Search results
- How does Spark differ from Hadoop, and what advantages does it offer for big data processing? Spark differs from Hadoop primarily in its data processing approach and performance.
- Can you explain the architecture of Spark, highlighting the roles of key components such as the Driver Program, Cluster Manager, and the Executors? Apache Spark’s architecture follows a master/worker paradigm, with the Driver Program acting as the master and Executors as workers.
- What is the role of the DAG scheduler in Spark, and how does it contribute to optimizing query execution? The DAG scheduler in Spark plays a crucial role in optimizing query execution by transforming the logical execution plan into a physical one, consisting of stages and tasks.
- What are the key differences between RDD, DataFrame, and Dataset in Spark, and when would you choose to use each one? RDD (Resilient Distributed Dataset) is Spark’s low-level data structure, providing fault tolerance and parallel processing.
Jun 27, 2024 · Essential Spark interview questions with example answers for job-seekers, data professionals, and hiring managers. Jun 27, 2024. Apache Spark is a unified analytics engine for data engineering, data science, and machine learning at scale. It can be used with Python, SQL, R, Java, or Scala.
Question: Can you explain how you can use Apache Spark along with Hadoop? Answer : Having compatibility with Hadoop is one of the leading advantages of Apache Spark. The duo makes up for a powerful tech pair.
- Spark Core acts as the base engine for large-scale parallel and distributed data processing. It is the distributed execution engine used in conjunc...
- GraphX – Implements graphs and graph-parallel computationMLib – Used for machine learningSpark Core – Base engine used for large-scale parallel and...
- GraphX is the API used for implementing graphs and graph-parallel computing in Apache Spark. It extends the Spark RDD with a Resilient Distributed...
- Spark SQL modules help in integrating relational processing with Spark’s functional programming API. It supports querying data via SQL or HiveQL (H...
- Parquet is a columnar format that is supported by several data processing systems. With it, Spark SQL performs both read as well as write operation...
- Apache Mesos – Commonly used cluster managerStandalone – A basic cluster manager for setting up a clusterYARN – Used for resource management
- Yes, it is possible to use Apache Spark for accessing as well as analyzing data stored in Cassandra databases using the Spark Cassandra Connector....
- Enhanced Speed – MapReduce makes use of persistent storage for carrying out any of the data processing tasks. On the contrary, Spark uses in-memory...
- Having compatibility with Hadoop is one of the leading advantages of Apache Spark. The duo makes up for a powerful tech pair. Using Apache Spark an...
- What is the spark? Spark is a general-purpose in-memory compute engine. You can connect it with any storage system like a Local storage system, HDFS, Amazon S3, etc.
- What is RDD in Apache Spark? RDDs stand for Resilient Distributed Dataset. It is the most important building block of any spark application. It is immutable.
- What is the Difference between SparkContext Vs. SparkSession? In Spark 1.x version, we must create different contexts for each API. For example:- SparkContext.
- What is the broadcast variable? Broadcast variables in Spark are a mechanism for sharing the data across the executors to be read-only. Without broadcast variables, we have to ship the data to each executor whenever they perform any type of transformation and action, which can cause network overhead.
- How to Programmatically Specify A Schema For Dataframe?
- Does Apache Spark Provide Checkpoints?
- What Do You Mean by Sliding Window Operation?
- What Are The Different Levels of Persistence in Spark?
- How Would You Compute The Total Count of Unique Words in Spark?
- What Are The Different MLlib Tools Available in Spark?
- What Are The Different Data Types Supported by Spark MLlib?
- What Is A Sparse vector?
- Describe How Model Creation Works with MLlib and How The Model Is applied.
- What Are The Functions of Spark Sql?
DataFrame can be created programmatically with three steps: 1. Create an RDD of Rows from the original RDD; 2. Create the schema represented by a StructType matching the structure of Rows in the RDD created in Step 1. 3. Apply the schema to the RDD of Rows via createDataFrame method provided by SparkSession.
This is one of the most frequently asked spark interview questions where the interviewer expects a detailed answer (and not just a yes or no!). Give as detailed an answer as possible here. Yes, Apache Spark provides an API for adding and managing checkpoints. Checkpointing is the process of making streaming applications resilient to failures. It al...
Controlling the transmission of data packets between multiple computer networks is done by the sliding window. Spark Streaming library provides windowed computations where the transformations on RDDs are applied over a sliding window of data.
DISK_ONLY - Stores the RDD partitions only on the disk MEMORY_ONLY_SER - Stores the RDD as serialized Java objects with a one-byte array per partition MEMORY_ONLY - Stores the RDD as deserialized Java objects in the JVM. If the RDD is not able to fit in the memory available, some partitions won’t be cached OFF_HEAP - Works like MEMORY_ONLY_SER but ...
1. Load the text file as RDD: sc.textFile(“hdfs://Hadoop/user/test_file.txt”); 2. Function that breaks each line into words: def toWords(line): return line.split(); 3. Run the toWords function on each element of RDD in Spark as flatMap transformation: words = line.flatMap(toWords); 4. Convert each word into (key,value) pair: def toTuple(word): retu...
ML Algorithms: Classification, Regression, Clustering, and Collaborative filteringFeaturization: Feature extraction, Transformation, Dimensionality reduction,Spark MLlib supports local vectors and matrices stored on a single machine, as well as distributed matrices. Local Vector: MLlib supports two types of local vectors - dense and sparse Example: vector(1.0, 0.0, 3.0) dense format: [1.0, 0.0, 3.0] sparse format: (3, [0, 2]. [1.0, 3.0]) Labeled point: A labeled point is a local vector, either dense or ...
A Sparse vector is a type of local vector which is represented by an index array and a value array. public class SparseVector extends Object implements Vector Example: sparse1 = SparseVector(4, [1, 3], [3.0, 4.0]) where: 4 is the size of the vector [1,3] are the ordered indices of the vector [3,4] are the value Do you have a better example for this...
MLlib has 2 components: Transformer: A transformer reads a DataFrame and returns a new DataFrame with a specific transformation applied. Estimator: An estimator is a machine learning algorithm that takes a DataFrame to train a model and returns the model as a transformer. Spark MLlib lets you combine multiple transformations into a pipeline to appl...
Spark SQL is Apache Spark’s module for working with structured data. Spark SQL loads the data from a variety of structured data sources. It queries data using SQL statements, both inside a Spark program and from external tools that connect to Spark SQL through standard database connectors (JDBC/ODBC). It provides a rich integration between SQL and ...
1. Apache Spark Interview Questions – Objective. Apache Spark is prevailing because of its capability to handle real-time streaming and processing big data faster than Hadoop MapReduce.
People also ask
Is Apache Spark faster than Hadoop?
How many Apache Spark interview questions are there?
Why should you use spark vs Hadoop?
Is spark better than Hadoop MapReduce?
Why do companies use Apache Spark?
What is Apache Spark & its role in the Big Data ecosystem?
Follow along and learn the 23 most common and advanced Apache Spark interview questions and answers to prepare for your next big data and machine learning interview. Q1 : Briefly compare Apache Spark vs Apache Hadoop
