HDFS Latency Issues: Hadoop Distributed File System Quiz and Flashcards

Study Notes

Introduction

Hadoop Distributed File System (HDFS) is an open-source distributed file system designed to store large volumes of data across clusters of computers. While HDFS offers several advantages for handling large-scale data sets, it has some drawbacks when it comes to data access, specifically in terms of low-latency access. This article explores the challenges of low-latency access in HDFS and why it is generally not recommended for applications requiring real-time or near-real-time data access.

HDFS Architecture

HDFS operates on a master-slave architecture, consisting of four parts: the HDFS Client, NameNode, DataNode, and SecondaryNameNode. The client serves as the interface between the user and HDFS, responsible for file segmentation, interacting with the NameNode, and performing read and write operations. The NameNode holds metadata information such as block location and manages the namespace of HDFS. It also handles read and write requests from clients. The DataNodes store actual data blocks and perform read/write operations under the control of the NameNode. Finally, the SecondaryNameNode acts as an auxiliary node, sharing some workload from the NameNode and assisting in restoring the NameNode in case of failure.

Low-Latency Access Drawbacks

One of the main drawbacks of HDFS in terms of low-latency access is its design philosophy. HDFS provides high throughput data access, making it suitable for applications dealing with large files that don't require immediate response times. However, this approach comes at the expense of latency, as HDFS is designed to handle sequences of reads rather than individual small files. When dealing with small files, this can lead to performance issues due to the overhead associated with managing the metadata and maintaining the distributed nature of the storage system.

Another factor contributing to HDFS's poor handling of low-latency access is its reliance on sequential reads. Hadoop uses MapReduce jobs to process data, which assumes that the input data is stored in contiguous blocks, leading to sequential disk access. This sequential access pattern further increases latency when trying to access random locations within a file.

Moreover, HDFS has limitations related to file size. Although HDFS supports large files efficiently, there are challenges when processing small files. By default, Hadoop stores files in blocks ranging from 128MB to 256MB. Processing a large number of small files can overload the NameNode due to the increased overhead of managing metadata. Additionally, storing millions of small files in HBase is a common practice to mitigate the issue with HDFS handling small files, but it introduces complexity and additional overhead.

Lastly, Hadoop lacks support for in-memory processing and only offers batch processing capabilities. This means that real-time or near-real-time data access is difficult, as it requires moving data in and out of memory frequently, which is less efficient compared to modern databases optimized for in-memory processing.

Conclusion

In summary, while HDFS performs well in scenarios requiring high throughput data access for large files, it is generally not ideal for applications seeking low-latency access. Its sequential read pattern, limitations with small files, and lack of in-memory processing make it more suited for batch-oriented, large-scale data processing tasks. For real-time or low-latency applications, alternative solutions like Apache Spark and Apache Flink may provide better performance characteristics.

Description

Explore the drawbacks of low-latency access in Hadoop Distributed File System (HDFS) and why it may not be suitable for real-time or near-real-time data access. Learn about the architecture of HDFS, sequential read patterns, limitations with small files, and lack of in-memory processing capabilities.