{"id":1999,"date":"2017-10-05T05:58:04","date_gmt":"2017-10-05T05:58:04","guid":{"rendered":"http:\/\/techvidvan.com\/tutorials\/?p=366"},"modified":"2017-10-05T05:58:04","modified_gmt":"2017-10-05T05:58:04","slug":"distributed-cache-in-hadoop","status":"publish","type":"post","link":"https:\/\/techvidvan.com\/tutorials\/distributed-cache-in-hadoop\/","title":{"rendered":"Introduction to Distributed Cache in Hadoop"},"content":{"rendered":"

In this tutorial we will provide you a detailed description of a Distributed Cache in Hadoop<\/strong>. First of all we will briefly understand what is Hadoop, then we will see what is Distributed Cache in Hadoop.<\/p>\n

We will also cover the working and implementation of Hadoop\u00a0Distributed Cache. At last in this blog we will also see the advantages and disadvantages of distributed caching in Hadoop.<\/p>\n

Introduction to Hadoop<\/h3>\n

 <\/p>\n

It is a mechanism that MapReduce framework provides to cache files needed by the applications. It can cache files like read-only text\/data files, and more complex types such as archives, jar files etc.<\/p>\n

Before we start with Distributed Cache, let us first discuss what is Hadoop?<\/p>\n

Hadoop<\/strong> is open-source, Java-based programming framework. It supports processing and storage of extremely large datasets in a distributed environment. Hadoop follows Master-Slave topology.<\/p>\n

Master is NameNode and Slave is DataNode. Datanode stores actual data in HDFS<\/strong>. And it performs read and write operation as per request for the client. Namenode stores meta-data.<\/p>\n

In Apache Hadoop, data chunks, process in parallel among Datanodes, using a program written by the user.\u00a0 If we want to access some files from all the Datanodes, then we will put that file into distributed cache.<\/p>\n

What is Distributed Cache in Hadoop?<\/h3>\n

Distributed Cache<\/strong>\u00a0in Hadoop is a facility provided by the MapReduce framework. Distributed Cache can cache files when needed by the applications. It can cache read only text files, archives, jar files etc.<\/p>\n

Once we have cached a file for our job, Apache Hadoop will make it available on each datanodes where map\/reduce tasks are running. Thus, we can access files from all the datanodes in our MapReduce job.<\/p>\n

Size of Distributed Cache<\/h4>\n

By default, distributed cache size is 10 GB. If we want to adjust the size of distributed cache we can adjust by using local<\/strong>.cache<\/strong>.size.<\/strong><\/p>\n

Implementation<\/h4>\n

An application which is going to use distributed cache to distribute a file:<\/p>\n

    \n
  • Should first ensure that the file is available.<\/li>\n
  • After that, also make sure that the file can accessed via URLs. URLs can be either hdfs: \/\/ or https:\/\/.<\/strong><\/li>\n<\/ul>\n

    After the above validation, if the file is present on the mentioned urls. The Hadoop user mentions it to be a cache file to the distributed cache. The Hadoop MapReduce job will copy the cache file on all the nodes before starting of tasks on those nodes.<\/p>\n

    Follow the below process:<\/p>\n

    a) Copy the requisite file to the HDFS:<\/strong><\/h5>\n

    $ hdfs dfs-put\/user\/dataflair\/lib\/jar_file.jar<\/p>\n

    b) Setup the application\u2019s JobConf:<\/strong><\/h5>\n

    DistributedCache.addFileToClasspath(new Path (\u201c\/user\/dataflair\/lib\/jar-file.jar\u201d), conf).<\/p>\n

    c) Add it in Driver class.<\/strong><\/h5>\n

    Advantages of Distributed Cache<\/h3>\n
      \n
    • Single point of failure- <\/strong>As distributed cache run across many nodes. Hence, the failure of a single node does not result in a complete failure of the cache.<\/li>\n
    • Data Consistency- <\/strong>It tracks the modification timestamps of cache files. It then, notifies that the files should not change until a job is executing. Using hashing algorithm, the cache engine can always determine on which node a particular key-value resides. As we know, that there is always a single state of the cache cluster, so, it is never inconsistent.<\/li>\n
    • Store complex data –<\/strong> It distributes simple, read-only text file. It also stores complex types like jars, archives. These achieves are then un-archived at the slave node.<\/li>\n<\/ul>\n

      Disadvantage of Distributed Cache<\/h3>\n

      A Distributed Cache in Hadoop has overhead that will make it slower than an in-process cache:<\/p>\n

      a) Object serialization<\/strong>– It must serialize objects. But the serialization mechanism has two main problems:<\/p>\n

        \n
      • Very bulky<\/strong>– Serialization stores complete class name, cluster, and assembly details. It also stores references to other instances in member variables. All this makes the serialization very bulky.<\/li>\n
      • Very slow<\/strong>– Serialization uses reflection to inspect the type of information at runtime. Reflection is a very slow process as compared to pre-compiled code.<\/li>\n<\/ul>\n

        Conclusion<\/h3>\n

        In conclusion to Distributed cache, we can say that, it is a facility provided by the MapReduce. It caches files when needed by the applications. It can cache read only text files, archives, jar files etc.<\/p>\n

        By default, distributed cache size is 10 GB. If you find this blog, or you have any query related to Distributed Cache in Hadoop, so feel free to share with us.<\/p>\n","protected":false},"excerpt":{"rendered":"

        In this tutorial we will provide you a detailed description of a Distributed Cache in Hadoop. First of all we will briefly understand what is Hadoop, then we will see what is Distributed Cache...<\/p>\n","protected":false},"author":1,"featured_media":73108,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[544],"tags":[538,457,539,622,541,623],"class_list":["post-1999","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hadoop","tag-apache-hadoop","tag-big-data","tag-big-data-hadoop","tag-distributed-cache-in-hadoop","tag-hadoop","tag-hadoop-distributed-cache"],"yoast_head":"\nIntroduction to Distributed Cache in Hadoop - TechVidvan<\/title>\n<meta name=\"description\" content=\"Hadoop tutorial for Distributed Cache in Hadoop,Hadoop distributed cache implementation, Distributed Cache size, benefits & limitations of Distributed cache\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/techvidvan.com\/tutorials\/distributed-cache-in-hadoop\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Introduction to Distributed Cache in Hadoop - 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