Cracking the Coding Interview - Fooo

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Solutions to Chapter 11 | System Design and Memory Limits 11 5 If you were designing a web crawler, how would you avoid getting into infinite loops? SOLUTION CareerCup com pg 72 First, how does the crawler get into a loop? The answer is very simple: when we re-parse an already parsed page This would mean that we revisit all the links found in that page, and this would continue in a circular fashion Be careful about what the interviewer considers the “same” page Is it URL or content? One could easily get redirected to a previously crawled page So how do we stop visiting an already visited page? The web is a graph-based structure, and we commonly use DFS (depth first search) and BFS (breadth first search) for traversing graphs We can mark already visited pages the same way that we would in a BFS/DFS We can easily prove that this algorithm will terminate in any case We know that each step of the algorithm will parse only new pages, not already visited pages So, if we assume that we have N number of unvisited pages, then at every step we are reducing N (N-1) by 1 That proves that our algorithm will continue until they are only N steps SUGGESTIONS AND OBSERVATIONS » This question has a lot of ambiguity Ask clarifying questions! » Be prepared to answer questions about coverage » What kind of pages will you hit with a DFS versus a BFS? » What will you do when your crawler runs into a honey pot that generates an infinite subgraph for you to wander about? 2 0 6
Solutions to Chapter 11 | System Design and Memory Limits 11 6 You have a billion urls, where each is a huge page How do you detect the duplicate documents? SOLUTION Observations: 1 Pages are huge, so bringing all of them in memory is a costly affair We need a shorter representation of pages in memory A hash is an obvious choice for this 2 Billions of urls exist so we don’t want to compare every page with every other page (that would be O(n^2)) Based on the above two observations we can derive an algorithm which is as follows: 1 Iterate through the pages and compute the hash table of each one 2 Check if the hash value is in the hash table If it is, throw out the url as a duplicate If it is not, then keep the url and insert it in into the hash table This algorithm will provide us a list of unique urls But wait, can this fit on one computer? » How much space does each page take up in the hash table? 2 0 7 » Each page hashes to a four byte value » Each url is an average of 30 characters, so that’s another 30 bytes at least » Each url takes up roughly 34 bytes Cracking the Coding Interview | Concepts and Algorithms pg 72 » 34 bytes * 1 billion = 31 6 gigabytes We’re going to have trouble holding that all in memory! What do we do? » We could split this up into files We’ll have to deal with the file loading / unloading—ugh » We could hash to disk Size wouldn’t be a problem, but access time might A hash table on disk would require a random access read for each check and write to store a viewed url This could take msecs waiting for seek and rotational latencies Elevator algorithms could elimate random bouncing from track to track » Or, we could split this up across machines, and deal with network latency Let’s go with this solution, and assume we have n machines » First, we hash the document to get a hash value v » v%n tells us which machine this document’s hash table can be found on » v / n is the value in the hash table that is located on its machine
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CRACKING THE C O D I N G I N T E R
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CRACKING THE C O D I N G I N T E R
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Table of Contents Foreword 4 Introd
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Dear Readers, CareerCup com Forewor
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My Approach Introduction The focus
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Behind the Scenes | The Microsoft I
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Behind the Scenes | The Google Inte
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Behind the Scenes | The Yahoo Inter
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Interview War Stories the next few
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Interview War Stories | Failure to
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Interview War Stories | You Can (Ma
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Before the Interview
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Before the Interview | Resume Advic
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Before the Interview | Behavioral P
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What You Need To Know Before the In
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At the Interview | Handling Behavio
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At the Interview | Handling Technic
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At the Interview | Handling Technic
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At the Interview | Five Algorithm A
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At the Interview | The Offer and Be
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At the Interview | Top Ten Mistakes
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At the Interview | Frequently Asked
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Interview Questions How This Book i
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Part 1 Data Structures
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Chapter 1 | Arrays and Strings 1 1
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2 1 Write code to remove duplicates
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Chapter 3 | Stacks and Queues 3 1 D
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Chapter 4 | Trees and Graphs 4 1 Im
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Chapter 5 | Bit Manipulation How to
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Chapter 6 | Brain Teasers Do compan
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Chapter 7 | Object Oriented Design
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Chapter 8 | Recursion How to Recogn
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Chapter 9 | Sorting and Searching H
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Chapter 10 | Mathematical How to Ap
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Chapter 11 | Testing Testing Proble
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Chapter 12 | System Design and Memo
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Part 3 Knowledge Based
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Chapter 13 | C++ 13 1 Write a metho
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Chapter 14 | Java 14 1 In terms of
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Chapter 15 | Databases 15 1 Write a
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Chapter 16 | Low Level 16 1 Explain
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Chapter 17 | Networking 17 1 Explai
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Chapter 18 | Threads and Locks 18 1
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Chapter 19 | Moderate 19 1 Write a
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Chapter 20 | Hard 20 1 Write a func
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Each problem may have many 'optimal
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Solutions to Chapter 1 | Arrays and
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Solutions to Chapter 2 | Linked Lis
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Approach 2: Solutions to Chapter 3
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Solutions to Chapter 3 | Stacks and
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Solutions to Chapter 4 | Trees and
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Solutions to Chapter 6 | Brain Teas
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Solutions to Chapter 7 | Object Ori
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Solutions to Chapter 7 | Object Ori
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Solutions to Chapter 18 | Threads a
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Solutions to Chapter 19 | Moderate
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Solutions to Chapter 20 | Hard 20 1
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Solutions to Chapter 20 | Hard 17 }
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M matrixes 101, 102, 163, 169, 184,
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About the Author Gayle Laakmann’s
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Cracking the Coding Interview - Fooo

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