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DBMS
Designing an Efficient B-Tree Index for Large Databases
A practical guide that turns B‑Tree theory into concrete index designs for billion‑row tables, covering fan‑out math, bulk‑load tricks, maintenance knobs, and interview‑ready scenarios.
System Design
Designing a Scalable Distributed Cache
A deep dive into engineering an elastic, fault‑tolerant distributed cache—covering requirements, partitioning, replication, eviction, failure handling, real‑world trade‑offs, and interview‑ready testing.
DSA
Heap and Priority Queue
Learn the exact differences between binary heaps and priority‑queue APIs, master index math, O(n) heap construction, and avoid hidden interview bugs.
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DSA
Dynamic Programming
- Identify the smallest tuple of parameters that uniquely describes each subproblem.
- Show that the optimal solution contains optimal sub‑solutions; otherwise greedy may be correct.
- Derive the recurrence from the last decision; choose memoization for clarity, bottom‑up for safety.
System Design
Designing a Scalable Distributed Cache
- Pin down latency, consistency, scalability, and cost before picking any technology.
- Use consistent hashing for minimal key movement; pick rendezvous when variance matters.
- Pick cache‑aside for low write amplification; use quorum writes for strong consistency.
DSA
Graph BFS and DFS
- Match problem attributes (shortest‑path, memory, depth) to the traversal that guarantees the right answer.
- Initialize a deque, mark visited on enqueue, track level counters, and stop early when the target appears.
- Iterative DFS uses an explicit stack; decide when to mark visited (pre‑visit vs post‑visit) for cycles and ordering.
DSA
Heap and Priority Queue
- Use a custom binary heap when you need decrease‑key or direct index control; use a library PQ for simple min/max extraction.
- Derive parent/child indices for both 0‑based and 1‑based arrays to avoid off‑by‑one bugs.
- Insert and extract run in O(log n); decrease‑key is O(log n) for binary heaps but O(1) amortized for Fibonacci heaps.
60-second cards
Quick revise
Why does traversing a contiguous array of size N typically outperform traversing a singly linked list of the same size, even though both operations have an O(N) time complexity?
Array elements are stored in contiguous memory locations, which maximizes CPU cache locality and allows the system to prefetch data efficiently. In contrast, linked list nodes are scattered across memory, causing frequent cache misses as the CPU must repeatedly look up pointer addresses.
REVIEW 60s
In a hash table, why does the search performance of Open Addressing (like linear probing) degrade much faster than Separate Chaining as the load factor approaches 1?
As the load factor approaches 1, Open Addressing suffers from clustering, where long runs of occupied slots form and drastically increase the number of probes needed to find an empty slot. Separate Chaining handles collisions by appending to lists, meaning lookup time degrades gradually relative to the average list length rather than failing to locate a slot.
REVIEW 60s
For a deeply nested, narrow tree (e.g., a degenerate tree of depth N and width 1), would BFS or DFS require less auxiliary space, and why?
BFS would require less auxiliary space, specifically O(1) memory, because its queue will hold at most one node at any given level. In contrast, DFS would require O(N) auxiliary space because its recursion stack must store all N ancestor nodes from the root to the leaf.
REVIEW 60s
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Lemon.io · LATAM, Europe, USA, Canada, APAC
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Lemon.io · Europe
.NetandroidCC#C++data sciencegolangiosjavajavascriptnode.jsphppythonreactruby/railsscalashopifyswiftUI/UXwordpressblockchainAI/MLautomationproject managementreact nativerustunityelectronspringlaravelEthereumgraphic designTypescript cypressangular seleniumfirebasedata engineeringSite ReliabilitySymfonymanual testingmarketplacetestingnext.jsflutter