35+ [FREQUENTLY ASK]: Pyspark Interview Questions & Answers
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35+ [FREQUENTLY ASK]: Pyspark Interview Questions & Answers

Last updated on 20th Jun 2020, Blog, Interview Questions

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The large data processing and analytics platform Apache Spark is free and open-source. PySpark is the Python library for Apache Spark, which enables you to utilize the strength of Spark’s distributed computing capabilities with the simplicity and adaptability of the Python programming language.

1. What is PySpark?

Ans:

PySpark is Apache Spark interface in a Python. It is used for collaborating with the  Spark using APIs written in Python.PySpark supports to reading data from the multiple sources and different formats.

2. What are the characteristics of PySpark?

Ans:

 Abstracted Nodes: This means that individual worker nodes can not be addressed.

Spark API: PySpark provides the APIs for utilizing  a Spark features.

Map-Reduce Model: PySpark is based on the Hadoop’s Map-Reduce model this means that programmer provides the map and  reduce functions.

Abstracted Network: Networks are abstracted in a PySpark which means that only possible communication is  an implicit communication.

3. What are the advantages PySpark?

Ans:

Error Handling: PySpark framework simply  handles the errors.

Inbuilt Algorithms: PySpark provides more of the useful algorithms in a Machine Learning or Graphs.

Library Support: Compared to be Scala, Python has huge library collection for working in  field of data science and data visualization.

 Easy to Learn: PySpark is an simple  to learn language.

4. What are the disadvantages of PySpark?

Ans:

Sometimes, it becomes complex to express problems using MapReduce model.

The Spark Streaming API in the PySpark is not mature when compared to be Scala. It still requires the improvements.  PySpark cannot be used for modifying internal function of a Spark due to the abstractions provided.

5. What is Spark Context?

Ans:

PySpark SparkContext is initial entry point of a spark functionality. It also represents the  Spark Cluster Connection and can be used for creating Spark RDDs (Resilient Distributed Datasets) and broadcasting variables on the cluster.

6. Why do use PySpark SparkFiles?

Ans:

PySpark’s SparkFiles are used for loading files onto Spark application. SparkFiles can also be used for getting path using the SparkFiles.get() method. It can be used to resolve paths to files added for using the sc.addFile() method.

7. What are PySpark serializers?

Ans:

The serialization process is used to conduct the  performance tuning on a Spark. The data sent or received over network to the disk or memory should be persisted. PySpark supports the  serializers for this purpose.

Serializer Process

8. What are the types of serializers?

Ans:

 PickleSerializer: This serializes objects using the Python’s PickleSerializer (class pyspark.PickleSerializer). This supports almost each  Python object.

MarshalSerializer: This performs serialization of the objects. can use it by using the class pyspark.

9. What are RDDs in PySpark?

Ans:

RDDs expand to the  Resilient Distributed Datasets. These are elements that are used for running and operating on the multiple nodes to perform parallel processing on cluster. Since RDDs are suited for a parallel processing, they are immutable elements.

10. What are the different cluster manager types supported by PySpark?

Ans:

Standalone: This is the  simple cluster manager that is included with the Spark.

 Apache Mesos:This manager can run the  Hadoop MapReduce and PySpark apps.

Hadoop YARN : This manager is used in a Hadoop2.

Kubernetes: This is open-source cluster manager that helps in the  automated deployment, scaling and automatic management of a containerized apps.

11. What advantages does PySpark RDD offer?

Ans:

In-Memory Processing: PySpark’s RDD helps in loading a data from a disk to the memory.

Parallel Processing: RDDs are partitioned across a cluster, enabling parallel processing of data. 

Ease of Use: RDDs provide a high-level API that abstracts the complexity of distributed computing. 

Fault Tolerance: The RDDs are the fault-tolerant. This means that whenever operation fails, the data gets automatically reloaded from the other available partitions

12. Is PySpark faster than pandas?

Ans:

PySpark supports the parallel execution of statements in the  distributed environment, i.e on different cores and various  machines which are not present in Pandas. This is PySpark is faster than a  pandas.

13. What do you understand about PySpark DataFrames?

Ans:

PySpark DataFrame is the  distributed collection of a well-organized data that is equivalent to tables of relational databases and are placed into the named columns. PySpark DataFrame has a better optimisation when compared to the R or python. 

14. What is SparkSession in Pyspark?

Ans:

SparkSession is  an  entry point to PySpark and is replacement of SparkContext since PySpark version 2.0. This acts as starting point to access all of the PySpark functionalities related to the RDDs, DataFrame, Datasets etc. It is also Unified API that is used in replacing  SQLContext, StreamingContext, HiveContext and all the  other contexts.

15. Explain the types of PySpark’s shared variables.

Ans:

Accumulator variables : These variables are called the updatable shared variables. They are added through the associative and commutative operations and are used for performing a counter or sum operations. 

Named Accumulators: These accumulators are the  visible under the “Accumulator” tab in  PySpark web UI

Unnamed Accumulators: The PySpark Web UI page does not display these accumulators. It is generally advisable to utilize named accumulators.

16. What is PySpark UDF?

Ans:

The User Defined Function udf() acts as function wrapper for a Python functions to use on the DataFrames and SQL. UDFs are used to expand framework’s functions to re-use them across the various DataFrames. If want to perform an operation on the data structure and PySpark doesn’t have that function, may write it as UDF and reuse it as many times on the multiple DataFrames.

17. What is PySpark Architecture?

Ans:

PySpark similar to the Apache Spark works in master-slave architecture pattern. Here, the master node is called Driver and the slave nodes are called the workers. When Spark application is run, the Spark Driver creates the SparkContext which acts as entry point to  spark application.

18. What is PySpark DAGScheduler?

Ans:

DAG stands for  a Direct Acyclic Graph. DAGScheduler constitutes scheduling layer of Spark which implements the scheduling of tasks in a stage-oriented manner using jobs and stages. 

19. What is the common workflow of spark program?

Ans:

  • The first step is to create an input RDDs depending on external data. A Data can be obtained from the various  data sources.
  • Post RDD creation, the RDD transformation operations  are  filter() or map() are run for creating a new RDDs depending on business logic.
  • If any intermediate RDDs are needed to be reused for a later purposes, and  can persist those RDDs.

20. Why is PySpark SparkConf  used?

Ans:

PySpark SparkConf is used for setting configurations and parameters required to run the  applications on cluster or local system.

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    21. How will you create PySpark UDF?

    Ans:

    To create a PySpark User-Defined Function (UDF), you first import the necessary modules from the pyspark.sql package, including udf and StructType. Then, define your custom Python function that performs the desired transformation on DataFrame columns.

    22. What are the profilers in PySpark?

    Ans:

    In a PySpark, custom profilers are supported. These can be used to create prediction models. Profilers are helpful for reviewing data to make sure it is accurate and suitable for consumption. 

    23. How to create the SparkSession?

    Ans:

    To create SparkSession, use the builder pattern. The SparkSession class from  pyspark.sql library has getOrCreate() method which creates the  new SparkSession if there is none or else it returns an  existing SparkSession object.

    Spark Session

    24. Is it possible to create a PySpark DataFrame from external data sources?

    Ans:

    Yes, it is absolutely possible to create a PySpark DataFrame from external data sources. PySpark provides built-in support for reading data from a wide range of external sources and converting that data into DataFrame objects for further processing and analysis.

    25. What do you understand by Pyspark’s startsWith() and endsWith() methods?

    Ans:

    PySpark’s startsWith() and endsWith() methods are convenient functions for filtering and selecting rows in DataFrames based on specific string conditions. startsWith() allows you to filter rows where a column’s value begins with a specified substring, while endsWith() filters rows where a column’s value ends with a given substring.

    26. What is PySpark SQL?

    Ans:

    PySpark SQL is an Apache Spark component that provides a programming interface for dealing with structured and semi-structured data using SQL (Structured Query Language) queries and the DataFrame API.

    27. What are the transformations and actions in PySpark?

    Ans:

    Transformations in a PySpark are operations that create new RDD from an existing one. Examples of a transformations include map(), filter(), and distinct(). Actions, on other hand, are operations that return the  value to the driver program or write a data to an external storage system. Examples of the actions include the count(), collect(), and saveAsTextFile().

    28. What do you understand by Pyspark Streaming?

    Ans:

    PySpark Streaming is a highly scalable, fault-tolerant, high throughput processing streaming system that supports both streaming and batch loading for real-time data from data sources such as TCP Socket, S3, Kafka, Twitter, file system folders, and so on.

    29. What happen if lose RDD partitions due to the failure of worker node?

    Ans:

    If any RDD partition is lost, then partition can be recomputed using the operations lineage from original fault-tolerant dataset.

    30. Why do we employ PySpark SparkFiles?

    Ans:

    We employ PySpark’s SparkFiles utility to distribute external files and resources to worker nodes within a Spark cluster. This is essential for ensuring that all nodes have access to the necessary files and dependencies required for tasks such as data preprocessing, custom libraries, configuration files, or lookup tables.

    31.  Why are partitions immutable in PySpark?

    Ans:

    In PySpark, each  transformation generates new partition. Partitions use the HDFS API to make partitions immutable, distributed, and fault-tolerant. A Partitions are aware of  a data locality.

    32. What is the usage of PySpark  Storage Level?

    Ans:

    The PySpark Storage Level is used to control a  storage of RDD. It controls how and where  RDD is stored. PySpark StorageLevel decides if RDD is stored on the memory, over disk, or both. It also specifies whether need to replicate RDD partitions or serialize the RDD.

    33. What is data cleaning?

    Ans:

    Data cleaning is the process of preparing data by examining data and eliminating or changing it if it is inaccurate, lacking, unnecessary, duplicated, or structured incorrectly.

    34. What is PySpark SparkConf?

    Ans:

    PySpark SparkConf is majorly used if have to set a few configurations and parameters to run  the  Spark application on the local/cluster. In other words, can say that PySpark SparkConf is used to provide the configurations to run a Spark application.

    35. What are the  different types of algorithms supported in PySpark?

    Ans:

    • Supervised Learning Algorithms
    • Unsupervised Learning Algorithms
    • Recommendation Algorithms
    • Feature Selection and Transformation
    • Natural Language Processing (NLP)

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    36. What is SparkCore? 

    Ans:

    The Spark platform’s general execution engine, which includes all features, is called SparkCore. It includes Java, Scala, and Python APIs to simplify development, in-memory processing capabilities to deliver high performance, and a flexible execution paradigm to accommodate a variety of applications.

    37. What are key functions of SparkCore?

    Ans:

    • Perform all basic I/O functions
    • Job scheduling
    • Monitoring jobs
    • Memory management
    • Fault-tolerance

    38. What is  PySpark Array type?

    Ans:

    PySpark array type is the collection data type that extends PySpark’s DataType class, which is  superclass for all kinds.It accepts the two arguments:

    • Value Type: The valueType should extend a DataType class in PySpark.
    • Value Contains Null: It is optional argument. It specifies whether the value can accept null and is set to be True by default.

    39. What are the most frequently used Spark ecosystems?

    Ans:

    • Spark SQL for a developers.
    • Spark Streaming for processing the live data streams.
    • Graphx for a generating and computing graphs.
    • MLlib (also known as a Machine Learning Algorithms)
    • SparkR to promote the R programming language in Spark engine.

    40. What is the PySpark Partition?

    Ans:

    PySpark Partition is a technique for dividing a big dataset into smaller datasets depending on one or more partition keys. Because each partition’s changes are conducted in parallel, transformations on partitioned data run more quickly.

    41. What is Parquet file in PySpark?

    Ans:

    In PySpark, Parquet file is the  column-type format supported by a several data processing systems. By using Parquet file, Spark SQL can perform the both read and write operations.

    42. What is the cluster manager?

    Ans:

    In PySpark, cluster manager is  the  cluster mode platform that facilitates a Spark to run by providing all the resources to worker nodes according to requirements. The prime work of cluster manager is to divide the resources across applications. It works as external service for acquiring a resources on the cluster. The cluster manager dispatches the  work for cluster. Spark supports the pluggable cluster management.

    43. Explain the different cluster manager types supported by PySpark.

    Ans:

    • Standalone: It is the part of spark distribution and available as simple cluster manager to us. Standalone cluster manager is a resilient in nature, it can handle work failures. It has the capabilities to manage the resources according to requirement of application.
    • Apache Mesos: It is the  distributed cluster manager. As like yarn, it is highly available for master and slaves. It can manage resource per application. can run spark jobs, Hadoop MapReduce or any other service applications easily.
    • Hadoop YARN: This cluster manager works as distributed computing framework. It also maintains a job scheduling as well as a resource management. In this cluster, masters and slaves are more available for us. And also available with the  executors and pluggable scheduler.

    44. Distinguish difference between map() and flatMap().

    Ans:

    When using the map() function, each element of an RDD is subjected to the function, and the new RDD containing the results is returned. The method flatMap(), on the other hand, applies a function on each element of an RDD and then returns the new RDD by flattening the results. To put it another way, map() returns a list of lists, but flatMap() delivers the results in a flattened list.

    45. What is the difference between reduce() and fold()?

    Ans:

    Reduce Fold
    reduce() takes a binary function (a function that takes two arguments) and applies it cumulatively to the elements in the collection, starting from the first element. fold() also takes a binary function, but it requires an initial accumulator value (sometimes called a seed or zero value).
    It combines elements one by one and updates the accumulator with the result of each combination. It starts with the initial accumulator value and combines it with the first element, then takes the result and combines it with the second element, and so on.

    46. What do you know about Spark driver?

    Ans:

    The Spark driver is a pivotal component in Apache Spark applications, serving as the central control and coordination point for distributed data processing tasks. It initializes the SparkContext, schedules jobs, and manages the entire execution process. 

    47. What is  PySpark SparkJobinfo?

    Ans:

    The PySpark SparkJobinfo is used to get an information about SparkJobs that are in execution.

    Following is a code for using SparkJobInfo:

    • class SparkJobInfo(namedtuple(“SparkJobInfo”, “jobId stageIds status “)):

    48. What are the  main functions of Spark core?

    Ans:

    A Spark Core’s primary responsibility is to execute numerous crucial procedures, including memory management, fault tolerance, job monitoring, job setup, and connectivity with storage systems. It also includes more libraries that were added to the middle and utilized to diversify the workloads for SQL, machine learning, and streaming.

    49. Why Spark Core is mainly used for?

    Ans:

    • A Fault tolerance and recovery.
    • To interact with the  storage systems.
    • Memory management.
    • Scheduling and monitoring jobs on the  cluster.

    50. Distinguish difference between cache() and persist().

    Ans:

    The cache() function is used to be persist an RDD in memory. It is shorthand for persist(StorageLevel.MEMORY_ONLY). The persist() function allows for the more fine-grained control over storage level of an RDD. It can be used to be  persist an RDD in memory, on disk, or in the combination of both.

    51. What is the use of Akka in PySpark?

    Ans:

    Akka is used in the  PySpark for scheduling. When a worker requests a task to  master after registering, the master assigns  the  task to him. In this case, Akka sends and receives the messages between workers and masters.

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    52. What is RDD Lineage?

    Ans:

    The RDD lineage is the  procedure that is used to reconstruct a lost data partitions. The Spark does not hold up  a data replication in the memory. If any data is lost, have to rebuild it using the RDD lineage. This is best use case as RDD always remembers how to construct from the  other datasets.

    53. Does PySpark have a machine learning API?

    Ans:

    Yes, PySpark includes a powerful machine learning library known as MLlib (Machine Learning Library). MLlib is designed for distributed machine learning tasks and provides a wide range of machine learning algorithms and tools.

    54. Explain main attributes used in SparkConf.

    Ans:

    • appName: This attribute sets the name of your Spark application, which is displayed in the Spark web UI and logs. 
    • master: Specifies the cluster manager to use (e.g., “local” for local mode, “yarn” for YARN, “mesos” for Mesos, or the URL of a standalone cluster manager).  
    • spark.executor.memory: Sets the amount of memory to be allocated per executor. 
    • spark.driver.memory: Configures the memory allocated to the driver program, which is the main entry point for your Spark application

    55. How does Spark associate with Apache Mesos?

    Ans:

    • First, configure a  sparkle driver program to associate with the  Mesos.
    • The Spark paired bundle must be in area open by Mesos.
    • After that, install Apache Spark in the  similar area as Apache Mesos and design property “spark.mesos.executor.home” to point to area where it is introduced.

    56. What are the main file systems supported by a Spark

    Ans:

    • Local File system.
    • Hadoop Distributed File System (HDFS).
    • Amazon S3

    57. How does trigger automatic cleanups in Spark to handle accumulated metadata?

    Ans:

    In Apache Spark, automatic cleanups to handle accumulated metadata are primarily achieved through a mechanism called “garbage collection” and Spark’s own built-in mechanisms for managing metadata and resource cleanup.

    58. How can limit information moves when working with Spark?

    Ans:

    Limit information moves when working with the Spark by using following manners:

    • Communicate
    • Accumulator factors

    59. How is Spark SQL different from HQL and SQL?

    Ans:

    Spark SQL is designed for distributed big data processing within the Apache Spark ecosystem, enabling SQL-like queries on DataFrames and Datasets. Hive Query Language (HQL), associated with Apache Hive, is used for batch processing on Hadoop. Traditional SQL, on the other hand, is a standardized language for querying relational databases. Spark SQL is ideal for distributed data processing, HQL for Hadoop-based batch processing, and traditional SQL for structured databases, each serving different data processing needs.

    60. What is DStream in PySpark?

    Ans:

    In PySpark, DStream stands for a Discretized Stream. It is group of information or gathering of RDDs separated into  little clusters. DStreams are based on the Spark RDDs and are used to be  enable Streaming to flawlessly coordinate with some other Apache Spark segments  are Spark MLlib and Spark SQL.

    61. What is the pipeline in PySpark?

    Ans:

    A pipeline in a PySpark is a sequence of stages that are executed in the  specific order to perform  specific task. Each stage in pipeline is a transformation or an action that is applied to input data. Pipelines are used to be automate the process of building and deploying the  PySpark.

    62. What is the PySpark Storage Level?

    Ans:

    PySpark Storage Level is the  mechanism used in PySpark to control how the RDDs (Resilient Distributed Datasets) are stored in a memory and on disk. It allows the users to specify the level of persistence or caching of a RDDs, which determines how often RDDs are recomputed from an  original data source.

    63. Explain broadcast variables in PySpark.

    Ans:

    In PySpark, broadcast variables are a mechanism for efficiently sharing read-only variables across worker nodes in a distributed computing environment. They are particularly useful when you have a large dataset that needs to be used in operations performed on each worker node but can be shared without the need for costly data shuffling.

    64. Why does a developer needs to do Serializers in PySpark?

    Ans:

    Serialization is the act of transforming complicated data structures, like Python objects, into a format that can be quickly transferred over the network. Deserialization is the process of reassembling the original data structure from the serialized form. Serializers are crucial to this data conversion procedure.

    65. What are the levels in PySpark Storage Level ?

    Ans:

    • MEMORY_ONLY: This level stores RDDs in memory as a deserialized Java objects. It provides a fast access to the data, but requires the enough memory to store  entire RDD.
    • MEMORY_AND_DISK: This level stores the  RDDs in memory as long as possible, and spills them to disk if there is a not enough memory available. It provides the balance between the performance and storage capacity.
    • MEMORY_ONLY_SER: This level stores the RDDs in memory as serialized Java objects, which can save memory but may incur a serialization and deserialization overhead.

    66. What is PySpark SparkStageinfo mean to you?

    Ans:

    In PySpark, “SparkStageInfo” refers to an essential component of Spark’s internal monitoring and management system. It represents detailed information about a specific stage in a Spark application’s execution.

    67. Can you use PySpark in small data set?

    Ans:

    Yes, PySpark can be used with small datasets, but it may not always be the most efficient choice for small-scale data processing tasks. PySpark and Apache Spark, in general, are designed for big data processing and are optimized for distributed computing across clusters of machines.

    68.  Different cluster manager types in PySpark.

    Ans:

    • Local: It simplifies a running mode for a Spark application through API.
    • Kubernetes: It helps in an automated deployment and a data scaling as an open-source cluster.
    • Hadoop YARN: This type of a cluster manages Hadoop environment.
    • Apache Mesos: In this cluster, can run the  Map-reduce.
    • Standalone: This cluster can operate a  Spark API.

    69. What function does the Spark execution engine serve?

    Ans:

    The Apache Spark execution engine is a chart execution engine that enables users to view large data sets with a high presentation. If you wish to alter data across several processing steps, you must keep Spark in memory to drastically improve speed.

    70. How is PySpark exposed in a Big Data?

    Ans:

    PySpark API is attached with  Spark programming model to the Python and Apache Spark. Apache Spark is open-source software, so most popular Big Data framework can scale up a   process in cluster and made it quicker. A Big Data use the  distributed database system -memory data structures to the smoother processing.

    71. Distinguish between  PySpark and Python.

    Ans:

    Pyspark Python
    PySpark is simple to write and also easy to develop parallel programming. Python is the cross-platform programming language, and one can simply handle it.
    It provides algorithm which is already implemented so that one can simply integrate it. As a python language is flexible, one can simply do the analysis of data.
    It is the memory computation. It uses the internal memory and nonobjective memory as well.

    72. Can use PySpark as a programming language?

    Ans:

    No, cannot use PySpark as a programming language. It is  the  computing framework.

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    73. What do you think PySpark is important in a Data Science?

    Ans:

    PySpark is built into Python. It have interface and inbuilt environment to use the Python and ML .That’s why PySpark is the  essential tool in Data Science. Once  process data set, a prototype models are converted into the  production-grade workflows.

    74. What are the different MLlib tools available in Spark?

    Ans:

    • ML Algorithms: The core of a MLlib is ML Algorithms. These include  the common learning algorithms like the  classification, clustering, regression, and collaborative filtering.
    • Featurization: It includes the extraction, transformation, selection, and dimensionality reduction.
    • Pipelines: Pipelines  are provide tools to construct, evaluate, and tune ML Pipelines.
    • Persistence: It aids in saving and loading  the models, algorithms, and pipelines.
    • Utilities: Utilities for the statistics, algebra, and handling data. 

    75.  Describe getrootdirectory ().

    Ans:

    The developers can obtain a root directory by using getrootdirectory().It assists in the obtaining root directory, which contains a  files added using SparkContext.addFile().

    76. Explain broadcast variables in PySpark.

    Ans:

    A Developers can store  data as a copy into all the nodes. All the data are variable fetched from the machines and not sent back to devices. Broadcast variables will do code block to save a  data copy as one of classes of PySpark. 

    77. Can you use PySpark in a small data set?

    Ans:

    Should  not use the Pyspark in a  small data set. It will not help us so much because it is  typical library systems that have a more complex objects than more accessible. It’s best for a massive amount of data set.

    78. What is the pivot() method’s function in PySpark?

    Ans:

    The pivot() method in the  PySpark is used to rotate/transpose a data from one column into more  Dataframe columns and back using unpivot() function (). Pivot() is aggregation in which values of one of grouping columns are transposed into the  separate columns containing a different data.

    79. How does PySpark differ from the other big data tools, such as Hadoop and Flink?

    Ans:

    • PySpark vs Hadoop: Hadoop is the  distributed storage and processing system that uses MapReduce programming model. PySpark, on other hand, is a part of Apache Spark ecosystem that provides the Python API for distributed data processing. PySpark offers the  in-memory processing, making it faster than Hadoop for a certain tasks.
    • PySpark vs Flink: Both the PySpark and Flink are distributed processing frameworks, but Flink is designed for a streaming data processing, while PySpark focuses on batch and an iterative processing. PySpark provides a better support for machine learning and graph processing, while Flink excels in the real-time data processing and event-driven applications.

    80. Describe DataFrame and Dataset APIs in PySpark.

    Ans:

    DataFrame API:
    A distributed collection of data with named columns is referred to as a DataFrame in PySpark. In a relational database or spreadsheet, where data is organized in rows and columns, it is conceptually comparable to a table.
    Dataset API:
    The Dataset API in PySpark is an extension of the DataFrame API, providing the benefits of strong typing while retaining the performance optimizations of DataFrames.

    81. Explain the role of the Spark UI in monitoring and debugging PySpark applications.

    Ans:

    Monitoring application progress: Track progress of stages, tasks, and jobs in real-time.

    Identifying bottlenecks: Identify the performance bottlenecks and areas for  an optimization by analyzing metrics like  task duration, input data size, and shuffle read/write data.

    Debugging: View the  logs and exceptions for every  task and stage to help debug issues.

    Resource usage: Analyse resource usage (CPU, memory, disk) for application to optimize resource allocation and configuration.

    82. Explain the  process of registering a DataFrame as a temporary table in PySpark SQL.

    Ans:

    To register a DataFrame as the temporary table in PySpark SQL, follow these steps:

    1.  Create the  DataFrame using data source API, or by loading a data from a file.

    2.  Use the registerTempTable() method to register  DataFrame as a temporary table. This method takes  the string parameter that specifies a  name of the table.

    3.  Once a  DataFrame is registered as temporary table, it can be queried using  the SQL queries using spark.sql() method.

    82. Is PySpark an ETL?

    Ans:

    PySpark is not specifically ETL tool, but it can be used for the  ETL (Extract, Transform, Load) operations as part of  larger data processing pipeline.

    83. Is it possible to create a PySpark DataFrame from external data sources?

    Ans:

    Yes, it is possible to create the  PySpark DataFrame from external data sources. PySpark supports the  reading data from various file formats, databases, and streaming sources.

    Can  create PySpark DataFrame from  an external data sources:

    1. Reading from CSV file

    2. Reading from JSON file

    3. Reading from Parquet file

    83. Is PySpark better than Python?

    Ans:

    PySpark is not necessarily “better” than a Python, but it is more powerful tool for processing the large datasets in a distributed computing environment.

    84. What is Spark Streaming?

    Ans:

    Stream processing is the  extension to Spark API that lets stream processing of a live data streams. Data from the multiple sources like  Flume, Kafka, Kinesis, etc., is processed and then pushed to the live dashboards, file systems, and databases. Compared to terms of input data, it is just similar to the  batch processing, and data is segregated into the  streams like batches in processing

    84. What module used to implement SQL in Spark?

    Ans:

    The module used is a Spark SQL, which integrates the relational processing with  the Spark’s functional programming API. It helps to query a data either through Hive Query Language or SQL. These are four libraries of Spark SQL.

    84. What module used to implement SQL in Spark?

    Ans:

    The term “Status Tracker” could be used in various domains to refer to a system or tool that monitors and tracks the status or progress of various processes, tasks, or events. The specific functionality and purpose of a “Status Tracker” would depend on the context in which it is used.

    85. How can save a DataFrame or RDD in PySpark?

    Ans:

    In PySpark, can save a DataFrame or RDD using a write method. For DataFrames, use df.write.format(‘format’).option(‘path’, ‘location’).save(). The format could parquet, csv, json etc., and location is where want to store it.

    86. Explain the significance of PySpark’s immutable nature of data.

    Ans:

    PySpark’s data immutability is significant for a two main reasons: reliability and optimization. Reliability is more enhanced as once an object is created, it cannot be changed, preventing  an accidental modifications. This ensures the consistency in computations across the  different stages of a Spark application.

    87. Define lazy evaluation in Spark.

    Ans:

    When Spark operates on the  any dataset, it remembers the instructions. When transformation such as a map() is called on an RDD,  operation is not performed instantly. Transformations in a Spark are not evaluated until perform an action, which aids in optimizing  overall data processing workflow, known as a lazy evaluation.

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