Elastic Coding Interview Questions
25 Elastic coding interview problems with full optimal solutions — 8 easy, 12 medium, 5 hard. Every problem ships with multiple approaches (brute-force first, then the optimal), complexity tables for each, company-specific tips on what an Elastic interviewer values, and a FAQ section.
Showing 5 problems of 25
- #3mediumvery frequently asked
3. Longest Substring Without Repeating Characters
Find the length of the longest substring with all unique characters. Elastic favors this problem because the sliding-window technique it demands is the same mental model used in Elasticsearch's windowed token analysis during text tokenization and shingle generation.
- #146mediumvery frequently asked
146. LRU Cache
Design a cache that evicts the least-recently-used item when full. Elastic asks this because LRU is a first-class concern in search infrastructure — Elasticsearch's request cache and field data cache both use LRU eviction, and candidates who can implement it from scratch demonstrate exactly the systems intuition Elastic values.
- #208mediumvery frequently asked
208. Implement Trie (Prefix Tree)
Build a prefix tree supporting insert, search, and startsWith. Tries are the foundational data structure behind Elasticsearch's prefix query, completion suggester, and edge-n-gram token filter — implementing one from scratch is one of the most domain-relevant questions Elastic asks.
- #347mediumvery frequently asked
347. Top K Frequent Elements
Return the k most frequent elements in an array. Elastic asks this because computing top-K term frequencies is at the heart of Elasticsearch's terms aggregation and relevance scoring — and bucket-sort vs. heap trade-offs map directly to how Elasticsearch chooses between different aggregation strategies.
- #658mediumsometimes asked
658. Find K Closest Elements
Return the k integers in a sorted array closest to a target value. Elastic asks this because nearest-neighbor lookup in a sorted index is the core of Elasticsearch's range queries and numeric field approximations — binary search to anchor + window expansion is a real optimization pattern in search engines.