Apache Spark Interview Questions

  1. Do you know the comparative differences between Apache Spark and Hadoop?
  2. Name few companies that are the uses of Apache spark?
  3. What is a Hive on spark?
  4. What is Spark SQL?
  5. What are accumulators in spark?
  6. Can you explain about the cluster manager of Apache spark?
  7. Name the operations supported by RDD?
  8. What do you understand by the partitions in spark?
  9. How can we create RDDs in Apache spark?
  10. If map reduce is inferior to Spark then is there any benefit of learning it?
  11. What do you understand about yarn?
  12. List few benefits of spark over map reduce?
  13. Name the languages which are supported by Apache Spark and which one is most popular?
  14. Can you define RDD?
Apache Spark Interview Questions

Reportedly a fact has been introduced that Apache Spark's market share is been around 4.9 to 5 %. So there is a lot in the market for everyone to gain. Many of the international firms have opened the doors for the research and development with the Apache Spark. With the absolute preparation through Apache Spark interview questions and practising the advanced technology, you can definitely achieve your dream job of an Apache Spark developer.

Apart from the fundamental proficiency, a candidate might also be asked skills in Hadoop, hive, Sqoop and many more. You will get a perfect combination of Apache spark interview questions for fresher as well as experienced candidates here. In the most specific segment like Spark SQL programming, there are enough job opportunities. If you have given a thought to it then keep yourself assure with your skills and below listed Apache Spark interview questions.

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Yes there are several segments on which they can be differentiated. Few of them are-

FeatureApache sparkHadoop
SpeedIt is almost 100 times faster than HadoopIt has moderate speed
ProcessingOffers real time and batch processing functionalityIt offers batch processing only
DifficultyIt has high level modules hence it is easyIt is tough to learn
RecoveryIt allows the partition recoveryMapReduce
InteractivityIt has interactive modesOther than Pig and Hive, it has no interactive mode

The companies that are using Apache Spark with the production are Pinterest, Shopify, Open Table, ComViva.

For the Apache Spark processing, Hive contains the support. High execution is formulated to spark.

Spark SQL is basically a module which is formulated to provide structured data processing. The advantage of SQL message queries running on the datasheets can be taken from it.

The write only variables which are initially executed once and send to the workers are accumulators. On the basis of the logic written, these workers will be updated and sent back to the driver which will process it on the basis of logic. A driver has the potential to exercise accumulator’s value.

There are three different cluster manager in Spark which are as-

  • YARN
  • Apache Mesos - Spark along with several other applications can easily be scheduled and run over it. Its priority is to scale down the allocations between several commands in order to provide interfaces when several users run their shells.
  • Standalone deployments - For the easy setup and convenience, for the new deployments.

As the major logical data units in Apache Spark, RDD possesses a distributed collection of data. It is a read-only data structure and you cannot change the original format but it can always be transformed into a different form with the changes. The two operations which are supported by RDD are -

  • Transformation - It creates a new RDD from the former one. They are executed only on demand.
  • Actions - The final outcomes of the RDD computations are returned by actions.

Partitions are done in order to simplify the data as they are the logical distribution of entire data. It is similar to the split in MapReduce. In order to enhance the processing speed, this logical distribution is carried out. Each and every association in Apache Spark is a partitioned RDD.

In order to create RDD there are basically two methods –

  • In a driver program parallelizes a collection. This will use the sparkcontext's “Parallelize”.
  • From the external storage, an external data sheet can be loaded into the file system.

Apache Spark is far better than MapReduce but still learning MapReduce is essential. MapReduce is a paradigm which is even used by Spark as big data tools. When the data is large and grows bigger, in that case, MapReduce is much relevant. Data tools like pig and hive convert their message queries into MapReduce in order to optimize them properly.

The YARN is a key feature in Spark which provides a central resource management for most of the operational deliveries across a cluster. It is also a container manager like Mesos. Spark can easily run on YARN which eventually emphasizes a binary distribution of Apache Spark built on its support.

The benefits are –

  • With the in-memory processing, Spark implements it around 100 times faster than the head of MapReduce.
  • Spark provides inbuilt libraries for most of the multidimensional task as compared to map reduce.
  • Spark is independent of the disk storage on the other hand Hadoop is highly dependent.
  • Spark can perform multiple computations within the same datasheet.

Apache Spark supports the languages Java, Python, Scala and R. among them Scala and Python have interactive shares for Apache Spark and Scala shell can be easily accessed through the ./bin/spark-shell and Python can be accessed through ./bin/pyspark. Among them, Scala is the most popular because Apache Spark is written in Scala.

The acronym for Resale in Distributed Datasheets is RDD. It is a fault-tolerant collection for all of the elements that run parallel. The sorted data in RDD is immutable and primarily of two types –

  • Parallelized collections
  • Hadoop datasets

On a general note, the most essential features of Apache Spark are-

  • It allows the integration with Hadoop and files including HDFS.
  • It also has an interactive language shell.
  • It consists of RDD (Resilient Distributed Datasheets) it can be cached across multiple computing nodes in a cluster.
  • It also supports analytical tools which are utilized for interactive analysis and real-time analysis.

Apache Spark is basically a processing framework which is extremely fast and convenient to use. With an advanced execution engine supporting it offers the cyclic data flow and in-memory computation. Apache spark can also run on Hadoop, cloud or standalone. It is capable to access the diverse data including Cassandra, HDFS, HBase.