)
Number of concurrent users likely to access or search the data indexed

For more information, see Sizing drivers and their impact.

Additionally, there are other factors that impact the product performance, for example, the number of fields defined in the data patterns, the number of tags specified in the data collectors, the number of notification set, and so on. These factors impact the resources that support the product functioning (such as processor, memory, storage) and thereby affect the product performance. The amount by which these factors impact the product performance depends on the manner in which you use the product. For more information about the list of factors and the level at which they impact performance, see Variables that impact product performance.

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