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 12 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.
- #15mediumfrequently asked
15. 3Sum
Find all unique triplets in an array that sum to zero. Elastic uses this to assess whether candidates can combine sorting and two-pointer techniques without double-counting — the same deduplication challenge that arises when Elasticsearch merges results from replicated shards.
- #49mediumfrequently asked
49. Group Anagrams
Group a list of strings so that anagrams appear together. This is a canonical Elastic question — the sorted-string or frequency-vector canonical key pattern is exactly the normalization step Elasticsearch's token filters apply before building the inverted index.
- #56mediumfrequently asked
56. Merge Intervals
Merge all overlapping intervals into a minimal non-overlapping set. Elastic frequently asks this because merging time-range queries, log time spans, and APM trace windows are everyday operations in Elasticsearch and Kibana — getting the overlap condition exactly right under edge cases is a real engineering concern.
- #139mediumfrequently asked
139. Word Break
Determine if a string can be segmented into words from a dictionary. Elastic interviews this because token segmentation is at the core of Elasticsearch's text analysis pipeline — understanding which substrings can be valid tokens is exactly what analyzers compute before indexing.
- #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.
- #200mediumfrequently asked
200. Number of Islands
Count connected components of '1' cells in a 2D grid. Elastic reaches for this problem because BFS/DFS on a grid is a direct analogue of cluster connectivity analysis — the same reasoning applies when determining which Elasticsearch nodes belong to the same cluster partition after a network split.
- #207mediumfrequently asked
207. Course Schedule
Determine if you can finish all courses given prerequisite dependencies — essentially cycle detection in a directed graph. Elastic interviews this because topological ordering and DAG validation are fundamental to Elasticsearch's pipeline processor chains and ingest node dependency resolution.
- #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.
- #238mediumfrequently asked
238. Product of Array Except Self
Return an array where each element is the product of all other elements, without using division. Elastic uses this to test prefix/suffix scan thinking — the same cumulative-scan pattern drives Elasticsearch's pipeline aggregations that compute running totals across document sets.
- #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.