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 11 problems of 25
- #1easyvery frequently asked
1. Two Sum
Find two indices in an array whose values sum to a target. At Elastic, this tests your instinct for trading memory for speed — the same hash-map lookup pattern powers fast term lookups inside Elasticsearch's inverted index.
- #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.
- #4hardsometimes asked
4. Median of Two Sorted Arrays
Find the median of two sorted arrays in O(log(m+n)) time. Elastic includes this hard problem because computing global percentiles across distributed shards — a core Elasticsearch aggregation feature — requires exactly this kind of partitioned-binary-search reasoning over sorted shard segments.
- #23hardvery frequently asked
23. Merge K Sorted Lists
Merge K sorted linked lists into one sorted list. This is one of the most Elastic-relevant hard problems in existence — merging sorted posting lists from K shards is the exact operation Elasticsearch performs on every multi-shard search query, and the min-heap approach maps one-to-one to the coordinating node's result collection logic.
- #70easysometimes asked
70. Climbing Stairs
Count the distinct ways to climb n stairs taking 1 or 2 steps at a time. Elastic uses this as an entry-level dynamic programming probe — if you can articulate why the recurrence is Fibonacci and spot the O(1) space optimization, you signal that you think about caching and state compression the way Elasticsearch's query cache does.
- #127hardsometimes asked
127. Word Ladder
Find the shortest transformation sequence from one word to another, changing one letter at a time. Elastic uses this BFS-on-a-word-graph problem because edit-distance graph traversal and fuzzy query shortest paths underpin Elasticsearch's fuzziness and Levenshtein automaton query routing.
- #141easysometimes asked
141. Linked List Cycle
Detect whether a linked list contains a cycle. Elastic uses Floyd's tortoise-and-hare algorithm here as a proxy for distributed-systems intuition — detecting liveness vs. livelock in a cluster follows the same principle of needing two probes at different rates to distinguish 'still making progress' from 'spinning in a loop'.
- #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.