Hadoop Overview - MCNG Marketing

Hi there!

With data continuing to explode from social, mobile, IoT and more – Hadoop has emerged as the leading distributed framework to store and process these huge datasets efficiently.

I have been working for over 5 years as a Hadoop Data Engineer helping organizations leverage analytics at scale. The demand for quality Hadoop professionals is massive!

As per research by Markets and Markets:

The hadoop and big data analytics market is projected to grow from $23 billion in 2020 to over $59 billion by 2025!

So if you are looking to pursue an exciting career in this space, mastering the basics is crucial.

Let me take you through the commonly asked interview questions on Hadoop and its ecosystem components. I‘ll try to keep the discussion as friendly as possible! Feel free to poke me if something is not clear.

But before jumping into the questions, let‘s quickly recap what Hadoop actually is.

In simple terms, Hadoop provides a distributed software framework to efficiently store and process huge volumes of data on commodity hardware clusters.

The core components that make up Hadoop are:

HDFS (Hadoop Distributed File System): A distributed and scalable filesystem optimized for large data workloads. It divides data blocks and distributes them across local disks of cluster nodes.
YARN (Yet Another Resource Negotiator): Responsible for managing compute resources and job scheduling in Hadoop cluster. Think of it as the operation system for scheduling jobs.
MapReduce: The data processing framework built atop YARN that enables parallel processing on distributed nodes. Popular for batch processing workloads.

The Hadoop ecosystem also contains several components for data processing likeHive, Pig, Spark etc. that integrate with the core.

Now over the years working on analytics platforms – I have noticed good Hadoop professionals not only understand these components well but also how they interact with each other.

So let‘s get to the common interview questions and answers!

General Hadoop Interview Questions

What is commodity hardware in context of Hadoop?

Commodity hardware refers to affordable, widely accessible non-proprietary computers – like the usual servers or desktop computers.

Hadoop achieves distributed parallel processing by leveraging these commodity servers instead of high-end expensive hardware.
So unlike legacy systems, it doesn‘t need highly reliable specialized hardware upfront.
Using commodity infrastructure significantly reduces capital costs for deploying Hadoop clusters at scale.
It also provides native fault tolerance capabilities through replication of data blocks across multiple cluster nodes. Failure of few nodes doesn‘t affect overall cluster.

I have worked with clusters ranging from 10 nodes to over 1500 nodes using commodity hardware without issues. The huge cost savings plus horizontal scalability makes Hadoop a preferred choice for modern data platforms.

What are the different Hadoop ecosystem components?

Hadoop has a diverse ecosystem beyond the core components. The major technologies are:

Technology	Description
HDFS	Distributed, scalable filesystem
YARN	Cluster resource manager
MapReduce	Data processing framework
Hive	Data warehouse for querying
Pig	Platform for procedural data flows
Sqoop	Bulk data transfer
Flume	Streaming data ingestion
Spark	In-memory data processing
HBase	Distributed NoSQL database
Oozie	Job workflow scheduler
Zookeeper	Centralized coordination

Each technology targets a specific capability – like data processing, storage, workflow management etc. They integrate well to build a comprehensive data architecture.

Understanding where each technology fits in the grand scheme is helpful to be an expert Hadoop architect!

How does this discussion so far feel? Does the breadth of ecosystem components feel overwhelming? Let me know if you need any clarification or have additional questions before we look at other areas!

HDFS Interview Questions

HDFS or Hadoop Distributed File System is the primary storage layer for Hadoop-based applications. Let‘s explore some commonly asked interview questions around it.

What is block size in HDFS and why is it important?

Files stored in HDFS are broken down into smaller blocks during writes for distribution across the cluster.
These blocks are the basic units of read/write operation. By default block size is 64 MB but can be increased if needed.

Maintaining block architecture provides following advantages:

Large files can be stored by splitting across blocks instead of a single node
Different blocks can be read/processed in parallel across the cluster for efficiency
Reduces network overhead as smaller block sizes are transferred over network

So block size allows configuring HDD throughput versus network bandwidth tradeoffs.

What happens when DataNodes storing HDFS block replicas fail?

Reliability for block data in HDFS comes from replicating blocks across DataNodes. The replication factor controls the number of replicas for each block.

HDFS maintains metadata info about which block replicas are stored on which DataNode.
If a DataNode fails due to hardware crash or other reasons, the NameNode figures out absence of block replicas on that node.
Using the remaining replicas, HDFS re-replicates the affected blocks to other DataNodes to ensure replication factor is maintained.

So even if a few nodes go down, the file data remains available without loss due to in-built replication support. This makes HDFS fault-tolerant by design to handle failures.

MapReduce Interview Questions

Let‘s now talk about MapReduce which has become synonymous with data processing on Hadoop.

Can you explain the workflow of a MapReduce job through a diagram?

Sure, so in a typical MapReduce job data flows through different transforms across cluster nodes in following sequence:

Input Data: Resides in HDFS
Input Splits: Data nodes read data blocks from HDFS and divide into splits for mappers
Mapper: Maps process splits in parallel, output intermediate records
Shuffling: Mapper output sorted, partitioned and transferred to Reducers
Reducer: Reduces process grouped data per Reducer into final output
Final Output: Written back to HDFS

Some key characteristics of the workflow:

Breaks job into Mapper and Reducer phases
Runs map/reduce tasks parallel across commodity hadoop nodes
Shuffle handles sorting/transfer of intermediate records
Great for batch processing workloads, not optimal for iterative processing

Let me know if this helps explain what happens behind the scenes in a MapReduce job!

What are the key differences between Hadoop 1.0 and Hadoop 2.0?

The release of Hadoop 2.0 was significant from architecture standpoint. Some salient updates include:

Introduction of YARN – Generic cluster resource manager to replace classic MapReduce for job scheduling and cluster management. Provides a central platform to handle allocation of compute resources.
Better cluster utilization – YARN enabled running different kinds of distributed applications beyond just MapReduce. Allows running MPI, Spark, Flink etc on same cluster.
Architecture decoupling – Core components like HDFS and YARN are evolving independent of each other. Enables more agile development.
Application portability – Defines stable APIs for MapReduce framework. This enables engineers to run MapReduce version 1 apps on Hadoop 2 cluster.

The updates in Hadoop 2.0 like YARN brought in fundamental shifts that shaped technology evolution of related big data technologies as well.

I‘ll be happy to discuss more architectural differences between Hadoop 1.0 vs 2.0. So feel free ask follow-up questions!

YARN Interview Questions

We just discussed how YARN was a game changer in Hadoop 2.0 to manage cluster resources. Expanding on that..

What is the role of ApplicationMaster in YARN cluster?

The ApplicationMaster is responsible for:

Managing application execution in the cluster
Negotiating appropriate resource containers from ResourceManager
Tracking progress of running containers
Managing data flow between map and reduce containers

It has been crucial in improving scalability for large clusters while providing visibility into application lifecycle.

Some benefits like:

Better resource utilization with centralized allocation
Enables long running applications on cluster
Log aggregation for debugging
Dynamic scaling capability to handle grows

So in summary, ApplicationMaster is at the heart of enabling job orchestration through YARN. It works with Resource Manager to manage cluster resources forapplications.

Hive Interview Questions

Let‘s move to discussing Hive which has emerged as the go-to technology for data warehousing on Hadoop.

What are advantages of using Hive for analyzing big data?

Apache Hive brings in powerful data warehousing capabilities to Hadoop. Using HiveQL (SQL construct) and metadata around tables has following key advantages:

Allows traditional data analysts familiar with SQL to leverage Hadoop
Hide complexity of java map reduce programming
Powerful constructs like partitions, buckets accelerate queries
Integrates well with custom scripts when needed
Managed Tables and Storage helps avoid data duplication

I have used Hive extensively for building enterprise data warehouses on Hadoop handling workloads from 100s of users simultaneously. Performance tuning hive queries is an art!

The rich metadata architecture and tight integration with REST of Hadoop ecosystem makes it a versatile component for modern data lakes.

What are the different table types in Hive?

Based on various warehouse design considerations, Hive supports different table types:

Managed Table: Default table where data is controlled by hive
External Table: Table with metadata in hive pointing to data external location
Partition Table: Physically divides table based on partition keys to accelerate queries
Bucket Table: Adds logical buckets on partition data for extra structure

Choosing between the table types depends on expected data lifecycle and pipeline:

For ingesting streaming data via Spark, its better to use external tables pointing to cloud storage locations.
Partitioning works great for queries filtered on date/city kind of columns.
Bucketing evenly distributes data to optimize parallel processing.

So understanding these table types and when to apply them based on access patterns is key to designing high performance Hive data models.

We have covered quite a bit of ground discussing various Hadoop components. Let‘s wrap up the discussion with some quick fire questions!

Quickfire Interview Questions

What is the difference between NameNode and DataNode daemons in HDFS?

NameNode: Master daemon that manages the file system metadata and access
DataNode: Slave daemon which holds actual data blocks belonging to files

The separation of responsibilities improves performance and scalability.

How is Pig Latin different from Hive QL?

Pig Latin is procedural, data flow language
Hive QL is declarative using SQL constructs

So Hive is simpler if you just need to query data while Pig programs control flow and transformations required for ETL processing.

Can Hadoop YARN manage other applications like Spark and MPI?

Yes absolutely! YARN provides the generic cluster manager that can handle different kinds of applications beyond just MapReduce jobs.

Where does Oozie fit in the Hadoop ecosystem?

Oozie allows creating workflows that coordinate execution and scheduling of multiple Hadoop jobs. Useful for pipeline automation.

You mentioned about Hadoop 3.0.0 released recently – can you highlight some key updates?

Major highlights of Hadoop 3 include:

Decoupled HDFS from main release for independent development
Support for erasure coding to reduce storage costs
YARN timeline service enhancements
Improved scalability limits (files, blocks, nodes)

I can definitely elaborate on these based on your interest areas!

I loved discussing Hadoop in detail with you. Let me know if you have any other questions! I am happy to explain further till you are comfortable with the concepts.

Conclusion

So in this discussion we went wide and deep across commonly asked interview questions covering the Hadoop ecosystem. I tried touching upon some critical aspects like:

Architecture diagrams to build clear visual mental models
Real world usage from my past project experiences
Difference between related technologies and where they fit
Recent releases like Hadoop 3.0 and improvements
Additional references for you to dig deeper

Getting hands-on experience will be invaluable to reinforce the theoretical concepts we discussed. Let me know if you need any help on that front!

Wishing you the very best for your Hadoop/big data interviews !