Spark Interview Questions and Answers
Apache Spark is a unified analytics engine designed to handle large volumes of data. With over 80 high-level operators, it facilitates the straightforward development of parallel applications and can execute workloads 100 times faster. Spark can access data from many sources and run standalone, in the cloud, on Apache Mesos, Hadoop, or Kubernetes. Learn how to build applications with Spark by reading the Spark overview for Scala Analytics.
The most significant Apache Spark interview questions that you might encounter in a Spark interview are also covered in this post. After reading this post, you should be able to respond to the majority of the questions posed in your next Spark interview. The Spark interview questions have been divided into distinct sections according to the different components of Apache Spark.
Spark Interview Questions and Answers for Beginners
What is a spark?
A framework for parallel data processing is called Spark. It makes it possible to quickly create cohesive big data applications that integrate interactive, streaming, and batch analytics.
Which elements of the Spark ecosystem are crucial?
The Apache Spark ecosystem is divided into three primary areas. They are as follows:
Language support: Spark can do analytics and interface with applications written in a variety of languages. Scala, R, Java, and Python are these languages.
Core components: Spark is compatible with five primary core components. GraphX, Spark MLlib, Spark Streaming, Spark Core, and Spark SQL are the available options.
Cluster Management: Three environments are available to operate Spark. They are YARN, Apache Mesos, and the Standalone cluster.
For what reason, Spark?
The third-generation platform for distributed data processing is called Spark. For all big data processing issues, including batch, interactive, and streaming processing, this is the one-stop shop. Thus, it can help with a lot of large data issues.
RDD: What is it?
Resilient Distributed Datasets are the main fundamental abstraction of Spark. A grouping of partitioned data that meets these requirements is called an RDD. Common features of RDDs are immutability, distribution, lazy evaluation, and catchability.
What does immutability mean?
This quality, known as immutability, means that once something is produced and assigned a value, it cannot be changed. Spark is immutable by default; updates and alterations are not permitted. Note that while data values are unchangeable, data collection is not.
What does distributed mean?
RDD can automatically distribute data among several nodes for parallel computation.
How is Lazy assessed?
If you run a lot of projects, you don’t have to evaluate them right away. This laziness is a trigger, particularly in transformations.
What Does the Word “Catchable” Mean?
For computing, keep all of the data in memory instead of transferring it to a disc. As a result, Spark can process data 100 times quicker than Hadoop.
Recommended Article: Analyzing Big Data in R using Apache Spark
What is the accountability of the Spark engine?
Spark is in charge of allocating, scheduling and keeping an eye on the application throughout the cluster.
Typical Spark Ecosystems: What Are They?
Common Spark ecosystems include
- Spark SQL (Shark) for SQL developers,
- Spark Streaming for streaming data,
- MLLib for machine learning methods,
- GraphX for graph processing,
- SparkR for running R on the Spark engine,
- BlinkDB for enabling interactive searches over large amounts of data. GraphX, SparkR, and BlinkDB are currently in the incubation phase.
Partitions: What Are They?
A logical divide of the data is called partitioning, and the concept comes from split (or map-reduce). To process the data, logical data is specially derived. Small data sets can facilitate scalability and expedite the procedure. All of the data—input, intermediate, and output—is partitioned in RDD.
How is the data partitioned using Spark?
Spark partitions the data using the map-reduce API. It is possible to generate many partitions in the input format. HDFS block size determines partition size by default; however, partition sizes can be altered using Split.
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How is the data stored by Spark?
There is no storage engine; Spark is a processing engine. Any storage engine, including HDFS, S3, and other data resources, can be used to retrieve data.
Does the Spark application require Hadoop to be started to run?
No, it’s not required; however, Spark stores the data on a local file system because it lacks dedicated storage. Data can be loaded and processed from the local system; Spark does not require Hadoop or HDFS to operate.
SparkContext establishes a connection with the Spark cluster to generate a new SparkContext object whenever a programmer builds an RDD. Spark knows how to reach the cluster, thanks to SparkContext. An essential component in developing a programming application is SparkConf.
What features does SparkCore offer?
The Apache Spark framework’s underlying engine is called SparkCore. The main features of Spark include memory management, fault tolerance, scheduling, job monitoring, and storage system interaction.
What distinguishes SparkSQL from SQL and HQL?
Without requiring any syntax changes, SparkSQL is a unique part of the Spark core engine that supports both SQL and HiveQuery Language. The SQL table and the HQL table can be joined.
Our article that explains Spark Fundamentals will help you get started with Apache Spark for a promising career in database management.
When was Spark Streaming put to use?
Real-time streaming data API processing is known as Spark streaming. Spark streaming collects streaming data from a variety of sources, including social media, web server log files, the financial market, and Hadoop ecosystems like Kafka and Flume.
Spark Interview Questions for Experienced
How can you programmatically define a DataFrame schema?
Three steps can be taken to generate a ‘DataFrame’ programmatically:
- Make an RDD of rows by using the initial RDD.
- Create the schema, which is represented via a StructType that matches the Step 1-established row structure in the RDD.
- Use SparkSession’s createDataFrame function to apply the schema to the RDD of rows.
Suggested Read: Spark and Scala Fundamentals
Does Apache Spark provide checkpoints?
One of the most popular spark interview questions is this one, to which the interviewer is expecting a thorough response rather than a simple affirmative or negative response. Provide as much information as you can in this response.
Indeed, there is an API for adding and managing checkpoints provided by Apache Spark. The practice of making streaming programs resilient to errors is called checkpointing. The data and information can be saved in a checkpointing directory. The spark can recover this data in the event of a failure and resume where it left off.
In Spark, we may utilize checkpointing for two sorts of data.
Checkpointing Metadata: Metadata refers to information about information. It speaks about storing the metadata on HDFS or another fault-tolerant storage system. Configurations, DStream operations, and incomplete batches are examples of metadata.
Data Checkpointing: In this case, the RDD is saved to dependable storage since certain stateful transformations require it. In this instance, the future RDD depends on the RDDs of earlier batches.
What are the various Spark persistence levels?
DISK_ONLY: On the disk, only the RDD partitions are stored.
MEMORY_ONLY_SER: This option serializes Java objects with a one-byte array for each partition to store the RDD.
MEMORY_ONLY: This option stores the RDD in the JVM as deserialized Java objects. Partitions won’t be cached if the RDD can’t fit in the available RAM.
OFF_HEAP: This function is similar to MEMORY_ONLY_SER, but it saves data in off-heap memory.
MEMORY_AND_DISK: In the JVM, RDD is stored as deserialized Java objects. Additional partitions are kept on the disk in case the RDD cannot fit in the memory.
MEMORY_AND_DISK_SER: Same as MEMORY_ONLY_SER, but it stores partitions to the disk that are too big to fit in memory.
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How would you use Spark to calculate the total number of unique words?
- Open the text file in RDD mode.
- The function that divides each sentence into words is:
- Apply the toWords function as a flatMap transformation to every RDD member in Spark:
words = line.flatMap(toWords);
- Create a (key,value) pair out of every word:
return (word, 1);
wordTuple = words.map(toTuple);
- Execute the reduceByKey() procedure:
def sum(x, y):
counts = wordsTuple.reduceByKey(sum)
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Let’s say you have a large text document. How are you going to use Spark to determine whether a certain keyword exists?
lines = sc.textFile(“hdfs://Hadoop/user/test_file.txt”);
if line.find(“my_keyword”) > -1
foundBits = lines.map(isFound);
sum = foundBits.reduce(sum);
if sum > 0:
print “Not Found”;
What function do accumulators provide in Spark?
Variables called accumulators are employed to combine data from all of the executors. This data may include diagnostic information about the API or the data itself, such as the number of damaged entries or calls to a library API.
What kinds of MLlib tools does Spark provide?
ML Algorithms: Collaborative filtering, clustering, regression, and classification
Featurization: Getting smaller and more streamlined Selection, dimension reduction, Transformation, and Feature extraction
Pipelines: Tools for building, assessing, and fine-tuning machine learning pipelines
Persistence: Models, pipelines, and algorithms saved and loaded
Services: Data processing, statistics, and linear algebra
Which various data formats does Spark MLlib support?
Spark MLlib supports both local vectors and matrices stored on a single computer, as well as distributed matrices.
Local Vector: MLib supports dense and sparse local vectors.
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 that has a label or response attached to it. It can be sparse or dense.
Example: A label in binary classification needs to be either 1 (positive) or 0 (negative).
Local Matrix: Double-type values are kept in a single machine and integer type row and column indices are present in a local matrix.
Distributed Matrix: A distributed matrix is distributedly stored in one or more RDDs, with double-type values and long-type row and column indices.
Different forms of distributed matrices
A Sparse Vector: What Is It?
An index array and a value array represent a sparse vector, a kind of local vector.
public class SparseVector
Example: sp1 = SparseVector(4, [1, 3], [3.0, 4.0])
4 is the size of the vector
[1,3] are vector’s ordered indices
[3,4] are values
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Explain the MLlib model-building process and the model’s application.
MLlib consists of two parts:
Transformer: A transformer takes in a ‘DataFrame’, applies a certain transformation on it, and then outputs a new DataFrame.
Estimator: A machine learning approach known as an estimator uses a ‘DataFrame’ to train a model and then outputs the model as a transformer.
With Spark MLlib, you can apply complicated data transformations by combining many transformations into a pipeline.
What uses does Spark SQL provide?
The Apache Spark module for handling structured data is called Spark SQL.
Numerous structured data sources are loaded via Spark SQL.
Both from within a Spark program and from outside tools that connect to Spark SQL via common database connectors (JDBC/ODBC), it queries data using SQL commands.
It offers a sophisticated connection between SQL and standard Python, Java, and Scala code, enabling the joining of RDDs with SQL tables as well as the exposure of bespoke SQL procedures.
Recommended Read: Simplifying Data Pipelines with Apache Kafka
How can Spark SQL be connected to Hive?
Place the hive-site.xml file in Spark’s conf directory to establish a connection between Hive and Spark SQL.
The Spark Session object can be used to build a DataFrame.
result=spark.sql(“select * from <hive_table>”)
Which categories of operators does the Apache GraphX library offer?
When answering questions like these in a Spark interview, try providing more information than just the operators’ names.
Property Operator: Using a user-defined map function, property operators alter the vertex or edge properties to create a new graph.
Structural Operator: A structural operator creates a new graph by manipulating the structure of an input graph.
Join Operator: Participate in operators Create new graphs and add data to existing ones.
In Apache Spark GraphX, which analytical methods are available?
The graph-parallel processing API from Apache Spark is called GraphX. A collection of graph algorithms is included in GraphX to make analytics work easier. The algorithms can be directly accessible as methods on Graph via GraphOps and are part of the org.apache.spark.graphx.lib package.
PageRank is a graph parallel calculation that calculates each vertex’s significance inside a graph. For instance, you can use PageRank to determine which Wikipedia pages are the most significant.
Connected Components: The connected components algorithm uses the ID of the lowest-numbered vertex in the network to identify each related component. Connected components, for instance, can roughly represent clusters in a social network.
Triangle Counting: When two nearby vertices of a triangle have an edge connecting them, then that vertex is a part of the triangle. The TriangleCount object in GraphX implements a triangle counting technique that counts the number of triangles that pass through each vertex to provide a clustering metric.
Useful Link: Data Science for Scala
Here is a compilation of some of the most frequently requested, theoretical, and conceptual Apache Spark interview questions that you may encounter when attending a Spark-related interview. However, you may also sign up for Softlogic Systems’ Big Data Certification Training, which will enable you to become proficient with the Big Data Hadoop Ecosystem.