MapReduce

Master the distributed computing framework that enables processing of massive datasets across clusters

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MapReduce Job Performance Calculator

Input Data Size (GB)1000 GB

Number of Mappers100 mappers

Number of Reducers20 reducers

HDFS Block Size (MB)128 MB

Cluster Nodes50 nodes

Network Bandwidth (Mbps)1000 Mbps

Job Complexity

Replication Factor3x replication

Job Performance

Map Phase:426.7 min

Shuffle & Sort:41 min

Reduce Phase:85.3 min

Total Time:553 min

Throughput:109 GB/hr

Resource Utilization

CPU Utilization:95%

Network Utilization:48%

Storage Required:4000 GB

Data per Mapper:10240 MB

Efficiency & Reliability

Parallel Efficiency:80%

Fault Tolerance:High

Scalability:Optimal

Total Blocks:8000

Cost Breakdown

Compute Cost:$6635.52

Storage Cost:$200

Network Cost:$6

Total Cost:$6841.52

MapReduce Programming Model

MapReduce is a programming model for processing large datasets across distributed clusters. It automatically handles parallelization, fault tolerance, and load balancing, allowing developers to focus on business logic rather than distributed systems complexity.

Automatic Parallelization

Automatically distributes computation across hundreds or thousands of machines without manual coordination.

Fault Tolerance

Built-in handling of machine failures with automatic task restart and data replication.

Scalable Storage

Integrates with distributed file systems like HDFS to handle petabyte-scale datasets.

MapReduce Execution Phases

1. Input Split & Map Phase

Input data is split into chunks and processed in parallel by mapper functions that emit key-value pairs.

• Input Split: Data divided into HDFS blocks

• Record Reader: Converts input to key-value pairs

• Mapper Function: User-defined processing logic

• Output: Intermediate key-value pairs

# Word count mapper example

def mapper(line):

words = line.split()

for word in words:

emit(word, 1)

# Input: "hello world hello"

# Output: (hello,1), (world,1), (hello,1)

2. Shuffle & Sort Phase

Intermediate data is grouped by key, sorted, and distributed to reducer nodes across the network.

• Partitioning: Keys assigned to reducers

• Sorting: Data sorted by key within partitions

• Grouping: Values with same key grouped together

• Network Transfer: Data moved to reducer nodes

Data Flow

Mapper 1

→

Reducer 1

Mapper 2

→

Reducer 2

Network shuffle by key hash

3. Reduce Phase

Reducer functions process grouped values for each key to produce final aggregated results.

• Input: Key and list of associated values

• Processing: User-defined aggregation logic

• Output: Final results written to storage

• Format: Configurable output format

# Word count reducer example

def reducer(key, values):

count = sum(values)

emit(key, count)

# Input: hello, [1, 1, 1]

# Output: (hello, 3)

# Final: hello 3, world 1

Advanced MapReduce Features

Combiners for Optimization

Combiners perform local aggregation on mapper output before the shuffle phase, reducing network traffic.

Benefits:

Reduces network bandwidth usage
Decreases shuffle phase time
Lower reducer input size
Improved overall job performance

Without Combiner

Mapper → (hello,1), (hello,1), (hello,1)

Network → 3 records transferred

With Combiner

Mapper → Combiner → (hello,3)

Network → 1 record transferred

Fault Tolerance Mechanisms

Task Failure Handling

Failed tasks are automatically restarted on different nodes up to a configured retry limit.

Data Replication

HDFS replicates data blocks across multiple nodes to prevent data loss from hardware failures.

Speculative Execution

Slow-running tasks are speculatively executed on other nodes to prevent stragglers.

Custom Partitioning & Input Formats

Custom Partitioners

Control how intermediate keys are distributed to reducers for load balancing.

def custom_partitioner(key, num_reducers):

return hash(key.country) % num_reducers

# Partition by country

Input Formats

Support for various data formats and custom input splitting strategies.

• TextInputFormat: Line-based text files

• SequenceFileInputFormat: Binary format

• DBInputFormat: Database records

• CustomInputFormat: User-defined formats

Hadoop Ecosystem & Tools

Core Hadoop Components

HDFS (Hadoop Distributed File System)

Distributed storage layer with automatic replication and fault tolerance

YARN (Yet Another Resource Negotiator)

Resource management and job scheduling for the cluster

MapReduce Framework

Programming model and runtime for distributed data processing

Hadoop Common

Common utilities and libraries used by other Hadoop modules

Ecosystem Tools & Frameworks

🐷

Apache Pig

High-level platform for analyzing large datasets

🐝

Apache Hive

SQL-like interface for querying large datasets

🗂️

HBase

NoSQL database built on top of HDFS

📊

Apache Sqoop

Data transfer between Hadoop and relational databases

🌊

Apache Flume

Service for collecting and moving large amounts of data

🔍

Apache Mahout

Machine learning library for scalable algorithms

Evolution Beyond MapReduce

Limitations of MapReduce

• High latency due to disk I/O between phases
• Inefficient for iterative algorithms
• No support for real-time processing
• Complex multi-stage job management

Modern Alternatives

• Apache Spark: In-memory processing
• Apache Flink: Stream processing
• Apache Storm: Real-time computation
• Presto/Trino: Interactive analytics

Real-World MapReduce Applications

Google

• PageRank: Web page ranking algorithm

• Index Building: Search index construction

• Log Analysis: Petabyte-scale log processing

• Machine Learning: Large-scale model training

Facebook

• Data Warehousing: ETL for analytics

• Ad Targeting: User behavior analysis

• Graph Processing: Social graph analytics

• A/B Testing: Experiment result analysis

Yahoo!

• Web Crawling: Internet content indexing

• Spam Detection: Email and content filtering

• Recommendation: Content recommendation systems

• Analytics: User behavior insights

Netflix

• Viewing Analytics: Content performance analysis

• Recommendation: Personalized content suggestions

• Quality Control: Video encoding optimization

• A/B Testing: Interface and algorithm testing

• People Recommendations: Social connection suggestions

• Job Matching: Career opportunity matching

• Content Ranking: Feed personalization

• Skill Inference: Professional skill analysis

eBay

• Search Optimization: Product search improvement

• Fraud Detection: Transaction security analysis

• Pricing Analysis: Market pricing insights

• Seller Analytics: Merchant performance metrics

MapReduce Best Practices

🎯 Design Optimization

• Use combiners to reduce network traffic
• Optimize input split size (64MB-256MB)
• Avoid small files that create many mappers
• Balance mapper and reducer counts

📊 Performance Tuning

• Configure appropriate heap sizes
• Use compression for intermediate data
• Optimize sort buffer sizes
• Enable speculative execution carefully

⚡ Data Management

• Use appropriate file formats (Parquet, ORC)
• Implement proper data partitioning
• Consider data locality for performance
• Clean up intermediate and temporary data

🛡️ Reliability & Monitoring

• Monitor job progress and resource usage
• Set appropriate retry policies
• Implement proper error handling
• Use checkpoint and recovery mechanisms

🔧 Development Practices

• Test with small datasets first
• Use unit tests for mapper and reducer logic
• Implement proper logging and debugging
• Document job configurations and parameters

📈 Scalability Planning

• Plan cluster capacity for peak loads
• Consider data growth and retention policies
• Implement proper resource allocation
• Design for horizontal scaling

📝 MapReduce Knowledge Quiz

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