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Commit d666844

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StoneLyuazl397985856
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feat: more translations (azl397985856#109)
* correct typos * Add translations * 新增HTTP/1.0, HTTP/1.1, HTTP/2的对比 * 增加HTTP/1.1和HTTP/2的性能对比的Demo * 翻译大部分基础数据结构,但是REACT部分没有翻译 * update description of `HTTP/1.0` * add WIP tag, update some translation details * change 'fundamental' to 'basic' * remove some trivial details about HTTP/1.0 * error correction * translation for DP * translation for string problems * error correction * correct wrongly typed * translation for binary-tree-traversal.md * correct wrongly typed * update README.en.md * update description of Huffman encoding * update file path of Binary-tree-traversal-en.md. * correct wrongly typed * update paths of thinkings * translation for basic-algorithm.md (Draft) * translation for bloom-filter.md
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‎README.en.md‎

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### Summary of Data Structures and Algorithms
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- [Data Structure](./thinkings/basic-data-structure-en.md) (Drafts)
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- [Basic Algorithm](./thinkings/basic-algorithm.md)Drafts
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- [Basic Algorithm](./thinkings/basic-algorithm-en.md)Drafts
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- [Binary Tree Traversal](./thinkings/binary-tree-traversal-en.md)
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- [Dynamic Programming](./thinkings/dynamic-programming-en.md)
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- [Huffman Encode and Run Length Encode](./thinkings/run-length-encode-and-huffman-encode.md)
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- [Bloom Filter](./thinkings/bloom-filter.md)
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- [Huffman Encode and Run Length Encode](./thinkings/run-length-encode-and-huffman-encode-en.md)
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- [Bloom Filter](./thinkings/bloom-filter-en.md)
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- [String Problems](./thinkings/string-problems-en.md)
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‎thinkings/basic-algorithm-en.md‎

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# Basic Algorithms
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## Analysis of Time Complexity
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## Algorithm Thoughts
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### Greedy Algorithms
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### Divide-and-Conquer
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### Dynamic Programming
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### Backtracking
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### Enumeration
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## Meta Algorithm
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### Sorting Algorithms
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Sorting algorithms are meta algorithm. Although they are not tested quit often, the core ideas are very useful.
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#### O(n^2)
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- Insertion Sort
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- Selection Sort
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- Shell's Sort (aka Diminishing Increment Sort)
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- Bubble Sort
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#### O(nlogn)
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- Quick Sort
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- Merge Sort
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- Heap Sort
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#### O(n)
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- Bucket Sort
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- Counting Sort
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- Radix Sort
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### Searching Algorithm
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- BFPRT Algorithm
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- Search Trees
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- Search by Hashing
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## Algorithms for String
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- 朴素
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- KMP
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- RK
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- BM
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- trie
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## Other
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- Number Theory
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- Probability Theory
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- Union-find Algorithm
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- Matrix Algorithms

‎thinkings/basic-algorithm.md‎

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- 插入排序
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- 选择排序
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- 希尔排序
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-冒泡排序
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-冒泡排序
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#### O(nlogn)
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‎thinkings/bloom-filter-en.md‎

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# Bloom-filter
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## Scenes
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Assume that you have a website which has so many audiences, what will you do if you want to know whether an IP address of a visitor is the first time to access your server or not.
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### Can hashtable do this?
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Of course yes.
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Obviously, a hashtable with storing all IP addresses can tell us whether we have known one IP already. But, imagine that, if there are more than 1 billion IP addresses have been recorded, at least `4 Byte * 1,000,000,000 = 4,000,000,000 Byte = 4 GB` RAM is required. If it is not IP, but URL, the RAM required will be much larger.
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### Bit
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Another solution is using 1 bit to represent the access status of 1 IP, accessed or not.
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For the same 1 billion IP addresses, now only `1 * 1,000,000,000 = 128 MB` RAM is required. If it is URL, this method uses much less spaces.
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With this method, only two operations are needed: `set(ip)` and `has(IP)`.
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However, this method has two fatal weakness:
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1. If elements are not distributed uniformly, a lot of spaces will not be used which is inefficient in space.
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> A good Hash function can be used to overcome this weakness.
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2. If the elements are not integer (e.g. URL), `BitSet` is inapplicable.
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> one or more Hash functions can also solve this.
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### Bloom Filter
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A Bloom filter is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set.
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Actually, Bloom Filter is the second method with multiple hash functions.
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Here are four interesting properties of Bloom filter:
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- Unlike a standard hash table, a Bloom filter of a fixed size can represent a set with an arbitrarily large number of elements.
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- Adding an element never fails. However, the false positive rate increases steadily as elements are added until all bits in the filter are set to 1, at which point all queries yield a positive result.
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- Bloom filters never generate false negative result, i.e., telling you that a username doesn’t exist when it actually exists.
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- Deleting elements from filter is not possible because, if we delete a single element by clearing bits at indices generated by k hash functions, it might cause deletion of few other elements.
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![bloom-filter-url](../assets/thinkings/bloom-filter-url.png)
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### Application of Bloom Filter
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1. Network crawler
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whether an URL has been crawled or not.
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2. `key-value` database
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Whether a Key exists in this database or not.
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Example:
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Each region of HBase has a Bloom Filter, which can be used to find whether a key exists in this region or not quickly.
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3. Phishing websites detection
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Sometimes browsers may alert that a website you are accessing is a phishing website.
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Browsers use Bloom Filter to find wheter URL of one website exists in the phishing website database.
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> Hope this Algorithm can help you have a better understanding to `TRADEOFF`.

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