When it comes to interview questions, there are a few different types that you might encounter. One type is the scenario-based question, which can be a bit tricky to answer.
Scenario-based questions are designed to test your critical thinking and problem-solving skills. In a nutshell, you’ll be given a situation and asked what you would do in that particular situation.
Answering these questions can be challenging, but there are a few things you can do to prepare. First, take a few deep breaths and try to relax. Then, take your time to think through the situation and consider all of your options.
Once you’ve thought through the situation, formulate your answer. Be sure to explain your thought process and why you made the decisions you did.
If you’re feeling nervous about scenario-based questions, don’t worry – you can prepare for them. With a little practice, you
- Question 1: What are ‘partitions’? …
- Question 2: What is Spark Streaming used for? …
- Question 3: Is it normal to run all of your processes on a localized node? …
- Question 4: What is ‘SparkCore’ used for? …
- Question 5: Does the File System API have a usage in Spark?
Spark Scenario Based Interview Question | Missing Code
Note: This work is ongoing; some questions will have links to further information and answers, while others won’t.
To use the beast to its full potential in each of these dimensions, specific domain knowledge is required!
Since Apache Spark is now a required skill for data engineers, it can be challenging for developers to keep up with all of its nitty-gritty details as it grows.
Update Oct-2021: Check this Youtube channel for more live demo with interview Q&A: https://www.youtube.com/channel/UCl8BC-R6fqITW9UrSXj5Uxg
A straightforward guy using Big Data tools to pursue AI and Deep Learning:) @ https://www linkedin. com/in/mageswaran1989/.
Top 50 PySpark Interview Questions and Answers
To assist you in achieving your goal of working as a PySpark Data Engineer or Data Scientist, we are here to provide you with the top 50 PySpark Interview Questions and Answers for both new and seasoned professionals. We have placed the questions into five categories below-.
Let’s examine each of these categories individually.
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Apache Spark Interview Questions for Beginners
Hadoop MapReduce is slower when processing large amounts of data, whereas Apache Spark MapReduce processes data both in batches and in real-time. Spark processes data almost one hundred times faster than MapReduce, which only processes data in batches. e. in-memory. Since Spark offers caching and in-memory data storage and Hadoop MapReduce data is stored in HDFS, it is quicker to retrieve the data. Hadoop is heavily dependent on disk.
Apache Spark has 3 main categories that comprise its ecosystem. Those are:.
The interviewer will expect you to provide a thorough response to one of the most typical spark interview questions.
The SparkSession object in the driver program controls how independently running Spark applications interact with one another. One task per partition is given to the worker nodes by the resource manager or cluster manager. Iterative algorithms benefit from caching datasets across iterations because they repeatedly apply operations to the data. A task produces a new partition dataset after applying its unit of work to the dataset in its partition. The outcomes are then sent back to the driver application or may be saved to disk.
The core data structure of Apache Spark is resilient distributed datasets. It is embedded in Spark Core. RDDs are distributed collections of objects that are fault-tolerant, immutable, and allow for parallel processing. RDDs are divided into partitions and can be run on various cluster nodes.
RDDs are produced by either loading an external dataset from a reliable storage system like HDFS or HBase or by transforming already-existing RDDs.
Here is how the architecture of RDD looks like:
As of now, if you have any questions about the interview questions and answers for Apache Spark, please leave a comment below.
When Spark operates on any dataset, it remembers the instructions. When a map() or similar transformation is used on an RDD, the operation is not immediately completed. Lazy evaluation, which improves the efficiency of the entire data processing workflow, prevents transformations in Spark from being evaluated until you take a certain action.
In order to process data more quickly and create machine learning models, Apache Spark stores data in memory. To create an ideal model, machine learning algorithms need to go through several iterations and different conceptual stages. To create a graph, graph algorithms go through all the nodes and edges. Performance can improve with these low latency workloads that demand multiple iterations.
You must set the spark parameter in order to start the cleanups. cleaner. ttlx.
There are four steps in all to connecting Spark to Apache Mesos.
Many data processing systems support the columnar format Parquet. Spark can perform both read and write operations on the Parquet file. Â.
Some of the advantages of having a Parquet file are:
The process of shuffling involves redistributing data among partitions, which could result in data moving among executors. Comparing Spark and Hadoop, the shuffle operation is implemented differently. Â.
Shuffling has 2 important compression parameters:
spark. shuffle. Whether the engine would compress shuffle outputs or not is determined by the compress command. shuffle. spill. determines whether or not to compress intermediate shuffle spill files.
When combining two tables or using byKey operations like GroupByKey or ReduceByKey, it happens.
In order to minimize the number of partitions in a DataFrame, Spark uses a coalesce method.
Consider reading data into an RDD with four partitions from a CSV file.
This is how a filter operation to remove all multiples of 10 from the data is carried out.
The RDD has some empty partitions. Coalesce can be used to reduce the number of partitions, which makes sense.
After applying to coalesce, the final RDD would look like this.
Consider the following cluster information:
Here is the number of core identification:
To calculate the number of executor identification:
The Spark Core engine is used to process large data sets in parallel and distributed fashion. The various functionalities supported by Spark Core include:.
A Spark RDD can be transformed into a DataFrame in one of two ways:
You can convert an RDD[Row] to a DataFrame by
calling createDataFrame on a SparkSession object
def createDataFrame(RDD, schema:StructType)
An elementary Spark data structure is called Resilient Distributed Dataset (RDD). RDDs are distributed collections of objects of any type that are immutable. It collects data from various nodes and guards against serious errors.
Two different types of operations are supported by Spark’s Resilient Distributed Dataset (RDD). These are:Â.
In Spark, the transformation function creates fresh RDDs from the existing ones. The transformation creates a new RDD whenever it happens by using an existing RDD as input and generating one or more RDD as output. The input RDDs don’t change and remain constant because it’s immutable. Â.
Additionally, if we use Spark transformation, it creates RDD lineage, including all of the final RDDs’ parents. This RDD lineage may also be referred to as an RDD operator graph or RDD dependency graph. The logically executed plan is known as RDD Transformation, and as a result, it is a Directed Acyclic Graph (DAG) of the continuous parent RDDs of RDD.
The RDD Action operates on a real dataset by carrying out a number of distinct actions. The new RDD does not generate whenever the action is triggered, unlike in transformation. It illustrates how Spark RDD operations called “Actions” produce non-RDD values. These non-RDD values of action are stored by the drivers and external storage systems. This brings all the RDDs into motion.
The action is how the data is sent from the Executor to the driver if it is properly defined. Executors are responsible for carrying out a task and acting as agents. In contrast, the driver functions as a JVM process that makes task execution and worker coordination easier. Â.
This is another frequently asked spark interview question. A lineage graph shows the dependencies between the old and new RDDs. Instead of the original data, it means that all of the dependencies between the RDD will be documented in a graph.
When computing a new RDD or trying to recover lost data from a lost persisted RDD, an RDD lineage graph is required. Spark does not support data replication in memory. Consequently, RDD lineage can be used to rebuild any lost data. It is also known as an RDD dependency graph or an RDD operator graph.
A Discretized Stream (DStream) is the basic abstraction in Spark Streaming and is a continuous sequence of RDDs. These RDD sequences are all of the same type and represent an ongoing data stream. Every RDD contains data from a specific interval.
Spark’s DStreams can receive input from a variety of sources, including TCP sockets, Flume, Kafka, and Kinesis. As a data stream created by converting the input stream, it can also function. It facilitates developers with a high-level API and fault tolerance.
Caching, also referred to as persistence, is a Spark computation optimization method. DStreams, like RDDs, let programmers keep the data from a stream in memory. That is, when a DStream’s persist() method is called, all of its RDDs are automatically stored in memory. Saving interim partial results for use in later stages is beneficial.
For input streams that receive data over the network and for fault tolerance, the default persistence level is set to replicate the data to two nodes.
Instead of sending a copy of a variable with tasks, broadcast variables enable programmers to keep a read-only variable cached on each machine. They can be used to effectively distribute copies of a sizable input dataset to each node. To cut down on communication costs, Spark distributes broadcast variables using effective broadcast algorithms.
scala> val broadcastVar = sc.broadcast(Array(1, 2, 3))
broadcastVar: org.apache.spark.broadcast.Broadcast[Array[Int]] = Broadcast(0)
res0: Array[Int] = Array(1, 2, 3)
If you still have questions about the spark interview questions for beginners, please post them in the comments section below. Â.
Let’s move on and comprehend the spark interview questions for knowledgeable candidates.
Question:Write a script for the below scenario either in PySpark (or) Spark Scala.
1. Read the provided “Spark RDD” input testfile (Pipe delimited).
2. Remove the Header Record from the RDD
3. Calculate Final_Price:
Final_Price = (Size * Price_SQ_FT)
4. Save the final rdd asTextfile with three fields
