As per Splunk Enterprise Search Performance documentation, the most significant factor affecting search performance when querying across a large number of buckets is disk I/O throughput. A search that spans “All Time” forces Splunk to inspect all historical buckets (hot, warm, cold, and potentially frozen if thawed), even if only a few events match the query. This dramatically increases the amount of data read from disk, making the search bound by I/O performance rather than CPU or memory.

Increasing the number of indexing pipelines (Option B) only benefits data ingestion, not search performance. Changing to a real-time search (Option D) does not help because real-time searches are optimized for streaming new data, not historical queries. The indexed_realtime_use_by_default setting (Option C) applies only to streaming indexed real-time searches, not historical “All Time” searches.

To improve performance for such searches, Splunk documentation recommends enhancing disk I/O capability — typically through SSD storage, increased disk bandwidth, or optimized storage tiers. Additionally, creating summary indexes or accelerated data models may help for repeated “All Time” queries, but the most direct improvement comes from faster disk performance since Splunk must scan large numbers of buckets for even small result sets.

References (Splunk Enterprise Documentation):

• Search Performance Tuning and Optimization

• Understanding Bucket Search Mechanics and Disk I/O Impact

• limits.conf Parameters for Search Performance

• Storage and Hardware Sizing Guidelines for Indexers and Search Heads