RDD in Spark

RDD, short for Resilient Distributed Dataset, is a crucial concept in Apache Spark. It represents a read-only collection of records that are distributed and partitioned across nodes within a cluster. RDDs can be transformed into other RDDs through various operations, and once created, they are immutable, meaning they cannot be modified. Instead, new RDDs are generated from existing ones.

An RDD is a fundamental abstraction in Spark that addresses the limitations of Hadoop. Unlike Hadoop, which replicates data redundantly across machines for fault tolerance, Spark’s RDDs store data across the cluster’s nodes and can recover lost data using a lineage graph. This approach provides fault tolerance while minimizing data redundancy.There are several ways to create an RDD:
1. Loading an external dataset: 

2. Using the Parallelize method: 

3. Transforming an existing RDD:Table of Contents

Launching Spark-Shell

1. Download and unzip Spark: 

Visit the official Spark website and download the latest version of Spark. Unzip the downloaded file to a desired location on your system.

2. Set up Scala: 

Download Scala from scala-lang.org and install it on your machine. Set the SCALA_HOME environment variable and add the Scala bin directory to your PATH variable.

3. Start the Spark shell: 

Open the command prompt and navigate to the bin folder of the Spark installation directory. Execute the command “spark-shell” to launch the Spark shell, which serves as the driver program for Spark.

Please note that the code snippets provided in this introduction are written in Scala, but RDDs support Python, Java, and Scala objects, including user-defined classes.

1. Loading an external data set: 

Spark provides the `textFile` method in the `SparkContext` class, which can be used to load data from various sources such as Hadoop, HBase, Amazon S3, etc. For example, you can create an RDD by loading a text file using `sc.textFile(“file_path”)`.

2. Parallelizing a collection: 

You can create an RDD by parallelizing an existing collection in your driver program. The `parallelize` method in the `SparkContext` class can be used to convert a collection into an RDD. For example, you can create an RDD from an array of integers using `sc.parallelize(Array(1, 2, 3, 4, 5))`.

3. Transforming an existing RDD: 

You can create a new RDD by applying transformations on an existing RDD. Spark provides various transformation operations like `map`, `filter`, `flatMap`, etc., which can be used to transform the data in an RDD and create a new RDD with the transformed data. For example, you can create a new RDD by applying a `map` transformation on an existing RDD.

4. Caching an RDD: 

If you have an RDD that you want to reuse multiple times in your application, you can cache it in memory using the `cache` method. Caching an RDD allows Spark to store the RDD’s partitions in memory, enabling faster access during subsequent operations on the RDD. For example, you can create an RDD by loading a text file and then cache it using the `cache` method.

5. Reading from other data sources: 

Spark provides APIs to read data from various data sources directly into an RDD. For example, you can use the `jdbc` method to read data from a relational database into an RDD. Similarly, you can use other methods like `cassandraTable`, `hbaseTable`, `jsonFile`, etc., to create RDDs from different data sources.

These are some of the common ways to create RDDs in Spark. Depending on your specific use case and data source, you can choose the appropriate method to create an RDD that suits your needs.

Introduction to Spark DataFrame

Basically A Spark DataFrame is a distributed collection of data that is organized(tabler format columns and rows) into named columns. It provides various operations such as filtering, aggregations, and grouping, and can be seamlessly integrated with Spark SQL. DataFrames can be created from structured data files, existing RDDs, external databases, or Hive tables. They act as an abstraction layer built on top of RDDs, and in later versions of Spark (2.0+), they are followed by the introduction of the dataset API. Unlike datasets, DataFrames were introduced not only in Scala but also in PySpark. They provide a logical columnar format that simplifies working with RDDs and offers the same functions as RDDs. From a conceptual standpoint, DataFrames are similar to relational tables, offering optimization features and techniques for efficient data processing.

Creating a DataFrame can be done using various methods such as utilizing Hive tables, external databases, structured data files, or existing RDDs. These approaches allow for the creation of named columns, forming DataFrames for data processing in Apache Spark. Applications can leverage SQLContext or SparkSession to create DataFrames

Operations on Spark DataFrames:

In Spark, a DataFrame represents a distributed and organized collection of data in named columns, similar to a relational database or a structured data frame in languages like R or Python. It offers enhanced optimizations for efficient data manipulation.

The following are some fundamental operations for structured data processing using DataFrames:

1. Data Input: 

To create a DataFrame, you can read data from a file, such as a JSON file, and the field names will be inferred automatically. Here’s an example:

“`scala

val dfs = sqlContext.read.json("student.json")

“`

2. Displaying Data: 

To visualize the data in a DataFrame, you can use the `show()` command. For instance:

“`scala

dfs.show()

“`

This will present the student data in a tabular format.

3. Examining Schema: 

To view the structure or schema of a DataFrame, you can use the `printSchema()` method. Here’s an example:

“`scala

df.printSchema()

“`

This will output the structure of the DataFrame.

Advantages of spark DataFrame 

DataFrames are distributed collections of data (partition data) where it has organized into named columns. They are resemble tables in relational databases and offer a wide range of optimizations. DataFrames enable SQL queries and can be used for pyspark programming big data processing both structured- data and unstructured-data.

The Catalyst(tungsten)optimizer simplifies and enhances optimization processes. These libraries are available in multiple languages such as Python, Scala, Java, and R.

DataFrames provide seamless compatibility with Hive, allowing unmodified Hive queries to be executed on existing Hive warehouses. They exhibit excellent scalability, ranging from a few kilobytes on personal systems to petabytes on large clusters.

DataFrames facilitate easy integration with various big data technologies and frameworks. The abstraction they offer for RDDs is efficient, resulting in faster processing speeds.

By employing these operations, you can effectively work with DataFrames in Spark and perform various data manipulation tasks.

Read more: what is RDD in pyspak

Percentage

Percent means many hundredths.Example: z% is z percent which means z hundredths. It will be written as:

z% = ^z⁄₁₀₀

^p⁄_q as percent: (^p⁄_q x 100)%

Commodity

If the price of a commodity increases by R%, then the reduction in consumption so as not to increase the expenditure is:[^R⁄_{(100 + R)}x 100]%

If the price of a commodity decreases by R%, then the increase in consumption so as not to decrease the expenditure is:[^R⁄_{(100 – R)}x 100]%

Population

The population of a city is P and let it increases at the rate of R% per annum:

Population after t years: P(1 + ^R⁄₁₀₀)^t

• If P is R% more than Q, then Q is less than P by how many percent?
  [^R⁄_{(100 + R)}x 100]%
• If P is R% more than Q, then Q is more than P by how many percent?
  [^R⁄_{(100 – R)}x 100]%

Population t years ago: ^P⁄_{(1 + ^R⁄100)^t}

Depreciation

Let V be the present value of machine. Suppose it depreciates at the rate of R% per annum:

Machine’s value after t years:P(1 – ^R⁄₁₀₀)^t

Machine’s value t years ago: ^P⁄_{(1 – ^R⁄100)^t}

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