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 Meetup on the sidelines of Strata Hadoop Conference - Part 2

Read part 1 of this here

Day 2 of the meetup was equally exciting, if not better.  Lined up were talks from Qubit and Google, William Hill (a surprise for me - more later on that) and then PostCodeAnywhere, all very exciting from the synopsis.

Google & Qubit showcased basically a stream processing engine, with pluggable components, many of them can be written in different technologies and programming languages.

Of course Google Cloud Data flow is much more than just a stream processing engine, however, for real time data ingestion perspective, that feature is pretty significant.  

A completely managed system, it woks on the publish-subscribe (pub-sub) model.  As Reza put it, “pub-sub is not just data delivery mechanism, its used as a glue to hold the complete system together”.  Pluggable components is another differentiator for Google’s offering, in today’s demo they showcased bigtable as one of the consumers at the end.

From my own knowledge of stream processing, which is not significant in anyway, i could relate to many similarities with IBM’s info sphere streams and some with apache kafka.  However, a question around comparisons with these sites remained unanswered from Google (though in very good spirit, in a chat with the speaker Reza later on, it came out as more of a philosophical question avoidance than anything else).

The william hill talk (by Peter Morgan, their head of engineering), was a genuine surprise, at least for me.  Perhaps due to my ignorance, due to which i didn't realize, their systems are far more sophisticated and load bearing than I would have imagined.  As an example, they process 160TB of data through their systems on a daily basis.

Including many complexities managed through their system are their main components, the betting engine, the settlement engine among others. 

William Hill supports an open API as well, enabling app developers to pick up data elements and innovate. However, for obvious reasons, very limited data is thrown open in the public domain.  Would that be a deterrent for app developers ? not having enough data ?   For example, if i would want to report in an app, who’s betting on a  certain game, cross referenced with geo location data .. I cant do that, since William hill doesn't publish demographic data.  I personally feel alright with it, there are possibilities that many of those data elements can be used in ways to influence the betting system itself, becoming counter-productive.

I would imagine their IT systems to be one of the top notch systems around the place, to be able to manage such data volumes, with such speeds and accuracy. Commendable job.  I would probably write exclusively on their architecture once i get my hands on the presentation slides (couple of days may be).

The talk from PostCodeAnywhere was more educative to me, personally.  Got to understand a bit about Markov Models, chains and how they can be used for machine Learning.  Very interesting stuff there too.

Apache Spark is being seen more and more as the tool to be perform analytics on the fly, specially on large volumes of data.  It would be very interesting to see how R and python analytical capabilities compare with what spark offers.

Speaking to another attendee today, it came out the people prefer to use R more and more for massaging and cleansing purposes, however, its not seen as fit for heavy lifting required for performing real analytic and/or predictive pieces. For these areas, people still prefer to use Python.

IBM’s bigR is a possible contender for the job, where they talk about having optimised R for a hadoop cluster and have enabled it to work on top of hdfs.  However, bigR is not open source and that could be its biggest challenge in adoption.

my experiments with solr :)

Its a catchy title, but yes.. thats what I am going to talk about...

I came across hadoop, when I was looking for a new solution for one of our in-house projects. The need was quite clear, however, the solution had to be dramatically different.

The one statement we received from business was, "We need an exceptionally fast search interface". And for that fast interface to search upon they had more than a hundred million rows worth of data in a popular RDBMS.

So, when I sat about thinking, how to make a fast search application, the first thing that came to my mind was, Google. Actually, whenever we talk about speed or performance of web sites, Google is invariably the first name that comes across.


Further, Google has a plus point that there is always some activity at the back end to generate the page or results that we see, its never static content. And, then, another point, Google has a few trillion pieces of information to store/index/search whereas our system was going to have significantly lower volume of data to manage.   So, going with that, Google looked like a very good benchmark for this fast search application. 

Then I started to look for "How Google generates that kind of performance". There are quite a few pages on the web talking about just that.   But, probably none of them has the definitive/authoritative view on Google's technology or for that matter the insider's view on how it actually does what it does so fast.

Some pages pointed towards their storage technology, some talked about their indexing technology, some about their access to huge volumes of high performance hardware and what not...

For me, some of them turned out to be genuinely interesting, one of them was the indexing technology. There has to be a decent indexing mechanism to which the crawler's would feed and the search algorithms hit.  The storage efficiency is probably the next thing to come in the play. How fast can they access the corresponding item ?

Another of my observation is that, the search results (the page mentioning page titles and stuff) comes real fast, mostly less than 0.25 seconds, but the click on the links does take some time.  So, I think it has to be their indexing methodology that plays the bigger role.

With that in mind, I sat about finding what can do similar things and how much of Google's behaviour they can simulate/implement.

Then I found Hadoop project on apache (http://hadoop.apache.org/) which to a large extent reflects the way Google kind of system would work. It provides distributed computing(hadoop core), it provides a bigTable kind of database (hbase), provides map/reduce layer, and more.  Reading into it more, I figured out that this system is nice for a batch processing kind for mechanism, but not for our need of real time search.

Then I found solr(http://lucene.apache.org/solr/), a full text search engine under Apache Lucene.  It is a java written, xml indexing based genuinely fast search engine.  It provides many features that we normally wish for in more commercial applications, an being from apache, I would like to think of it as much more reliable and stable than compared to many others.

When we sat about doing a Proof of Concept with it, I figured out a few things –

•    It supports only one schema, as in, rdbms tables – only one. So, basically you would have to denormalize all your content to fit into this one flat structure.
•    It supports interactions with the server interface only through http methods be it the standard methods get/put etc or be it REST like interfaces.
•    It allows you loading data in varying formats, through xml documents, through delimited formats and through db interactions as well.
•    It has support for clustering as well. Either you can host it on top of something like hadoop or you can just configure it to do it within solr as well.
•    It supports things like expression and function based searches
•    It supports faceting
•    Extensive caching and “partitioning” features.

Besides other features, the kind of performance without any specific tuning efforts made me think of it as a viable solution.

In a nutshell, I loaded around 50 million rows on a “old” Pentium-D powered desktop box with 3 GB RAM running ubutnu 10.04 server edition (64 bit) with two local hard disks configured over a logical volume manager.

The loading performance was not quite great. Though its not that bad either. I was able to load a few million rows (in a file that was sized about 6 GB) in about 45 minutes when the file was on the same file system.

In return, it gave me query performances in the range of 2-4 seconds for the first query. For subsequent re-runs of the same query (within a span of an hour or so), it came back in approx 1-2 milliseconds.  I would like to think that its pretty great performance given the kind of hardware I was running upon, and the kind of tuning effort I put in (basically none – zero, I just ran the default configuration).

Given that, I wont say that I have found the equivalent or replacement of Google’s search for our system, but yeah, we should be doing pretty good with this.

Although there is more testing and experimentation that is required to be able to judge solr better, the initial tests look pretty good.. pretty much in line with the experiences of others who are using it.

Apache Experience on Ubuntu@HP 6710b

Well, I have this feeling that the ubuntu apache package that we receive from the repositories is built a bit differently than the standard build for apache.

I was looking into all sorts of apache configuration files after I installed the ubuntu apache package. (apache2 actually). I wanted to change the document root, and enable/disable some modules...couldnt really succeed.

So, for me, the solution eventually was to download the source from httpd.apache.org, its a newer version there, and compile it myself. That was pretty easy. And it worked after that. Then I found all the regular file locations, config files etc. that I have always known about apache.

But, there the catch is, the default installation does not seem to provide/setup automatic startup scripts for init.d kind of locations.

I am still after that task. would post as soon as i find the solution/fix it myself...

regards
raghav..