Alation Interview Prep
Everything you need for Monday's interview — DSA, OOP, System Design, Kafka & more.
Interview Structure
Introduction
Problem Statements & Clarifications
Coding (DSA)
1 Problem · Approach → Code → Optimize
Low-Level Design
OOP Design · Class Structure · Trade-offs
STUDY TOPICS
DSA — Python
Arrays, Trees, Graphs, DP, Heaps, Sorting
30+ Problems with SolutionsOOP & LLD — Java
SOLID, Patterns, Parking Lot, Library, ATM
5 LLD Problems + PatternsSystem Design
CAP, Sharding, Caching, URL Shortener, Twitter
10 Design ProblemsKafka & Message Queues
Topics, Partitions, Consumers, RabbitMQ, SQS
Full Architecture GuideAlation Focus
Company-specific patterns, Data Catalog Design
Must Review!OS & Concurrency
Threads, deadlocks, locks, rate limiting, Java concurrency
Java + Python examplesDocker & AWS
Containers, Kubernetes, EC2, S3, Lambda, VPC, ECS
Architecture diagramsNetworking & Security
HTTP/2/3, TCP/UDP, OAuth 2.0, SSO, JWT, TLS, Cryptography
Auth flows + crypto primitivesDatabases — SQL & NoSQL
Indexes, window functions, ACID, MongoDB, Cassandra, Redis
Query patterns + NoSQL deep divesKey DSA Patterns
| Pattern | When to Use |
|---|---|
| Sliding Window | Contiguous subarray/substring |
| Two Pointers | Sorted array, sum/pair problems |
| BFS | Shortest path, level-order |
| DFS | Explore all paths, backtracking |
| Binary Search | Sorted data, minimize/maximize |
| DP | Overlapping subproblems, optimal sub |
| Heap | K-th element, streaming data |
| Union-Find | Connected components |
Interview Strategy
1. Understand (2–3 min)
Repeat the problem. Clarify input size, constraints, edge cases.
2. Brute Force First (2 min)
Verbally state the naive O(n²) or O(2ⁿ) approach.
3. Optimize (5 min)
Identify the right pattern. Think about what data structure reduces complexity.
4. Code (15–20 min)
Write clean code. Name variables well. Think aloud.
5. Test (5 min)
Trace through examples. Check edge cases: empty, single element, negatives.
⏱ Complexity Cheatsheet
| Algorithm | Time | Space |
|---|---|---|
| Binary Search | O(log n) | O(1) |
| Merge Sort | O(n log n) | O(n) |
| Quick Sort | O(n log n) avg | O(log n) |
| BFS / DFS | O(V+E) | O(V) |
| Hash Table Ops | O(1) avg | O(n) |
| Heap Push/Pop | O(log n) | O(n) |
| Trie Insert | O(L) | O(L) |
| DP (2D table) | O(m×n) | O(m×n) |
Python Quick Ref
# Collections
from collections import defaultdict, Counter, deque
import heapq
d = defaultdict(int) # default value 0
c = Counter([1,1,2,3]) # freq map
q = deque([1,2,3]) # O(1) popleft
q.appendleft(0); q.popleft()
# Heap (min-heap by default)
heap = []; heapq.heapify(heap)
heapq.heappush(heap, 5)
heapq.heappop(heap) # min element
# Max-heap: push -val, pop -val
# Sort
arr.sort(key=lambda x: x[1])
sorted(arr, reverse=True)
# Two Pointers
l, r = 0, len(arr)-1
while l < r:
# ... process
l += 1; r -= 1
# Binary Search
import bisect
bisect.bisect_left(arr, x) # leftmost pos