Spark interview questions and answers in 2022

This is a common Spark interview question. The cluster managers are:

Standalone Mode: The standalone mode cluster executes applications in FIFO order by default, with each application attempting to use all available nodes. You can manually start a standalone cluster by manually starting a master and workers. It is also possible to test these daemons on a single system.

Apache Mesos: Apache Mesos is an open-source project that can run Hadoop applications as well as manage computer clusters. The benefits of using Mesos to deploy Spark include dynamic partitioning between Spark and other frameworks, as well as scalable partitioning across several instances of Spark.

Hadoop YARN: Apache YARN is Hadoop 2's cluster resource manager. Spark can also be run on YARN.

Kubernetes: Kubernetes is an open-source solution for automating containerized application deployment, scaling, and management.

Apache Spark caches data in memory to allow for faster processing and the development of machine learning models. To construct an optimal model, machine learning algorithms require several iterations and distinct conceptual processes. To construct a graph, graph algorithms explore all of the nodes and edges. These low-latency workloads that necessitate repeated iterations can result in improved performance.

This is another common Spark interview question. A Lineage Graph is a graph of dependencies between an existing RDD and a new RDD. It means that instead of the original data, all of the dependencies between the RDD will be represented in a graph.

An RDD lineage graph is required when we want to calculate a new RDD or recover lost data from a persisted RDD that has been lost. Spark does not support in-memory data replication. As a result, if any data is lost, it can be recreated using RDD lineage. It's sometimes referred to as an RDD operator graph or an RDD dependency graph.

Spark remembers the instructions when it operates on any dataset. When a transformation, such as a map(), is invoked on an RDD, the action is not completed immediately. Lazy evaluation occurs when transformations in Spark are not assessed until you execute an action that aids in optimizing the overall data processing workflow.

You will often come across this Spark coding interview question. A Discretized Stream (DStream) is the rudimentary abstraction in Spark Streaming and is a continuous succession of RDDs. These RDD sequences are all of the same types and represent a continuous stream of data. Every RDD holds information from a specified time interval.

DStreams in Spark accepts input from a variety of sources, including Kafka, Flume, Kinesis, and TCP connections. It can also be used to generate a data stream by transforming the input stream. It helps developers by providing a high-level API and fault tolerance.

The process of dispersing data across partitions, which may result in data migration across executors, is known as shuffling. When opposed to Hadoop, Spark does the shuffle process differently.

Shuffling has 2 important compression parameters:

• Spark.shuffle.compress – checks whether the engine would compress shuffle outputs
• Spark.shuffle.spill.compress – decides whether to compress intermediate shuffle spill files or not.

You will often come across this Spark coding interview question. Spark Core is the engine that handles huge data sets in parallel and distributed mode. Spark Core provides the following functionalities:

• Job scheduling and monitoring
• Memory management
• Fault detection and recovery
• Interacting with storage systems
• Task distribution, etc.

Caching, often known as Persistence, is a strategy for optimizing Spark calculations. DStreams, like RDDs, allow developers to store the stream's data in memory. That is, calling the persist() method on a DStream will automatically keep all RDDs in that DStream in memory. It is beneficial to store interim partial results so that they can be reused in later stages. For input streams that receive data via the network, the default persistence level is set to replicate the data to two nodes for fault tolerance.

You will often come across this Spark coding interview question. Yes, there is an API for adding and managing checkpoints in Apache Spark. The practice of making streaming applications resilient to errors is known as checkpointing. It lets you save data and metadata to a checkpointing directory. In the event of a failure, Spark can recover this data and resume where it left off.

Checkpointing in Spark can be used for two sorts of data.

• Checkpointing Metadata: Metadata is data about data. It refers to storing the metadata in a fault-tolerant storage system such as HDFS. Configurations, DStream actions, and incomplete batches are all examples of metadata.

• Data Checkpointing: In this case, we store the RDD in a reliable storage location because it is required by some of the stateful transformations.

Accumulators are variables that are used to aggregate information between executors. This information can be about the data or an API diagnosis, such as how many damaged records there are or how many times a library API was called.