Hadoop Distributed File System (HDFS) is designed for storing very large files and processing them in a distributed computing environment. However, it's not the best fit for a large number of small files, for a few reasons:
So, while HDFS can technically handle small files, it is not designed to do so efficiently. If your use case involves many small files, you might want to consider other storage systems or design patterns (like compaction or SequenceFiles in Hadoop) more suited to that kind of workload.
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The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications. HDFS employs a NameNode and DataNode architecture to implement a distributed file system that provides high-performance access to data across highly scalable Hadoop clusters.
Hadoop itself is an open source distributed processing framework that manages data processing and storage for big data applications. HDFS is a key part of the many Hadoop ecosystem technologies. It provides a reliable means for managing pools of big data and supporting related big data analytics applications. HDFS enables the rapid transfer of data between compute nodes. At its outset, it was closely coupled with MapReduce, a framework for data processing that filters and divides up work among the nodes in a cluster, and it organizes and condenses the results into a cohesive answer to a query. Similarly, when HDFS takes in data, it breaks the information down into separate blocks and distributes them to different nodes in a cluster.With HDFS, data is written on the server once, and read and reused numerous times after that. HDFS has a primary NameNode, which keeps track of where file data is kept in the cluster.HDFS also has multiple DataNodes on a commodity hardware cluster -- typically one per node in a cluster. The DataNodes are generally organized within the same rack in the data center. Data is broken down into separate blocks and distributed among the various DataNodes for storage. Blocks are also replicated across nodes, enabling highly efficient parallel processing.The NameNode knows which DataNode contains which blocks and where the DataNodes reside within the machine cluster. The NameNode also manages access to the files, including reads, writes, creates, deletes and the data block replication across the DataNodes.The NameNode operates in conjunction with the DataNodes. As a result, the cluster can dynamically adapt to server capacity demand in real time by adding or subtracting nodes as necessary.The DataNodes are in constant communication with the NameNode to determine if the DataNodes need to complete specific tasks. Consequently, the NameNode is always aware of the status of each DataNode. If the NameNode realizes that one DataNode isn't working properly, it can immediately reassign that DataNode's task to a different node containing the same data block. DataNodes also communicate with each other, which enables them to cooperate during normal file operations.Moreover, the HDFS is designed to be highly fault-tolerant. The file system replicates -- or copies -- each piece of data multiple times and distributes the copies to individual nodes, placing at least one copy on a different server rack than the other copies. Dr.Sundus Fadhil Hantoosh
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