The 4 types of searches in Splunk, categorized by their performance characteristics, are Dense, Sparse, Super-sparse, and Rare. These categories describe how efficiently Splunk can retrieve and process matching events, with significant implications for system resource utilization, particularly CPU and I/O.
What are the 4 types of searches in Splunk by performance?
Splunk intelligently classifies searches into different types based on the number of matching events found in each index bucket. This classification directly influences how Splunk handles the search internally, impacting performance and resource consumption. Understanding these types is crucial for optimizing Splunk deployments and writing efficient searches.
Here are the four types of searches in Splunk by performance:
| Search Type | Reference Indexer Throughput | Performance Impact |
|---|---|---|
| Dense | Up to 50,000 matching events per second | CPU-bound |
| Sparse | Up to 5,000 matching events per second | CPU-bound |
| Super-sparse | Up to 2 seconds per index bucket | I/O bound |
| Rare | From 10 to 50 index buckets per second | I/O bound |
Let's explore each type in more detail:
Dense Searches
Dense searches are characterized by a very high number of matching events within each index bucket. Splunk can process these searches with impressive throughput, potentially up to 50,000 matching events per second. Due to the sheer volume of data being processed and evaluated per CPU, dense searches are typically CPU-bound. This means that the performance bottleneck is usually the processing power of the Splunk indexers or search heads, rather than disk I/O. These searches are efficient when a large percentage of indexed data matches the search criteria.
Sparse Searches
Similar to dense searches, sparse searches also have a significant number of matching events, but at a lower density than dense searches. Splunk can achieve throughput of up to 5,000 matching events per second for sparse searches. Like dense searches, sparse searches are also primarily CPU-bound. The system spends most of its resources on evaluating and processing the found events rather than on retrieving them from disk.
Super-sparse Searches
Super-sparse searches represent a significant shift in performance characteristics. In these searches, very few events match the search criteria within each index bucket. Instead of measuring throughput in events per second, performance for super-sparse searches is often measured in seconds per index bucket, reaching up to 2 seconds per index bucket. Because Splunk has to read through many buckets to find the few matching events, these searches become I/O-bound. The system spends more time reading data from disk than processing it, making disk speed and I/O capacity critical.
Rare Searches
Rare searches are at the extreme end of the sparsity spectrum. They are even less common than super-sparse searches, meaning an extremely small number of events match the search criteria across a large dataset. Performance is measured in terms of how many index buckets can be scanned per second, ranging from 10 to 50 index buckets per second. Like super-sparse searches, rare searches are I/O-bound. The overhead of searching across numerous index buckets for very few matches makes disk I/O the primary performance constraint. Optimizing indexes and ensuring efficient data storage is crucial for improving the performance of rare searches.
Understanding these search types helps in designing effective searches, optimizing Splunk configurations, and troubleshooting performance issues by identifying whether CPU or I/O is the bottleneck.
