Datasets In Apache Spark - Part 3 | Writing Datasets to Disk

In the last tutorial we've seen how to create parametrized datasets. Once you create datasets and perform some operations on them, you would like to save those results back into storage. This is what we'll try to do in this article - Saving Datasets to storage.

The first thing we'll do as always is to create the spark-session variable.

// Initialize Sparksession
SparkSession spark = SparkSession.builder().appName("Freblogg-Spark").master("local").getOrCreate();

Using that session variable, we read the fake-people.csv file which has data like this:

id,first_name,last_name,email,gender,ip_address
1,Netti,McKirdy,nmckirdy0@slideshare.net,Female,148.3.248.193
2,Nickey,Curreen,ncurreen1@tripadvisor.com,Male,206.9.48.216
3,Allayne,Chatainier,achatainier2@trellian.com,Male,191.118.4.217
...

We read this file into a dataset as following:

// Read csv file
Dataset<Row> peopleDs = spark.read().option("header", "true").csv("fake-people.csv");

After we have the dataset, Let's assume you've performed some operations on it. Some column selections, some filtering, some sorting etc. And we have a new dataset after all those operations.

// After performing several awesome operations
Dataset<Row> newDs = ....

We want to store this dataset back on the disk. We can do that with the write() on spark session variable, just like read().

newDs.write().csv("processed-data");

The processed-data in the above command is not the name for the output CSV file but instead for the output directory. When you write a Dataset to a file, it will store the data in the format you asked for, CSV in this case, along with adding some check files and status flags as well creating a directory with that name.

These are the files that get created in the processed-data folder.

$ ls ../../apache-spark/processed-data
_SUCCESS  part-00000-311049cf-3e48-4286-b93c-7d2096a18678-c000.csv

There are two more hidden CRC files that I'm not showing here. The part-00000-31hxxxxxxxxx.csv is the actual data file which has the data from the new dataset.

You can also create a json file by running

newDs.write().json("processed-data")

And that will create another folder with json file and the _SUCCESS file inside it.

You can also save this data to an external Database if you want to. You'll use the jdbc() method along with the connection string and the table name. And Spark will write it to the DB.

Apart from the CSV and JSON formats, there is one more popular data format in the Data Science and Big Data world. That is Parquet. Parquet is a data format that is highly optimized and well suited for column-wise operations. It is widely used in a lot of projects in the Big Data ecosystem as a data serialization format. And In Spark, Parquet is the default file storage format. Of course one main difference between Parquet and formats like CSV, JSON is that Parquet is not meant to be used for humans. It can only be read by a parquet reader. A sample file looks something like this:

PAR1   �k �>, �          999  1     �5,   �      1   2   3   4   5   6   7   8   9  - 0   1   2   3   4   5   6   7   8   < 2 < 2 < 2 < 2 < 2 < 2 < 2 <
.....

Utterly gibberish. But spark can read and understand it. In fact, As Parquet is designed for speed and throughput, it can be 10-100 times faster than reading/writing from an ordinary data format like CSV or JSON, depending on the type of data.

You save dataset to Parquet as follows:

newDs.write().parquet("processed");

And this will save the dataset as a parquet file along with the _SUCCESS status file.

That is all for this article.

---

For more programming articles, checkout Freblogg, Freblogg/Java, Freblogg/Spark

Articles on Apache Spark:

Map Vs Flat map

Spark Word count with Java

Datasets in Spark | Part I

Datasets in Spark | Part II

---

This is the 17th article as part of my twitter challenge #30DaysOfBlogging. Thirteen more articles on various topics, including but not limited to, Java, Git, Vim, Software Development, Python, to come.

If you are interested in this, make sure to follow me on Twitter @durgaswaroop. While you're at it, Go ahead and subscribe here on medium and my other blog as well.

---

If you are interested in contributing to any open source projects and haven't found the right project or if you were unsure on how to begin, I would like to suggest my own project, Delorean which is a Distributed Version control system, built from scratch in scala. You can contribute not only in the form of code, but also with usage documentation and also by identifying any bugs in its functionality.

---

Thanks for reading. See you again in the next article.

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Datasets In Apache Spark | Part 2

In the two last tutorials we have covered what Apache Spark is and also got ourselves familiar with Datasets in Apache Spark, which is the primary data abstraction in Spark. In this tutorial we will see how to read a data file as a parametrized Bean object Dataset using Encoders.

 

This tutorial is going to be short, but this is very important as you would find yourself doing this frequently. In the last article you've seen how to read a CSV or JSON file as a Dataset. You might have noticed that we were using Dataset<Row> for everything. If you're not familiar with Generics in Java, Dataset<Row> can be thought of as a Dataset consisting of Row objects. The Row object is a spark sql class and is the default when creating a Dataset.

Although the Row class has some useful methods, as a generic object suitable for all types, it is not suitable for everything. Since Datasets usually store data that usually corresponds to a Bean class, it is better to create a Dataset of that bean class instead of Row. With this, you'll have access to all your usual getters and setters of the bean class. That's what We'll do in this article. We'll create a Dataset of POJO's instead of Row objects.

I'm using the same fake-people.csv file that I used in the last article that looks like this:

id,first_name,last_name,email,gender,ip_address
1,Netti,McKirdy,nmckirdy0@slideshare.net,Female,148.3.248.193
2,Nickey,Curreen,ncurreen1@tripadvisor.com,Male,206.9.48.216
3,Allayne,Chatainier,achatainier2@trellian.com,Male,191.118.4.217
...

To represent this data, I've created a POJO called FakePeople.java, which looks like this:

import lombok.Data;
public @Data class FakePeople {
    final int id;
    final private String firstName;
    final private String lastName;
    final private String email;
    final private String gender;
    final private String ipAddress;
}

I'm using Project Lombok here, to generate the required Getters, Setters and other POJO methods. (If you don't know about Lombok, you should definitely check that out. It is quite handy).

We have our POJO now, Let's get a parametrized Dataset. To achieve this we first need to create an Encoder. We do that for the FakePeople class as following:

Encoder<FakePeople> fakePeopleEncoder = Encoders.bean(FakePeople.class);

This will register our encoder which will help us parse our CSV data.

Of course we need our spark session variable as well.

// Initialize Sparksession
SparkSession spark = SparkSession.builder().appName("Freblogg-Spark").master("local").getOrCreate();

Now we can go ahead and read the CSV file, very much like the way we did before with just one addition.

// Without Encoder
Dataset<Row> people = spark.read().option("header", "true").csv("fake-people.csv");

// With Encoder
Dataset<FakePeople> people = spark.read().option("header", "true").csv("fake-people.csv").as(fakePeopleEncoder);

And the output of people.show(5) is the same as what you'd expect.

+---+----------+----------+--------------------+------+--------------+
| id|first_name| last_name|               email|gender|    ip_address|
+---+----------+----------+--------------------+------+--------------+
|  1|     Netti|   McKirdy|nmckirdy0@slidesh...|Female| 148.3.248.193|
|  2|    Nickey|   Curreen|ncurreen1@tripadv...|  Male|  206.9.48.216|
|  3|   Allayne|Chatainier|achatainier2@trel...|  Male| 191.118.4.217|
|  4|     Tades|    Emmett|temmett3@barnesan...|  Male|153.113.87.195|
|  5|     Shawn|    McGenn|smcgenn4@shop-pro.jp|  Male|  247.45.80.68|
+---+----------+----------+--------------------+------+--------------+

As you can see the only difference in creating the Dataset is .as(fakePeopleEncoder) and that gets us Dataset<FakePeople> instead of Dataset<Row>. And with that, we now have access to all the getters, setters of FakePeople class which we wouldn't otherwise have with a Row object. We'll explore more about how this is useful in a future tutorial.

For more information on Datasets: Spark SQL, DataFrames and Datasets Guide

That is all for this article.

---

For more programming articles, checkout Freblogg, Freblogg/Java, Freblogg/Spark

Apache Spark articles:

Word count with Apache Spark and Java

Datasets in Apache Spark | Part 1

Datasets in Apache Spark | Part 2

---

This is the 11th article as part of my twitter challenge #31DaysOfBlogging. Nineteen more articles on various topics, including but not limited to, Java, Git, Vim, Software Development, Python, to come.

If you are interested in this, make sure to follow me on Twitter @durgaswaroop. While you're at it, Go ahead and subscribe here on medium and my other blog as well.

---

If you are interested in contributing to any open source projects and haven't found the right project or if you were unsure on how to begin, I would like to suggest my own project, Delorean which is a Distributed Version control system, built from scratch in scala. You can contribute not only in the form of code, but also with usage documentation and also by identifying any bugs in its functionality.

---

Thanks for reading. See you again in the next article.

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Datasets in Apache Spark | Part 1

In my previous post I have talked about Apache Spark. We have also built an application for counting the number of words in a file, which is the hello world equivalent of the big data world.

It has been over 18 months since that article and spark has changed quite a lot in this time. A new major release of spark, which is spark-2.0 came out and now the latest version is 2.2.1. And with a new version comes new API’s and improvements. In-fact the first thing you’ll probably notice is that, you don’t need to create SparkContext or JavaSparkContext objects anymore. The various context and configurations have been put together into a new class SparkSession. You can still access the SparkContext or the SqlContext from the SparkSession object itself. So, you’ll be starting your programs with this now:

SparkSession spark = SparkSession.builder().appName("Freblogg-Spark").master("local").getOrCreate();

And you can use this spark variable the way you’d use other context variables.

Another change in Spark 2.0 is that, there is a heavy emphasis on the usage of Dataset API’s, and for a good reason. Datasets are more performant and memory efficient than RDD’s. RDD (Resilient Distributed Datasets) have been pushed to second place now. You can still use RDD’s if you want but Datasets are the preferred API. In fact, datasets have some nice convenience methods that we can use them for even unstructured data like text as well. Let’s generate some cool lipsum from Malevole. It looks something like this:

Ulysses, Ulysses - Soaring through all the galaxies. In search of Earth, flying in to the night. Ulysses, Ulysses - Fighting evil 
and tyranny, with all his power, and with all of his might. Ulysses - no-one else can do the things you do. Ulysses - like a bolt of
thunder from the blue. Ulysses - always fighting all the evil forces bringing peace and justice to all....

Now, you might try to use an RDD to read this, but let’s see what we can do with Datasets.

Dataset<String> lipsumDs = spark.read().textFile("fake-text.txt");
lipsumDs.show(5);

Here we are reading the text file using the spark object we created earlier and that gives us a Dataset<String> lipsumDs. The show() method on the dataset object prints the dataset. And we get the following output:

+--------------------+
|               value|
+--------------------+
|Ulysses, Ulysses ...|
|Ulysses, Ulysses ...|
| no-one else can ...|
|  always fighting...|
|                    |
+--------------------+

What we see here are the lines of the text file. Each line in the file is now a row in the Dataset. There are now a rich set of functions available to you in Datasets which weren’t in RDD’s. You can do filters on the rows for certain words, do a count on the table, perform groupBy operations, etc all like you would on a Database table. For a full list of all the available operations on Dataset, read this: Dataset: Spark Documentation.

I hope that’s enough talk about unstructured data analysis. Let’s get to the main focus of this article, which is using Datasets for structured data. More specifically, csv and json. For this tutorial, I am using the data created from Mockaroo, an online data generator. I’ve created 1000 csv records that look like this:

id,first_name,last_name,email,gender,ip_address
1,Netti,McKirdy,nmckirdy0@slideshare.net,Female,148.3.248.193
2,Nickey,Curreen,ncurreen1@tripadvisor.com,Male,206.9.48.216
3,Allayne,Chatainier,achatainier2@trellian.com,Male,191.118.4.217
4,Tades,Emmett,temmett3@barnesandnoble.com,Male,153.113.87.195
5,Shawn,McGenn,smcgenn4@shop-pro.jp,Male,247.45.80.68
6,Giuseppe,Scobbie,gscobbie5@twitter.com,Male,123.114.131.200
...

We’ll use this data, which I’ve put in a file named fake-people.csv, to work with Datasets. Let’s create a Dataset out of this csv data.

Dataset<Row> peopleDs = spark.read().option("header", "true").csv("fake-people.csv");
peopleDs.show(5);

Since we’ve column headers in our data, we add the .option("header", "true") and the output is a nicely formatted table of the data with all the columns like this:

+---+----------+----------+--------------------+------+--------------+
| id|first_name| last_name|               email|gender|    ip_address|
+---+----------+----------+--------------------+------+--------------+
|  1|     Netti|   McKirdy|nmckirdy0@slidesh...|Female| 148.3.248.193|
|  2|    Nickey|   Curreen|ncurreen1@tripadv...|  Male|  206.9.48.216|
|  3|   Allayne|Chatainier|achatainier2@trel...|  Male| 191.118.4.217|
|  4|     Tades|    Emmett|temmett3@barnesan...|  Male|153.113.87.195|
|  5|     Shawn|    McGenn|smcgenn4@shop-pro.jp|  Male|  247.45.80.68|
+---+----------+----------+--------------------+------+--------------+

You can read in json data similarly as well. So, I generated some json this time from mockaroo.

{"id":1,"first_name":"Zenia","last_name":"Joberne","email":"zjoberne0@foxnews.com","gender":"Female","ip_address":"214.207.159.43"}
{"id":2,"first_name":"Renard","last_name":"Kezor","email":"rkezor1@elpais.com","gender":"Male","ip_address":"199.3.18.104"}
{"id":3,"first_name":"Briant","last_name":"Patel","email":"bpatel2@odnoklassniki.ru","gender":"Male","ip_address":"111.184.217.23"}
{"id":4,"first_name":"Robinett","last_name":"Heasley","email":"rheasley3@tiny.cc","gender":"Female","ip_address":"21.40.190.226"}
{"id":5,"first_name":"Rosalinda","last_name":"Glandfield","email":"rglandfield4@indiegogo.com","gender":"Female","ip_address":"26.16.4.132"}
{"id":6,"first_name":"Haslett","last_name":"Culligan","email":"hculligan5@meetup.com","gender":"Male","ip_address":"201.191.72.10"}
....

Note: Spark can read json only of this format where we have one object per row. Otherwise you will see _corrupt_record when you print your dataset. That’s your cue to make sure the json is formatted as per spark’s need.

And you read json very similar to the way you read csv. Since in json we don’t have headers, we don’t need the header option.

Dataset<Row> peopleJsonDs = spark.read().json("fake-people.json");
peopleJsonDs.show(5);

And the output is,

+--------------------+----------+------+---+--------------+---------+
|               email|first_name|gender| id|    ip_address|last_name|
+--------------------+----------+------+---+--------------+---------+
|psurgison0@istock...|   Prissie|Female|  1| 48.151.89.171| Surgison|
| rsewell1@jalbum.net|    Robena|Female|  2| 184.16.37.210|   Sewell|
|aluxon2@list-mana...| Annamarie|Female|  3| 254.69.187.23|    Luxon|
|sodoherty3@twitpi...|   Shannah|Female|  4| 0.245.101.197|O'Doherty|
| alodford4@jigsy.com|     Alice|Female|  5|70.217.170.182|  Lodford|
+--------------------+----------+------+---+--------------+---------+

You can see the order of columns is jumbled. This is because JSON data doesn’t usually keep any specified order and so, when you read JSON data into a dataset, the order might not be same as what you’ve given. Of course if you want to display the columns in a particular order, you can always do a select operation.

peopleJsonDs.select("id", "first_name", "last_name", "email", "gender", "ip_address").show(5);

And that would print it in the right order. This is exactly like the SELECT query in SQL, if you’re familiar with it.

Now, that we have seen how to create Datasets, let’s see some of the operations we can perform on them.

Operations on Datasets

Datasets are built on top of Dataframes. So, if you’re already familiar with Dataframes in the spark 1.x releases you already know a ton about Datasets. Some of the operations you can perform on Dataset are as follows:

Column selection

Select one or more columns from the dataset.

peopleDs.select("email").show(5); // Selecting one column
peopleDs.select(col("email"), col("gender")).show(5); // Selecting multiple columns

Note: col is a static import of org.apache.spark.sql.functions.col;

Filtering on columns

Filter a subset of rows in the dataset based on conditions.

// Filter rows with id > 5 and \<= 10
peopleDs.filter(col("id").$less$eq(10).and(col("id").$greater(5))).show();

Dropping columns

Remove one or more columns from the dataset

peopleDs.drop("last_name", "ip_address").show(5);

Sorting on columns

peopleDs.sort(desc("first_name")).show(5);

And that sorts the dataset in the reverse order of the column first_name.

Output:

+---+----------+---------+--------------------+------+-------------+
| id|first_name|last_name|               email|gender|   ip_address|
+---+----------+---------+--------------------+------+-------------+
|685|  Zedekiah|  Brockie|zbrockiej0@mozill...|  Male|105.119.18.98|
|308|     Zarla| Bryceson|zbryceson8j@redif...|Female|55.118.168.15|
|636|  Zacherie|   Kermon|zkermonhn@prnewsw...|  Male| 120.36.10.87|

Those are some of the functions that you can use with Datasets. There are still several Database table type operations on Datasets, like group By, aggregations, joins, etc. We’ll look at them in the next article on Spark as I think this article already has a lot of information already and I don’t want to overload you with information.

So, that is all for this article. If you’re someone that has never tried Datasets or Dataframes, I hope this article gave a good introduction on the topic to keep you interested in learning more.

The full code is available as gist.

---

For more Java, Apache Spark, Big data and other programming articles, checkout Freblogg, Freblogg/Java, Freblogg/Spark

---

This is the fifth article as part of my twitter challenge #30DaysOfBlogging. Twenty-five more articles on various topics including but not limited to Java, Git, Vim, Software Development, Python, to come.

If you are interested in this, make sure to follow me on Twitter @durgaswaroop. While you’re at it, Go ahead and subscribe to this blog and my blog on Medium as well.

---

If you are interested in contributing to any open source projects and haven’t found the right project or if you were unsure on how to begin, I would like to suggest my own project, Delorean which is a Distributed Version control system, built from scratch in scala. You can contribute not only in the form of code, but also with usage documentation and also by identifying any bugs in the functionality.

---

Thanks for reading. See you again in the next article.

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Quick Vim Tips

Vim is one of the most powerful text editors available. And, hence it is not really possible for everyone to know everything or get the same ideas on improving their work experience. And, so this article includes a few tips and handy shortcuts that will help your productivity just as we have been doing in the Vim series, but individually not extensive enough to get their own dedicated article.

So, here are some useful tips for Vim

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RegEx : The Right Way | Dot Operator

Let's continue with Regular Expressions. All articles in this series can be found here. I will be using Regexr.com for most of these tutorials. It is a great site, where you can write and validate your regular expressions against your desired input text.

Now, Let's look at how regular expressions usually look like..

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RegEx : The Right Way |Tutorial 1

Regular Expressions or RegEx is  a sequence of characters that define a search pattern. Regex is every where these days and you can use it to extract information from Text files, Log files, Dictionaries, Spread sheets and even webpages. Every major programming language has support for Regular Expressions. Most importantly grep, awk and  sed use regex to find/replace matches.

Regular Expressions can help you save a lot of time. Instead of writing complex String pattern searches which span over multiple lines, regex gets the job done really easily and really fast.

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Switch Case With Strings | Java

Switch Case is certainly one of the most widely used programming constructs. It is just as widely used in Java as in any other language.

Up until Java 7, switch in Java did not support String type in the case statement. So, if you want to perform multiple comparisons on Strings, the only way you were able to do it was by using multiple If - Else Statements which is certainly not pretty!

But, then in Java 7 they have introduced Switch With Strings and it was welcomed by everyone who had to write those terrible if-else ladders. So, Let’s see how we can use Strings in a Case statement.

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How to Open Vlc using Java

Java is a very versatile programming language and so needless to say you can do a lot of things with it. Even if it is playing a video or audio using VLC!

So, what do you need to do this?

• You obviously need Java installed on your system
• You need VLC. Duh!!  (You can use just about any other player if you wish)
• (Optional) Source code for this program from github

So, With that said, let's get started.

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How to use Aliases in Linux | Superuse your Terminal

Well, let’s face it, Linux is cool. Using Linux makes you look cool. But, for new users, may be not so much. Typing those long  commands can be a little intimidating and may even scare them off.
Wouldn’t it be awesome if there was a way, so that you won’t have to type long commands? Or even better, to create your own new commands by which you can do a lot of stuff?
You are in luck, because Aliases help you with that and much more. 

What are Aliases anyway? 
Alias, as the name suggests is an alternate form of a previously existing command(s). So basically you are creating your own custom command with which you can do a lot of functions.
Enough with the definitions, let’s see an example.
Say you want to go to the Desktop folder. You would have to do something like this,

 [dsp@freblogg]$:~ cd /home/dsp/Desktop

How about instead of  typing all that, you just say desk like this and it takes you to Desktop?

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C Program that prints its own Source Code | C Programming Quine

Quine in programming terms is a program which outputs its own Source code. These are self-replicating programs and are very popular in the programming community. So, in this article we will see how we can achieve this.
           This problem may sound complex, but this is actually quite easy and simple. So, the way we proceed with this is by opening the source file from the program and print out each and every character.
Sounds simple, doesn't it? Lets code it.
Things you need to know for this : Usage of File Pointers

Procedure: Lets break down the approach step by step

1. Open the current file (fopen()) using a file pointer (in Read Mode)
2. Read characters one after the other and print them until you reach the end of the file (EOF)
3. Close the file

To open the file we'll use the fopen() function which takes in two parameters, the location of the file and mode of opening (Read/Write). Fortunately for us, there is a macro in C, to give us file's location, called  _FILE_  macro. Using this we can get the location/path of the current file.

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Things you don't know about scanf()

Scanf() is an inbuilt function in the C Standard libraries. This is used to get input from the user and store that in a variable.

scanf provides inbuilt mechanisms to tackle with de-limiters which many people are not aware of. So, today we'll show you how you can limit your input till a certain symbol.

Consider we have a variable declared as

char a[20];

Reading a String:

To read a string usually we use '%S'. Instead of that we can also use the following way to get the value till you encounter '\n' and discard it.

scanf("%[^\n]\n",a);

This only stores the value till '\n' is encountered and discards it.

To read till a Comma:

Just like the above scenario we will use comma (,) as our delimiter. That would be,

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Factorial of a number in C & C++ | The Simplest Way

Factorial of a number is a very commonly used function and so, not surprisingly programmers run in to this quite often. But, there is no direct function we can call to do this for us. So, each time i have to use this, i end up writing a 5-10 line code using recursion or some other method and then call that for the factorial, which is pretty boring.

But, I have found a very easy way to implement factorial with a built-in function of "math.h" called tgamma(). So, lets see how to implement this program

#include <math.h>
#include <stdio.h>

double fact(double x)
{
  return tgamma(x+1);
}
int main()
{

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How to create Mail merge with Gmail and Google docs?

Mail merge is a very interesting feature in every mail. And, being as popular as it is gmail supports this.
So, What is Mail merge?
Imagine you have a mail that needs to be sent to 50 of your friends/clients. One thing you can do is to add all the mail id's in the 'TO' field and send it.
This sure is one way to do it, but if you want to send a personalized message to each of them, probably something like, Hello Mr.A, for friend A and Hello Mr.B for B and so on. To be able to do this, you need to send the mails individually. Wouldn't it be easier if you can send this to all the persons at once?
Behold the Mail Merge man. He will make this task simple and easy. You just need to write a single mail and that will be sent to each of them with the corresponding personalization.

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C program for Anagram checking

      The below program is to check whether two words are anagrams or not.The concept used is that we store the frequency of an alphabet in a word .By checking those counts we determine whether they are anagrams  or not ....count same == anagrams.

#include <stdio.h>
#include<conio.h>
int anagramchecker(char [],char []); //declaration of funtion
int main()
{
   char word1[50], word2[50];
   printf("Enter first word:\n");
   gets(word1);
   printf("Enter second word:\n");
   gets(word2);
   anagramchecker(word1,word2); //calling function to check anagrams
   getch(); return 0;
}

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Restart Your Computer Using C Program

You can easily do all your typical system operations using C programming. This article will show you how to restart from C programming console. Just Compile and execute the following program.

#include <stdio.h>
#include <stdlib.h>
 main()
{
   char Restart;
 
   printf("Do you want to restart your computer now (y/n)\n");
   scanf("%c",&Restart);
 
   if (Restart== 'y' ||Restart == 'Y')
      system("C:\\windows\\System32\\shutdown /r"); //for windows 7
//  system("C:\\windows\\System32\\shutdown -r"); for windows xp

/* for ubuntu linux
      system("shutdown -"your option") */

   return 0;
}

 This program calls the system function of "stdlib.h" which is used to execute  shutdown.exe which is present in C:\windows\system32 in Windows XP,Windows 7. For more options take a look at this.

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How To Unlock Widgets In Blogger

UNLOCK LOCKED WIDGETS

                 By default most of the widgets in Blogger are not locked , i.e., you can edit them by clicking on 'edit' and even remove them completely (if u want) from the 'LAYOUT' section. But You might have noticed some widgets like Navbar widget, Header widget, Attribution widget by default comes locked. So, You can't edit them directly. Here i'll show you how to UNLOCK them to make them editable and then you can do        what ever you want with them.Here is a easy step wise procedure to help you through till the end.

How To UNLOCK : 

1)Open Your Blogger Dash Board and click on the 'TEMPLATE' button

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Copying folders and files

#include<stdio.h>
#include<stdlib.h>
int main ()
   {
 int i;
 printf ("\n");
 i = system(" XCOPY [source path] [destination path] [options] ");
 printf ("Returned value is: %d.\n",i);
        return 0;
   }

Copy files and/or directories to another folder. XCOPY is similar to the COPY

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Know Your Current Directory | Using C Programming

The below program shows the usage of system() function to list down all the files and directories in the current directory:

#include<stdio.h>
#include<stdlib.h> //system( ) is included in this library

int main ()
    {
       int i;
       printf ("\n");
       i=system ("dir");// on windows
       // i=system ("ls"); on linux or unix platforms
       printf ("Returned value is: %d.\n",i);
       return 0;
    }

Another way

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C Programming Tutorial-5 - Data Types

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System( ) Function in stdlib.h Library

The C library function system() passes the command name or program name specified by a command to the host environment to be executed by the command processor. After the execution of the command it returns a value depending on the status of the execution. 

Declaration: 
To use this function,

 int system( const  char *command )    

         When you call this function, It  will invoke the command processor to execute a command. If the command execution is terminated the processor will trasnfer the control back to the program that has called the system command.

Parameters:
                 String containing name of system command or requested variable. 

Return Value:
            It will return an integer value, but the interpretation of that is system dependant. So, on different systems it gets interpreted differently. If an error occured in the program execution, '-1' is returned, Ootherwise it returns the status of command.

Click here to know how you can restart your computer using system function. 

As, always, Have a happy reading and Stay Awesome !

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