HDFS: A Conceptual View

There has been a significant boom in distributed computing over the past few years. Various components communicate with each other over network inspite of being deployed on different physical machines. A distributed file system (DFS) is a file system with data stored on a server. The data is accessed and processed as if it was stored on the local client machine. The DFS makes it convenient to share information and files among users on a network in a controlled and authorized way. So why exactly do we need these multiple physical machines?
Let’s have a look

  • When data outgrows the storage capacity of a single physical machine, it becomes necessary to partition it across a number of separate machines.
  • File systems that manage the storage across a network of machines are called Distributed Filesystems. Since they are network-based, all the difficulty of network programming comes up, thus making distributed file systems more complex than regular disk file systems.
  • So, one of the biggest challenges is making the file system tolerate node failures without data loss. Hadoop comes with a distributed file system called HDFS i.e., Hadoop Distributed File System.
  • Image result for distributed

    Design of HDFS:

    HDFS is a filesystem designed for storing very large files, with streaming data access patterns, running on clusters of commodity hardware. Now let us understand this statement in detail.

    • Very large” here means files that are hundreds of megabytes, gigabytes, or terabytes in size. There are Hadoop clusters running today that store petabytes of data.
    • Streaming data access” HDFS is built around the idea that the most efficient data processing pattern is a write-once, read-many-times pattern. A dataset is typically generated or copied from the source, then various analyses are performed on that dataset over time. Each analysis will involve a large proportion, if not all, of the dataset, so the time to read the whole dataset is more important than the latency in reading the first record.
    • Commodity hardware” Hadoop doesn’t require expensive, highly reliable hardware to run on. It’s designed to run on clusters of commodity hardware (commonly available hardware available from multiple vendors) for which the chance of node failure across the cluster is high, at least for large clusters. HDFS is designed to carry on working without a noticeable interruption to the user in the face of such failure.

    HDFS Concepts:

    Image result for hdfs architecture


    • Blocks:
      A disk has a block size, which is the minimum amount of data that it can read or write. Filesystems for a single disk build on this by dealing with data in blocks, which are an integral multiple of the disk block size. Filesystem blocks are typically a few kilobytes in size, while disk blocks are normally 512 bytes. This is generally transparent to the filesystem user who is simply reading or writing a file of whatever length.HDFS too has the concept of a block, but it is a much larger uni – 64 MB by default.
      Like in a filesystem for a single disk, files in HDFS are broken into block-sized chunks, which are stored as independent units. Unlike a filesystem for a single disk, a file in HDFS that is smaller than a single block does not occupy a full block’s worth of underlying storage.Why Is a Block in HDFS So Large?
      HDFS blocks are large compared to disk blocks, and the reason is to minimize the cost of seeks. By making a block large enough, the time to transfer the data from the disk can be made to be significantly larger than the time to seek to the start of the block. Thus the time to transfer a large file made of multiple blocks operates at the disk transfer rate. A quick calculation shows that if the seek time is around 10ms, and the transfer rate is 100 MB/s, then to make the seek time 1% of the transfer time, we need to make the block size around 100 MB. The default is actually 64 MB, although many HDFS installations use 128 MB blocks. This figure will continue to be revised upward as transfer speeds grow with new generations of disk drives.
      This argument shouldn’t be taken too far, however. Map tasks in MapReduce normally operate on one block at a time, so if you have too few tasks (fewer than nodes in the cluster), your jobs will run slower than they could otherwise
    • Namenodes and Datanodes:
      A HDFS cluster has two types of node operating in a master-worker pattern: a HDFS namenode (the master) and a number of datanodes (workers).The namenode manages the filesystem namespace. It maintains the filesystem tree and the metadata for all the files and directories in the tree. This information is stored persistently on the local disk in the form of two files: the namespace image and the edit log. The namenode also knows the datanodes on which all the blocks for a given file are located, however, it does not store block locations persistently, since this information is reconstructed from datanodes when the system starts.

      A client accesses the filesystem on behalf of the user by communicating with the namenode and datanodes. The client presents a POSIX-like filesystem interface, so the user code does not need to know about the namenode and datanode to function.

      Datanodes are the work horses of the filesystem. They store and retrieve blocks when they are told to (by clients or the namenode), and they report back to the namenode periodically with lists of blocks that they are storing. Without the namenode, the filesystem cannot be used. In fact, if the machine running the namenode were obliterated, all the files on the filesystem would be lost since there would be no way of knowing how to reconstruct the files from the blocks on the datanodes. For this reason, it is important to make the namenode resilient to failure, and Hadoop provides two mechanisms for this.

      The first way is to back up the files that make up the persistent state of the filesystem metadata. Hadoop can be configured so that the namenode writes its persistent state to multiple filesystems. These writes are synchronous and atomic. The usual configuration choice is to write to local disk as well as a remote NFS mount.

    • Checkpoint Node and Backup Node:
      The Checkpoint node is a node which periodically creates checkpoints of the namespace. Checkpoint Node in Hadoop first downloads FsImage and edits from the Active Namenode. Then it merges them (FsImage and edits) locally, and at last, it uploads the new image back to the active NameNode. It stores the latest checkpoint in a directory that has the same structure as the Namenode’s directory. This permits the checkpointed image to be always available for reading by the namenode if necessary.

      Backup node provides the same checkpointing functionality as the Checkpoint node. In Hadoop, Backup node keeps an in-memory, up-to-date copy of the file system namespace. It is always synchronized with the active NameNode state. The backup node in HDFS Architecture does not need to download FsImage and edits files from the active NameNode to create a checkpoint. It already has an up-to-date state of the namespace state in memory. The Backup node checkpoint process is more efficient as it only needs to save the namespace into the local FsImage file and reset edits. NameNode supports one Backup node at a time.

      References: Hadoop The Definitive Guide


Written by 

Ayush is a Software Consultant having more than 11 months of experience. He has knowledge of various programming languages like C, C++, Java, Scala, JavaScript and is currently working on Big Data Technologies like Spark, Kafka, ElasticSearch. He is always eager to learn new and advance concepts in order to expand his horizon and apply them in project development with his existing knowledge. His hobbies includes playing Cricket, Travelling, Watching Movies

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