optimize algorithm performance

When implementing algorithms, prioritize performance optimization through careful algorithm selection, query structure, and evaluation strategy choices. Key considerations include:

Algorithm Selection: Choose appropriate algorithms for the task. Avoid inefficient patterns like flawed cycle detection that only checks subsets of data or tree walking when more efficient alternatives exist.

Query Optimization: Structure database queries and datalog rules for optimal performance. Place filter clauses early in the query to reduce the search space: (or [?block :logseq.task/scheduled ?n] [?block :logseq.task/deadline ?n]) [(>= ?n ?start-time)] [(<= ?n ?end-time)] rather than filtering later.

Evaluation Strategy: Understand the difference between lazy and eager evaluation. Use mapv or doseq instead of map when side effects are needed, as map is lazy and won’t execute the operations.

Rule Optimization: For recursive queries, carefully order clauses for performance. Write rules optimized for your primary access pattern - descendant queries vs ancestor queries require different clause ordering.

Example of inefficient cycle detection to avoid:

;; Problematic - only checks every 100 steps, misses cycles in smaller datasets
(when (and (zero? steps)
           (seq (set/intersection (set @seen) (set uuids-to-add))))
  ;; cycle detection logic

Consider computational complexity early in design and choose data structures and algorithms that scale appropriately with expected input sizes.