Algorithm

This page is about key points about Algorithm

Methodology

  • The easiest way to improve search efficiency on a set of data is to put it in a data structure that allows more efficient searching. What data structures can be searched more efficiency than O(n)? Binary tree can be searched in O(log(n)). Arrays and hash tables both have constant time element look up (has tables have worse-case lookup of O(n) but the average case is O(1)).
  • Then need to determine which data structure to be used. If the underlying characters are just ASCII, then a array[128] would be enough. But characters are UNICODe, then it need 100,000 (100K) array, which is a concern of memory, so hash table would be a better option, which only keep exist characters. In general, arrays are a better choice for long strings with a limited set of possible characters values, hash tables are more efficient for shorter strings or when there are many possible character values.
  • For some problems, obvious iterative alternatives like the one just shown don’t exist, but it’s always possible to implement a recursive algorithm without using recursive calls.
  • For a simple recursive function like factorial, many computer architectures spend more time on call overhead than on the actual calculation. Iterative functions, which use looping constructs instead of recursive function calls, do not suffer from this overhead and are frequently more efficient.
  • NOTE Iterative solutions are usually more efficient than recursive solutions.
  • NOTE Every recursive case must eventually lead to a base case.
  • NOTE Recursive algorithms have two cases: recursive cases and base cases

Sort

I collections.sort()

Object[] a = list.toArray();
        Arrays.sort(a);
        ListIterator<T> i = list.listIterator();
        for (int j=0; j<a.length; j++) {
            i.next();
            i.set((T)a[j]);
        }

Arrays.sort

public static void sort(Object[] a) {
        if (LegacyMergeSort.userRequested)
            legacyMergeSort(a);
        else
            ComparableTimSort.sort(a);
    }
private static void mergeSort(Object[] src,
                                  Object[] dest,
                                  int low,
                                  int high,
                                  int off) {
        int length = high - low;

        // Insertion sort on smallest arrays
        if (length < INSERTIONSORT_THRESHOLD) { // threshold is 7
            for (int i=low; i<high; i++)
                for (int j=i; j>low &&
                         ((Comparable) dest[j-1]).compareTo(dest[j])>0; j--)
                    swap(dest, j, j-1);
            return;
        }

// else use mergeSort

Self review

CeasarCipher:

Generally: it’s a rotation of English alphabic. E.g. if rotation is 2, the encode is start from A+2, i.e. A is at -2 of the encode array. And decode is start with 26-2, and “A” start at positon 2, then the increase by 1 character to constitute the array That’s why need to “%26”, to make it loop across 26 characters

If rotation is 2:
--- encrytpion code is:[C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, X, Y, Z, A, B]
--- decrytpion code is:[Y, Z, A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, X]
If rotation is 4:
--- encrytpion code is:[E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, X, Y, Z, A, B, C, D]
--- decrytpion code is:[W, X, Y, Z, A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V]

ABC:
Msg ={‘A’,’B’,’C’};
Char[] encode=’A’+(k+rotation)%26);
Char[] decode=’A’+(k-rotation+26)%26); // +26 to avoid negative

Encode={‘C’,’D’,’E’}; // rotation=3, so A+3, A+4,A+5,xxx, A+26=>3,4,5,6,xxx,0
Decode={‘M’,’N’,’O’};//as k-rotation+26 % 26, so it’s 26-3+0,26-3+1 ,xx: => 23,24,25,0,1,2,3,4,is: A+23,A+24,A+25=>‘M’,’N’,’O’. that’s rotation, rotain-1, rotaion -2 xxxx
For(int i=0;i<msg.length;i++){
  Int j=msg[i]-‘A’; // to remove the base ‘A”, so sync with the “k” in encode, 3,4,5,xxx 3+26
  Msg[i]=codes[j];
}
// encode
Int j=’A’-‘A’; //0
Msg[0]=’C’;
Msg[1]=’D’;
Msg[2]=’E’;

//decode
Int j=msg[i]-‘A’; //’C’-‘A’=3
Msg[i]=decode[j]; // correspoindg to the postion 0,-xxx, 26 in decode, 
Msg[0]=
  • If you says “tree,” it’s a good idea to clarify whether she is referring to a generic tree or a binary tree.

To print content of Array

Import java.util.Arrays;
Arrays.toString(ary);
Arrays.deepToString(ary);

search without recursive

Node findNode( Node root, int value ){ while( root != null ){ int currval = root.getValue(); if( currval == value ) break; if( currval < value ){ root = root.getRight(); } else { // currval > value root = root.getLeft(); } }

return root; }

  • preceding lookup operation can be reimplemented recursively as follows: Node findNode( Node root, int value ){ if( root == null ) return null; int currval = root.getValue();
    if( currval == value ) return root;
    if( currval < value ){ return findNode( root.getRight(), value ); } else { // currval > value return findNode( root.getLeft(), value ); }
    }

  • This subtree property is conducive to recursion because recursion generally involves solving a problem in terms of similar subproblems and a base case.

Big O sequencey

1, logn, n, n log n, n2, n3, 2n(2 power n).

Big O

  • It is also considered poor taste to include constant factors and lower-order terms in the big-Oh notation. For example, it is not fashionable to say that the function 2n2 is O(4n2 + 6n log n), although this is completely correct. We should strive instead to describe the function in the big-Oh in simplest terms.
  • So, for example, we would say that an algorithm that runs in worst-case time 4n2 + n log n is a quadratic-time algorithm, since it runs in O(n2) time. Likewise, an algorithm running in time at most 5n + 20logn + 4 would be called a linear-time algorithm.

Big Omega

  • Just as the big-Oh notation provides an asymptotic way of saying that a function is “less than or equal to” another function, the following notations provide an asymptotic way of saying that a function grows at a rate that is “greater than or equal to” that of another.
  • Example 4.14: 3n log n ? 2n is Ω(n log n).

Big-Theta

  • In addition, there is a notation that allows us to say that two functions grow at the same rate, up to constant factors. We say that f(n) is Θ(g(n)), pronounced “f(n) is big-Theta of g(n),”

Comparative Analysis

  • asymptotically[,?simp’t?tik,-k?l] better
  • Suppose two algorithms solving the same problem are available: an algorithm A, which has a running time of O(n), and an algorithm B, which has a running time of O(n2). Which algorithm is better? We know that n is O(n2), which implies that algorithm A is asymptotically better than algorithm B, although for a small value of n, B may have a lower running time than A.

Some Words of Caution

  • First, note that the use of the big-Oh and related notations can be somewhat misleading should the constant factors they “hide” be very large. For example, while it is true that the function 10100n is O(n), if this is the running time of an algorithm being compared to one whose running time is 10n log n, we should prefer the O(nlog n)-time algorithm, even though the linear-time algorithm is asymptotically faster. This preference is because the constant factor, 10100, which is called “one googol,” is believed by many astronomers to be an upper bound on the number of atoms in the observable universe. So we are unlikely to ever have a real-world problem that has this number as its input size.

Exponential [,eksp?’nen?(?)l] Running Times

  • To see how fast the function 2n grows, consider the famous story about the inventor of the game of chess. He asked only that his king pay him 1 grain of rice for the first square on the board, 2 grains for the second, 4 grains for the third, 8 for the fourth, and so on. The number of grains in the 64th square would be 263 = 9, 223, 372, 036, 854, 775, 808, which is about nine billion billions!
  • If we must draw a line between efficient and inefficient algorithms, therefore, it is natural to make this distinction be that between those algorithms running in polynomial [,p?l?’n??m??l] time and those running in exponential time. That is, make the distinction between algorithms with a running time that is O(nc) (power c based on n), for some constant c > 1, and those with a running time that is O(bn) (power n based on b), for some constant b > 1. Like so many notions we have discussed in this section, this too should be taken with a “grain of salt,” for an algorithm running in O(n100) time should probably not be considered “efficient.” Even so, the distinction between polynomial-time and exponential-time algorithms is considered a robust measure of tractability.

Examples of Algorithm Analysis

constant time operation

  • All of the primitive operations, originally described on page 154, are assumed to run in constant time; Assume that variable A is an array of n elements. The expression A.length in Java is evaluated in constant time, because arrays are represented internally with an explicit variable that records the length of the array. Another central behavior of arrays is that for any valid index j, the individual element, A[j], can be accessed in constant time. This is because an array uses a consecutive block of memory. The jth element can be found, not by iterating through the array one element at a time, but by validating the index, and using it as an offset from the beginning of the array in determining the appropriate memory address. Therefore, we say that the expression A[j] is evaluated in O(1) time for an array.

Finding the Maximum of an Array

Proposition 4.16: The algorithm, arrayMax, for computing the maximum element of an array of n numbers, runs in O(n) time.

Justification: The initialization at lines 3 and 4 and the return statement at line 8 require only a constant number of primitive operations. Each iteration of the loop also requires only a constant number of primitive operations, and the loop executes n ? 1 times.

Composing Long Strings

  • Therefore, the overall time taken by this algorithm is proportional to 1 + 2 + ··· + n, which we recognize as the familiar O(n2) summation from Proposition 4.3. Therefore, the total time complexity of the repeat1 algorithm is O(n2).

  • x = logbn if and only if bx = n. The value b is known as the base of the logarithm. Note that by the above definition, for any base b > 0, we have that logb 1 = 0.

Three-Way Set Disjointness

Origional solution

Suppose we are given three sets, A, B, and C, stored in three different integer arrays. We will assume that no individual set contains duplicate values, but that there may be some numbers that are in two or three of the sets. The three-way set disjointness problem is to determine if the intersection of the three sets is empty, namely, that there is no element x such that x ∈ A, x ∈ B, and x ∈ C.

    private static boolean disjoint1(int[]  groupA, int[] groupB, int[] groupC){
        for ( int i : groupA) {
            for (int j : groupB) {
                for (int k : groupC) {
                    if(i==j && j==k){
                        return false;
                    }
                }
            }
        }
        return true;
    }
  • This simple algorithm loops through each possible triple of values from the three sets to see if those values are equivalent. If each of the original sets has size n, then the worst-case running time of this method is O(n3) . 
    private static boolean disjoint2(int[]  groupA, int[] groupB, int[] groupC){
      for ( int i : groupA) {
          for (int j : groupB) {
              if(i==j){
                  // add this checking to reduce complexitiy
                  for (int k : groupC) {
                      if(j==k){
                          return false;
                      }
                  }
              }

          }
      }
      return true;
  }

In the improved version, it is not simply that we save time if we get lucky. We claim that the worst-case running time for disjoint2 is O(n2).

by sorting

Sorting algorithms will be the focus of Chapter 12. The best sorting algorithms (including those used by Array.sort in Java) guarantee a worst-case running time of O(nlog n). Once the data is sorted, the subsequent loop runs in O(n) time, and so the entire unique2 algorithm runs in O(n log n) time. Exercise C-4.35 explores the use of sorting to solve the three-way set disjointness problem in O(n log n) time.

prefixAverage

Check the source code at PrefixAverage.java, the inital implementation is two for loop, which is O(n2), while the better approach is reuse existing total sum.

// naiive approach,
for (int i = 0; i < n; i++) {
			double total=0;
			for (int j = 0; j <=i; j++) { // be awre it's <=, instead of "<"
				total+=x[j];				
			}
			a[i]=total/(i+1);			
		}
// better approach
double total=0;
		for (int i = 0; i < n; i++) {
			total += x[i];
			a[i]=total/(i+1);			
		}

Recursive

Definitions

  1. We have one or more base cases, which refer to fixed values of the function. e.g. for n!=1 as n=1 is base.
  2. Then we have one or more recursive cases, which define the function in terms of itself. for n!, it’s =n*(n-1)! for n>=1
    • Repetition is achieved through repeated recursive invocations of the method. The process i finite because each time the method is invoked, its argument is smaller by one, and when a base case is reached, no further recursive calls are made.
    • In the case of computing the factorial function, there is no compelling reason for prefereing recursion over a direct iteration with a loop.

Tree

ADT (Abstract Data Type)

  • we define a tree ADT using the concept of a position as an abstraction for a node of a tree. An element is stored at each position, and positions satisfy parent-child relationships that define the tree structure.

Depth and Height

Depth

  • The depth of p is the number of ancestors of p, other than p itself.
  • The running time of depth(p) for position p is O(dp + 1), where dp denotes the depth of p in the tree, because the algorithm performs a constant-time recursive step for each ancestor of p. Thus, algorithm depth(p) runs in O(n) worst-case time, where n is the total number of positions of T, because a position of T may have depth n - 1 if all nodes form a single branch.
  • Method depth, as implemented within the AbstractTree class. 
    public int depth(Position<E> p){
  if(isRoot(p))
      return 0;
  else
      return 1+depth(parent(p));
}

    Height

  • We next define the height of a tree to be equal to the maximum of the depths of its positions (or zero, if the tree is empty).
  • Folloing worst time cost is O(n), it progresses in a top-down fashion.
  • If the method is initially called on the root of T, it will eventually be called once for each position of T. This is because the root eventually invokes the recursion on each of its children, which in turn invokes the recursion on each of their children, and so on. 
    public int height(Position<E> p){
  int h=0;
  for(Position<E> c: children(p))
      h=Math.max(h,1+height(c));
  return h;
}

Binary Tree

  • A binary tree is an ordered tree with the following properties:
    • Every node has at most two children.
    • Each child node is labeled as being either a left child or a right child.
    • A left child precedes a right child in the order of children of a node.
  • A binary tree is proper if each node has either zero or two children. Some people also refer to such trees as being full binary trees. Thus, in a proper binary tree, every internal node has exactly two children. A binary tree that is not proper is improper.

    Some binary trees

    decision tree

  • An important class of binary trees arises in contexts where we wish to represent a number of different outcomes that can result from answering a series of yes-or-no questions. Each internal node is associated with a question. Starting at the root, we go to the left or right child of the current node, depending on whether the answer to the question is “Yes” or “No.” With each decision, we follow an edge from a parent to a child, eventually tracing a path in the tree from the root to a leaf. Such binary trees are known as decision trees, because a leaf position p in such a tree represents a decision of what to do if the questions associated with p’s ancestors are answered in a way that leads to p. A decision tree is a proper binary tree.

    Arithmetic expression

  • An arithmetic expression can be represented by a binary tree whose leaves are associated with variables or constants, and whose internal nodes are associated with one of the operators +, ?, *, and /, as demonstrated in Figure 8.6. Each node in such a tree has a value associated with it.
    • If a node is leaf, then its value is that of its variable or constant.
    • If a node is internal, then its value is defined by applying its operation to the values of its children.

      Properties of Binary trees

  • level d has at most 2d nodes
  • Let T be a nonempty binary tree, and let n, nE, nI, and h denote the number of nodes, number of external nodes, number of internal nodes, and height of T, respectively. Then T has the following properties:
    • h + 1 ≤ n ≤ 2h+1 - 1
    • 1 ≤ nE ≤ 2h
    • h ≤ nI ≤ 2h - 1
    • log(n + 1) - 1 ≤ h ≤ n - 1
  • Also, if T is proper, then T has the following properties:
    • 2h + 1 ≤ n ≤ 2h+1 - 1
    • h + 1 ≤ nE ≤ 2h
    • h ≤ nI ≤ 2h - 1
    • log(n + 1) - 1 ≤ h ≤ (n - 1)/2
  • In a nonempty proper binary tree T, with nE external nodes and nI internal nodes, we have nE = nI + 1.

Why use tree

  • You can search, insert/delete items quickly in a tree
  • Ordered Arrays are bad at Insertions/Deletions
  • Finding items in a Linkedlist is slow
  • Time needed to perform an operation on a tree is O(log N)
  • On average a tree is more efficient if you need to perform many different types of operations.

Code practice

http://www.practice.geeksforgeeks.org/problem-page.php?pid=700159

Geek IDE

http://code.geeksforgeeks.org/index.php

Reference

  • http://www.geeksforgeeks.org/maximum-width-of-a-binary-tree/

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NodeJs Notes

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commands to read files var lineReader = require(‘readline’).createInterface({ input: require(‘fs’).createReadStream(‘C:\dev\node\input\git_reset_files.tx...

Minium Viable Product

less than 1 minute read

https://blog.leanstack.com/minimum-viable-product-mvp-7e280b0b9418

CORS :Cross-Origin Resource Sharing

1 minute read

Cross-Origin Request Sharing - CORS (A.K.A. Cross-Domain AJAX request) is an issue that most web developers might encounter, according to Same-Origin-Policy,...

ngrx

2 minute read

Why @Effects? In a simple ngrx/store project without ngrx/effects there is really no good place to put your async calls. Suppose a user clicks on a button or...

iOS programming

3 minute read

View A view is also a responder (UIView is a subclass of UIResponder). This means that a view is subject to user interactions, such as taps and swipes. Thus,...

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2017

cloud computering

less than 1 minute read

openshift vs openstack The shoft and direct answer is `OpenShift Origin can run on top of OpenStack. They are complementary projects that work well together....

cloud computering

3 minute read

Concepts Cloud computing is the on-demand demand delivery of compute database storage applications and other IT resources through a cloud services platform v...

Redux

less than 1 minute read

whats @Effects You can almost think of your Effects as special kinds of reducer functions that are meant to be a place for you to put your async calls in suc...

reactive programing

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The second advantage to a lazy subscription is that the observable doesn’t hold onto data by default. In the previous example, each event generated by the in...

Container

2 minute read

The Docker project was responsible for popularizing container development in Linux systems. The original project defined a command and service (both named do...

promise vs observiable

less than 1 minute read

The drawback of using Promises is that they’re unable to handle data sources that produce more than one value, like mouse movements or sequences of bytes in ...

JDK source

less than 1 minute read

interface RandomAccess Marker interface used by List implementations to indicate that they support fast (generally constant time) random access. The primary ...

SSH SFTP

4 minute read

Secure FTP SFTP over FTP is the equivalant of HTTPS over HTTP, the security version

AWS Tips

less than 1 minute read

After establishing a SSH session, you can install a default web server by executing sudo yum install httpd -y. To start the web server, type sudo service htt...

Oracle

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ORA-12899: Value Too Large for Column

Spring notes

28 minute read

Spring Bean Life Cycle Callback Methods

Kindle notes

113 minute read

#《亿级流量网站架构核心技术》目录一览 TCP四层负载均衡 使用Hystrix实现隔离 基于Servlet3实现请求隔离 限流算法 令牌桶算法 漏桶算法 分布式限流 redis+lua实现 Nginx+Lua实现 使用sharding-jdbc分库分表 Disruptor+Redis...

Java Security Notes

8 minute read

Java Security well-behaved: programs should be prevent from consuming too much system resources

R Language

less than 1 minute read

s<-read.csv("C:/Users/xxx/dev/R/IRS/SHH_SCHISHG.csv") # aggregate s2<-table(s$Original.CP) s3<-as.data.frame(s2) # extract by Frequency ordered s3...

SSH and Cryptography

35 minute read

SFTP versus FTPS SS: Secure Shell An increasing number of our customers are looking to move away from standard FTP for transferring data, so we are ofte...

Eclipse notes

less than 1 minute read

How do I remove a plug-in? Run Help > About Eclipse > Installation Details, select the software you no longer want and click Uninstall. (On Macintosh i...

Java JVM

less than 1 minute read

Class loading subsystem

Maven-Notes

27 minute read

Maven philosophy “It is important to note that in the pom.xml file you specify the what and not the how. The pom.xml file can also serve as a documentatio...

Java New IO

less than 1 minute read

Notes JDK 1.0 introduced rudimentary I/O facilities for accessing the file system (to create a directory, remove a file, or perform another task), accessi...

IT-Architect

1 minute read

SOA SOA is a set of design principles for building a suite of interoperable, flexible and reusable services based architecture. top-down and bottom-up a...

Algorithm

15 minute read

This page is about key points about Algorithm

Java-Tricky-Tech-Questions.md

19 minute read

What is the difference between Serializable and Externalizable in Java? In earlier version of Java, reflection was very slow, and so serializaing large ob...

Compare-In-Java

6 minute read

Concepts If you implement Comparable interface and override compareTo() method it must be consistent with equals() method i.e. for equal object by equals(...

Java Collections Misc

3 minute read

Difference between equals and deepEquals of Arrays in Java Arrays.equals() method does not compare recursively if an array contains another array on oth...

HashMap in JDK

4 minute read

Hashmap in JDK Some note worth points about hashmap Lookup process Step# 1: Quickly determine the bucket number in which this element may resid...

Java 8 Tips

66 minute read

This blog is listing key new features introduced in Java 8

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2016

Java GC notes

28 minute read

verbose:gc verbose:gc prints right after each gc collection and prints details about each generation memory details. Here is blog on how to read verbose gc

Hash Code Misc

12 minute read

contract of hashCode : Whenever it is invoked on the same object more than once during an execution of a Java application, the hashCode method must consis...

Angulary Misc

2 minute read

Dependency Injection Angular doesn’t automatically know how you want to create instances of your services or the injector to create your service. You must co...

Random number in java

less than 1 minute read

ThreadLocalRandom, SecureRandm, java.util.Random, java.math.Random

Java new features

less than 1 minute read

JDK Versions JDK 1.5 in 2005 JDK 1.6 in 2006 JDK 1.7 in 2011 JDK 1.8 in 2014 Sun之前风光无限，但是在2010年1月27号被Oracle收购。 在被Oracle收购后对外承诺要回到每2年一个realse的节奏。但是20...

Simpler chronicle of CI(Continuous Integration) “乱弹系列”之持续集成工具

less than 1 minute read

引言 有句话说有人的地方就有江湖，同样，有江湖的地方就有恩怨。在软件行业历史长河（虽然相对于其他行业来说，软件行业的历史实在太短了，但是确是充满了智慧的碰撞也是十分的精彩）中有一些恩怨情愁，分分合合的小故事，比如类似的有，从一套代码发展出来后面由于合同到期就分道扬镳，然后各自发展成独门产品的Sybase DB和微...

浅谈软件单元测试中的“断言” (assert)，从石器时代进步到黄金时代。

less than 1 minute read

大家都知道，在软件测试特别是在单元测试时,必用的一个功能就是“断言”（Assert)，可能有些人觉得不就一个Assert语句，没啥花头，也有很多人用起来也是懵懵懂懂，认为只要是Assert开头的方法，拿过来就用。一个偶然的机会跟人聊到此功能，觉得还是有必要在此整理一下如何使用以及对“断言”的理解。希望可以帮助大家...

Head First Blockchina 1

less than 1 minute read

深入浅出区块链系统：第一章. what you should know about blockchain

Kubernetes 与 Docker Swarm的对比

less than 1 minute read

Kubernetes 和Docker Swarm 可能是使用最广泛的工具，用于在集群环境中部署容器。但是这两个工具还是有很大的差别。

http methods

11 minute read

RFC origion http://www.w3.org/Protocols/rfc2616/rfc2616-sec9.html#sec9.1.2)

Spark-vs-Storm

less than 1 minute read

The stark difference among Spark and Storm. Although both are claimed to process the streaming data in real time. But Spark processes it as micro-batches; wh...

微服务

less than 1 minute read

可以想像一下，之前的传统应用系统，像是一个大办公室里面，有各个部门，销售部，采购部，财务部。办一件事情效率比较高。但是也有一些弊端，首先，各部门都在一个房间里。

kibana, view layer of elasticsearch

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What’s Kibana kibana is an open source data visualization plugin for Elasticsearch. It provides visualization capabilities on top of the content indexed on...

kibana, view layer of elasticsearch

less than 1 minute read

What’s Kibana kibana is an open source data visualization plugin for Elasticsearch. It provides visualization capabilities on top of the content indexed on...

iConnect

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UI HTML5, AngularJS, BootStrap, REST API, JSON Backend Hadoop core (HDFS), Hive, HBase, MapReduce, Oozie, Pig, Solr

Business Analysis

less than 1 minute read

Purpose of BA 带来一些商业价值（收益） 解决业务痛点

Data Structure

2 minute read

Binary Tree A binary tree is a tree in which no node can have more than two children. A property of a binary tree that is sometimes important is that th...

equity trading

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Difference between mutal funds and hedge funds

SQL

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Differences between not in, not exists , and left join with null

HTTPS/2

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concepts

Github page commands notes

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404 error for customized domain (such as godday) 404 There is not a GitHub Pages site here. Go to github master branch for gitpages site, manually add CN...

RenMinBi International

1 minute read

RQFII RQFII stands for Renminbi Qualified Foreign Institutional Investor. RQFII was introduced in 2011 to allow qualified foreign institutional investors to ...

Load Balancing

less than 1 minute read

Concepts LVS means Linux Virtual Server, which is one Linux built-in component.

Python

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(‘—–Unexpected error:’, <type ‘exceptions.TypeError’>) datetime.datetime.now()

Setup Git in Mint Linux

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How to setup Git in Mint Linux =================================================

Microservices vs. SOA

1 minute read

Microservice Services are organized around capabilities, e.g., user interface front-end, recommendation, logistics, billing, etc. Services are small in ...

Java Class Loader

33 minute read

Codecache The maximum size of the code cache is set via the -XX:ReservedCodeCacheSize=N flag (where N is the default just mentioned for the particular com...

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