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    <p>Because the log file is fairly large you should read the log-file using a stream-reader. Don't read it all in the memory. I would expect it is feasible to have the number of possible distinct links in the memory while we work on the log-file.</p> <pre><code>// Pseudo Hashmap map&lt;url,count&gt; while(log file has nextline){ url = nextline in logfile add url to map and update count } List list foreach(m in map){ add m to list } sort the list by count value take top n from the list </code></pre> <p>The runtime is O(n) + O(m*log(m)) where n is the size of the log-file in lines and where the m is number of distinct found links.</p> <p>Here's a C# implementation of the pseudo-code. An actual file-reader and a log-file is not provided. A simple emulation of reading a log-file using a list in the memory is provided instead. </p> <p>The algorithm uses a hashmap to store the found links. A sorting algorithm founds the top 100 links afterward. A simple data container data-structure is used for the sorting algorithm.</p> <p>The memory complexity is dependent on expected distinct links. The hashmap must be able to contain the found distinct links, else this algorithm won't work.</p> <pre class="lang-c# prettyprint-override"><code>// Implementation using System; using System.Collections.Generic; using System.Linq; public class Program { public static void Main(string[] args) { RunLinkCount(); Console.WriteLine("press a key to exit"); Console.ReadKey(); } class LinkData : IComparable { public string Url { get; set; } public int Count { get; set; } public int CompareTo(object obj) { var other = obj as LinkData; int i = other == null ? 0 : other.Count; return i.CompareTo(this.Count); } } static void RunLinkCount() { // Data setup var urls = new List&lt;string&gt;(); var rand = new Random(); const int loglength = 500000; // Emulate the log-file for (int i = 0; i &lt; loglength; i++) { urls.Add(string.Format("http://{0}.com", rand.Next(1000) .ToString("x"))); } // Hashmap memory must be allocated // to contain distinct number of urls var lookup = new Dictionary&lt;string, int&gt;(); var stopwatch = new System.Diagnostics.Stopwatch(); stopwatch.Start(); // Algo-time // O(n) where n is log line count foreach (var url in urls) // Emulate stream reader, readline { if (lookup.ContainsKey(url)) { int i = lookup[url]; lookup[url] = i + 1; } else { lookup.Add(url, 1); } } // O(m) where m is number of distinct urls var list = lookup.Select(i =&gt; new LinkData { Url = i.Key, Count = i.Value }).ToList(); // O(mlogm) list.Sort(); // O(m) var top = list.Take(100).ToList(); // top urls stopwatch.Stop(); // End Algo-time // Show result // O(1) foreach (var i in top) { Console.WriteLine("Url: {0}, Count: {1}", i.Url, i.Count); } Console.WriteLine(string.Format("Time elapsed msec: {0}", stopwatch.ElapsedMilliseconds)); } } </code></pre> <p><strong>Edit:</strong> This answer has been updated based on the comments</p> <ul> <li>added: running time and memory complexity analysis</li> <li>added: pseudo-code</li> <li>added: explain how we manage a fairly large log-file</li> </ul>
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    1. POGetting top 100 URL from a log file
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    1. COThis answer may show us that you know C# but no one cares about it. The tags of the question are `algorithm` and `data-structures`, not `C#`.
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    2. CONot meant as to demonstrate I know C#, but the intend was to 'show' the algorithm through code.
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    3. COI believe what @Thomash wants to point out is that you should provide your answer concisely. You will prefer reading a short answer to a long code. Don't you? By the way, the keyword in OP's question is "fairly large" but you clearly ignored it.
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