Apache Spark Interview Questions

1. What is Spark?

Apache Spark is a fast, easy-to-use and flexible data processing framework. It has an advanced execution engine supporting cyclic data  flow and in-memory computing. Spark can run on Hadoop, standalone or in the cloud and is capable of accessing diverse data sources including HDFS, HBase, Cassandra and others. 

2. Key features of Spark.

• Allows Integration with Hadoop and files included in HDFS.
• Spark has an interactive language shell as it has an independent Scala (the language in which Spark is written) interpreter.
• Spark consists of RDD’s (Resilient Distributed Datasets), which can be cached across computing nodes in a cluster.
• Spark supports multiple analytic tools that are used for interactive query analysis , real-time analysis and graph 

3. What is an RDD?

RDD is the acronym for Resilient Distribution Datasets – a fault-tolerant collection of operational elements that run parallel. The partitioned data in RDD is immutable and distributed. There are primarily two types of RDD:

1. Parallelized Collections : The existing RDD’s running parallel with one another.
2. Hadoop datasets : perform function on each file record in HDFS or other storage system

4. What are Partitions? 

As the name suggests, partition is a smaller and logical division of data  similar to ‘split’ in MapReduce. Partitioning is the process to derive logical units of data to speed up the processing process. Everything in Spark is a partitioned RDD.

5. What kind of operations RDD support?

• Transformations.
• Actions

6. What do you understand by Transformations in Spark?

Transformations are functions applied on RDD, resulting into another RDD. It does not execute until an action occurs. map() and filer() are examples of transformations, where the former applies the function passed to it on each element of RDD and results into another RDD. The filter() creates a new RDD by selecting elements form current RDD that pass function argument.

7. What are Actions in Spark ?

An action helps in bringing back the data from RDD to the local machine. An action’s execution is the result of all previously created transformations. reduce() is an action that implements the function passed again and again until one value if left. take() action takes all the values from RDD to local node.

8. Define functions of SparkCore?

Serving as the base engine, SparkCore performs various important functions like memory management, monitoring jobs, fault-tolerance, job scheduling and interaction with storage systems.

9. What is RDD Lineage?

Spark does not support data replication in the memory and thus, if any data is lost, it is rebuild using RDD lineage. RDD lineage is a process that reconstructs lost data partitions. The best is that RDD always remembers how to build from other datasets.

10. What is Spark Driver?

Spark Driver is the program that runs on the master node of the machine and declares transformations and actions on data RDDs. In simple terms, driver in Spark creates SparkContext, connected to a given Spark Master.
The driver also delivers the RDD graphs to Master, where the standalone cluster manager runs.

11. What is Hive on Spark?

Hive contains significant support for Apache Spark, wherein Hive execution is configured to Spark:

hive> set spark.home=/location/to/sparkHome;hive> set hive.execution.engine=spark;

Hive on Spark supports Spark on yarn mode by default.

12. Name commonly-used Spark Ecosystems.

• Spark SQL (Shark)- for developers.
• Spark Streaming for processing live data streams.
• GraphX for generating and computing graphs.
• MLlib (Machine Learning Algorithms).
• SparkR to promote R Programming in Spark engine.

13. What is Spark Streaming ?

Spark supports stream processing – an extension to the Spark API , allowing stream processing of live data streams. The data from different sources like Flume, HDFS is streamed and finally processed to file systems, live dashboards and databases. It is similar to batch processing as the input data is divided into streams like batches.

14. What is Spark SQL?

SQL Spark, better known as Shark is a novel module introduced in Spark to work with structured data and perform structured data processing. Through this module, Spark executes relational SQL queries on the data. The core of the component supports an altogether different RDD called SchemaRDD, composed of rows objects and schema objects defining data type of each column in the row. It is similar to a table in relational database.

15. What is a Parquet file?

Parquet is a columnar format file supported by many other data processing systems. Spark SQL performs both read and write operations with Parquet file and consider it be one of the best big data analytics format so far.

16. What file systems Spark support?

• Hadoop Distributed File System (HDFS).  Learn more about HDFS in these Top Interview questions.
• Local File system.
• S3

17. What is Yarn?

Similar to Hadoop, Yarn is one of the key features in Spark, providing a central and resource management platform to deliver scalable operations across the cluster . Running Spark on Yarn necessitates a binary distribution of Spar as built on Yarn support.

18. List the functions of Spark SQL.?

Spark SQL is capable of:

• Loading data from a variety of structured sources.
• Querying data using SQL statements, both inside a Spark program and from external tools that connect to Spark SQL through standard database connectors (JDBC/ODBC). For instance, using business intelligence tools like Tableau. Get to know more about Tableau in this Tableau Tutorial.
• Providing rich integration between SQL and regular Python/Java/Scala code, including the ability to join RDDs and SQL tables, expose custom functions in SQL, and more.

19. What are benefits of Spark over MapReduce?

• Due to the availability of in-memory processing, Spark implements the processing around 10-100x faster than Hadoop MapReduce. MapReduce makes use of persistence storage for any of the data processing tasks.
• Unlike Hadoop, Spark provides in-built libraries to perform multiple tasks form the same core like batch processing, Steaming, Machine learning, Interactive SQL queries. However, Hadoop only supports batch processing.
• Hadoop is highly disk-dependent whereas Spark promotes caching and in-memory data storage.
• Spark is capable of performing computations multiple times on the same dataset. This is called iterative computation while there is no iterative computing implemented by Hadoop.

Read more in this blog about the comparison of Spark and MapReduce.

20. What is Spark Executor?
When SparkContext connect to a cluster manager, it acquires an Executor on nodes in the cluster. Executors are Spark processes that run computations and store the data on the worker node. The final tasks by SparkContext are transferred to executors for their execution.

21. Name types of Cluster Managers in Spark.

The Spark framework supports three major types of Cluster Managers:

1. Standalone : a basic manager to set up a cluster.
2. Apache Mesos : generalized/commonly-used cluster manager, also runs Hadoop MapReduce and other applications.
3. Yarn : responsible for resource management in Hadoop

22. What do you understand by worker node?

Worker node refers to any node that can run the application code in a cluster.

23. Do you need to install Spark on all nodes of Yarn cluster while running Spark on Yarn?

No because Spark runs on top of Yarn.

Hadoop - Small Files vs Big Files

Credits- https://blogs.msdn.microsoft.com/cindygross/2015/05/04/hadoop-likes-big-files/

One of the frequently overlooked yet essential best practices for Hadoop is to prefer fewer, bigger files over more, smaller files. How small is too small and how many is too many? How do you stitch together all those small Internet of Things files into files "big enough" for Hadoop to process efficiently?

The Problem

One performance best practice for Hadoop is to have fewer large files as opposed to large numbers of small files. A related best practice is to not partition “too much”. Part of the reason for not over-partitioning is that it generally leads to larger numbers of smaller files.

Too small is smaller than HDFS block size (chunk size), or realistically small is something less than several times larger than chunk size. A very, very rough rule of thumb is files should be at least 1GB each and no more than maybe around 10,000-ish files per table. These numbers, especially the maximum total number of files per table, vary depending on many factors. However, it gives you a reference point. The 1GB is based on multiples of the chunk size while the 2nd is honestly a bit of a guess based on a typical small cluster.

Why Is It Important?

One reason for this recommendation is that Hadoop’s name node service keep track of all the files and where the internal chunks of the individual files are. The more files it has to track the more memory it needs on the head node and the longer it takes to build a job execution plan. The number and size of files also affects how memory is used on each node.

Let’s say your chunk size is 256MB. That’s the maximum size of each piece of the file that Hadoop will store per node. So if you have 10 nodes and a single 1GB file it would be split into 4 chunks of 256MB each and stored on 4 of those nodes (I’m ignoring the replication factor for this discussion). If you have 1000 files that are 1MB each (still a total data size of ~1GB) then every one of those files is a separate chunk and 1000 chunks are spread across those 10 nodes. NOTE: In Azure and WASB this happens somewhat differently behind the scenes – the data isn’t physically chunked up when initially stored but rather chunked up at the time a job runs.

With the single 1GB file the name node has 5 things to keep track of – the logical file plus the 4 physical chunks and their associated physical locations. With 1000 smaller files the name node has to track the logical file plus 1000 physical chunks and their physical locations. That uses more memory and results in more work when the head node service uses the file location information to build out the plan for how it will split out any Hadoop job into tasks across the many nodes. When we’re talking about systems that often have TBs or PBs of data the difference between small and large files can add up quickly.

The other problem comes at the time that the data is read by a Hadoop job. When the job runs on each node it loads the files the task tracker identified for it to work with into memory on that local node (in WASB the chunking is done at this point). When there are more files to be read for the same amount of data it results in more work and slower execution time for each task within each job. Sometimes you will see hard errors when operating system limits are hit related to the number of open files. There is also more internal work involved in reading the larger number of files and combining the data.

Stitching

There are several options for stitching files together.

• Combine the files as they land using the code that moves the files. This is the most performant and efficient method in most cases.
• INSERT into new Hive tables (directories) which creates larger files under the covers. The output file size can be controlled with settings like hive.merge.smallfiles.avgsize and hive.merge.size.per.task.
• Use a combiner in Pig to load the many small files into bigger splits.
• Use the HDFS FileSystem Concat APIhttp://hadoop.apache.org/docs/current/api/org/apache/hadoop/fs/FileSystem.html#concat.
• Write custom stitching code and make it a JAR.
• Enable the Hadoop Archive (HAR). This is not very efficient for this scenario but I am including it for completeness.

There are several writeups out there that address the details of each of these methods so I won’t repeat them.

• Merging small files on HDInsight http://blogs.msdn.com/b/mostlytrue/archive/2014/04/10/merging-small-files-on-hdinsight.aspx which uses a Java MapReduce JAR https://github.com/mooso/smallfilesmerge.
• Quick Tip for Compressing Many Small Text Files within HDFS via Pighttp://dennyglee.com/2014/01/06/quick-tip-for-compressing-many-small-text-files-within-hdfs-via-pig/.
• FileCrush https://github.com/edwardcapriolo/filecrush.
• HDFS FileSystem Concat API
  • http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/WebHDFS.html#Concat_Files
  • http://hadoop.apache.org/docs/current/api/org/apache/hadoop/fs/FileSystem.html#concat
  • https://issues.apache.org/jira/browse/HDFS-222
• CombineFileInputFormat (splits)
  • This may not work with really large numbers of files and has to be used EVERY time a job is run.
  • http://www.ibm.com/developerworks/library/bd-hadoopcombine/index.html
  • Process Small Files on Hadoop Using CombineFileInputFormat (1)http://www.idryman.org/blog/2013/09/22/process-small-files-on-hadoop-using-combinefileinputformat-1/
• Dealing with Hadoop’s small files problem http://snowplowanalytics.com/blog/2013/05/30/dealing-with-hadoops-small-files-problem/  “aggregating with the small files first reduced total processing time from 2 hours 57 minutes to just 9 minutes – of which 3 minutes was the aggregation, and 4 minutes was running our actual Enrichment process. That’s a speedup of 1,867%.”
• The Small Files problem in Hadoop http://piglog4j.blogspot.com/2013/06/the-small-files-problem-in-hadoop.html
• Hadoop Archive: File Compaction for HDFS https://developer.yahoo.com/blogs/hadoop/hadoop-archive-file-compaction-hdfs-461.html
• The Small Files Problem http://blog.cloudera.com/blog/2009/02/the-small-files-problem/ “Reading through files in a HAR is no more efficient than reading through files in HDFS, and in fact may be slower since each HAR file access requires two index file reads as well as the data file read (see diagram). And although HAR files can be used as input to MapReduce, there is no special magic that allows maps to operate over all the files in the HAR co-resident on a HDFS block.”

The key here is to work with fewer, larger files as much as possible in Hadoop. The exact steps to get there will vary depending on your specific scenario.

Eclipse - installing Scala plugin manually?

I have been playing around with Scala for some time, and was always using the Scala IDE (www.scala-ide.org) which is based on a relatively older version of Eclipse (Luna).

I recently discovered this, wherein you could install the scala plug-in on a regular Eclipse installation.

Just add the following url as a new update site in your local eclipse installation and you'd be able to install the scala plugin just like that -


http://download.scala-ide.org/sdk/lithium/e44/scala211/stable/site