Which technique can improve search performance in Splunk?

Using summary indexing and optimizing queries is an effective technique to improve search performance in Splunk. Summary indexing allows you to create smaller, more manageable datasets by storing aggregated results of searches rather than the full raw data. This significantly reduces the amount of data that needs to be searched in real-time queries, resulting in faster search performance.

Additionally, optimizing queries—such as by using appropriate search commands, defining time ranges efficiently, and minimizing wildcard usage—can help reduce the processing time required for searches. It helps direct the search engine to focus on the most relevant data, minimizing resource use and speeding up query execution.

In contrast, while deploying additional indexers can help handle larger volumes of data, it doesn’t directly enhance search performance unless combined with other strategies, such as load balancing. Limiting user access to searches may control data exposure but does not inherently speed up search processing. Reducing the data ingestion rate could limit the amount of data being indexed but does not necessarily contribute to improving search performance for existing data.