Casandra vs Hadoop

Introduction

Big data refers to the large amount of data that should be effectively managed and processed for making informed business decisions and gaining a competitive edge. This is where comparision of distributed systems like Casandra vs Hadoop come into play. While both systems are designed to handle large-scale data processing, they have different underlying principles and use cases.

The selection of the right tool, significantly influences an organization's big data strategy. Organizations must assess their specific use cases and business goals to determine which distributed system aligns best with their needs.

What is Hadoop?

Hadoop is one part of the Casandra vs Hadoop comparision and is an open-source framework that provides a distributed storage and processing infrastructure capable of handling massive volumes of structured and unstructured data.

The core components of Hadoop are:

• Hadoop Distributed File System (HDFS) for efficient data storage across multiple nodes in the cluster.
• MapReduce programming model for parallel processing of data.

Key Features

• Scalability: Scale horizontally by adding more hardware for handling massive data.
• Fault Tolerance: Replicates data blocks across nodes, preventing data loss.
• Cost-Effective: Runs on inexpensive commodity hardware.

To know more: Hadoop

What is Cassandra?

Cassandra is another part of the Casandra vs Hadoop comparision and is a distributed NoSQL database designed for high availability and linear scalability. It is well-suited for managing large amounts of time-series data and can handle both structured and unstructured data. Cassandra is based on a peer-to-peer architecture, where all nodes in the cluster have equal status and communicate with each other.

Components

• Node: A single instance of Cassandra running on a physical or virtual machine.
• Data Center: A collection of related nodes forming a single deployment.
• Cluster: A group of one or more data centers that act together to store and process data.
• Commit Log: A write-ahead log that records all changes to data for crash recovery.
• Memtable: An in-memory data structure that temporarily holds recent writes before flushing to SSTables.
• SSTables (Sorted String Tables): Immutable on-disk data files that store data sorted by primary key for efficient reads.
• Read Repair: A mechanism to maintain data consistency during read operations.
• Hinted Handoff: Ensures data availability and consistency when a node is temporarily unavailable.
• Gossip Protocol: A peer-to-peer communication protocol for exchanging cluster state information.
• Compaction: The process of merging and optimizing SSTables to reclaim disk space.
• Snitches: Determines the network topology and data center awareness for better data distribution.
• Partitioners: Responsible for distributing data across the nodes in the cluster based on the partition key.

Working

1. Cassandra uses consistent hashing to evenly distribute data across nodes in the cluster, ensuring efficient load balancing.
2. When data is written to Cassandra, it first goes to a write-ahead log (Commit Log) for durability. Then, it is stored in an in-memory data structure (Memtable) for fast writes.
3. The Memtable is moved periodically to disk as Sorted String Tables (SSTables), which store data sorted by primary key for efficient retrieval.
4. When a read request is made, Cassandra checks the Memtable first, followed by a search in the SSTables, using the partition key to locate the data.
5. Users can specify the desired consistency level for read and write operations to maintain data integrity.
6. Cassandra allows data to be replicated across multiple nodes to ensure fault tolerance and high availability.
7. Nodes communicate using the gossip protocol to share cluster state information, helping them discover and learn about each other.
8. SSTables are merged and optimized through compaction at regular intervals to manage disk space efficiently.

Key Features

• Scalability: Cassandra can scale seamlessly by adding more nodes to the cluster, without any downtime or reconfiguration.
• High Availability: It ensures that data remains available even if some nodes in the cluster fail.
• Flexible Schema: Cassandra allows flexible and dynamic schemas, enabling easy adaptation to changing data requirements.

Limitations

• Complexity: Cassandra's distributed nature can make operations and troubleshooting complex.
• Eventual Consistency: Achieving strong consistency in a distributed environment might result in trade-offs with availability.
• Data Model Limitations: It may not be the best fit for highly interconnected data models, as Cassandra does not support joins.

Difference between Hadoop vs Cassandra

Both Hadoop and Cassandra are powerful distributed systems, but their design and primary use cases differ significantly. Here are the key features that differentiate Casandra vs Hadoop:

• Data Model: Hadoop follows the batch processing model for processing and analyzing large volumes of data. Cassandra is a distributed NoSQL database that allows real-time querying and is more suitable for applications requiring low-latency access to data.
• Processing Paradigm: Hadoop's MapReduce paradigm is ideal for complex data transformations and aggregations, making it suitable for data analytics and batch processing jobs. Cassandra, with its focus on real-time data access, is better suited for applications requiring quick and frequent read-and-write operations.
• Scalability: Casandra vs Hadoop, both systems offer scalability, but do so differently. Hadoop scales by adding more hardware to the cluster, while Cassandra scales by adding more nodes to the peer-to-peer architecture.
• Data Distribution: Hadoop replicates data across nodes to ensure fault tolerance. In contrast, Cassandra distributes data evenly across nodes using consistent hashing, providing high availability and data redundancy.
• Data Consistency: Hadoop guarantees strong consistency across the cluster during data processing. In contrast as of Casandra vs Hadoop, Cassandra offers tunable consistency levels, allowing users to choose between strong consistency and high availability based on their application needs.

Hadoop vs Cassandra: Comparison Table