Which index type is optimized for speed and uses less storage?

The metrics index type is specifically designed to handle large volumes of numeric data efficiently and is optimized for both speed and storage. Unlike event indexing, which deals with individual log entries and may require more detailed storage due to the complex nature of the logs, metrics indexing focuses on aggregated data points like counters and gauges. This allows for more efficient processing and reduces the overall storage requirements as it can store the data in a more condensed format.

In addition, metrics data is often time-series data, which makes it easier for Splunk to perform performance optimizations when querying this type of index. It enables faster searches and retrieval of data, making it ideal for scenarios where real-time monitoring and analytics are needed.

The other index types do serve distinct purposes, but they do not match the combination of optimized speed and reduced storage offered by metrics. Event indexes, for example, are more comprehensive but can be heavier on both performance and storage due to the nature of the unstructured data they handle. Summary indexes store pre-computed summaries of events, which can improve query speed but still require significant underlying data and processing overhead. Time-series is a broad category and not an actual index type in Splunk's terminology, making metrics the most appropriate choice for this question.