Observer Pattern

-

Observer Pattern

An Observer Pattern says that “just define a one-to-one dependency so that when one object changes state, all its dependents are notified and updated automatically”.

The Memento pattern is also known as Dependents or Publish-Subscribe.

Benefits:

  • It describes the coupling between the objects and the observer.
  • It provides the support for broadcast-type communication.

Usage:

  • When the change of a state in one object must be reflected in another object without keeping the objects tight coupled.
  • When the framework we writes and needs to be enhanced in future with new observers with minimal chamges.

UML for Observer Pattern:

Observer Pattern

 

Implementation of Observer Pattern

Step 1:

Create a ResponseHandler1 class the will implement the java.util.Observer interface.

import java.util.Observable;

import java.util.Observer;

public class ResponseHandler1 implements Observer {

privateString resp;

publicvoidupdate(Observable obj, Object arg) {

if(arg instanceof String) {

resp = (String) arg;

System.out.println(“\nReceived Response: ” + resp);

}

}

}// End of the ResponseHandler1 interface.

Step 2:

Create a ResponseHandler2 class the will implement the java.util.Observer interface.

import java.util.Observable;

import java.util.Observer;

public class ResponseHandler2 implements Observer {

privateString resp;

publicvoidupdate(Observable obj, Object arg) {

if(arg instanceof String) {

resp = (String) arg;

System.out.println(“\nReceived Response: ” + resp);

}

}

}// End of the ResponseHandler2 interface.

Step 3:

Create an EventSource class that will extend the java.util.Observable class .

import java.io.BufferedReader;

import java.io.IOException;

import java.io.InputStreamReader;

import java.util.Observable;

public class EventSource extends Observable implements Runnable {

@Override

publicvoidrun() {

try{

final InputStreamReader isr = new InputStreamReader(System.in);

final BufferedReader br = new BufferedReader(isr);

while(true) {

String response = br.readLine();

setChanged();

notifyObservers(response);

}

} catch (IOException e) {

e.printStackTrace();

}

}

}// End of the Eventsource class.

Output:

Observer Pattern

Popular posts from this blog

How to change column name in Dataframe and selection of few columns in Dataframe using Pyspark with example

-
Input details: #● File has json records #● Each record has fields: #○ user_id #○ card_num #○ merchant #○ category #○ amount #○ ts ### Below analysis to be done Sample data: +------+--------+---------+--------+----------+-------+|amount|card_num| category|merchant| ts|user_id|+------+--------+---------+--------+----------+-------+| 243| C_108| food| M_102|1579532902| U_104|| 699| C_106|cosmetics| M_103|1581759040| U_103|| 228| C_104| children| M_110|1584161986| U_103| Solution: from pyspark.sql.functions import col # this can be done without using window function creditCardData = spark.read.json(“card_transactions.json”) useridMaxSpendDF = df.groupby(‘user_id’).max(‘amount’) useridMaxSpendDF=useridMaxSpendDF.withColumnRenamed(“max(amount)”,”max_amount”) useridMaxSpendDF=useridMaxSpendDF.withColumnRenamed(“user_id”,”m_user_id”) cond = [creditCardData.user_id == useridMaxSpen...
Read more

What is Garbage collection in Spark and its impact and resolution

-
Image
Garbage Collection Spark runs on the Java Virtual Machine (JVM). Because Spark can store large amounts of data in memory, it has a major reliance on Java’s memory management and garbage collection (GC). Therefore, garbage collection  (GC) can be a major issue that can affect many Spark applications. Common symptoms of excessive GC in Spark are: Slowness of application Executor heartbeat timeout GC overhead limit exceeded error Spark’s memory-centric approach and data-intensive applications make it a more common issue than other Java applications. Thankfully, it’s easy to diagnose if your Spark application is suffering from a GC problem. The Spark UI marks executors in red if they have spent too much time doing GC. Spark executors are spending a significant amount of CPU cycles performing garbage collection. This can be determined by looking at the “Executors” tab in the Spark application UI. Spark will mark an executor in  red  if the executor has spent more than 10% of the time in gar...
Read more

Complex SQL: fetch the users who logged in consecutively 3 or more times (lead perfect example)

-
From the login_details table, fetch the users who logged in consecutively 3 or more times. Table Name : LOGIN_DETAILS Approach : We need to fetch users who have appeared 3 or more times consecutively in login details table. There is a window function which can be used to fetch data from the following record. Use that window function to compare the user name in current row with user name in the next row and in the row following the next row. If it matches then fetch those records. –Table Structure: drop table login_details; create table login_details( login_id int primary key, user_name varchar(50) not null, login_date date); delete from login_details; insert into login_details values (101, ‘Michael’, current_date), (102, ‘James’, current_date), (103, ‘Stewart’, current_date+1), (104, ‘Stewart’, current_date+1), (105, ‘Stewart’, current_date+1), (106, ‘Michael’, current_date+2), (107, ‘Michael’, cur...
Read more