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In the technical market, developers are always in search of advanced data processing tools that can process data faster to meet the flexible needs of the superior market. Also, advanced tools are able to handle real-time data processing within seconds. Here, Apache is getting quick momentum when it comes to enterprises and large-sized businesses that generally have plenty of big data to work on. Highlights of Apache Spark and why it has gone so popular during last few years??
The increasing demand of Apache Spark has triggered us to compile a list of Apache Spark interview questions and answers that will surely help you in the successful completion of your interview. These questions are good for both fresher and experienced Spark developers to enhance their knowledge and data analytics skills both.
In the earlier section, we have given the list of 30 questions after careful research and analysis that will surely help you in interview selection. During later sections, we will provide answers to each question by dividing whole 30 questions into three sets – Apache Spark SQL interview questions, Apache Spark Scala interview questions, and Apache Spark Coding interview questions.
Here is a list of the key features of Apache Spark:
Here are the core components of the Spark ecosystem:
Apache Spark supports the accompanying four languages: Scala, Java, Python and R. Among these languages, Scala and Python have intuitive shells for Spark. The Scala shell can be gotten to through ./canister/start shell and the Python shell through ./receptacle/pyspark. Scala is the most utilized among them since Spark is composed in Scala and it is the most prominently utilized for Spark.
Apache Spark SQL is a popular ecosystem or interfaces to work with structured or semi-structured data. The multiple data sources supported by Spark SQL includethe text file, JSON file, Parquet file etc.
MLlib is a versatile machine learning library given by Spark. It goes for making machine adapting simple and versatile with normal learning calculations and utilize cases like grouping, relapse separating, dimensional decrease, and alike.
Like Hadoop, YARN is one of the key highlights in Spark, giving a focal and asset administration stage to convey versatile activities over the group. YARN is an appropriated compartment supervisor, as Mesos for instance, though Spark is an information preparing instrument. Spark can keep running on YARN, a similar way Hadoop Map Reduce can keep running on YARN. Running Spark on YARN requires a parallel dissemination of Spark as based on YARN support.
Yes, Spark SQL helps in big data analytics through external tools too. Let us see how it is done actually –
Spark SQL is advance database component able to support multiple database tools without changing their syntax. This is the way how Spark SQL accommodates both HQL and SQL superiorly.
Real-time data processing is not possible directly but obviously, we can make it happen by registering existing RDD as a SQL table and trigger the SQL queries on priority.
RDD is an abbreviation for Resilient Distribution Datasets. An RDD is a blame tolerant accumulation of operational components that keep running in parallel. The divided information in RDD is permanent and distributed in nature. There are fundamentally two sorts of RDD:
RDDs are essential parts of information that are put away in the memory circulated crosswise over numerous hubs. RDDs are sluggishly assessed in Spark. This apathetic assessment is the thing that adds to Spark's speed.
There are two types of operations that RDDs support: transformations and actions.
Parquet is a columnar arrangement record upheld by numerous other information preparing frameworks. Start SQL performs both read and write operations with Parquet document and think of it as an extraordinary compared to other enormous information examination arranges up until this point.
Parquet is a popular columnar file format compatible with almost all data processing systems. This is the reason why it is taken as one of the best choices for big data analytics so far. Spark SQL interface is able to perform read and write operation on Parquet file and it can be accessed quickly whenever required.
Spark SQL is parallel data processing framework where batch streaming and interactive data analytics is performed altogether.
Catalyst framework is advanced functionality in Spark SQL for automatic transformation of SQL queries by addition of optimized functions that help in processing data faster and accurately than your expectations.
Each spark application has the same settled load estimate and settled a number of centers for a spark agent. The pile measure is the thing that alluded to as the Spark agent memory which is controlled with the spark.executor.memory property of the – agent memory signal. Each spark application will have one agent on every laborer hub. The agent memory is fundamentally a measure on how much memory of the specialist hub will the application use.
To maintain query accuracy and response time in Spark SQL, you are advised to go with BlinkDB query engine. The engine renders queries with meaningful results and significant error to maintain the accuracy.
The programming in Hadoop was really tough that has been made easier with Spark by usage of interactive APIs for the different programming language. Obviously, Spark is a preferable choice than Hadoop in terms of usage.
Spark has the ability to perform data processing 100 times faster than MapReduce. Also, Spark has inbuilt memory processing and libraries to perform multiple tasks together like batch processing, streaming, interactive processing etc. The above discussion makes sure than Apache Spark is surely better than any other data processing frameworks exist as of now.
The Array is a mutable data structure that is sequential in nature while Lists are immutable data structures that are recursive in nature. Size of array is predefined while lists change its size based on operational requirements. In other words, Lists are variable in size while the array is fixed size data structure.
The most wonderful solution to map data and forms together in Scala is “apply” and “unapply" methods. As the name suggests, the apply method is used to map data while the unapply method can be used to unmap the data. The unapply method follows the reverse operation of the apply method.
Yes, it is possible that private members of Companion classes can be accessed through companion objects in Scala.
Every time when working with concurrent programs and other similar equality issues then immutable design in Scala programming language works amazingly. It helps in resolving coding related issues and makes programming easy for Scala developers.
The keywords "def" and "this" is used to declare secondary or auxiliary constructors in Scala programming language. They are designed to overload constructors similar to Java. This is necessary to understand the working of each constructor deeply so that the right constructor can be invoked at the right time. Even declaration of constructor differs from each other in terms of data types or parameters.
Yield keyword can be used either before or after expressions. It is taken more useful when declared before expression. The return value from every expression will be stored as the collection. The returned value can either be used as a normal collection or iterate in another loop.
In case, when we want to invoke functions silently without passing all the parameters, we should use implicit parameters. The parameters that you want to use implicit, you need to provide default values for the same.
Scala trait is an advanced class in Scala that enables the use of multiple inheritances and it can be extended to multiple classes together. In other words, one class can have multiple Scala traits based on requirement. Traits are used commonly when you need dependency injection. You just need to initiate class with Scala traits and dependency will be injected immediately.
Normal users are generally confused between two terms parallelism and concurrency in Scala programming language. Here, we will discuss in simple words how they are different from each other and their significance too. When processes are executed sequentially then it is termed as concurrency while processes are executed simultaneously then it is named as parallelism technology. There are several library functions available in Scala to achieve parallelism.
If you want to understand Monads in simple words then it would not be wrong comparing them with a wrapper. As wrappers are used to protect any product and to make it attractive, Monads are used for the same purpose in Scala. They are used to wrap objects together and perform two important functions further. These functions are –
Transformations are created early in programs and these are generally used along with RDD. These functions are applied on already existed RDD to make a new RDD. Transformations cannot be used without implementing actions in Apache Spark. The most popular examples of transformation are amap () and filter () that helps to create new RDD by selecting elements in available RDD.
The data is taken back to the local machine from RDD with the help of “actions” in Apache Spark. The popular example of the action is folded () passes value again and again until the time it is left only one. The actions are executed with the assistance of transformations that are created early in programs. The most popular examples of transformation are amap () and filter () that helps to create new RDD by selecting elements in available RDD.
Spark Core in Apache Spark is used for memory management, job monitoring, tolerate faults, scheduling jobs and interactive storage features. RDD is an advanced feature in Spark Core suitable for tolerating faults. RDD is a collection of distributed objects available across multiple nodes that are generally manipulated in parallel.
No framework can come to the top without the functionality of live data streaming or handling live events. This is the reason why Apache Spark has used the most advanced techniques to allow the same. For this purpose, Apache uses complex algorithms and high-level functions like reduce, map, join or window etc. These functions push data to file systems and live dashboards further.
Out of all, one attractive feature supported in Apache Spark includes graph processing. Spark uses advanced multimedia component GraphX to create or explore graphs used to explore data more wisely and accurately.
Spark MLib is a popular library function in Apache Spark to support machine learning algorithms. The common learning algorithms and utilities included in MLib library functions are a regression, clustering, classification, dimensional reduction, low-level optimization, advance level pipelining APIs, and collaborative filtering etc. The main objective of the machine learning algorithm is recommendations, predictions and similar other functions.
Apache Spark is an advanced data processing system that can access data from multiple data sources. It creates distributed datasets from the file system you use for data storage. The popular file systems used by Apache Spark include HBase, Cassandra, HDFS, and Amazon S3 etc.
The three popular cluster modes supported in Apache Spark include – Standalone, Apache Mesos, and YARN cluster managers. YARN is the cluster management technology in Apache Spark stands for yet another resource negotiator. The idea was taken from Hadoop where YARN technology was specially introduced to reduce the burden on MapReduce function.
Yes, the cluster management technology in Apache Spark is popular with the name YARN technology. YARN stands for yet another resource negotiator. The idea was taken from Hadoop where YARN technology was specially introduced to reduce the burden on MapReduce function.
There are two popular techniques that can be used to create RDD in Apache Spark – First is Parallelize and other is text File method. Here is a quick explanation of how both methods can be used for RDD creation. val x= Array(5,7,8,9) val y= sc.parallelize(x) val input = sc.textFile(“input.txt”);
The key distinction between Hadoop and Spark lies in the way to deal with processing: Spark can do it in-memory, while Hadoop MapReduce needs to peruse from and keep in touch with a disc. Thus, the speed of handling varies altogether – Spark might be up to 100 times quicker. Be that as it may, the volume of information prepared likewise varies: Hadoop MapReduce can work with far bigger informational indexes than Spark.
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