Comprehensive and Detailed Step by Step Explanation:
Summary indexing should be used forreports that run on small datasets over long time ranges. It is particularly useful when you need to aggregate data over extended periods without querying raw events repeatedly.
Here’s why this works:
Efficiency: Summary indexing pre-aggregates data into summary indexes, reducing the amount of data that needs to be processed during runtime. This improves performance for reports that span long time ranges.
Small Datasets: Summary indexing is most effective when working with smaller datasets because aggregating large volumes of data can become resource-intensive.
Other options explained:
Option B: Incorrect because summary indexing is not a fallback for reports that fail to qualify for acceleration methods like report or data model acceleration.
Option C: Incorrect because summary indexing is less beneficial for short time ranges, where querying raw data is often faster.
Option D: Incorrect because Smart Mode is unrelated to summary indexing; it is a search optimization feature.
Example: Suppose you want to calculate daily sales totals over a year. Instead of querying raw sales data every time, you can use summary indexing to store daily totals and query the summary index instead.
[References:, Splunk Documentation on Summary Indexing:https://docs.splunk.com/Documentation/Splunk/latest/Knowledge/Usesummaryindexing, Splunk Documentation on Report Acceleration:https://docs.splunk.com/Documentation/Splunk/latest/Knowledge/Acceleratedatamodels, , , ]
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