Are you preparing for the Spark SQL interview? Are you sure you have covered all the basic and advanced level questions? If not, then our guide on Spark SQL interview questions will help you crack the interview. In this blog, we have listed Spark SQL interview questions and answers prepared by the industry experts so that you can ace your interview.
Based on Hadoop and MapReduce, Apache Spark is an open-source, blazingly fast computation technology that supports a variety of computational techniques for quick and effective processing. The primary feature of Spark that contributes to the acceleration of its applications' processing speed is its in-memory cluster computation.
Matei Zaharia created Spark in 2009 at UC Berkeley's AMPLab as a component of the Hadoop subproject. It was subsequently released under the BSD License in 2010 and decided to donate to the Apache Software Foundation in 2013. Spark surpassed all other projects carried out by the Apache Foundation starting in 2014.
Before we begin the interview questions, let us analyze certain important facts about Spark SQL.
RDDs and relationship tables are conveniently conflated by Spark SQL. Developers can easily combine Sql statements querying external data with organization . This means within a single application by combining these potent abstractions. Specific capabilities provided by Spark SQL include:
Import data items from Parquet files and Hive tables
To speed up queries, Spark SQL also has an expense optimizer, crystalline storage, and code generation. Without having to worry about using a different engine for historical data, it scales to hundreds of nodes and multi-hour queries using the Spark engine, which offers full mid-query fault tolerance.
Clearly, the demand for Spark SQL professionals is quite high. We are certain that these interview questions will help you bag your dream job and make the process a whole lot easier.
For easy understanding, we have divided the questions into two categories that is,
Top 10 Spark SQL Questions
Receivers are the organizations that take in data from various data sources and then transfer it to Spark for computation. They are made using long-running tasks that are planned to run in a round-robin fashion in streaming contexts. The receivers are set up to only use one core per receiver. To carry out the task of data streaming, the receivers are designed to function on a variety of executors. Guess it depends on how the data is transmitted to Spark, there are two different types of receivers:
For effective reading and processing, Spark supports both the raw file formats and the organized file formats. Spark can read files in paraquet, XML, JSON, CSV, Avro, TSV, RC, and other formats.
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Shuffling/repartitioning is the process of redistributing data among various partitions, which may or may not result in data movement among JVM processes or executors on various machines. The only thing a partition is is a smaller rational division of data.
One of the key components of Spark is YARN, a platform for central resource management that enables scalable operations across the cluster. Spark is a data processing tool, and YARN is a grouping management technology.
Redistributing data among partitions is a process called shuffling that could result in data moving among executors. Compared to Hadoop, Spark has a different implementation of the shuffle operation. There are 2 crucial compression parameters for shuffling:
You must set the spark.cleaner.ttl parameter in order to start the cleanups.
A Discrete - time Stream (DStream) is a stream of RDDs that runs continuously and serves as the basic abstract concept in Spark Streaming. These RDD sequences are all of the same type and represent an ongoing data stream. Each RDD includes information from a particular interval. Spark's DStreams can receive data from a variety of sources, including TCP sockets, Flume, Kafka, and Kinesis. As a data stream created by transforming the input stream, it can also function. With a high-level API and fault tolerance, it helps developers.
Instead of sending a copy of a read-only variable along with tasks, the programmer can use broadcast variables to keep it cached on each machine. They can be effectively used to distribute copies of a sizable input dataset to each node. To cut down on communication costs, Spark distributes telecast variables using effective broadcast algorithms.
Directed acyclic graph (DAG) refers to a graph without directed cycles. The number of vertices and edges would be finite. Each edge through one vertex is sequentially directed to another vertex. Connected by edges the operations to be carried out on the Spark RDDs, and the vertices refer to those RDDs.
In Spark, there are two deploy modes. As follows:
A basic Spark data structure is the Resilient Distributed Dataset (RDD). RDDs are distributed compilations of objects of any type that are immutable. It collects information from various nodes and guards against serious errors. Two different types of operations are supported by Spark's Resilient Distributed Dataset (RDD). Which are:
The RDD Action operates on a real dataset by carrying out a few particular operations. The new RDD does not generate as it does during transformation whenever the action is triggered. It shows how Spark RDD operations called "Actions" produce non-RDD values. These non-RDD values of action are stored by the drivers and external memory systems.
This starts the RDDs all in motion. The action, if properly defined, is the method by which the Executor transmits data to the driver. Executors carry out a task's execution while acting as agents. In contrast, the driver functions as a JVM process that makes task execution and worker coordination easier.
Three steps can be followed to create a DataFrame programmatically:
Yes, Apache Spark has a checkpoint management and addition API. The process of checkpointing makes streaming applications fault-tolerant. You can store the information and metadata in a checkpointing directory. In the event of a failure, the spark can recoup this data and resume operations where it left off. Spark offers checkpointing for two different kinds of data.
Checkpointing for Metadata: Metadata is information about information. The saving of the metadata to a fault-tolerant storage system, such as HDFS, is meant. Configurations, DStream processes, and incomplete batches are examples of metadata.
Data checkpointing: We save the RDD to dependable storage in this instance because some stateful transformations call for it. Here, the forthcoming RDD
The sliding window regulates the transfer of information of data packets between various computer networks. The windowed computations offered by the Spark Streaming library involve applying RDD transformations to a moving window of data.
Variables called accumulators are employed to combine data from various executors. This data may include API diagnosis, such as how many corrupted records there are or how frequently a library API was used.
Spark Dataframes are a dispersed collection of datasets that have been organized into SQL-like columns. It is primarily designed for big data operations and is comparable to a table in a relational database. Data from a variety of sources, including external databases, pre-existing RDDs, Hive Tables, etc., can be used to build dataframes.
The attributes of Spark Dataframes are as follows:
MLlib consists of two parts:
To apply complex data transformations, Spark MLlib enables you to combine multiple transformations into a pipeline.
The Apache Spark module for working with structured data is called Spark SQL. Several structured data sources are used by Spark SQL to load the data. Both from within a Spark programme and from external tools that link up to Spark SQL through common database connectors (JDBC/ODBC), it queries data utilizing SQL statements. It offers a thorough integration of SQL with standard Python, Java, and Scala code, allowing for the joining of RDDs and SQL tables as well as the exposure of custom SQL functions.
Position the hive-site.xml file in the Spark conf directory to connect Hive to Spark SQL.
In a novel way, Catalyst Optimizer makes use of advanced computer language features like Scala's quasi quotes and pattern matching to create an expandable query optimizer.
Property Operator: Using a user-defined map function, property operators create a new graph by changing the vertex or edge properties.
Structural Operator: Structural operators alter the input graph's structure to create a new graph.
Enter Operator: Enter operators Create new graphs and add data to existing ones.
The API for graphs and graph-parallel integer arithmetic in Apache Spark is called GraphX. To make analytics tasks simpler, GraphX includes a collection of graph algorithms. The algorithms can be directly accessed as methods on Graph via GraphOps and are part of the org.apache.spark.graphx.lib package.
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The fixed core count and fixed heap size characterized for spark executors are the same for applications developed in Spark. The property spark.executor.memory, which is a part of the -executor-memory flag, controls how much memory the Spark executor has available. This property is referred to as the heap size. On each worker node where a Spark application runs, one executor is set aside for it. The probate court memory is a measurement of the memory used by the application's worker node.
Worker nodes are the nodes in a cluster that manage the Spark application. The executors send connections to the Spark driver programme, which accepts them, and addresses people to the worker nodes for execution. A worker node functions similarly to a slave node in that it receives instructions out of its master node and executes them.
The worker nodes process data and inform the master of the resources used. The tasks are then planned for the worker nodes by the maestro based on the amount of resources that need to be allocated and their availability.
Data transfers are equivalent to the shuffling process. Spark applications run faster and more reliably when these transfers are minimized. These can be minimized in a number of different ways. As follows:
Another popular strategy is to stay away from the operations that cause these reshuffles.
Instead of including a copy of the variable with tasks, broadcast variables allow the developers to keep read-only variables cached on each machine. They are employed to effectively distribute copies of a sizable input dataset to each node. To cut the cost of communication, these variables are broadcast to the nodes utilizing different algorithms.
Setting the spark.cleaner.ttl parameter or batch-wise dividing the long-running jobs and then writing the middleman results to disc can automatically start the cleanup tasks.
Data from a data stream is split up into DStreams, or batches of X seconds, as part of Spark Streaming. When the data from a DStream is used for multiple computations, these DStreams allow developers to cache the data into memory, which can be very helpful. Data can be cached using the cache() method or the persist() method with the right levels of persistence. The input streams receiving default persistence level
The pipe() method on RDDs, which Apache Spark offers, allows users to compose various components of jobs that can use any language as needed in accordance with UNIX Standard Streams. The RDD transformation can be written using the pipe() method and used to read each element of the RDD as a String. These can be altered as needed, and the outcomes can be shown as Strings.
In order to support graphs and graph-based computations, Spark offers a potent API called GraphX that stretches Spark RDD. The resilient distributed property graph, which is a directed multi-graph with numerous parallel edges, is the name of the extended property of the spark RDD.
An open-source framework engine called Apache Spark is renowned for its efficiency and user-friendliness in the area of big data analysis. Additionally, it includes built-in modules for SQL, streaming, machine learning, and graph processing. The spark implementation engine supports cyclic data flow and in-memory computation. It can operate in standalone or cluster mode and can access a variety of data sources, including HBase, Cassandra, HDFS, etc.
There are three main subcategories that make up the Apache Spark ecosystem. Which are:
The interviewer will count on you to provide an in-depth response to one of the most typical spark interview questions. Spark applications function as separate processes under the control of the driver program's SparkSession object. One task is given to each partition of the worker nodes by the task scheduler or cluster manager.
Iterative algorithms benefit from caching datasets all over iterations by repeatedly applying operations to the data. A task creates a new partition dataset by applying its unit of work to the set of data in its partition. The outcomes are then either saved to the disc or sent back to the driver application.
Spark retains the instructions when processing any dataset. An RDD undergoes a transformation when a function like map() is called, but the operation is not completed immediately. Lazy evaluation, which improves the efficiency of the entire data processing workflow, prevents transformations in Spark from being evaluated until you take a specific action.
In order to process data more quickly and create machine learning models, Apache Spark stores data in memory. To produce an ideal model, machine learning algorithms go through several conceptual iterations. To create a graph, graph algorithms go through each of the nodes and edges. Performance may improve with these low latency caseloads that demand multiple iterations.
You can connect Spark to Apache Mesos using a total of 4 steps.
A number of data processing systems support the columnar Parquet format. Spark is able to both read from and write to the Parquet file. The following are some advantages of owning a Parquet file:
The Spark Core engine is used to process large data sets in parallel and over a distributed network. The various functions that Spark Core supports include:
Caching, also referred to as persistence, is a Spark computation optimization technique. DStreams give programmers the same ability to keep stream data in memory as RDDs do. That is, every RDD of a DStream will be automatically stored in memory when the persist() method is called on the DStream. Saving interim partial results for use in later stages is beneficial. For fault tolerance and input streams that receive data over the network, the default perseverance level is set to recreate the data to two nodes.
This is yet another query that comes up frequently in spark interviews. A lineage graph shows the relationships between the old and new RDDs. In place of the raw data, all of the relationships between the RDD would be represented as 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 doesn't support in-memory data replication. As a result, RDD lineage can be used to rebuild any lost data. An RDD operating company graph or RDD addiction graph is another name for it.
Interactive SQL queries are frequently used by data scientists, analysts, and users of general business intelligence to explore data. A Spark module for processing structured data is Spark SQL. It offers the DataFrame programming abstraction and functions as a distributed SQL query engine. It makes it possible for existing implementations and data to process Hadoop Hive queries up to 100 times faster than before. Additionally, it offers strong integration with the remainder of the Spark ecosystem.
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