Hadoop vs LevelDB: What are the differences?

The comparison between Hadoop and LevelDB focuses on their key differences.

1. Scalability: Hadoop is designed for distributed processing on large datasets across clusters of computers, making it highly scalable for big data tasks. On the other hand, LevelDB is a key-value storage library designed for embedded applications and may not exhibit the same level of scalability as Hadoop for distributed processing.

2. Use case: Hadoop is typically used for batch processing, data storage, and processing large-scale data where fault tolerance is crucial. In contrast, LevelDB is more suited for applications that require low latency reads and writes for a small to moderate amount of data.

3. Data model: Hadoop follows a file-based storage system known as the Hadoop Distributed File System (HDFS) which breaks data into manageable blocks distributed across the cluster. LevelDB, on the other hand, utilizes a key-value storage model where data is stored as a collection of key-value pairs, providing fast access to data.

4. Consistency: Hadoop focuses on achieving high throughput by allowing eventual consistency in data processing, which means updates are eventually propagated to all nodes in the cluster. In comparison, LevelDB provides strong consistency guarantees where data is immediately consistent across all operations.

5. Complexity: Hadoop can be complex to set up and maintain due to its distributed nature, requiring expertise in cluster management and configuration. On the contrary, LevelDB is lightweight, easy to embed within applications, and requires less operational overhead.

In Summary, Hadoop and LevelDB differ in terms of scalability, use case, data model, consistency, and complexity, catering to different needs in big data processing and storage.