Woodstock Blog

a tech blog for general algorithmic interview questions

[NineChap 9] Big Date, System Design and Resume (`)


  1. Do not write anything unrelated to CS.
  2. Do not write too long - 1 or 2 pages are fine. Senior engineer 3 pages.
  3. Do not write low GPA
  4. Never ever write “proficient in anything”

Big Data

Most classic question is “Frequent items” (refer to July’s blog).

Find top k hot queries in a daily access log of Google.


  1. k = 1 vs k = 100000 - majority numbers
  2. low RAM vs sufficient RAM
  3. single machine vs multiple machines
  4. accurate vs inaccurate

Sufficient RAM

  1. HashTable + Heap (min-heap)
  2. Time O(n * logk), Space O(n)


  1. Split into 1000 (i.e. LOG/M) files by hash(query) % 1000
  2. Using HashTable + Heap to get top k for each files
  3. Collect 1000 top k queries and get global top k
  4. This method requires a lot of disk access and r/w, still slow.

Inaccurate (reduce memory from O(n) to O(k))

  1. Hash Count (only need to know this one) Limit the size of HashMap. The bigger the RAM, the more accurate is the result.
  2. Space Saving
  3. Lossy Counting
  4. Sticky Sampling
  5. Count Sketch

Bloom Filter

  1. Regular bloom filter - use 4 线性无关 formula
  2. Counting bloom filter - support delete
  3. Better DS than HashMap, but can loose some accuracy



Find all unique queries - use bigmap to store 3 types of states

System Design

Design a short url system

  1. Cache

to store hot urls

  1. Load Balance

Too many click in short time

  1. Storage balance

Hash value of an url and then store in individual machine


  1. Consistent Hash

Node, can increase # of machines to store information

Migration process

  1. Router

check which machine response my query

light-weight calculations

what is router is down?

  1. Locale

url frequently access by China, then put the url storage in Beijing

Need-to-know Design patterns

  1. Singleton
  2. Factory
  3. Master-slave (esp. for relational DB)

MapReduce: Simplified Data Processing on Large Clusters

The Google File System

BigTable: A Distributed Storage System for Structured Data