What is Hadoop? Architecture, Ecosystem & Components

By : Prafulla Ranadive Prafulla Ranadive
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⚡ Smart Summary

Apache Hadoop is an open-source framework that stores huge datasets across clusters of commodity machines and moves the processing logic to the data, so analysis scales by adding cheap nodes rather than bigger servers.

  • 🔘 Core modules: HDFS stores the blocks, MapReduce processes them, and YARN schedules cluster resources from Hadoop 2.x onward.
  • ☑️ Data locality: Compiled processing logic travels to the node holding the block, so far less network bandwidth is consumed.
  • ✅ Ecosystem: Hive, HBase, Mahout, Sqoop, Flume and ZooKeeper extend the core with SQL, NoSQL, ingestion and coordination.
  • 🧪 Master-slave layout: A NameNode tracks namespace metadata while DataNodes hold the blocks and report their state back.
  • 🛠️ Fault tolerance: Every block is replicated across nodes, so a single failed machine never stops a running job.
  • ⚠️ Network topology: Hadoop models the cluster as a tree of data centres, racks and nodes to keep traffic local.
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What is Hadoop architecture, ecosystem and components

What is Hadoop?

Apache Hadoop is an open source software framework used to develop data processing applications which are executed in a distributed computing environment.

Applications built using Hadoop are run on large data sets distributed across clusters of commodity computers. Commodity computers are cheap and widely available. These are mainly useful for achieving greater computational power at low cost.

Similar to data residing in a local file system of a personal computer system, in Hadoop, data resides in a distributed file system which is called the Hadoop Distributed File System. The processing model is based on the ‘Data Locality’ concept, wherein computational logic is sent to the cluster nodes (servers) containing the data. This computational logic is simply a compiled version of a program written in a high-level language such as Java. Such a program processes data stored in Hadoop HDFS.

Do you know? A computer cluster consists of a set of multiple processing units (storage disk + processor) which are connected to each other and act as a single system.

Version 3.5.0, published on 2 April 2026, is the current stable release; the 3.4 line still receives maintenance updates.

Hadoop Ecosystem and Components

The diagram below shows the various components in the Hadoop ecosystem, grouped by the job each one does — storage, processing, and the query, ingestion and coordination tools around them.

Hadoop ecosystem diagram showing HDFS, MapReduce and the surrounding Apache projects

Apache Hadoop consists of two sub-projects –

  1. Hadoop MapReduce: MapReduce is a computational model and software framework for writing applications which are run on Hadoop. These MapReduce programs are capable of processing enormous data in parallel on large clusters of computation nodes.
  2. HDFS (Hadoop Distributed File System): HDFS takes care of the storage part of Hadoop applications. MapReduce applications consume data from HDFS. HDFS creates multiple replicas of data blocks and distributes them on compute nodes in a cluster. This distribution enables reliable and extremely rapid computations.

Current releases ship two further core modules. Hadoop YARN, added in Hadoop 2.x, schedules work on the cluster, and Hadoop Common holds the shared Java libraries every module depends on. YARN is what lets engines such as Spark, Tez and Flink run alongside MapReduce.

Although Hadoop is best known for MapReduce and its distributed file system HDFS, the term is also used for a family of related projects that fall under the umbrella of distributed computing and large-scale data processing. Other Hadoop-related projects at Apache include Hive, HBase, Mahout, Sqoop, Flume, and ZooKeeper.

Hadoop Architecture

Hadoop has a master-slave architecture for data storage and distributed data processing using MapReduce and HDFS methods. The diagram below places those roles side by side, with the storage layer on one side and the processing layer on the other.

High level Hadoop architecture diagram showing NameNode, DataNodes, master node and slave nodes

High Level Hadoop Architecture

NameNode

The NameNode stores the metadata for every file and directory used in the namespace, including which blocks make up each file and where those blocks live.

DataNode

A DataNode manages the state of an HDFS node and lets you interact with the blocks it stores, reporting back to the NameNode with periodic block reports and heartbeats.

MasterNode

The master node allows you to conduct parallel processing of data using Hadoop MapReduce.

Slave Node

The slave nodes are the additional machines in the Hadoop cluster that let you store data and run complex calculations. Moreover, every slave node runs a TaskTracker and a DataNode. This allows you to synchronise the processes with the NameNode and the JobTracker respectively.

In Hadoop, master or slave systems can be set up in the cloud or on-premise.

One naming note: JobTracker and TaskTracker belong to the first-generation MapReduce runtime (MRv1). From Hadoop 2.x onward, YARN splits their duties between a cluster-wide ResourceManager, a NodeManager per worker, and one ApplicationMaster per job. NameNode and DataNode are unchanged.

Features of Hadoop

Suitable for Big Data Analysis

As Big Data tends to be distributed and unstructured in nature, Hadoop clusters are best suited for the analysis of Big Data. Since it is the processing logic (not the actual data) that flows to the computing nodes, less network bandwidth is consumed. This is called the data locality concept, and it helps increase the efficiency of Hadoop-based applications.

Scalability

Hadoop clusters can easily be scaled to any extent by adding additional cluster nodes, and thus allow for the growth of Big Data. Also, scaling does not require modifications to application logic.

Fault Tolerance

The Hadoop ecosystem has a provision to replicate the input data on to other cluster nodes. That way, in the event of a cluster node failure, data processing can still proceed by using data stored on another cluster node. HDFS keeps three copies of every block by default, a value set by the dfs.replication property, and the NameNode re-replicates any block that drops below that count.

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Network Topology in Hadoop

The topology (arrangement) of the network affects the performance of the Hadoop cluster as the size of the cluster grows. In addition to performance, one also needs to care about high availability and the handling of failures. To achieve this, Hadoop cluster formation makes use of network topology.

The tree below shows how that arrangement is modelled, from the data centre down to the nodes inside each rack.

Hadoop network topology tree with data centre, racks and nodes used to measure distance between nodes

Typically, network bandwidth is an important factor to consider while forming any network. However, as measuring bandwidth can be difficult, in Hadoop a network is represented as a tree, and the distance between nodes of this tree (the number of hops) is considered an important factor in the formation of a Hadoop cluster. Here, the distance between two nodes is equal to the sum of their distances to their closest common ancestor.

A Hadoop cluster consists of a data center, the rack and the node which actually executes jobs. Here, the data center consists of racks and a rack consists of nodes. Network bandwidth available to processes varies depending upon the location of the processes. That is, the bandwidth available becomes lesser as we go away from-

  • Processes on the same node
  • Different nodes on the same rack
  • Nodes on different racks of the same data center
  • Nodes in different data centers

Rack awareness uses the same tree: HDFS places replicas on more than one rack, so losing a rack switch does not take every copy of the data with it.

FAQs

Version 3.5.0, published on 2 April 2026, is the first stable release of the 3.5 line and the usual choice for new clusters. The 3.4 line is still maintained if you need a longer-settled branch.

Yes. Hadoop supports standalone mode, which runs as one Java process with no daemons, and pseudo-distributed mode, where every daemon runs separately on one host. Both are meant for learning and testing, not production.

Models trained on job history predict runtimes, recommend container sizes and flag data skew before a job finishes. Similar models scan NameNode and DataNode logs to spot failing disks and hot racks earlier than threshold alerts do.

It speeds up boilerplate: mapper and reducer skeletons, job driver configuration and XML property blocks. Always check generated code against the API version you run, because Copilot mixes the old mapred and newer mapreduce packages.

Three by default, controlled by dfs.replication. One replica stays on the writing node, a second goes to a different rack, and a third joins the second rack. The NameNode restores any block that falls below the target.

It remains common where petabyte-scale batch storage must stay on owned hardware. Most new processing is written for Spark or Flink, which run on YARN, so HDFS often survives as the storage layer after MapReduce is retired.

YARN is the resource manager introduced in Hadoop 2.x. It separates cluster scheduling from the MapReduce programming model, which removed the JobTracker bottleneck and let engines such as Spark, Tez and Flink share one cluster.

Java is native. Hadoop Streaming lets any executable that reads standard input work as a mapper or reducer, so Python, Ruby, Perl and C++ are all usable, and Hive adds a SQL-like layer over the same data.

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