HDFS and MapReduce in Big Data
HDFS and MapReduce are the two core pillars of the Hadoop framework.
HDFS handles the storage of massive datasets, while MapReduce processes
those datasets in a distributed and parallel manner.
This chapter explains how data is stored across clusters using HDFS and
how large-scale computation is performed using the MapReduce programming model.
⭐ Hadoop Distributed File System (HDFS)
HDFS is a distributed file system designed to store very large files across
multiple machines. It provides high fault tolerance and is optimized for
high-throughput data access.
📌 Key Features of HDFS
- Distributed storage across multiple nodes
- Fault tolerance using data replication
- High scalability on commodity hardware
- Write-once, read-many access model
📌 HDFS Architecture
- NameNode: Manages metadata and file system namespace
- DataNode: Stores actual data blocks
- Secondary NameNode: Assists in metadata checkpointing
📌 Data Storage in HDFS
- Files are split into blocks (default 128 MB)
- Each block is replicated across multiple DataNodes
- Replication factor ensures fault tolerance
⭐ MapReduce Programming Model
MapReduce is a distributed processing framework that allows large-scale data
processing by dividing tasks into smaller sub-tasks that run in parallel.
📌 MapReduce Workflow
- Input Split: Data is divided into chunks
- Map Phase: Processes input and produces key-value pairs
- Shuffle & Sort: Groups similar keys together
- Reduce Phase: Aggregates results
📌 Example: Word Count Using MapReduce
// Mapper
map(String key, String value):
for each word in value:
emit(word, 1)
// Reducer
reduce(String key, Iterator values):
sum = 0
for each value in values:
sum += value
emit(key, sum)
📌 Advantages of MapReduce
- Automatic parallelization
- Fault tolerance
- Scales to petabytes of data
📌 Limitations of MapReduce
- High latency for iterative tasks
- Complex programming model
- Not suitable for real-time processing
📌 Real-Life Applications
- Log processing and analysis
- Search engine indexing
- Data warehousing
- Batch data processing
📌 Project Title
Distributed Word Count and Log Analysis Using HDFS and MapReduce
📌 Project Description
In this project, you will store large text or log files in HDFS and process
them using a MapReduce job to perform word count and log analysis.
This project helps you understand distributed storage and batch processing
in real-world Big Data systems.
📌 Summary
HDFS and MapReduce form the foundation of Hadoop-based Big Data systems.
HDFS enables reliable distributed storage, while MapReduce allows large-scale
batch processing. Understanding these concepts is essential before moving
to faster and more flexible frameworks like Apache Spark.
