Apache Spark is one of the most popular distributed, general-purpose cluster-computing frameworks. The open-source tool offers an interface for programming an entire computer cluster with implicit data parallelism and fault-tolerance features.
3 What is the role of Catalyst Optimizer in Spark SQL?
Catalyst optimizer leverages advanced programming language features (such as Scalaâs pattern matching and quasi quotes) in a novel way to build an extensible query optimizer.
1 How can you calculate the executor memory?
Consider the following cluster information:
Here is the number of core identification:
To calculate the number of executor identification:
Check out this insightful video on Spark Tutorial for Beginners:
Serving as the base engine, Spark Core performs various important functions like memory management, basic I/O functionalities, monitoring jobs, providing fault-tolerance, job scheduling, interaction with storage systems, distributed task dispatching, and many more. Spark Core is the base of all projects. The above-mentioned functions are Spark Core’s primary functions.
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2 What is Directed Acyclic Graph in Spark?
Directed Acyclic Graph or DAG is an arrangement of edges and vertices. As the name implies the graph is not cyclic. In this graph, the vertices represent RDDs, and the edges represent the operations applied to RDDs. This graph is unidirectional, which means it has only one flow. DAG is a scheduling layer that implements stage-oriented scheduling and converts a plan for logical execution to a physical execution plan.
2 What are Accumulators in Spark?
Ans: Spark of-line debuggers are called accumulators. Spark accumulators are similar to Hadoop counters, to count the number of events and what’s happening during the job you can use accumulators. Only the driver program can read an accumulator value, not the tasks.
2 What is a Spark Executor?
When SparkContext connects to Cluster Manager, it acquires an executor on the nodes in the cluster. Executors are Spark processes that run computations and store data on worker nodes. The final tasks by SparkContext are transferred to executors for their execution.
1 What do you understand about DStreams in Spark?
A Discretized Stream (DStream) is a continuous sequence of RDDs and the rudimentary abstraction in Spark Streaming. These RDDs sequences are of the same type representing a constant stream of data. Every RDD contains data from a specific interval.
The DStreams in Spark take input from many sources such as Kafka, Flume, Kinesis, or TCP sockets. It can also work as a data stream generated by converting the input stream. It facilitates developers with a high-level API and fault tolerance.
What is the PageRank algorithm in Apache Spark GraphX?
It is a plus point if you are able to explain this spark interview question thoroughly, along with an example! PageRank measures the importance of each vertex in a graph, assuming an edge from u to v represents an endorsement of vâs importance by u.
If a Twitter user is followed by many other users, that handle will be ranked high.
PageRank algorithm was originally developed by Larry Page and Sergey Brin to rank websites for Google. It can be applied to measure the influence of vertices in any network graph. PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The assumption is that more important websites are likely to receive more links from other websites.
A typical example of using Scalas functional programming with Apache Spark RDDs to iteratively compute Page Ranks is shown below:
Spark Driver is the programme that runs on the machines master node and tells RDDs how to be changed and what to do with them. In simple terms, a Spark driver creates a SparkContext linked to a specific Spark Master.
The driver also sends the RDD graphs to Master, where the cluster manager runs independently.