Hadoop vs RocksDB: What are the differences?

Introduction:

Hadoop and RocksDB are both powerful tools used in big data processing, but they have key differences that make them suitable for different use cases. Below are the key differences between Hadoop and RocksDB:

1. Storage Type: Hadoop is designed for distributed storage and processing of large data sets across clusters of computers using a simple programming model. On the other hand, RocksDB is an embedded key-value store optimized for fast storage and retrieval of data on local storage devices like hard drives or solid-state drives (SSDs).

2. Use Case: Hadoop is commonly used for batch processing of large data sets where fault tolerance and scalability are essential. It is ideal for processing large volumes of data in a distributed environment. In contrast, RocksDB is suitable for applications that require low-latency reads and writes, making it a good choice for real-time processing and caching.

3. Consistency Model: Hadoop follows a strong consistency model, ensuring that all nodes see the same data at the same time. This ensures data integrity but may impact performance in certain scenarios. RocksDB, on the other hand, allows for eventual consistency, where some nodes may have slightly outdated data at any given time, prioritizing performance over strict consistency.

4. Query Language: Hadoop uses MapReduce as its processing model, which involves writing code in Java, Python, or other languages to process data. RocksDB, being a key-value store, provides an API for storing and retrieving data directly without the need for complex query languages.

5. Data Processing Speed: Due to its distributed nature, Hadoop may face issues related to data transfer and network latency, impacting processing speed. RocksDB, being a local storage engine, can offer faster data processing speeds by minimizing data transfer over a network and accessing data directly from local storage.

6. Scalability: Hadoop is highly scalable and can handle petabytes of data across large clusters of machines, making it suitable for organizations dealing with massive data volumes. RocksDB, while not designed for distributed processing, can scale vertically by leveraging faster storage devices or increasing memory capacity for improved performance.

In Summary, Hadoop is suited for distributed batch processing of large data sets, while RocksDB excels in low-latency read/write operations for real-time processing and caching applications.