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 8 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.
- #20easyfrequently asked
20. Valid Parentheses
Determine whether a string of brackets is balanced. Elastic reaches for this in phone screens because the stack-based parse pattern is directly analogous to query-string and JSON document validation that Elasticsearch performs on every indexed document.
- #21easyfrequently asked
21. Merge Two Sorted Lists
Merge two sorted linked lists into one sorted list. Elastic interviews this because it is the atomic merge step in merge sort — and merge sort is the exact mechanism Elasticsearch uses when combining sorted posting lists during multi-term query execution.
- #53easyfrequently asked
53. Maximum Subarray
Find the contiguous subarray with the largest sum. Elastic reaches for Kadane's algorithm because it illustrates how a single-pass decision — keep accumulating or restart — is the same greedy insight driving Elasticsearch's segment-level scoring and relevance windowing.
- #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.
- #121easyfrequently asked
121. Best Time to Buy and Sell Stock
Find the maximum profit from a single buy-low-sell-high transaction. Elastic uses this pattern to test sliding-window and running-minimum thinking — the same mental model used when computing rolling aggregations over time-series log data in Elasticsearch.
- #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'.
- #206easyfrequently asked
206. Reverse Linked List
Reverse a singly-linked list in place. Elastic uses pointer-manipulation questions like this to verify that candidates can reason about mutable state without a garbage-collection safety net — critical intuition for writing correct segment merging and linked-structure traversal in distributed search engines.