Kampland Nature Resort Interview Questions and Answers

Spark Submit command is used to submit Spark applications to a cluster.

• Spark Submit command is used to launch applications on a Spark cluster.

• It allows users to specify application parameters like main class, jars, and arguments.

• Users can also configure properties like memory allocation and number of executors.

• Example: spark-submit --class com.example.Main --master yarn --deploy-mode cluster myApp.jar

Common file formats used in data storages include CSV, JSON, Parquet, Avro, and ORC. Parquet is best for compression.

• CSV (Comma-Separated Values) - simple and widely used, but not efficient for large datasets

• JSON (JavaScript Object Notation) - human-readable and easy to parse, but can be inefficient for storage

• Parquet - columnar storage format that is highly efficient for compression and query performance

• Avro - efficient binary format with schema support, good for data serial...read more

Python program to find the most repeating substring in a list of words.

• Iterate through each word in the list

• Generate all possible substrings for each word

• Count the occurrences of each substring using a dictionary

• Find the substring with the highest count

Cache() and Persist() are both used for caching RDDs in Apache Spark, but Persist() allows for more customization.

• Cache() is a shorthand for Persist(StorageLevel.MEMORY_ONLY)

• Persist() allows for specifying different storage levels like MEMORY_ONLY, MEMORY_AND_DISK, etc.

• Persist() also allows for specifying serialization formats like Java serialization, Kryo serialization, etc.

Window functions in SQL are used to perform calculations across a set of table rows related to the current row.

• Window functions operate on a set of rows related to the current row

• They can be used to calculate running totals, ranks, and averages

• Examples include ROW_NUMBER(), RANK(), and SUM() OVER()

Application Master in Spark is responsible for negotiating resources with the ResourceManager and executing tasks on the cluster.

• Responsible for negotiating resources with the ResourceManager

• Manages the execution of tasks on the cluster

• Monitors the progress of tasks and reports back to the driver program

Spark is a distributed computing framework that provides in-memory processing capabilities for big data analytics.

• Spark has a master-slave architecture with a central coordinator called the Spark Master and distributed workers called Spark Workers.

• It uses Resilient Distributed Datasets (RDDs) for fault-tolerant distributed data processing.

• Spark supports various data sources like HDFS, Cassandra, and S3 for input/output operations.

• It includes components like Spark SQL for stru...read more

Optimizing Spark involves tuning configurations, partitioning data, using efficient transformations, and caching intermediate results.

• Tune Spark configurations for optimal performance

• Partition data to distribute workload evenly

• Use efficient transformations like map, filter, and reduce

• Cache intermediate results to avoid recomputation