Skip to main content

Design Patterns (aka DP), Creational - Factory Pattern

DP is a well-described solution to a common software problem. Its benefits:

  • Already defined to solve a problem.
  • Increase code reusability and robustness.
  • Faster devlopment and new developers in team can understand it easily
DP defined in to 3 categories:
  • Creational - Used to construct objects such that they can be decoupled from their implementing system.
  • Structural - Used to form large object structures between many disparate objects
  • Behavioral - Used to manage algorithms, relationships, and responsibilities between objects.
  • Factory - Exposes a method for creating objects, allowing sub-classes to control the actual creation process.
Consider a scenario as below:-
  • You have an interface or abstract class or a normal class. Computer class
  • There are 2 sub-classes of of Computer. PC class and Server class
  • Now you have a factory which provides you Computers. And you can give specification to factory whether you want a PC or Server.
Below code implements above scenario:

package com.test.command.dp.creational.factory;

/**Abstract class**/ 
public abstract class Computer {

       public abstract String getRAM();

       public abstract String getHDD();

       public abstract String getCPU();

       @Override
       public String toString() {

              return "RAM= " + this.getRAM() + ", HDD=" + this.getHDD() + ", CPU="
                           + this.getCPU();
       }

}

package com.test.command.dp.creational.factory;

public class PC extends Computer {

       private String ram;
       private String hdd;
       private String cpu;

       public PC(String ram, String hdd, String cpu) {
              this.ram = ram;
              this.hdd = hdd;
              this.cpu = cpu;
       }

       @Override
       public String getRAM() {
              // TODO Auto-generated method stub
              return this.ram;
       }

       @Override
       public String getHDD() {
              // TODO Auto-generated method stub
              return this.hdd;
       }

       @Override
       public String getCPU() {
              // TODO Auto-generated method stub
              return this.cpu;
       }

}

package com.test.command.dp.creational.factory;

public class Server extends Computer {

       private String ram;
       private String hdd;
       private String cpu;
      
      

       public Server(String ram, String hdd, String cpu) {
             
              this.ram = ram;
              this.hdd = hdd;
              this.cpu = cpu;
       }

       @Override
       public String getRAM() {
              // TODO Auto-generated method stub
              return this.ram;
       }

       @Override
       public String getHDD() {
              // TODO Auto-generated method stub
              return this.hdd;
       }

       @Override
       public String getCPU() {
              // TODO Auto-generated method stub
              return this.cpu;
       }

}


package com.test.command.dp.creational.factory;

/**Factory**/
public class ComputerFactory {

       public static Computer getComputer(String type, String ram, String hdd, String cpu){
              if("PC".equalsIgnoreCase(type)){
                     return new PC(ram, hdd, cpu);
              }
              else if("Server".equalsIgnoreCase(type)){
                     return new Server(ram, hdd, cpu);
              }
              return null;
       }
      
       public static void main(String[] args) {
              Computer pc = ComputerFactory.getComputer("PC", null, null, null);
              Computer server = ComputerFactory.getComputer("SERVER", "12", null, null);
             
              System.out.println("Your PC Dinesh Sachdev. Delivered to Indore:"+pc);
              System.out.println("Your Server Dinesh Sachdev. Delivered to Indore:"+server);
       }
}

Comments

Popular posts

Read from a hive table and write back to it using spark sql

In context to Spark 2.2 - if we read from an hive table and write to same, we get following exception- scala > dy . write . mode ( "overwrite" ). insertInto ( "incremental.test2" ) org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; org . apache . spark . sql . AnalysisException : Cannot insert overwrite into table that is also being read from .; 1. This error means that our process is reading from same table and writing to same table. 2. Normally, this should work as process writes to directory .hiveStaging... 3. This error occurs in case of saveAsTable method, as it overwrites entire table instead of individual partitions. 4. This error should not occur with insertInto method, as it overwrites partitions not the table. 5. A reason why this happening is because Hive table has following Spark TBLProperties in its definition. This problem will solve for insertInto met




Hive Parse JSON with Array Columns and Explode it in to Multiple rows.

 Say we have a JSON String like below -  { "billingCountry":"US" "orderItems":[       {          "itemId":1,          "product":"D1"       },   {          "itemId":2,          "product":"D2"       }    ] } And, our aim is to get output parsed like below -  itemId product 1 D1 2 D2   First, We can parse JSON as follows to get JSON String get_json_object(value, '$.orderItems.itemId') as itemId get_json_object(value, '$.orderItems.product') as product Second, Above will result String value like "[1,2]". We want to convert it to Array as follows - split(regexp_extract(get_json_object(value, '$.orderItems.itemId'),'^\\["(.*)\\"]$',1),'","') as itemId split(regexp_extract(get_json_object(value, '$.orderItems.product'),'^\\["(.*)\\"]$',1),&




Caused by: java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainIntegerDictionary

Exception -  Caused by: java.lang.UnsupportedOperationException: org.apache.parquet.column.values.dictionary.PlainValuesDictionary$PlainIntegerDictionary at org.apache.parquet.column.Dictionary.decodeToBinary(Dictionary.java:44) at org.apache.spark.sql.execution.vectorized.ColumnVector.getUTF8String(ColumnVector.java:645) at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source) Analysis - This might occur because of data type mismatch between Hive Table & written Parquet file. Solution - Correct the data type to match between Hive Table & Parquet




org.apache.spark.sql.AnalysisException: Cannot overwrite a path that is also being read from.;

  Caused by: org.apache.spark.sql.AnalysisException: Cannot overwrite a path that is also being read from.; at org.apache.spark.sql.execution.command.DDLUtils$.verifyNotReadPath(ddl.scala:906) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:192) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:134) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266) at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256) at org.apache.spark.sql.execution.datasources.DataSourceAnalysis.apply(DataSourceStrategy.scala:134) at org.apache.spark.sql.execution.datasource




Hadoop Distcp Error Duplicate files in input path

  One may face following error while copying data from one cluster to other, using Distcp  Command: hadoop distcp -i {src} {tgt} Error: org.apache.hadoop.toolsCopyListing$DulicateFileException: File would cause duplicates. Ideally there can't be same file names. So, what might be happening in your case is you trying to copy partitioned table from one cluster to other. And, 2 different named partitions have same file name. Your solution is to correct Source path  {src}  in your command, such that you provide path uptil partitioned sub directory, not the file. For ex - Refer below : /a/partcol=1/file1.txt /a/partcol=2/file1.txt If you use  {src}  as  "/a/*/*"  then you will get the error  "File would cause duplicates." But, if you use  {src}  as  "/a"  then you will not get error in copying.