Hadoop Ecosystem Quiz

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Questions and Answers

What is a significant advantage of Hadoop compared to traditional RDBMS?

Supports low-latency data access

Requires expensive hardware

Offers better performance with small files

Can handle vast amounts of data efficiently (correct)

What is NOT a typical application for Hadoop?

Processing high-volume datasets

Large-scale data analysis

Streaming data processing

Low-latency data access (correct)

Which component of the Hadoop ecosystem is responsible for job scheduling and resource management?

Hadoop Common

Hadoop MapReduce

HDFS

Hadoop YARN (correct)

What limitation does Hadoop have regarding file management?

The number of small files is limited by the memory of the name node Signup and view all the answers

Which statement is true about the architecture of a typical Hadoop cluster?

The uplink from rack is generally around 3-4 gigabits Signup and view all the answers

What is the primary function of the NameNode in HDFS?

Map file names to their corresponding data blocks Signup and view all the answers

What does the Secondary NameNode primarily do in an HDFS architecture?

Copy and merge the FsImage and Transaction Log for checkpointing Signup and view all the answers

What is the block size typically set for HDFS files?

64MB-128MB Signup and view all the answers

How does HDFS handle hardware failures?

By replicating files across multiple nodes Signup and view all the answers

Which of the following describes the data access model used by HDFS?

Write-once-read-many access model Signup and view all the answers

What distinguishes a Standby NameNode in HDFS architecture?

It serves as a backup without processing requests. Signup and view all the answers

What is the primary goal of HDFS?

To function as a very large distributed file system Signup and view all the answers

Which subproject of Hadoop is primarily used for machine learning tasks?

Mahout Signup and view all the answers

What role does the standby NameNode have in the Hadoop architecture?

It maintains synchronization with the active NameNode. Signup and view all the answers

How often do DataNodes send heartbeats to the NameNode?

Once every 3 seconds Signup and view all the answers

What is the primary function of the Quorum Journal Manager (QJM) in the NameNode?

To communicate with JournalNodes using RPC Signup and view all the answers

In the current block placement strategy, where is the first replica of a block stored?

On the local node Signup and view all the answers

What is the main goal of the Rebalancer in Hadoop?

To ensure disk usage is similar across DataNodes Signup and view all the answers

How does the NameNode react when a DataNode failure is detected?

It chooses new DataNodes for new replicas. Signup and view all the answers

What type of file system do Block Servers in DataNodes typically use?

ext3 Signup and view all the answers

Which command would an HDFS user use to create a new directory?

hadoop dfs -mkdir /newdir Signup and view all the answers

Which component does the ResourceManager contact to launch the ApplicationMaster?

NodeManager Signup and view all the answers

What is the role of the ApplicationMaster in the YARN architecture?

To launch the Driver Program and manage its resources. Signup and view all the answers

Which scheduling policy in YARN utilizes a first-come, first-served approach?

FIFO Scheduler Signup and view all the answers

How does the Capacity Scheduler manage cluster resources?

It divides resources into multiple queues with reserved resources. Signup and view all the answers

What does the Driver Program do after being launched by the ApplicationMaster?

It assigns tasks to executor containers and tracks their status. Signup and view all the answers

In which scenario is the FIFO Scheduler most suitable?

In a small cluster with simpler, predictable workloads. Signup and view all the answers

Which of the following best describes the Fair Scheduler?

It aims to balance resources fairly among jobs without reserved capacity. Signup and view all the answers

What information does the ApplicationMaster communicate with the NameNode to obtain?

File block locations within the cluster. Signup and view all the answers

What does the Mapper output in the Word Count example?

Key: word, Value: 1 Signup and view all the answers

What is the role of the Reducer in the Word Count example?

To sum the occurrences of each word Signup and view all the answers

During which step does Hadoop divide the sample input file into parts?

Split Signup and view all the answers

What does the JobTracker do after the Mapper is executed?

Generates TaskTrackers for the map tasks Signup and view all the answers

If a sample input file contains 5 lines, how many splits are generated in this example?

5 Signup and view all the answers

What is the initial value associated with each word during the mapping process?

1 Signup and view all the answers

What would be the output key-value pair when reducing the word 'human' with occurrences 1 and 1?

human, 2 Signup and view all the answers

Which component is responsible for defining and submitting the MapReduce job to the cluster?

JobTracker Signup and view all the answers

What is the primary function of the ResourceManager in a YARN cluster?

To track resources and assign tasks to NodeManagers Signup and view all the answers

Which two resources are currently defined by YARN for monitoring?

v-cores and memory Signup and view all the answers

What role does the ApplicationMaster serve within a YARN application?

It manages task scheduling and coordination for the application Signup and view all the answers

Which of the following statements about a YARN container is true?

A container request includes v-cores and memory Signup and view all the answers

What happens after the ApplicationMaster has requested and received all necessary containers?

The ApplicationMaster exits and the last container is de-allocated Signup and view all the answers

Which of the following correctly describes the order of actions when a YARN application is started?

The application starts, the ResourceManager requests a container, and then the ApplicationMaster runs Signup and view all the answers

What is the role of NodeManagers in the YARN architecture?

To launch and track processes spawned on worker hosts Signup and view all the answers

Which statement accurately describes the communication flow in a YARN cluster?

The ResourceManager communicates with the client, tracks resources, and interacts with NodeManagers Signup and view all the answers

Study Notes

Big Data Analytics - Chapter III: Software Layers

Hadoop foundations are crucial for understanding the system.
The HDFS file system is key; understanding and applying the Map/Reduce paradigm is vital.
Servers, racks, and network architectures are parts of the Hadoop ecosystem.
Layers within Hadoop's architecture are interconnected.

RDBMS vs. Hadoop Properties

Traditional RDBMS typically handles gigabytes of data whereas Hadoop manages petabytes.
RDBMS supports interactive and batch access while Hadoop mainly supports batch processing.
RDBMS allows for frequent read and write operations, while Hadoop favors write-once, read-many times.
RDBMS works with static schemas, whereas Hadoop uses dynamic schemas.
RDBMS ensures high integrity, whereas Hadoop's integrity is relatively lower.
RDBMS scaling is nonlinear, while Hadoop scaling is linear.

Advantages of Hadoop

Hadoop handles vast amounts of data effectively.
It is an economical solution.
Hadoop's architecture allows for efficient processing.
Hadoop is scalable to manage growing data volumes.
Hadoop is robust and reliable

Applications Not for Hadoop

Low-latency data access is not Hadoop's forte.
HBase is a better choice for low-latency needs.
Processing large numbers of small files is not ideal on Hadoop.
File system metadata stored in memory limits the number of files Hadoop can process effectively.
Multiple writers and arbitrary file modifications are not supported by Hadoop.

Hadoop Cluster - Servers, Racks, and Networks

Hadoop Clusters usually have a two-level arrangement.
Nodes in the cluster are typically standard/commodity computers.
The typical number of nodes per rack is 30-40.
Uplink connections from a rack are typically 3-4 Gigabit.
Rack-internal connections often use 1 Gigabit connections.
An aggregation switch connects racks to each other.
An 8-Gigabit connection will connect to the aggregated switch.
A 1-Gigabit connection connects to the racks.
Standard computer components are used for the network.

The Core Apache Hadoop Project

Hadoop Common provides Java libraries required by other Hadoop modules.
HDFS (Hadoop Distributed File System) handles data storage.
Hadoop YARN (Yet Another Resource Negotiator) manages scheduling and cluster resource management.
Hadoop MapReduce is a programming model for large-scale data processing.

Hadoop Layers

Hadoop MapReduce (data processing)
YARN (cluster resource management)
HDFS (storage)
Spark (data processing)
Flink (data processing)
Others

Pig is a high-level language for data analysis.
HBase is table storage for semi-structured data.
ZooKeeper coordinates distributed applications.
Hive is an SQL-like query language.
Mahout is a machine learning library.

Hadoop Distributed File System (HDFS)

HDFS is a distributed file system built for very large files.
The design features of HDFS include a very large distributed file system with 10,000+ nodes, 100 million files, and 10 PB of data.
Data in HDFS is replicated for fault tolerance. Replication is critical for availability and to recover from hardware failure.
The design also includes optimization for batch processing to match computing to where data resides.
HDFS supports heterogeneous operating systems (OS).

HDFS Design

HDFS uses a single namespace for the entire cluster.
The system is coherent and data is accessible using a write-once-read-many approach.
Clients only append to existing files.
Files are broken into blocks (typically 64-128 MB).
Blocks are replicated on multiple DataNodes.
Clients can find block locations from the NameNode.
Data access is direct from DataNodes.

HDFS Architecture

Namenode manages the metadata of files and blocks.
DataNodes store the data blocks.
Metadata includes information about files, and their replication scheme.
Clients interact with the Namenode to locate blocks and with DataNodes to retrieve data.
Replication is critical to ensure high availability in case of failure of a particular DataNode.

NameNode Functions

The NameNode manages the file system namespace.
The NameNode maps blocks to DataNodes and file names to sets of blocks.
The NameNode manages cluster configuration.
The NameNode manages replication of blocks.
To ensure high availability, both active and standby NameNodes are necessary, operating as dedicated master nodes.

How Files Are Stored in HDFS

Data files are divided into blocks and distributed to DataNodes.
Each block is replicated for redundancy (default is 3 times).
The Namenode stores metadata about files and blocks, including block locations.

NameNode Metadata

Namenode metadata is stored in main memory.
There is no demand paging for metadata.
Metadata types include lists of files, blocks, DataNodes, and file attributes such as creation time.
A transaction log records file creations and deletions.

HDFS NameNode Availability

The NameNode must be running for the cluster to be accessible.
High availability mode (in CDH4 and later) features two NameNodes (active and standby).
Classic mode uses one NameNode with a secondary helper node for bookkeeping but no backup.

Secondary NameNode

The Secondary NameNode copies the FsImage and the transaction log from the Namenode to a temporary directory.
The Secondary NameNode merges the copied FsImage and Transaction Log into a new FsImage in a directory.
The secondary Namenode ensures a checkpoint in HDFS for recovery if the primary Namenode fails.
Edits regarding data files are copied to the Secondary node for safety from primary.

Standby Name Node-QJM

Hadoop 2 introduced high availability with two NameNodes.
One is active, handling client requests, while the other is standby, synchronized to take over if the active one fails.
The Quorum Journal Manager (QJM) runs on each NameNode facilitating communication with Journal Nodes using RPC, handling namespace modifications, and maintaining data synchronization.

ZooKeeper

ZooKeeper coordinates distributed applications.
It monitors the health of the NameNode and other components.
It handles block reports and ensures data synchronization.

DataNode

DataNodes are block servers.
They store data blocks on local file systems with checksums (e.g., CRC).
DataNodes are responsible for serving data and metadata to clients.
They periodically report block status to the Namenode.
Datanodes facilitate pipelining of data by forwarding data to other specified datanodes, improving processing efficiency and minimizing network overhead.

Block Placement

Hadoop places data replications on local nodes, remote racks, and other remote racks for fault tolerance and better performance.
Placement follows a rack awareness algorithm, replicating data across multiple racks.
Clients access the nearest replica for optimized read performance.

Heartbeats

DataNodes periodically send heartbeats to the Namenode.
Frequency is often once every 3 seconds.
Heartbeats enable Namenode to detect DataNode failures.

NameNode as Replication Engine

After detecting DataNode failures, the NameNode selects new DataNodes to host those blocks (replicas).
The NameNode balances disk usage across all DataNodes.
The NameNode balances communication traffic to DataNodes.

Data Pipelining (i)

Clients request a list of DataNodes to store a block's replicas.
The data is written to the first Datanode in the sequence of placement on the cluster.
Pipelining occurs where the first DataNode delivers the block data to the next DataNode in the sequence (pipeline).
The process continues until the appropriate number of replicas is stored as requested by the client.

Data Pipelining (ii)

The writing procedure uses a client JVM to signal a write request to HDFS, including the IP addresses of the target DataNodes.
The request is processed by the NameNode and sent to the appropriate Core Swtich for networking across the data nodes.
The DataNodes that are ready store the block.

Rebalancer

The goal of the rebalancer is to ensure similar disk space utilization across all the DataNodes within the cluster by rebalancing data distribution.
Rebalancing occurs frequently, especially after the addition of a new DataNode.

User Interface

Commands for HDFS users (e.g., creating directories, reading/writing files).
Commands for HDFS administrators (e.g., monitoring, de-commissioning DataNodes).
A web interface for monitoring and administration.

Introduction to Hadoop YARN

YARN is a resource manager crucial for enterprise Hadoop.
It provides centralized resource management, security, and data governance tools across Hadoop clusters.

Applications that run on YARN

A variety of applications/programs can run on top of YARN.

Before/After 2012: Hadoop Versions

Pre-2012 Hadoop relied primarily on the MapReduce programming model for its processing tasks.
Post-2012, Hadoop 2.7 and later versions greatly broadened the capabilities to support other processing techniques beyond MapReduce.

YARN Cluster Basics

The ResourceManager (RM) is the master daemon that directs resource allocation, tracks cluster resources, and schedules work.
NodeManagers are worker daemons on the worker nodes to handle tasks.

YARN Resource Monitoring (i) & (ii)

YARN uses v-cores and memory as primary resources.
Node Managers track their own resources and report to the RM.
The RM manages the total resources in the cluster.

Yarn Container

A container in YARN is a request for resources.
Containers manage the resources allocated (vcores and memory) to run a program.
Containers are run as processes.

YARN Application and ApplicationMaster

YARN applications comprise tasks (Map/Reduce).
ApplicationMaster manages running tasks and coordinates the application execution.

Interactions among YARN Components (i), (ii), (iii), (iv), & (v)

Steps outlining how applications interact with YARN components: submission, container request, ApplicationMaster launch processes, task assignment, task execution, and exit.

How Applications run on YARN - Steps 1, 2, 3, 4, 5, 6, 7

Step-by-step details on how applications using YARN operate within the Hadoop distributed computing system.

Schedulers

YARN's scheduler manages cluster resources, following a defined policy and allowing constraints like capacity, fairness, and SLA.

FIFO, Capacity, and Fair Schedulers

Algorithms/protocols for managing jobs (first-come, first-served, capacity allocation, balanced scheduling).

### MapReduce - Overview

MapReduce is a programming model for executing parallel computations over large datasets.
It consists of map and reduce phases (map & reduce functions).

MapReduce: Terminology

Explains job execution in MapReduce.
Defining 'job' within MapReduce (a program).
Understanding a 'task' as a part of execution.
Clarifying 'task attempts' to address failures within distributed tasks.

Hadoop Components: MapReduce

Mappers operate on one HDFS block at a time; local data processing when possible.
Mappers generate results as intermediate value/key pairs and send to reducers.
Reducers aggregate data in the data processing phase by combining similar value/key pairs.

Mappers Run in Parallel

In parallel execution, Mappers run on multiple nodes, processing/gathering information locally, to concurrently process/improve resource utilization and minimize network overhead.

MapReduce: The Mapper

The Mapper function reads data, typically as key value pairs (in text formatting).
Mappers process/transform data according to the program's requirements.
Mappers produce key-value pairs as intermediate results, then the reducer collects those.

MapReduce: The Reducer

The Reducer function combines intermediate results by processing/combining key-related value pairs.
Reducer tasks receive sorted data from Mappers, combining value pairs for that key, creating a final output.
Reducers output final results as (key, value) pairs.

Features of MapReduce

Automated parallelization and data distribution.
Built-in fault tolerance (processes failures).
Clean abstraction that hides underlying cluster management.
Tools for monitoring execution status.

Word Count Example

A simple MapReduce example demonstrating word counting.
The application logic for (key, value) pairs in the execution process.

Example

Steps of distributed execution demonstration.
Shows the generation of multiple tasks and their execution on different cluster components.

SORT and SHUFFLE

Demonstrates how Hadoop sorts and rearranges intermediate data before reducing it.

MapReduce - Word Count Example Flow

Visual representation of MapReduce word count processing from input to output with various intermediate results.

MAPREDUCE-Steps

Detail steps within MapReduce algorithm/process.
Includes input/splitting, mapping, combining, shuffling/sorting, reducing, and output generation.

Input and Output Formats

Formats for data input and output within MapReduce and how to specify them.
Standard options/formats such as TextInputFormat, TextOutputFormat.

INTRODUCTION TO YARN AND MAPREDUCE INTERACTION

Introduction to interactions between YARN and MapReduce.

MapReduce on Yarn

Description of the mapping of MapReduce tasks onto YARN tasks, showing how efficient the allocation can be.

Putting it Together: MapReduce and YARN

Visualization of how MapReduce tasks operate within a YARN container environment on the worker nodes.

Scheduling in YARN

Describes the Resource Manager's role in tracking resources in the cluster, including the scheduler process responsible for managing allocations.

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Test your knowledge of the Hadoop ecosystem with this quiz. Explore its advantages over traditional RDBMS, use cases, job scheduling components, file management limitations, and architecture. Ideal for students and professionals interested in big data technology.