Spark Training- Post Test

In-memory computing is the reason why Spark is 100x faster than MapReduce. By keeping the data in memory, Spark eliminates the need to read and write data from disk, which significantly speeds up data processing. This allows Spark to perform operations much faster than MapReduce, which relies heavily on disk I/O. By leveraging the power of in-memory computing, Spark is able to achieve impressive performance gains and process large datasets more efficiently.

All of the statements are correct. Spark is designed to run on top of Hadoop and can process data stored in HDFS. It can also use Yarn as a resource management layer, which allows for efficient allocation of resources and scheduling of tasks in a Hadoop cluster. Therefore, all three statements are true.

Caching is indeed an optimizing technique. It involves storing frequently accessed data or resources in a cache, which is a high-speed memory or storage system. By doing so, the system can retrieve the data or resources more quickly, reducing the need to access slower or more resource-intensive components. This can greatly improve the performance and efficiency of a system, making caching an effective optimization technique.

The features of Spark RDD include in-memory computation, lazy evaluations, and fault tolerance. In-memory computation allows Spark to store data in memory, which significantly speeds up data processing. Lazy evaluations enable Spark to optimize the execution of transformations on RDDs by postponing their execution until an action is called. Fault tolerance ensures that if a node fails, Spark can recover the lost data and continue processing without any disruption. Therefore, the correct answer is "All of the above."

The SparkContext is the entry point for accessing the Spark cluster. It is responsible for coordinating the execution of tasks and distributing data across the cluster. It provides methods for creating RDDs (Resilient Distributed Datasets) and performing operations on them. Therefore, it guides how to access the Spark cluster, making the answer TRUE.

The Spark Engine performs multiple tasks including scheduling, distributing data across a cluster, and monitoring data across the cluster. It is responsible for managing the execution of Spark applications, allocating resources, and coordinating tasks across the cluster. By handling these tasks, the Spark Engine enables efficient and parallel processing of large datasets, making it a powerful tool for big data analytics and processing.

The code creates a list called "lyrics" with 4 elements: "all", "that", "i", and "know". The "println" statement prints the size of the list, which is 4.

Apache Spark is a powerful data processing framework that can handle various types of processing tasks. It supports batch processing, which involves processing large volumes of data in a scheduled manner. It also supports stream processing, which involves processing real-time data as it arrives. Additionally, Apache Spark can handle graph processing, which involves analyzing and processing graph-based data structures. Lastly, it supports interactive processing, which involves querying and analyzing data interactively in real-time. Therefore, the correct answer is "All of the above" as Apache Spark is capable of handling all these types of processing.

Apache Spark has APIs in Java, Scala, and Python. This means that developers can use any of these programming languages to interact with and manipulate data in Apache Spark. The availability of multiple APIs allows developers to choose the language they are most comfortable with, making it easier for them to work with Spark and perform tasks such as data analysis, machine learning, and distributed processing.

The given answer "All the above" is correct because all the mentioned options - count, first, take(n), and collect - are actions that can be performed on a DataFrame. These actions are used to retrieve or manipulate data from the DataFrame. The count action returns the number of rows in the DataFrame, the first action returns the first row, the take(n) action returns the first n rows, and the collect action retrieves all the rows from the DataFrame. Therefore, all the mentioned options are valid DataFrame actions.

The correct answer is df.printSchema(). This is because the printSchema() function is a method in Spark DataFrame that prints the schema of the DataFrame in a tree format. It displays the column names and their corresponding data types, providing a concise overview of the structure of the DataFrame.

The correct answer is "All of the above" because the question is asking to identify the correct transformation, and all three options - Map, Filter, and Join - are valid transformations in data processing. Map is used to transform each element in a dataset, Filter is used to select specific elements based on a condition, and Join is used to combine two datasets based on a common key. Therefore, all three transformations can be used depending on the specific requirements of the data processing task.

RDD stands for Resilient Distributed Dataset, which is a fundamental data structure in Apache Spark. It is not a database or a programming paradigm. RDD is a distributed data structure that allows data to be processed in parallel across a cluster of computers. RDDs are fault-tolerant and can be cached in memory, which enables faster processing. They provide a high-level abstraction for distributed data processing and are a key component in Spark's computational model.

Apache Spark can potentially run batch-processing programs 100 times faster than MapReduce when processed in memory. This is because Spark is designed to store data in memory, which allows for faster data processing and eliminates the need to read and write data from disk, as in the case of MapReduce. Additionally, Spark utilizes a directed acyclic graph (DAG) execution engine, which optimizes the execution plan and minimizes the overhead of data shuffling. These factors contribute to the significant speed improvement of Spark over MapReduce.

In the production world, tasks are primarily launched on the Yarn cluster. Yarn is a distributed processing framework that allows for efficient resource management and job scheduling in Hadoop. It provides a flexible and scalable platform for running various types of applications, including MapReduce, Spark, and Hive. Yarn's ability to handle large workloads and optimize resource utilization makes it the preferred choice for launching tasks in the production environment.

The given code initializes a list of numbers [11, 22, 33] and a variable total with the value 0. It then iterates over each element in the list using a for loop and adds each element to the total. Finally, it prints the value of total, which is 66.

Spark supports all of the above cluster managers, which include Standalone Cluster Manager, Mesos, and YARN. This means that Spark can be deployed and run on any of these cluster managers, providing flexibility and compatibility with different environments and infrastructures.

The given code defines a function called "min" which takes two parameters (a and b) and returns the smaller value between them. In this case, the function is called with arguments 78 and 44, so it will return 44. The "println" statement then prints the returned value, which is 44.

The default block size in Hadoop 2 is 128MB. This means that when data is stored in Hadoop, it is divided into blocks of this size. Each block is then distributed across the cluster for processing. The default block size of 128MB is chosen to strike a balance between efficient storage utilization and parallel processing. It allows for optimal performance by ensuring that each block can be processed independently by a single node in the cluster.

The correct answer is "only one" because in Apache Spark, there can only be one active Spark Context per job. A Spark Context represents the entry point to the Spark cluster and coordinates the execution of tasks. Having multiple active Spark Contexts can lead to conflicts and inconsistencies in the execution environment. Therefore, it is recommended to have only one active Spark Context at a time.

Dataframes are immutable, meaning that once they are created, their contents cannot be changed. This ensures data integrity and prevents accidental modifications to the dataframe. If any changes need to be made to a dataframe, a new dataframe must be created with the desired modifications. This immutability property also allows for easier debugging and reproducibility, as the original dataframe remains unchanged throughout the data processing pipeline.

The default storage level of cache() is MEMORY_ONLY. This means that the RDD will be stored in memory as deserialized Java objects. This storage level provides fast access to the data but does not persist it on disk. If the memory is not sufficient to store the entire RDD, some partitions may be evicted and recomputed on the fly when needed.

RDD (Resilient Distributed Dataset) is a fundamental data structure in Apache Spark. It is immutable, meaning that once created, its data cannot be modified. RDDs are also recomputable, which means that if a node fails, the RDD can be reconstructed from the lineage information. Finally, RDDs are fault-tolerant, as they automatically recover from failures. Therefore, the correct answer is "All of the above" as RDDs possess all these characteristics.

The given code creates two lists, "numbers" and "letters", and then parallelizes them using the SparkContext "sc". It then combines the two RDDs using the "union" function, which concatenates the elements of both RDDs. Finally, it prints the combined RDD, which will be "List(one, two, a, b)".

The code creates a Map called "simple" with two key-value pairs: "r" -> "red" and "g" -> "green". The code then prints the value associated with the key "g" in the map, which is "green". Therefore, the code will print "green".

Spark can use Yarn, Mesos, and Standalone cluster manager for resource management. Yarn is a popular choice for managing resources in Hadoop clusters, while Mesos is a distributed systems kernel that can also handle resource allocation. Additionally, Spark can run in a standalone cluster manager mode where it manages its own resources. Therefore, the correct answer is "All of the above" as Spark provides the flexibility to use any of these options for resource management based on the specific requirements and infrastructure of the system.

Both A and B are correct because in Scala, there are two types of variables: var and val. The var keyword is used to declare mutable variables, which means their values can be changed. On the other hand, the val keyword is used to declare immutable variables, whose values cannot be changed once assigned. In the given code, myVar is a var variable and myVal is a val variable, so both types of variables are present.

Data transformations are executed lazily. This means that the transformations are not immediately performed when the code is executed, but rather when the result is needed or requested. Laziness allows for more efficient execution as only the necessary transformations are performed, reducing unnecessary computation. It also enables the use of lazy evaluation strategies, such as memoization, which can further optimize the execution of data transformations.

The Spark session variable was introduced in Spark 2.0. This release of Spark introduced the concept of a Spark session, which is the entry point for interacting with Spark functionality and allows for managing various Spark configurations and settings. Prior to Spark 2.0, users had to create a SparkContext object to interact with Spark, but the introduction of the Spark session simplified the process and provided a more user-friendly interface.

Parquet is a file format that provides optimized binary storage of structured data. It is designed to efficiently store and process large amounts of data. Parquet uses columnar storage, which allows for efficient compression and encoding techniques to be applied to individual columns, resulting in reduced storage space and improved query performance. This makes Parquet an ideal choice for big data processing frameworks like Apache Hadoop and Apache Spark.

Spark is developed in Scala. Scala is a programming language that runs on the Java Virtual Machine (JVM) and combines object-oriented and functional programming concepts. Spark was originally written in Scala because Scala provides concise syntax and strong support for functional programming, making it well-suited for building distributed data processing systems like Spark. However, Spark also provides APIs in other languages like Java, Python, and R, allowing developers to use Spark with their preferred programming language.

The default replication factor refers to the number of copies of data that are automatically created and stored across different nodes in a distributed system. In this case, the correct answer is 3, which means that by default, data is replicated three times to ensure fault tolerance and high availability. This replication factor helps in maintaining data integrity and durability, as it allows for data recovery in case of node failures or data corruption.

The code initializes a variable "aa" with the value "hello". Then it assigns the value "pretty" to the variable "aa". Finally, it prints the value of "aa", which is "pretty".

The correct answer is "both a and b" because both "select" and "filter" are common DataFrame transformations. The "select" transformation is used to select specific columns from a DataFrame, while the "filter" transformation is used to filter rows based on a condition. Therefore, both a and b are valid options for common DataFrame transformations.

The code will print "true" because it is using the logical AND operator (&&) to check if both conditions are true. The first condition, 5

The correct answer is mydf.distinct.count() because the distinct() function is used to remove duplicate records from a dataframe, and the count() function is used to get the total number of records in the dataframe after removing duplicates. This combination of distinct() and count() will give the count of distinct records in the dataframe.

Spark's core is a batch engine. This means that Spark is designed to process large amounts of data in batches rather than in real-time. It allows for efficient and parallel processing of data by dividing it into smaller chunks called batches. This batch processing approach is suitable for tasks such as data analytics, machine learning, and data transformations where processing large volumes of data at once is more efficient than processing individual records in real-time. Therefore, the statement "Spark's core is a batch engine" is true.

The HashPartitioner is the default Partitioner for partitioning key space. This means that when data is being distributed across partitions, the HashPartitioner is used to determine which partition a specific key should be assigned to. The HashPartitioner calculates a hash value for each key and then uses this value to determine the partition. This ensures an even distribution of keys across partitions, making it an efficient and balanced way to partition the key space.

Kafka maintains feeds of messages in categories called "topics". Topics in Kafka are used to organize and categorize messages, allowing for efficient and scalable message processing. Producers write messages to specific topics, and consumers can subscribe to one or more topics to consume the messages. Topics enable Kafka to handle large amounts of data and distribute it across multiple brokers in a fault-tolerant manner.

The show() method in a dataframe will display the first few rows in tabular format.

Both MapReduce and Spark do not have their own file system. They rely on external file systems such as Hadoop Distributed File System (HDFS) or any other compatible file system for storing and accessing data. MapReduce uses HDFS for data storage and retrieval, while Spark can work with various file systems including HDFS, Amazon S3, and local file systems.

Transformation in Spark RDD refers to the operations that are performed on an RDD to create a new RDD. These operations are lazily evaluated, meaning they are not executed immediately but rather when an action is called. The transformation takes an RDD as input and produces one or more RDDs as output. Examples of transformations include map, filter, and reduceByKey. These transformations allow for the transformation of data in a distributed and parallel manner, enabling efficient data processing in Spark.

HBase is a distributed database built on top of the Hadoop file system, and it is specifically designed to be column-oriented. This means that data is stored and retrieved based on columns rather than rows. This design allows for efficient querying and processing of large datasets, making it suitable for big data applications.

RDD stands for Resilient Distributed Dataset. It is a fundamental data structure in Spark that represents an immutable distributed collection of objects. RDDs are fault-tolerant and can be processed in parallel across a cluster of machines. They provide a high-level abstraction for performing distributed data processing tasks in Spark. RDDs are resilient, meaning they can recover from failures, and distributed, meaning they can be processed in parallel across multiple nodes. RDDs are the building blocks of Spark applications and provide a way to perform efficient and scalable data processing.

The correct answer is "mydf.rdd" because the ".rdd" method is used to convert a DataFrame to a Resilient Distributed Dataset (RDD) in Apache Spark. RDD is the fundamental data structure in Spark, and converting a DataFrame to RDD allows for lower-level operations and more flexibility in data processing.

The correct answer is SparkSession (spark). In Spark 2.0, SparkSession is the entry point of Spark SQL. SparkSession provides a single point of entry for interacting with Spark SQL and it encapsulates the functionality of SparkContext, SQLContext, and HiveContext. It allows users to easily create DataFrames, execute SQL queries, and access various Spark SQL features. Therefore, both options a and b are correct.

The given code defines a variable called "number" and assigns it a value using a closure. The closure calculates the value of "x" as 2 multiplied by 2, which is 4. Then, it adds 40 to "x", resulting in a final value of 44. The "println" statement then prints the value of "number", which is 44.

The correct answer is "Execution starts with the call of Action." In Spark, transformations are lazily evaluated, meaning they are not executed immediately when called. Instead, they create a plan of execution that is only triggered when an action is called. Actions are operations that trigger the execution of the transformations and produce a result or output. Therefore, the execution of a Spark program begins when an action is called, not when a transformation is called.

Scala has a powerful type inference system that allows the type of a variable to be determined by looking at its value. This means that in many cases, the data type of a variable does not need to be explicitly mentioned. The compiler analyzes the value assigned to the variable and infers its type based on that. This feature of Scala makes the code more concise and reduces the need for explicit type declarations, leading to cleaner and more expressive code.

Datasets are abstractions for representing structured data, along with DataFrames. Both DataFrames and Datasets are used in Apache Spark to handle structured data. While DataFrames provide a high-level API and are optimized for performance, Datasets provide a type-safe, object-oriented programming interface. Datasets combine the benefits of both DataFrames and RDDs, allowing for strong typing and providing a more efficient execution engine. Therefore, the correct answer is Datasets.