Hadoop vs MongoDB – What’s the Difference ? (Pros and Cons)
Both, Hadoop and MongoDB are two popular tools used in the field of processing and managing large data sets. Since they offer different approaches to data storage and processing they are also used in different business scenarios. Initially, Hadoop consists of two key components – Hadoop Distributed File System and Apache MapReduce. Well, HDFS is a distributed file system that allows large files to be stored on a Hadoop cluster. The other- MapReduce is a programming model that enables large scale distributed data processing.
On the other hand MongoDB is the NoSQL database written in C++, a large cloud provider, and also offers advanced analytics tools.
So shall we start with Hadoop vs MongoDB – What’s the Difference ? (Pros and Cons).
What is Hadoop?
Firstly, Hadoop is an open source framework designed to store and process large datasets across clusters of commodity hardware. Created by Doug Cutting and Mike Cafarella in 2005 and named after a toy elephant of Doug Cutting’s son.
Includes other components, such as Apache Pig, Apache Hive, and Apache Spark, which provide higher level abstractions and tools for data processing and analysis.
Features of Hadoop
Large Cluster of Nodes
Supports large node clusters. This means that a Hadoop cluster consists of millions of nodes. The main benefit of this feature is that it provides customers with massive processing power and massive storage.
Automatic Failover Management
If a particular computer in the cluster fails, the Hadoop network replaces that particular computer with another computer. Also replicates configuration settings and data from the failed computer to the new computer. If this feature is properly configured on the cluster, administrators do not need to worry about it.
Scalability
It refers to the ability to add or remove nodes and add or remove hardware components from or from a cluster. This is done without affecting or stopping cluster operations. Individual hardware components, such as RAM or hard drives, can also be added to or removed from a cluster.
Distributed Data
Distributed data refers to storing data on clusters of computers in a distributed manner. Well, so Hadoop uses a distributed file system called Hadoop Distributed File System (HDFS) to store data across multiple nodes in a cluster. Additionally, there is HDFS – designed to store large files and provide reliable, fault tolerant data storage.
Parallel Processing
Additionally, Hadoop is designed to process large data sets in parallel on clusters of computers, whereas traditional databases typically rely on a single server for processing. Parallel processing in Hadoop is achieved through a programming model called MapReduce. basically, MapReduce breaks data into smaller chunks and processes them independently on different cluster nodes.
Pros of Hadoop
- Helps to distribute data to different servers to avoid network congestion.
- A scalable storage platform. As a result, massive data sets are stored and distributed across hundreds of low cost servers.
- Businesses easily access new data sources and use different types of data to extract value from that data such as social networks and email correspondence.
- The framework has built in capabilities and flexibility to do things that weren’t possible before.
- Hadoop’s HDFS layer is self healing, replicable, and fault tolerant. Purposely used to automatically replicate data, if a server or drive fails.
- The unique storage method is based on a distributed file system that efficiently maps data wherever the cluster resides. The data analysis unit are also be located on the same server where the data resides, greatly speeding up data processing.
- Adding more nodes to your Hadoop cluster provides more storage and processing power. This feature eliminates the need to purchase external equipment. So it’s a cheaper solution.
Cons of Hadoop
- Not suitable for small real time data applications.
- Scaling issues arise whenever a single master manages Hadoop.
- Speaking of security, Hadoop’s own configuration puts management at risk. This framework is written in the Java language, which is actively used by cybercriminals.
- Hadoop is a complex application that is difficult to manage. Security is a major concern and is disabled by default due to extreme complexity.
What is MongoDB?
Firstly, MongoDB is a popular NoSQL document database management system that stores data in flexible JSON like documents. A MongoDB server is a software component that runs on a computer or server and provides an interface for client applications to interact with the database.
Furthermore, MongoDB server stores data in collections, which are similar to tables in relational databases, and documents, which are similar to rows in those tables. Also, MongoDB is known for its scalability and performance, and it handles large amounts of unstructured and semi structured data.
Features of MongoDB
Load balancing
Duplication of data
- Runs on multiple servers. Data is replicated to keep your system up and running even in the event of a hardware failure.
Support ad hoc queries
- Supports ad hoc queries using the MongoDB Query Language (MQL). MQL is a rich and powerful language that allows you to search, filter, and sort data in a variety of ways.
Indexing
- Supports indexing of all fields in a document which makes queries faster and more efficient. Indexing is the process of creating a data structure that allows MongoDB to quickly find and retrieve documents based on the values of specific fields.
Replication
- Generally MongoDB supports replication, the process of synchronizing data across multiple servers to provide high availability and data redundancy. Above all, replication in MongoDB is achieved through a primary secondary architecture where one node acts as a primary node and the other node acts as a secondary node.
Pros of MongoDB
- Deep query capabilities. Basically, MongoDB supports dynamic document queries using a document based query language that is almost as powerful as SQL.
- Structure of a single object is clear.
- Uses internal memory for storing the (windowed) working set, enabling faster access of data.
- Dynamic schemas allow you to try new functions at a fraction of the cost. No need to prepare your data before experimenting.
- Support for ACID (Atomicity, Consistency, Isolation, and Durability) properties for database transactions.
- Easy to install, use and schema less database.
- Replication is supported.
Cons of MongoDB
- Duplicates and joins.
- Stores the key name for each pair of values. Also, there is data redundancy because there is no join function. This increases unnecessary memory usage.
- It does not support connections like relational databases. However, use the linking feature by manually adding it. However, this slows down execution and affects performance.
We are now at the main part of this article about Hadoop vs MongoDB – What’s the Difference ?
Hadoop vs MongoDB – What’s the Difference ?
Since, Hadoop and MongoDB are both widely used data storage and processing technologies, there are some important differences:
Processing Differences
On one side, Hadoop is a batch processing system optimized for large scale data processing. For data processing, we use MapReduce, which divides the data into smaller chunks and processes them in parallel. On the other hand, MongoDB supports real time data processing and provides built in aggregation capabilities for executing complex queries.
Scalability
Here, both Hadoop and MongoDB scale well, but scale differently. Comparatively, Hadoop uses a distributed computing model and scales horizontally by adding more nodes to the cluster. Consequently, MongoDB also supports horizontal scaling, but at the cost of replicating data across multiple servers.
Memory Handling
Especially, with Hadoop it is designed to handle large amounts of data and processing tasks on a distributed network of nodes. Uses a two tier memory architecture with distributed memory on individual nodes in the cluster and a centralized storage layer for intermediate data. The Hadoop MapReduce framework is optimized for disk processing and handles large data sets that do not fit in memory.
The other hand, a document oriented NoSQL database handling, optimized for real time applications. Following, MongoDB uses memory mapped files for data storage. This means that database files are mapped into virtual memory, allowing the operating system to manage memory. In turn, this approach allows MongoDB to use the operating system’s memory management features to effectively cache data and reduce disk I/O.
Framework Comparison
Furthermore, Hadoop comprises of various software that is responsible for creating a data processing framework. While MongoDB it used to query, aggregate, index or replicate data stored. The stored data is in the form of Binary JSON (BJSON) and storing of data is done in collections.
Drawbacks of Both
Importantly, Hadoop highly depends upon ‘NameNode’, that can be a point of failure. Also, MongoDB have low fault tolerance that leads to loss of data occasionally.
Data structure
Indeed, Hadoop is designed for structured, semi structured and unstructured data types, including text, images, audio and video. Data must be converted to a structured format before processing. On the other hand, MongoDB is designed for semi structured and unstructured data types and does not require data transformation prior to processing.
Data model
Lastly, Hadoop is a distributed file system that supports batch processing. It uses the Hadoop Distributed File System (HDFS) to store data in a distributed and fault tolerant manner. On the other hand, MongoDB is a document oriented NoSQL database that stores data in JSON like documents.
Thank you for reading Hadoop vs MongoDB – What’s the Difference ? until the end. We shall conclude it now.
Hadoop vs MongoDB – What’s the Difference ? Conclusion
When considering Hadoop and MongoDB, it is important to understand the specific needs and requirements of your use case. Summing up, Hadoop is great for batch processing large data sets, while MongoDB is ideal for real time applications that require high throughput and flexible data modelling capabilities. Understanding the strengths and limitations of each technology help you choose the one that best fits your needs and achieves your data processing goals.
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