Hadoop Distributed File System (HDFS) is architected for large-scale, data-intensive applications. Its design prioritizes high-throughput, streaming access to very large datasets (gigabytes to terabytes) over low-latency access. A core feature is high fault tolerance, achieved by replicating data blocks across multiple nodes in a cluster, ensuring data availability even if some nodes fail. This makes HDFS ideal for batch processing workloads, such as those executed by MapReduce, where the system reads through massive datasets sequentially.

A. The NameNode in HDFS stores all file system metadata in memory. A large number of small files would create excessive metadata, overwhelming the NameNode and creating a performance bottleneck.

C. HDFS is optimized for high throughput (moving large amounts of data over time), not low latency (quick response to a single read request), making it unsuitable for interactive or real-time applications.

1. Huawei HCIA-Big Data V2.0 Course Material: In the module describing HDFS architecture and principles, it is explicitly stated that HDFS is suitable for scenarios requiring high throughput and fault tolerance for large files, using a streaming data access model. It also lists "not suitable for low-latency data access" and "inefficient for storing a large number of small files" as key limitations. (Reference: HCIA-Big Data V2.0 Training Material, Chapter 2: HDFS Distributed File System).

2. Official Vendor Documentation (Apache Hadoop): The official Apache Hadoop documentation for HDFS architecture outlines its core assumptions and goals. It states, "HDFS is built around the idea that the most efficient data processing pattern is a write-once/read-many-times pattern... A HDFS application needs high throughput data access... It is not a low-latency data access filesystem." (Reference: Apache Hadoop Documentation, HDFS Architecture Guide, Section: "Assumptions and Goals").

3. Academic Publication (Inspiration for HDFS): The design of HDFS was heavily influenced by Google's GFS. The original paper states, "The system is built from inexpensive commodity components that often fail... It must provide high aggregate throughput to many clients." This highlights the goals of fault tolerance and high throughput. (Reference: Ghemawat, S., Gobioff, H., & Leung, S. T. (2003). The Google file system. ACM SIGOPS operating systems review, 37(5), 29-43. Section 2.1, "Assumptions". DOI: https://doi.org/10.1145/1165389.945450).