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LEARN APACHE SPARK TUTORIAL 3.5 WITH EXAMPLES

In this Apache Spark Tutorial for Beginners, you will learn Spark version 3.5
with Scala code examples. All Spark examples provided in this Apache Spark
Tutorial for Beginners are basic, simple, and easy to practice for beginners who
are enthusiastic about learning Spark, and these sample examples were tested in
our development environment.

Note: If you can’t find the spark sample code example you are looking for on
this tutorial page, I would recommend using the Search option from the menu bar
to find your tutorial.

Table of Contents

 * What is Apache Spark
 * Features & Advantages
 * Architecture
 * Installation
 * RDD
 * DataFrame
 * SQL
 * Data Sources
 * Streaming
 * GraphFrame

Note that every sample example explained here is available at Spark Examples
Github Project for reference


WHAT IS APACHE SPARK?

Apache Spark Tutorial – Apache Spark is an Open source analytical processing
engine for large-scale powerful distributed data processing and machine learning
applications. Spark was Originally developed at the University of California,
Berkeley’s, and later donated to the Apache Software Foundation. In February
2014, Spark became a Top-Level Apache Project and has been contributed by
thousands of engineers making Spark one of the most active open-source projects
in Apache.

Apache Spark 3.5 is a framework that is supported in Scala, Python, R
Programming, and Java. Below are different implementations of Spark.

 * Spark – Default interface for Scala and Java
 * PySpark – Python interface for Spark
 * SparklyR – R interface for Spark.

Examples explained in this Spark tutorial are with Scala, and the same is also
explained with PySpark Tutorial (Spark with Python) Examples. Python also
supports Pandas which also contains Data Frame but this is not distributed.


FEATURES OF APACHE SPARK

 * In-memory computation
 * Distributed processing using parallelize
 * Can be used with many cluster managers (Spark, Yarn, Mesos e.t.c)
 * Fault-tolerant
 * Immutable
 * Lazy evaluation
 * Cache & persistence
 * Inbuild-optimization when using DataFrames
 * Supports ANSI SQL


ADVANTAGES OF APACHE SPARK

 * Spark is a general-purpose, in-memory, fault-tolerant, distributed processing
   engine that allows you to process data efficiently in a distributed fashion.
 * Applications running on Spark are 100x faster than traditional systems.
 * You will get great benefits from using Spark for data ingestion pipelines.
 * Using Spark we can process data from Hadoop HDFS, AWS S3, Databricks DBFS,
   Azure Blob Storage, and many file systems.
 * Spark also is used to process real-time data using Streaming and Kafka.
 * Using Spark Streaming you can also stream files from the file system and also
   stream from the socket.
 * Spark natively has machine learning and graph libraries.
 * Provides connectors to store the data in NoSQL databases like MongoDB.


WHAT VERSIONS OF JAVA & SCALA SPARK 3.5 SUPPORTS?

Apache Spark 3.5 is compatible with Java versions 8, 11, and 17, Scala versions
2.12 and 2.13, Python 3.8 and newer, as well as R 3.5 and beyond. However, it’s
important to note that support for Java 8 versions prior to 8u371 has been
deprecated starting from Spark 3.5.0.

LANGUAGESUPPORTED VERSIONPython3.8JavaJava 8, 11, 13, 17, and the latest
versions
Java 8 versions prior to 8u371 have been deprecatedScala2.12 and 2.13R3.5

Apache Spark Tutorial – Versions Supported


APACHE SPARK ARCHITECTURE

Spark works in a master-slave architecture where the master is called the
“Driver” and slaves are called “Workers”. When you run a Spark application,
Spark Driver creates a context that is an entry point to your application, and
all operations (transformations and actions) are executed on worker nodes, and
the resources are managed by Cluster Manager.

Source: https://spark.apache.org/

For additional learning on this topic, I would recommend reading the following.

 * What is Spark Job
 * What is the Spark Stage? Explained
 * What is Spark Executor
 * What is Apache Spark Driver?
 * What is DAG in Spark or PySpark
 * What is a Lineage Graph in Spark?
 * How to Submit a Spark Job via Rest API?


CLUSTER MANAGER TYPES

As of writing this Apache Spark Tutorial, Spark supports below cluster managers:

 * Standalone – a simple cluster manager included with Spark that makes it easy
   to set up a cluster.
 * Apache Mesos – Mesons is a Cluster manager that can also run Hadoop MapReduce
   and Spark applications.
 * Hadoop YARN – the resource manager in Hadoop 2. This is mostly used, a
   cluster manager.
 * Kubernetes – an open-source system for automating deployment, scaling, and
   management of containerized applications.

local – which is not really a cluster manager but still I wanted to mention that
we use “local” for master() in order to run Spark on our laptop/computer.


SPARK INSTALLATION

In order to run the Apache Spark examples mentioned in this tutorial, you need
to have Spark and its needed tools to be installed on your computer. Since most
developers use Windows for development, I will explain how to install Spark on
Windows in this tutorial. you can also Install Spark on a Linux server if
needed.

Related: Spark Installation on Mac (macOS)

Download Apache Spark by accessing the Spark Download page and selecting the
link from “Download Spark (point 3)”. If you want to use a different version of
Spark & Hadoop, select the one you wanted from dropdowns, and the link on point
3 changes to the selected version and provides you with an updated link to
download.



After downloading, untar the binary using 7zip and copy the underlying folder
spark-3.5.0-bin-hadoop3 to c:\apps

Now set the following environment variables.


SPARK_HOME  = C:\apps\spark-3.5.0-bin-hadoop3
HADOOP_HOME = C:\apps\spark-3.5.0-bin-hadoop3
PATH=%PATH%;C:\apps\spark-3.0.5-bin-hadoop3\bin


SETUP WINUTILS.EXE

Download wunutils.exe file from winutils, and copy it to %SPARK_HOME%\bin
folder. Winutils are different for each Hadoop version hence download the right
version from https://github.com/steveloughran/winutils


SPARK-SHELL

Spark binary comes with an interactive spark-shell. In order to start a shell,
go to your SPARK_HOME/bin directory and type “spark-shell“. This command loads
the Spark and displays what version of Spark you are using.

spark-shell

By default, spark-shell provides with spark (SparkSession) and sc (SparkContext)
objects to use. Let’s see some examples.

spark-shell create RDD

Spark-shell also creates a Spark context web UI and by default, it can access
from http://localhost:4041.


SPARK-SUBMIT

The spark-submit command is a utility to run or submit a Spark or PySpark
application program (or job) to the cluster by specifying options and
configurations, the application you are submitting can be written in Scala,
Java, or Python (PySpark) code. You can use this utility in order to do the
following.

 1. Submitting Spark applications on different cluster managers like Yarn,
    Kubernetes, Mesos, and Stand-alone.
 2. Submitting Spark application on client or cluster deployment modes


./bin/spark-submit \
  --master <master-url> \
  --deploy-mode <deploy-mode> \
  --conf <key<=<value> \
  --driver-memory <value>g \
  --executor-memory <value>g \
  --executor-cores <number of cores>  \
  --jars  <comma separated dependencies>
  --class <main-class> \
  <application-jar> \
  [application-arguments]



SPARK WEB UI

Apache Spark provides a suite of Web UIs
(Jobs, Stages, Tasks, Storage, Environment, Executors, and SQL) to monitor the
status of your Spark application, resource consumption of the Spark cluster, and
Spark configurations. On Spark Web UI, you can see how the operations are
executed.

Spark Web UI


SPARK HISTORY SERVER

Spark History server, keep a log of all completed Spark application you submit
by spark-submit, spark-shell. before you start, first you need to set the below
config on spark-defaults.conf


spark.eventLog.enabled true
spark.history.fs.logDirectory file:///c:/logs/path


Now, start the Spark history server on Linux or Mac by running.


$SPARK_HOME/sbin/start-history-server.sh


If you are running Spark on Windows, you can start the history server by
starting the below command.


$SPARK_HOME/bin/spark-class.cmd org.apache.spark.deploy.history.HistoryServer


By default, the History server listens at 18080 port and you can access it from
the browser using http://localhost:18080/

Spark History Server

By clicking on each App ID, you will get the details of the application in Spark
web UI.

The history server is very helpful when you are doing Spark performance tuning
to improve spark jobs where you can cross-check the previous application run
with the current run.


SPARK MODULES

 * Spark Core
 * Spark SQL
 * Spark Streaming
 * Spark MLlib
 * Spark GraphX

Spark Modules


SPARK CORE

In this section of the Apache Spark Tutorial, you will learn different concepts
of the Spark Core library with examples in Scala code. Spark Core is the main
base library of Spark which provides the abstraction of how distributed task
dispatching, scheduling, basic I/O functionalities etc.

Before getting your hands dirty on Spark programming, have your Development
Environment Setup to run Spark Examples using IntelliJ IDEA


SPARKSESSION

SparkSession introduced in version 2.0, is an entry point to underlying Spark
functionality in order to programmatically use Spark RDD, DataFrame, and
Dataset. It’s object spark is default available in spark-shell.

Creating a SparkSession instance would be the first statement you would write to
the program with RDD, DataFrame and Dataset. SparkSession will be created
using SparkSession.builder() builder pattern.


// Create SparkSession
import org.apache.spark.sql.SparkSession
val spark:SparkSession = SparkSession.builder()
      .master("local[1]")
      .appName("SparkByExamples.com")
      .getOrCreate()   



SPARK CONTEXT

SparkContext is available since Spark 1.x (JavaSparkContext for Java) and is
used to be an entry point to Spark and PySpark before introducing SparkSession
in 2.0. Creating SparkContext was the first step to the program with RDD and to
connect to Spark Cluster. It’s object sc by default available in spark-shell.

Since Spark 2.x version, When you create SparkSession, SparkContext object is by
default created and it can be accessed using spark.sparkContext

Note that you can create just one SparkContext per JVM but can create many
SparkSession objects.


RDD SPARK TUTORIAL

RDD (Resilient Distributed Dataset) is a fundamental data structure of Spark and
it is the primary data abstraction in Apache Spark and the Spark Core. RDDs are
fault-tolerant, immutable distributed collections of objects, which means once
you create an RDD you cannot change it. Each dataset in RDD is divided into
logical partitions, which can be computed on different nodes of the cluster. 

This Apache Spark RDD Tutorial will help you start understanding and using
Apache Spark RDD (Resilient Distributed Dataset) with Scala code examples. All
RDD examples provided in this tutorial were also tested in our development
environment and are available at GitHub spark scala examples project for quick
reference.

In this section of the Apache Spark tutorial, I will introduce the RDD and
explain how to create them and use their transformation and action operations.
Here is the full article on Spark RDD in case you want to learn more about it
and get your fundamentals strong.


RDD CREATION

RDDs are created primarily in two different ways, first parallelizing an
existing collection and secondly referencing a dataset in an external storage
system (HDFS, HDFS, S3 and many more). 

SPARKCONTEXT.PARALLELIZE()

sparkContext.parallelize is used to parallelize an existing collection in your
driver program. This is a basic method to create RDD.


//Create RDD from parallelize    
val dataSeq = Seq(("Java", 20000), ("Python", 100000), ("Scala", 3000))   
val rdd=spark.sparkContext.parallelize(dataSeq)


SPARKCONTEXT.TEXTFILE()

Using textFile() method we can read a text (.txt) file from many sources like
HDFS, S#, Azure, local e.t.c into RDD.


//Create RDD from external Data source
val rdd2 = spark.sparkContext.textFile("/path/textFile.txt")



RDD OPERATIONS

On Spark RDD, you can perform two kinds of operations.

RDD TRANSFORMATIONS

Spark RDD Transformations are lazy operations meaning they don’t execute until
you call an action on RDD. Since RDDs are immutable, When you run a
transformation(for example map()), instead of updating a current RDD, it returns
a new RDD.

Some transformations on RDDs
are flatMap(), map(), reduceByKey(), filter(), sortByKey() and all these return
a new RDD instead of updating the current.

RDD ACTIONS

RDD Action operation returns the values from an RDD to a driver node. In other
words, any RDD function that returns non RDD[T] is considered as an action. RDD
operations trigger the computation and return RDD in a List to the driver
program.

Some actions on RDDs are count(),  collect(),  first(),  max(),  reduce()  and
more.


RDD EXAMPLES

 * Read CSV file into RDD
 * RDD Pair Functions
 * Generate DataFrame from RDD


DATAFRAME SPARK TUTORIAL WITH BASIC EXAMPLES

DataFrame definition is very well explained by Databricks hence I do not want to
define it again and confuse you. Below is the definition I took from Databricks.

> DataFrame is a distributed collection of data organized into named columns. It
> is conceptually equivalent to a table in a relational database or a data frame
> in R/Python, but with richer optimizations under the hood. DataFrames can be
> constructed from a wide array of sources such as structured data files, tables
> in Hive, external databases, or existing RDDs.
> 
> – Databricks


DATAFRAME CREATION

The simplest way to create a Spark DataFrame is from a seq collection. Spark
DataFrame can also be created from an RDD and by reading files from several
sources.

Related: Spark Word Count Explained with Example

USING CREATEDATAFRAME()

By using createDataFrame() function of the SparkSession you can create a
DataFrame.


// Create DataFrame
val data = Seq(('James','','Smith','1991-04-01','M',3000),
  ('Michael','Rose','','2000-05-19','M',4000),
  ('Robert','','Williams','1978-09-05','M',4000),
  ('Maria','Anne','Jones','1967-12-01','F',4000),
  ('Jen','Mary','Brown','1980-02-17','F',-1)
)

val columns = Seq("firstname","middlename","lastname","dob","gender","salary")
df = spark.createDataFrame(data), schema = columns).toDF(columns:_*)


Since DataFrames are structure format that contains names and column, we can get
the schema of the DataFrame using the df.printSchema()

df.show() shows the 20 elements from the DataFrame.


+---------+----------+--------+----------+------+------+
|firstname|middlename|lastname|dob       |gender|salary|
+---------+----------+--------+----------+------+------+
|James    |          |Smith   |1991-04-01|M     |3000  |
|Michael  |Rose      |        |2000-05-19|M     |4000  |
|Robert   |          |Williams|1978-09-05|M     |4000  |
|Maria    |Anne      |Jones   |1967-12-01|F     |4000  |
|Jen      |Mary      |Brown   |1980-02-17|F     |-1    |
+---------+----------+--------+----------+------+------+


In this Apache Spark SQL DataFrame Tutorial, I have explained several mostly
used operation/functions on DataFrame & DataSet with working Scala examples.

 * Spark DataFrame – Rename nested column
 * How to add or update a column on DataFrame
 * How to drop a column on DataFrame
 * Spark when otherwise usage
 * How to add literal constant to DataFrame
 * Spark Data Types explained
 * How to change column data type
 * How to Pivot and Unpivot a DataFrame
 * Create a DataFrame using StructType & StructField schema
 * How to select the first row of each group
 * How to sort DataFrame
 * How to union DataFrame
 * How to drop Rows with null values from DataFrame
 * How to split single to multiple columns
 * How to concatenate multiple columns
 * How to replace null values in DataFrame
 * How to remove duplicate rows on DataFrame
 * How to remove distinct on multiple selected columns
 * Spark map() vs mapPartitions()


SPARK DATAFRAME ADVANCED CONCEPTS

 * Spark Partitioning, Repartitioning and Coalesce
 * How does Spark shuffle work?
 * Spark Cache and Persistence
 * Spark Persistance Storage levels
 * Spark Broadcast shared variable
 * Spark Accumulator shared variable
 * Spark UDF


SPARK ARRAY AND MAP OPERATIONS

 * How to create an Array (ArrayType) column on DataFrame
 * How to create a Map (MapType) column on DataFrame
 * How to convert an Array to columns
 * How to create an Array of struct column
 * How to explode an Array and map columns
 * How to explode an Array of structs
 * How to explode an Array of map columns to rows
 * How to create a DataFrame with nested Array
 * How to explode nested Arrays to rows
 * How to flatten nested Array to single Array
 * Spark – Convert array of String to a String column


SPARK AGGREGATE

 * How to group rows in DataFrame
 * How to get Count distinct on DataFrame
 * How to add row number to DataFrame
 * How to select the first row of each group


SPARK SQL JOINS

 * Spark SQL Join


SPARK PERFORMANCE

 * Spark Performance Improvement


OTHER HELPFUL TOPICS ON DATAFRAME

 * How to stop DEBUG & INFO log messages
 * Print DataFrame full column contents
 * Unstructured vs semi-structured vs structured files


SPARK SQL SCHEMA & STRUCTTYPE

 * How to convert case class to a schema
 * Spark Schema explained with examples
 * How to create array of struct column
 * Spark StructType & StructField
 * How to flatten nested column


SPARK SQL FUNCTIONS

Spark SQL provides several built-in functions, When possible try to leverage the
standard library as they are a little bit more compile-time safe, handle null,
and perform better when compared to UDF. If your application is critical on
performance try to avoid using custom UDF at all costs as these are not
guaranteed on performance.

In this section, we will see several Spark SQL functions Tutorials with Scala
examples.

 * Spark Date and Time Functions
 * Spark String Functions
 * Spark Array Functions
 * Spark Map Functions
 * Spark Aggregate Functions
 * Spark Window Functions
 * Spark Sort Functions


SPARK DATA SOURCE WITH EXAMPLES

Spark SQL supports operating on a variety of data sources through the DataFrame
interface. This section of the tutorial describes reading and writing data using
the Spark Data Sources with Scala examples. Using Data source API we can load
from or save data to RDMS databases, Avro, parquet, XML etc.


TEXT

 * Spark process Text file
 * How to process JSON from a Text file


CSV

 * How to process CSV file
 * How to convert Parquet file to CSV file
 * How to process JSON from a CSV file
 * How to Convert Avro file to CSV file
 * How to convert CSV file to Avro, Parquet & JSON


JSON

JSON’s readability, flexibility, language-agnostic nature, and support for
semi-structured data make it a preferred choice in big data Spark applications
where diverse sources, evolving schemas, and efficient data interchange are
common requirements.

Key characteristics and reasons why JSON is used in big data include
Human-Readable Format, Language-Agnostic, Semi-Structured Data, Schema Evolution
e.t.c

 * JSON Example (Read & Write)
 * How to Read JSON from multi-line
 * How to read JSON file with custom schema
 * How to process JSON from a CSV file
 * How to process JSON from a Text file
 * How to convert Parquet file to JSON file
 * How to convert Avro file to JSON file
 * How to convert JSON to Avro, Parquet, CSV file


PARQUET

 * Parquet Example (Read and Write)
 * How to convert Parquet file to CSV file
 * How to convert Parquet file to Avro file
 * How to convert Avro file to Parquet file


AVRO

 * Avro Example (Read and Write)
 * Spark 2.3 – Apache Avro Example
 * How to Convert Avro file to CSV file
 * How to convert Parquet file to Avro file
 * How to convert Avro file to JSON file
 * How to convert Avro file to Parquet file


ORC

 * Spark Read & Write ORC


XML

 * Processing Nested XML structured files
 * How to validate XML with XSD


HIVE & TABLES

 * Spark Save DataFrame to Hive Table
 * Spark JDBC Parallel Read
 * Read JDBC Table to Spark DataFrame
 * Spark saveAsTable() with Examples
 * Spark Query Table using JDBC
 * Spark Read and Write MySQL Database Table
 * Spark with SQL Server – Read and Write Table
 * Spark spark.table() vs spark.read.table()


SQL SPARK TUTORIAL

Spark SQL is one of the most used Spark modules which is used for processing
structured columnar data format. Once you have a DataFrame created, you can
interact with the data by using SQL syntax. In other words, Spark SQL brings
native RAW SQL queries on Spark meaning you can run traditional ANSI SQL on
Spark Dataframe. In the later section of this Apache Spark tutorial, you will
learn in detail using SQL select, where, group by, join, union e.t.c

In order to use SQL, first, we need to create a temporary table on DataFrame
using createOrReplaceTempView() function. Once created, this table can be
accessed throughout the SparkSession and it will be dropped along with your
SparkContext termination.

On a table, SQL query will be executed using sql() method of the SparkSession
and this method returns a new DataFrame.


df.createOrReplaceTempView("PERSON_DATA")
val df2 = spark.sql("SELECT * from PERSON_DATA")
df2.printSchema()
df2.show()


Let’s see another example using group by.


val groupDF = spark.sql("SELECT gender, count(*) from PERSON_DATA group by gender")
groupDF.show()


This yields the below output


+------+--------+
|gender|count(1)|
+------+--------+
|     F|       2|
|     M|       3|
+------+--------+


Similarly, you can run any traditional SQL queries on DataFrames using Spark
SQL.


SPARK HDFS & S3 TUTORIAL

 * Processing files from Hadoop HDFS (TEXT, CSV, Parquet, Avro, JSON)
 * Processing TEXT files from Amazon S3 bucket
 * Processing JSON files from Amazon S3 bucket
 * Processing CSV files from Amazon S3 bucket
 * Processing Parquet files from Amazon S3 bucket
 * Processing Avro files from Amazon S3 bucket


SPARK STREAMING TUTORIAL & EXAMPLES

Spark Streaming is a scalable, high-throughput, fault-tolerant streaming
processing system that supports both batch and streaming workloads. It is used
to process real-time data from sources like file system folders, TCP
sockets, S3, Kafka, Flume, Twitter, and Amazon Kinesis to name a few. The
processed data can be pushed to databases, Kafka, live dashboards e.t.c

source: https://spark.apache.org/
 * Spark Streaming – OutputModes Append vs Complete vs Update
 * Spark Streaming – Read JSON Files From Directory with Scala Example
 * Spark Streaming – Read data From TCP Socket with Scala Example
 * Spark Streaming – Consuming & Producing Kafka messages in JSON format
 * Spark Streaming – Consuming & Producing Kafka messages in Avro format
 * Using from_avro and to_avro functions
 * Reading Avro data from Kafka topic using from_avro() and to_avro()
 * Spark Batch Processing using Kafka Data Source


SPARK WITH KAFKA TUTORIALS

 * Spark Streaming – Consuming & Producing Kafka messages in JSON format
 * Spark Streaming – Consuming & Producing Kafka messages in Avro format
 * Using from_avro and to_avro functions
 * Reading Avro data from Kafka topic using from_avro() and to_avro()
 * Spark Batch Processing using Kafka Data Source


SPARK – HBASE TUTORIALS & EXAMPLES

In this section of the Spark Tutorial, you will learn several Apache HBase spark
connectors and how to read an HBase table to a Spark DataFrame and write
DataFrame to HBase table.

Apache HBase is an open-source, distributed, and scalable NoSQL database that
runs on top of the Hadoop Distributed File System (HDFS). It provides real-time
read and write access to large datasets and is designed for handling massive
amounts of unstructured or semi-structured data, making it suitable for big data
applications.

 * Spark HBase Connectors explained
 * Writing Spark DataFrame to HBase table using shc-core Hortonworks library
 * Creating Spark DataFrame from Hbase table using shc-core Hortonworks library


SPARK – HIVE TUTORIALS

In this section, you will learn what is Apache Hive and several examples of
connecting to Hive, creating Hive tables, reading them into DataFrame

Apache Hive is a data warehousing and SQL-like query language tool built on top
of Hadoop. It facilitates querying and managing large datasets stored in Hadoop
Distributed File System (HDFS) using a familiar SQL syntax. Hive translates
SQL-like queries into MapReduce or Apache Tez jobs, enabling users without
extensive programming skills to analyze big data. It supports schema-on-read,
allowing flexible data structures, and integrates with HBase and other Hadoop
ecosystem components. Hive is particularly useful for batch processing, data
summarization, and analysis tasks, making big data analytics accessible to a
broader audience within the Apache Hadoop ecosystem.

 * Start HiveServer2 and connect to hive beeline


SPARK GRAPHX AND GRAPHFRAMES

Spark GraphFrames are introduced in Spark 3.0 version to support Graphs on
DataFrames. Prior to 3.0, Spark had GraphX library which ideally runs on RDD,
and lost all Data Frame capabilities.

GraphFrames is a graph processing library for Apache Spark that provides
high-level abstractions for working with graphs and performing graph analytics.
It extends Spark’s DataFrame API to support graph operations, allowing users to
express complex graph queries using familiar DataFrame operations.

Below is an example of how to create and use Spark GraphFrame.


// Import necessary libraries
import org.apache.spark.sql.SparkSession
import org.graphframes.GraphFrame

// Create a Spark session
val spark = SparkSession.builder.appName("GraphFramesExample").getOrCreate()

// Define vertices and edges as DataFrames
val vertices = spark.createDataFrame(Seq(
  (1, "Scott", 30),
  (2, "David", 40),
  (3, "Mike", 45)
)).toDF("id", "name", "age")

val edges = spark.createDataFrame(Seq(
  (1, 2, "friend"),
  (2, 3, "follow")
)).toDF("src", "dst", "relationship")

// Create a GraphFrame
val graph = GraphFrame(vertices, edges)

// Display vertices and edges
graph.vertices.show()
graph.edges.show()

// Perform Graph Queries
val aliceFriends = graph.edges.filter("src = 1").join(graph.vertices, "dst").select("dst", "name")
aliceFriends.show()

// Graph Analytics - In-degrees
val inDegrees = graph.inDegrees
inDegrees.show()

// Subgraph Creation
val subgraph = graph.filterVertices("age >= 40").filterEdges("relationship = 'friend'")
subgraph.vertices.show()
subgraph.edges.show()

// Graph Algorithms - PageRank
val pageRankResults = graph.pageRank.resetProbability(0.15).maxIter(10).run()
pageRankResults.vertices.show()
pageRankResults.edges.show()

// Stop the Spark session
spark.stop()



WHAT ARE THE KEY FEATURES AND IMPROVEMENTS RELEASED IN SPARK 3.5.0

Following are some of the key features and improvements in Spark 3.5

 * Spark Connect: This release extends the general availability of Spark Connect
   with support for Scala and Go clients, distributed training and inference
   support, and enhanced compatibility for Structured streaming.
 * PySpark and SQL Functionality: New functionality has been introduced in
   PySpark and SQL, including the SQL IDENTIFIER clause, named argument support
   for SQL function calls, SQL function support for HyperLogLog approximate
   aggregations, and Python user-defined table functions.
 * Distributed Training with DeepSpeed: The release simplifies distributed
   training with DeepSpeed, making it more accessible.
 * Structured Streaming: It introduces watermark propagation among operators and
   dropDuplicatesWithinWatermark operations in Structured Streaming, enhancing
   its capabilities.
 * English SDK: Apache Spark for English SDK integrates the extensive expertise
   of Generative AI in Apache Spark.

REFERENCES:

 * https://spark.apache.org/
 * https://databricks.com/spark/about
 * https://github.com/apache/spark

--------------------------------------------------------------------------------

Author

Naveen

Naveen (NNK) is a Data Engineer with 20+ years of experience in transforming
data into actionable insights. Over the years, He has honed his expertise in
designing, implementing, and maintaining data pipelines with frameworks like
Apache Spark, PySpark, Pandas, R, Hive and Machine Learning. Naveen journey in
the field of data engineering has been a continuous learning, innovation, and a
strong commitment to data integrity. In this blog, he shares his experiences
with the data as he come across. Follow Naveen @ LinkedIn LinkedIn

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