Shows the number of SVCs assigned to your organization's subscription for your license entitlement.

Shows your organization's overall peak SVC usage as a single value and a percentage of your license entitlement.

Splunk deploys infrastructure based on your entitled SVCs. Overall peak SVC usage refers to the highest amount of resources used in a given time interval to perform system processes such as indexing, any running search processes, and shared services. It primarily measures the CPU usage across search and indexing workloads.

Generally, ensure SVC usage is less than 80% to maintain performance. Usage greater than or equal to 80% is considered elevated, and greater than or equal to 90% might cause degraded performance.

Shows your organization's search workload peak SVC usage as a single value.

Search peak SVC usage refers to the highest amount of resources used in a given time interval to perform search processes. It primarily measures the CPU usage across search workloads. The search workload can occur on both the search and indexing tiers.

Shows your organization's indexing workload peak SVC usage as a single value

Indexing peak SVC usage refers to the highest amount of resources used in a given time interval to perform indexing processes. It primarily measures the CPU usage across indexing workloads. The indexing workload occurs on the indexing tiers.

Shows the 90th percentile measurement of the memory used by all processes running across the time frame selected for all the indexer hosts.

The 90th percentile measurement captures 90% of the values below the estimate. 10% of the values above the estimate are excluded as outliers.

Shows the percentage of cache churn for your stack.

Indexer cache churn is the rate at which data is evicted from local disk cache to make room for new data. Cache churn occurs when the cache is unable to retain frequently accessed data due to capacity constraints or inefficient cache management, resulting in data being replaced more frequently than desired. Repeatedly evicted data needs to be reloaded from slower storage, which can lead to performance degradation, increased search latency, and inefficient caching. High cache churn is often an indication of inefficient searches or a need for more capacity, particularly in environments with high data volumes or complex search patterns.

For tips on how to improve your cache churn percentage, see Optimize indexing and search processes

Shows the 90th percentile measurement of the CPU used by all processes running across all indexers.

The 90th percentile measurement captures 90% of the values below the estimate. 10% of the values above the estimate are excluded as outliers.