Venkata Naresh Tanakam’s Post

**Interviewer:** Could you explain the architecture of Apache Spark?? **Candidate:** Sure! Apache Spark has a master-slave architecture that consists of a **Driver**, **Executors**, and a **Cluster Manager**. Let me break it down for you! **Driver Program:** - The **Driver** is the central coordinator in a Spark application. It is responsible for:  - Defining the main logic of the Spark application.  - Creating the **SparkContext**, which serves as the entry point for Spark functionality.  - Converting transformations into a logical Directed Acyclic Graph (DAG).  - Submitting jobs to the cluster manager and distributing tasks among the executors. **Executors:** - Executors are worker nodes in the cluster responsible for:  - Executing tasks assigned by the driver.  - Storing data for the application in memory or disk storage.  - Sending results back to the driver. **Cluster Manager:** - The Cluster Manager oversees resource management and job scheduling. Spark can run on various cluster managers, such as:  - **Standalone Cluster Manager:** A simple built-in cluster manager.  - **Apache Mesos:** A general cluster manager that can run Hadoop MapReduce and other applications.  - **Hadoop YARN:** The resource management layer of Hadoop.  - **Kubernetes:** An open-source platform for automating deployment, scaling, and operations of application containers. **Interviewer:** Great. Could you explain how the Driver and Executors interact during a Spark job execution? **Candidate:** Absolutely. 1. **Job Submission:**   - The user submits a Spark application using the SparkContext in the driver program. 2. **DAG Construction:**   - The driver builds a logical Directed Acyclic Graph (DAG) of stages representing transformations and actions. 3. **Task Scheduling:**   - The DAG is divided into smaller sets of tasks, which are then submitted to the cluster manager. 4. **Task Execution:**   - The cluster manager allocates resources and schedules the tasks on available executors.   - Executors perform the tasks assigned to them, which may involve reading data from a data source, performing computations, and storing intermediate results. 5. **Result Collection:**   - Executors send the results of their computations back to the driver.   - The driver program consolidates these results and performs any final actions required. **Interviewer:** You mentioned DAG and task scheduling. Can you explain the difference between transformations and actions in Spark? In Spark, operations on data are categorized into **transformations** and **actions**: - **Transformations:**  - These are operations that create a new RDD from an existing one. They are lazy, meaning they don’t execute immediately but instead create a logical plan of execution.  - Eg. `map()`, `filter()` - **Actions:**  - These operations trigger the execution of the transformations and return a result to the driver program or write data to an external storage system.  - Eg.`collect()`, `count()`

**Interviewer:** Could you explain the architecture of Apache Spark?? **Candidate:** Sure! Apache Spark has a master-slave architecture that consists of a **Driver**, **Executors**, and a **Cluster Manager**. Let me break it down for you! **Driver Program:** - The **Driver** is the central coordinator in a Spark application. It is responsible for:  - Defining the main logic of the Spark application.  - Creating the **SparkContext**, which serves as the entry point for Spark functionality.  - Converting transformations into a logical Directed Acyclic Graph (DAG).  - Submitting jobs to the cluster manager and distributing tasks among the executors. **Executors:** - Executors are worker nodes in the cluster responsible for:  - Executing tasks assigned by the driver.  - Storing data for the application in memory or disk storage.  - Sending results back to the driver. **Cluster Manager:** - The Cluster Manager oversees resource management and job scheduling. Spark can run on various cluster managers, such as:  - **Standalone Cluster Manager:** A simple built-in cluster manager.  - **Apache Mesos:** A general cluster manager that can run Hadoop MapReduce and other applications.  - **Hadoop YARN:** The resource management layer of Hadoop.  - **Kubernetes:** An open-source platform for automating deployment, scaling, and operations of application containers. **Interviewer:** Great. Could you explain how the Driver and Executors interact during a Spark job execution? **Candidate:** Absolutely. 1. **Job Submission:**   - The user submits a Spark application using the SparkContext in the driver program. 2. **DAG Construction:**   - The driver builds a logical Directed Acyclic Graph (DAG) of stages representing transformations and actions. 3. **Task Scheduling:**   - The DAG is divided into smaller sets of tasks, which are then submitted to the cluster manager. 4. **Task Execution:**   - The cluster manager allocates resources and schedules the tasks on available executors.   - Executors perform the tasks assigned to them, which may involve reading data from a data source, performing computations, and storing intermediate results. 5. **Result Collection:**   - Executors send the results of their computations back to the driver.   - The driver program consolidates these results and performs any final actions required. **Interviewer:** You mentioned DAG and task scheduling. Can you explain the difference between transformations and actions in Spark? In Spark, operations on data are categorized into **transformations** and **actions**: - **Transformations:**  - These are operations that create a new RDD from an existing one. They are lazy, meaning they don’t execute immediately but instead create a logical plan of execution.  - Eg. `map()`, `filter()` - **Actions:**  - These operations trigger the execution of the transformations and return a result to the driver program or write data to an external storage system.  - Eg.`collect()`, `count()`

**Interviewer:** Could you explain the architecture of Apache Spark? **Candidate:** Sure! Apache Spark has a master-slave architecture that consists of a **Driver**, **Executors**, and a **Cluster Manager**. Let me break it down for you! **Driver Program:** - The **Driver** is the central coordinator in a Spark application. It is responsible for:  - Defining the main logic of the Spark application.  - Creating the **SparkContext**, which serves as the entry point for Spark functionality.  - Converting transformations into a logical Directed Acyclic Graph (DAG).  - Submitting jobs to the cluster manager and distributing tasks among the executors. **Executors:** - Executors are worker nodes in the cluster responsible for:  - Executing tasks assigned by the driver.  - Storing data for the application in memory or disk storage.  - Sending results back to the driver. **Cluster Manager:** - The Cluster Manager oversees resource management and job scheduling. Spark can run on various cluster managers, such as:  - **Standalone Cluster Manager:** A simple built-in cluster manager.  - **Apache Mesos:** A general cluster manager that can run Hadoop MapReduce and other applications.  - **Hadoop YARN:** The resource management layer of Hadoop.  - **Kubernetes:** An open-source platform for automating deployment, scaling, and operations of application containers. **Interviewer:** Great. Could you explain how the Driver and Executors interact during a Spark job execution? **Candidate:** Absolutely. 1. **Job Submission:**   - The user submits a Spark application using the SparkContext in the driver program. 2. **DAG Construction:**   - The driver builds a logical Directed Acyclic Graph (DAG) of stages representing transformations and actions. 3. **Task Scheduling:**   - The DAG is divided into smaller sets of tasks, which are then submitted to the cluster manager. 4. **Task Execution:**   - The cluster manager allocates resources and schedules the tasks on available executors.   - Executors perform the tasks assigned to them, which may involve reading data from a data source, performing computations, and storing intermediate results. 5. **Result Collection:**   - Executors send the results of their computations back to the driver.   - The driver program consolidates these results and performs any final actions required. **Interviewer:** You mentioned DAG and task scheduling. Can you explain the difference between transformations and actions in Spark? In Spark, operations on data are categorized into **transformations** and **actions**: - **Transformations:**  - These are operations that create a new RDD from an existing one. They are lazy, meaning they don’t execute immediately but instead create a logical plan of execution.  - Eg. `map()`, `filter()` - **Actions:**  - These operations trigger the execution of the transformations and return a result to the driver program or write data to an external storage system.  - Eg.`collect()`, `count()`

**Interviewer:** Could you explain the architecture of Apache Spark? **Candidate:** Sure! Apache Spark has a master-slave architecture that consists of a **Driver**, **Executors**, and a **Cluster Manager**. Let me break it down for you! **Driver Program:** - The **Driver** is the central coordinator in a Spark application. It is responsible for:  - Defining the main logic of the Spark application.  - Creating the **SparkContext**, which serves as the entry point for Spark functionality.  - Converting transformations into a logical Directed Acyclic Graph (DAG).  - Submitting jobs to the cluster manager and distributing tasks among the executors. **Executors:** - Executors are worker nodes in the cluster responsible for:  - Executing tasks assigned by the driver.  - Storing data for the application in memory or disk storage.  - Sending results back to the driver. **Cluster Manager:** - The Cluster Manager oversees resource management and job scheduling. Spark can run on various cluster managers, such as:  - **Standalone Cluster Manager:** A simple built-in cluster manager.  - **Apache Mesos:** A general cluster manager that can run Hadoop MapReduce and other applications.  - **Hadoop YARN:** The resource management layer of Hadoop.  - **Kubernetes:** An open-source platform for automating deployment, scaling, and operations of application containers. **Interviewer:** Great. Could you explain how the Driver and Executors interact during a Spark job execution? **Candidate:** Absolutely. 1. **Job Submission:**   - The user submits a Spark application using the SparkContext in the driver program. 2. **DAG Construction:**   - The driver builds a logical Directed Acyclic Graph (DAG) of stages representing transformations and actions. 3. **Task Scheduling:**   - The DAG is divided into smaller sets of tasks, which are then submitted to the cluster manager. 4. **Task Execution:**   - The cluster manager allocates resources and schedules the tasks on available executors.   - Executors perform the tasks assigned to them, which may involve reading data from a data source, performing computations, and storing intermediate results. 5. **Result Collection:**   - Executors send the results of their computations back to the driver.   - The driver program consolidates these results and performs any final actions required. **Interviewer:** You mentioned DAG and task scheduling. Can you explain the difference between transformations and actions in Spark? In Spark, operations on data are categorized into **transformations** and **actions**: - **Transformations:**  - These are operations that create a new RDD from an existing one. They are lazy, meaning they don’t execute immediately but instead create a logical plan of execution.  - Eg. `map()`, `filter()` - **Actions:**  - These operations trigger the execution of the transformations and return a result to the driver program or write data to an external storage system.  - Eg.`collect()`, `count()`

*Interviewer:** Could you explain the architecture of Apache Spark? **Candidate:** Sure! Apache Spark has a master-slave architecture that consists of a **Driver**, **Executors**, and a **Cluster Manager**. Let me break it down for you! **Driver Program:** - The **Driver** is the central coordinator in a Spark application. It is responsible for:  - Defining the main logic of the Spark application.  - Creating the **SparkContext**, which serves as the entry point for Spark functionality.  - Converting transformations into a logical Directed Acyclic Graph (DAG).  - Submitting jobs to the cluster manager and distributing tasks among the executors. **Executors:** - Executors are worker nodes in the cluster responsible for:  - Executing tasks assigned by the driver.  - Storing data for the application in memory or disk storage.  - Sending results back to the driver. **Cluster Manager:** - The Cluster Manager oversees resource management and job scheduling. Spark can run on various cluster managers, such as:  - **Standalone Cluster Manager:** A simple built-in cluster manager.  - **Apache Mesos:** A general cluster manager that can run Hadoop MapReduce and other applications.  - **Hadoop YARN:** The resource management layer of Hadoop.  - **Kubernetes:** An open-source platform for automating deployment, scaling, and operations of application containers. **Interviewer:** Great. Could you explain how the Driver and Executors interact during a Spark job execution? **Candidate:** Absolutely. 1. **Job Submission:**   - The user submits a Spark application using the SparkContext in the driver program. 2. **DAG Construction:**   - The driver builds a logical Directed Acyclic Graph (DAG) of stages representing transformations and actions. 3. **Task Scheduling:**   - The DAG is divided into smaller sets of tasks, which are then submitted to the cluster manager. 4. **Task Execution:**   - The cluster manager allocates resources and schedules the tasks on available executors.   - Executors perform the tasks assigned to them, which may involve reading data from a data source, performing computations, and storing intermediate results. 5. **Result Collection:**   - Executors send the results of their computations back to the driver.   - The driver program consolidates these results and performs any final actions required. **Interviewer:** You mentioned DAG and task scheduling. Can you explain the difference between transformations and actions in Spark? In Spark, operations on data are categorized into **transformations** and **actions**: - **Transformations:**  - These are operations that create a new RDD from an existing one. They are lazy, meaning they don’t execute immediately but instead create a logical plan of execution.  - Eg. `map()`, `filter()` - **Actions:**  - These operations trigger the execution of the transformations and return a result to the driver program or write data to an external storage system.  - Eg.`collect()`, `count()`

*Interviewer:** Could you explain the architecture of Apache Spark?? **Candidate:** Sure! Apache Spark has a master-slave architecture that consists of a **Driver**, **Executors**, and a **Cluster Manager**. Let me break it down for you! **Driver Program:** - The **Driver** is the central coordinator in a Spark application. It is responsible for: - Defining the main logic of the Spark application. - Creating the **SparkContext**, which serves as the entry point for Spark functionality. - Converting transformations into a logical Directed Acyclic Graph (DAG). - Submitting jobs to the cluster manager and distributing tasks among the executors. **Executors:** - Executors are worker nodes in the cluster responsible for: - Executing tasks assigned by the driver. - Storing data for the application in memory or disk storage. - Sending results back to the driver. **Cluster Manager:** - The Cluster Manager oversees resource management and job scheduling. Spark can run on various cluster managers, such as: - **Standalone Cluster Manager:** A simple built-in cluster manager. - **Apache Mesos:** A general cluster manager that can run Hadoop MapReduce and other applications. - **Hadoop YARN:** The resource management layer of Hadoop. - **Kubernetes:** An open-source platform for automating deployment, scaling, and operations of application containers. **Interviewer:** Great. Could you explain how the Driver and Executors interact during a Spark job execution? **Candidate:** Absolutely. 1. **Job Submission:** - The user submits a Spark application using the SparkContext in the driver program. 2. **DAG Construction:** - The driver builds a logical Directed Acyclic Graph (DAG) of stages representing transformations and actions. 3. **Task Scheduling:** - The DAG is divided into smaller sets of tasks, which are then submitted to the cluster manager. 4. **Task Execution:** - The cluster manager allocates resources and schedules the tasks on available executors. - Executors perform the tasks assigned to them, which may involve reading data from a data source, performing computations, and storing intermediate results. 5. **Result Collection:** - Executors send the results of their computations back to the driver. - The driver program consolidates these results and performs any final actions required. **Interviewer:** You mentioned DAG and task scheduling. Can you explain the difference between transformations and actions in Spark? In Spark, operations on data are categorized into **transformations** and **actions**: - **Transformations:** - These are operations that create a new RDD from an existing one. They are lazy, meaning they don’t execute immediately but instead create a logical plan of execution. - Eg. `map()`, `filter()` - **Actions:** - These operations trigger the execution of the transformations and return a result to the driver program or write data to an external storage system. - Eg.`collect()`, `count()`

*Interviewer:** Could you explain the architecture of Apache Spark?? **Candidate:** Sure! Apache Spark has a master-slave architecture that consists of a **Driver**, **Executors**, and a **Cluster Manager**. Let me break it down for you! **Driver Program:** - The **Driver** is the central coordinator in a Spark application. It is responsible for:  - Defining the main logic of the Spark application.  - Creating the **SparkContext**, which serves as the entry point for Spark functionality.  - Converting transformations into a logical Directed Acyclic Graph (DAG).  - Submitting jobs to the cluster manager and distributing tasks among the executors. **Executors:** - Executors are worker nodes in the cluster responsible for:  - Executing tasks assigned by the driver.  - Storing data for the application in memory or disk storage.  - Sending results back to the driver. **Cluster Manager:** - The Cluster Manager oversees resource management and job scheduling. Spark can run on various cluster managers, such as:  - **Standalone Cluster Manager:** A simple built-in cluster manager.  - **Apache Mesos:** A general cluster manager that can run Hadoop MapReduce and other applications.  - **Hadoop YARN:** The resource management layer of Hadoop.  - **Kubernetes:** An open-source platform for automating deployment, scaling, and operations of application containers. **Interviewer:** Great. Could you explain how the Driver and Executors interact during a Spark job execution? **Candidate:** Absolutely. 1. **Job Submission:**   - The user submits a Spark application using the SparkContext in the driver program. 2. **DAG Construction:**   - The driver builds a logical Directed Acyclic Graph (DAG) of stages representing transformations and actions. 3. **Task Scheduling:**   - The DAG is divided into smaller sets of tasks, which are then submitted to the cluster manager. 4. **Task Execution:**   - The cluster manager allocates resources and schedules the tasks on available executors.   - Executors perform the tasks assigned to them, which may involve reading data from a data source, performing computations, and storing intermediate results. 5. **Result Collection:**   - Executors send the results of their computations back to the driver.   - The driver program consolidates these results and performs any final actions required. **Interviewer:** You mentioned DAG and task scheduling. Can you explain the difference between transformations and actions in Spark? In Spark, operations on data are categorized into **transformations** and **actions**: - **Transformations:**  - These are operations that create a new RDD from an existing one. They are lazy, meaning they don’t execute immediately but instead create a logical plan of execution.  - Eg. `map()`, `filter()` - **Actions:**  - These operations trigger the execution of the transformations and return a result to the driver program or write data to an external storage system.  - Eg.`collect()`, `count()`

*Interviewer:** Could you explain the architecture of Apache Spark? **Candidate:** Sure! Apache Spark has a master-slave architecture that consists of a **Driver**, **Executors**, and a **Cluster Manager**. Let me break it down for you! **Driver Program:** - The **Driver** is the central coordinator in a Spark application. It is responsible for:  - Defining the main logic of the Spark application.  - Creating the **SparkContext**, which serves as the entry point for Spark functionality.  - Converting transformations into a logical Directed Acyclic Graph (DAG).  - Submitting jobs to the cluster manager and distributing tasks among the executors. **Executors:** - Executors are worker nodes in the cluster responsible for:  - Executing tasks assigned by the driver.  - Storing data for the application in memory or disk storage.  - Sending results back to the driver. **Cluster Manager:** - The Cluster Manager oversees resource management and job scheduling. Spark can run on various cluster managers, such as:  - **Standalone Cluster Manager:** A simple built-in cluster manager.  - **Apache Mesos:** A general cluster manager that can run Hadoop MapReduce and other applications.  - **Hadoop YARN:** The resource management layer of Hadoop.  - **Kubernetes:** An open-source platform for automating deployment, scaling, and operations of application containers. **Interviewer:** Great. Could you explain how the Driver and Executors interact during a Spark job execution? **Candidate:** Absolutely. 1. **Job Submission:**   - The user submits a Spark application using the SparkContext in the driver program. 2. **DAG Construction:**   - The driver builds a logical Directed Acyclic Graph (DAG) of stages representing transformations and actions. 3. **Task Scheduling:**   - The DAG is divided into smaller sets of tasks, which are then submitted to the cluster manager. 4. **Task Execution:**   - The cluster manager allocates resources and schedules the tasks on available executors.   - Executors perform the tasks assigned to them, which may involve reading data from a data source, performing computations, and storing intermediate results. 5. **Result Collection:**   - Executors send the results of their computations back to the driver.   - The driver program consolidates these results and performs any final actions required. **Interviewer:** You mentioned DAG and task scheduling. Can you explain the difference between transformations and actions in Spark? In Spark, operations on data are categorized into **transformations** and **actions**: - **Transformations:**  - These are operations that create a new RDD from an existing one. They are lazy, meaning they don’t execute immediately but instead create a logical plan of execution.  - Eg. `map()`, `filter()` - **Actions:**  - These operations trigger the execution of the transformations and return a result to the driver program or write data to an external storage system.  - Eg.`collect()`, `count()`

*Interviewer:** Could you explain the architecture of Apache Spark? **Candidate:** Sure! Apache Spark has a master-slave architecture that consists of a **Driver**, **Executors**, and a **Cluster Manager**. Let me break it down for you! **Driver Program:** - The **Driver** is the central coordinator in a Spark application. It is responsible for:  - Defining the main logic of the Spark application.  - Creating the **SparkContext**, which serves as the entry point for Spark functionality.  - Converting transformations into a logical Directed Acyclic Graph (DAG).  - Submitting jobs to the cluster manager and distributing tasks among the executors. **Executors:** - Executors are worker nodes in the cluster responsible for:  - Executing tasks assigned by the driver.  - Storing data for the application in memory or disk storage.  - Sending results back to the driver. **Cluster Manager:** - The Cluster Manager oversees resource management and job scheduling. Spark can run on various cluster managers, such as:  - **Standalone Cluster Manager:** A simple built-in cluster manager.  - **Apache Mesos:** A general cluster manager that can run Hadoop MapReduce and other applications.  - **Hadoop YARN:** The resource management layer of Hadoop.  - **Kubernetes:** An open-source platform for automating deployment, scaling, and operations of application containers. **Interviewer:** Great. Could you explain how the Driver and Executors interact during a Spark job execution? **Candidate:** Absolutely. 1. **Job Submission:**   - The user submits a Spark application using the SparkContext in the driver program. 2. **DAG Construction:**   - The driver builds a logical Directed Acyclic Graph (DAG) of stages representing transformations and actions. 3. **Task Scheduling:**   - The DAG is divided into smaller sets of tasks, which are then submitted to the cluster manager. 4. **Task Execution:**   - The cluster manager allocates resources and schedules the tasks on available executors.   - Executors perform the tasks assigned to them, which may involve reading data from a data source, performing computations, and storing intermediate results. 5. **Result Collection:**   - Executors send the results of their computations back to the driver.   - The driver program consolidates these results and performs any final actions required. **Interviewer:** You mentioned DAG and task scheduling. Can you explain the difference between transformations and actions in Spark? In Spark, operations on data are categorized into **transformations** and **actions**: - **Transformations:**  - These are operations that create a new RDD from an existing one. They are lazy, meaning they don’t execute immediately but instead create a logical plan of execution.  - Eg. `map()`, `filter()` - **Actions:**  - These operations trigger the execution of the transformations and return a result to the driver program or write data to an external storage system.  - Eg.`collect()`, `count()`

*Interviewer:** Could you explain the architecture of Apache Spark? **Candidate:** Sure! Apache Spark has a master-slave architecture that consists of a **Driver**, **Executors**, and a **Cluster Manager**. Let me break it down for you! **Driver Program:** - The **Driver** is the central coordinator in a Spark application. It is responsible for: - Defining the main logic of the Spark application. - Creating the **SparkContext**, which serves as the entry point for Spark functionality. - Converting transformations into a logical Directed Acyclic Graph (DAG). - Submitting jobs to the cluster manager and distributing tasks among the executors. **Executors:** - Executors are worker nodes in the cluster responsible for: - Executing tasks assigned by the driver. - Storing data for the application in memory or disk storage. - Sending results back to the driver. **Cluster Manager:** - The Cluster Manager oversees resource management and job scheduling. Spark can run on various cluster managers, such as: - **Standalone Cluster Manager:** A simple built-in cluster manager. - **Apache Mesos:** A general cluster manager that can run Hadoop MapReduce and other applications. - **Hadoop YARN:** The resource management layer of Hadoop. - **Kubernetes:** An open-source platform for automating deployment, scaling, and operations of application containers. **Interviewer:** Great. Could you explain how the Driver and Executors interact during a Spark job execution? **Candidate:** Absolutely. 1. **Job Submission:** - The user submits a Spark application using the SparkContext in the driver program. 2. **DAG Construction:** - The driver builds a logical Directed Acyclic Graph (DAG) of stages representing transformations and actions. 3. **Task Scheduling:** - The DAG is divided into smaller sets of tasks, which are then submitted to the cluster manager. 4. **Task Execution:** - The cluster manager allocates resources and schedules the tasks on available executors. - Executors perform the tasks assigned to them, which may involve reading data from a data source, performing computations, and storing intermediate results. 5. **Result Collection:** - Executors send the results of their computations back to the driver. - The driver program consolidates these results and performs any final actions required. **Interviewer:** You mentioned DAG and task scheduling. Can you explain the difference between transformations and actions in Spark? In Spark, operations on data are categorized into **transformations** and **actions**: - **Transformations:** - These are operations that create a new RDD from an existing one. They are lazy, meaning they don’t execute immediately but instead create a logical plan of execution. - Eg. `map()`, `filter()` - **Actions:** - These operations trigger the execution of the transformations and return a result to the driver program or write data to an external storage system. - Eg.`collect()`, `count()` #Spark