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Hadoop interview questions & Answers

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Hadoop Interview Questions

Hadoop Interview questions

  • On What Concept The Hadoop Framework Works?

    It works on MapReduce, and it is devised by the Google.

  • What Is Mapreduce?

    Map reduce is an algorithm or concept to process Huge amount of data in a faster way. As per its name you can divide it Map and Reduce.

    • The main MapReduce job usually splits the input data-set into independent chunks. (Big data sets in the multiple small datasets)
    • MapTask: will process these chunks in a completely parallel manner (One node can process one or more chunks).The framework sorts the outputs of the maps.
    • Reduce Task : And the above output will be the input for the reducetasks, produces the final result.

    Your business logic would be written in the MappedTask and ReducedTask. Typically both the input and the output of the job are stored in a file-system (Not database). The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks

  • What Is Compute And Storage Nodes?

    Compute Node: This is the computer or machine where your actual business logic will be executed.

    torage Node: This is the computer or machine where your file system reside to store the processing data.

    In most of the cases compute node and storage node would be the same machine.

  • How Does Master Slave Architecture In The Hadoop?

    The MapReduce framework consists of a single master JobTracker and multiple slaves, each cluster-node will have one TaskTracker. The master is responsible for scheduling the jobs' component tasks on the slaves, monitoring them and re-executing the failed tasks. The slaves execute the tasks as directed by the master.

  • How Does An Hadoop Application Look Like Or Their Basic Components?

    Minimally an Hadoop application would have following components.

    • Input location of data
    • Output location of processed data.
    • A map task.
    • A reduced task.
    • Job configuration

    The Hadoop job client then submits the job (jar/executable etc.) and configuration to the JobTracker which then assumes the responsibility of distributing the software / configuration to the slaves, scheduling tasks and monitoring them, providing status and diagnostic information to the job-client.

  • Explain How Input And Output Data Format Of The Hadoop Framework?

    The MapReduce framework operates exclusively on pairs, that is, the framework views the input to the job as a set of pairs and produces a set of pairs as the output of the job, conceivably of different types.

    See the flow mentioned below

    (input) -> map -> -> combine/sorting -> -> reduce -> (output)

  • What Are The Restriction To The Key And Value Class ?

    he key and value classes have to be serialized by the framework. To make them serializable Hadoop provides a Writable interface. As you know from the java itself that the key of the Map should be comparable, hence the key has to implement one more interface Writable Comparable.

  • Explain The Wordcount Implementation Via Hadoop Framework ?

    We will count the words in all the input file flow as below

    • input Assume there are two files each having a sentence Hello World Hello World (In file 1) Hello World Hello World (In file 2)
    • Mapper : There would be each mapper for the a file For the given sample input the first map output:
    • < Hello, 1>
      < World, 1>
      < Hello, 1>
      < World, 1>

      The second map output:

      < Hello, 1>
      < World, 1>
      < Hello, 1>
      < World, 1>

    • Combiner/Sorting (This is done for each individual map) So output looks like this The output of the first map:
    • < Hello, 2>
      < World, 2>

      The output of the second map:

      < Hello, 2>
      < World, 2>

    • Reducer : It sums up the above output and generates the output as below
    • < Hello, 4>
      < World, 4>

    Output

    Final output would look like

    Hello 4 times
    World 4 times

  • Which Interface Needs To Be Implemented To Create Mapper And Reducer For The Hadoop?

    org.apache.hadoop.mapreduce.Mapper
    org.apache.hadoop.mapreduce.Reducer

  • What Mapper Does?

    Maps are the individual tasks that transform input records into intermediate records. The transformed intermediate records do not need to be of the same type as the input records. A given input pair may map to zero or many output pairs.

  • What Is The Inputsplit In Map Reduce Software?

    An InputSplit is a logical representation of a unit (A chunk) of input work for a map task; e.g., a file name and a byte range within that file to process or a row set in a text file.

  • What Is The Inputformat ?

    The InputFormat is responsible for enumerate (itemise) the InputSplits, and producing a RecordReader which will turn those logical work units into actual physical input records.

  • Where Do You Specify The Mapper Implementation?

    Generally mapper implementation is specified in the Job itself.

  • How Mapper Is Instantiated In A Running Job?

    The Mapper itself is instantiated in the running job, and will be passed a MapContext object which it can use to configure itself.

  • Which Are The Methods In The Mapper Interface?

    The Mapper contains the run() method, which call its own setup() method only once, it also call a map() method for each input and finally calls it cleanup() method. All above methods you can override in your code.

  • What Happens If You Don't Override The Mapper Methods And Keep Them As It Is?

    If you do not override any methods (leaving even map as-is), it will act as the identity function, emitting each input record as a separate output.

  • What Is The Use Of Context Object?

    The Context object allows the mapper to interact with the rest of the Hadoop system. It Includes configuration data for the job, as well as interfaces which allow it to emit output.

  • How Can You Add The Arbitrary Key-value Pairs In Your Mapper?

    You can set arbitrary (key, value) pairs of configuration data in your Job, e.g. with
    Job.getConfiguration().set("myKey", "myVal"), and then retrieve this data in your mapper with
    Context.getConfiguration().get("myKey"). This kind of functionality is typically done in the Mapper's setup() method.

  • How Does Mapper's Run() Method Works?

    The Mapper.run() method then calls map(KeyInType, ValInType, Context) for each key/value pair in the InputSplit for that task

  • Which Object Can Be Used To Get The Progress Of A Particular Job ?

    Context

  • What Is Next Step After Mapper Or Maptask?

    The output of the Mapper are sorted and Partitions will be created for the output. Number of partition depends on the number of reducer.

  • How Can We Control Particular Key Should Go In A Specific Reducer?

    Users can control which keys (and hence records) go to which Reducer by implementing a custom Partitioned.

  • What Is The Use Of Combiner?

    It is an optional component or class, and can be specify via Job.setCombinerClass(ClassName), to perform local aggregation of the intermediate outputs, which helps to cut down the amount of data transferred from the Mapper to the Reducer.

  • How Many Maps Are There In A Particular Job?

    The number of maps is usually driven by the total size of the inputs, that is, the total number of blocks of the input files.

    Generally it is around 10-100 maps per-node. Task setup takes awhile, so it is best if the maps take at least a minute to execute.

    Suppose, if you expect 10TB of input data and have a block size of 128MB, you'll end up with 82,000 maps, to control the number of block you can use the mapreduce.job.maps parameter (which only provides a hint to the framework). Ultimately, the number of tasks is controlled by the number of splits returned by the InputFormat.getSplits() method (which you can override).

  • What Is The Reducer Used For?

    Reducer reduces a set of intermediate values which share a key to a (usually smaller) set of values.

    The number of reduces for the job is set by the user via Job.setNumReduceTasks(int).

  • Explain The Core Methods Of The Reducer?

    The API of Reducer is very similar to that of Mapper, there's a run() method that receives a Context containing the job's configuration as well as interfacing methods that return data from the reducer itself back to the framework. The run() method calls setup() once, reduce() once for each key associated with the reduce task, and cleanup() once at the end. Each of these methods can access the job's configuration data by using Context.getConfiguration().

    As in Mapper, any or all of these methods can be overridden with custom implementations. If none of these methods are overridden, the default reducer operation is the identity function; values are passed through without further processing.

    The heart of Reducer is its reduce() method. This is called once per key; the second argument is an Iterable which returns all the values associated with that key.

  • What Are The Primary Phases Of The Reducer?

    Shuffle, Sort and Reduce.

  • Explain The Shuffle?

    Input to the Reducer is the sorted output of the mappers. In this phase the framework fetches the relevant partition of the output of all the mappers, via HTTP.

  • Explain The Reducer's Sort Phase?

    The framework groups Reducer inputs by keys (since different mappers may have output the same key) in this stage. The shuffle and sort phases occur simultaneously; while map-outputs are being fetched they are merged (It is similar to merge-sort).

  • Explain The Reducer's Reduce Phase?

    In this phase the reduce(MapOutKeyType, Iterable, Context) method is called for each pair in the grouped inputs. The output of the reduce task is typically written to the FileSystem via Context.write (ReduceOutKeyType, ReduceOutValType). Applications can use the Context to report progress, set application-level status messages and update Counters, or just indicate that they are alive. The output of the Reducer is not sorted.

  • How Many Reducers Should Be Configured?

    The right number of reduces seems to be 0.95 or 1.75 multiplied by
    (<no.of nades> * mapreduce.tasktracker.reduce.tasks.maximum).

    With 0.95 all of the reduces can launch immediately and start transfering map outputs as the maps finish. With 1.75 the faster nodes will finish their first round of reduces and launch a second wave of reduces doing a much better job of load balancing. Increasing the number of reduces increases the framework overhead, but increases load balancing and lowers the cost of failures.

  • It Can Be Possible That A Job Has 0 Reducers?

    It is legal to set the number of reduce-tasks to zero if no reduction is desired.

  • What Happens If Number Of Reducers Are 0?

    In this case the outputs of the map-tasks go directly to the FileSystem, into the output path set by setOutputPath(Path). The framework does not sort the map-outputs before writing them out to the FileSystem.

  • How Many Instances Of Jobtracker Can Run On A Hadoop Cluster?

    Only one

  • What Is The Jobtracker And What It Performs In A Hadoop Cluster?

    JobTracker is a daemon service which submits and tracks the MapReduce tasks to the Hadoop cluster. It runs its own JVM process. And usually it run on a separate machine, and each slave node is configured with job tracker node location. The JobTracker is single point of failure for the Hadoop MapReduce service. If it goes down, all running jobs are halted.

    JobTracker in Hadoop performs following actions

    • Client applications submit jobs to the Job tracker.
    • The JobTracker talks to the NameNode to determine the location of the data
    • The JobTracker locates TaskTracker nodes with available slots at or near the data
    • The JobTracker submits the work to the chosen TaskTracker nodes.
    • A TaskTracker will notify the JobTracker when a task fails. The JobTracker decides what to do then: it may resubmit the job elsewhere, it may mark that specific record as something to avoid, and it may may even blacklist the TaskTracker as unreliable.
    • When the work is completed, the JobTracker updates its status.
    • The TaskTracker nodes are monitored. If they do not submit heartbeat signals often enough, they are deemed to have failed and the work is scheduled on a different TaskTracker.
    • A TaskTracker will notify the JobTracker when a task fails. The JobTracker decides what to do then: it may resubmit the job elsewhere, it may mark that specific record as something to avoid, and it may may even blacklist the TaskTracker as unreliable.
    • When the work is completed, the JobTracker updates its status.
    • Client applications can poll the JobTracker for information.
  • How A Task Is Scheduled By A Jobtracker?

    The TaskTrackers send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These messages also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster work can be delegated. When the JobTracker tries to find somewhere to schedule a task within the MapReduce operations, it first looks for an empty slot on the same server that hosts the DataNode containing the data, and if not, it looks for an empty slot on a machine in the same rack.

  • How Many Instances Of Tasktracker Run On A Hadoop Cluster?

    There is one Daemon Tasktracker process for each slave node in the Hadoop cluster.

  • What Are The Two Main Parts Of The Hadoop Framework?

    Hadoop consists of two main parts.

    • Hadoop distributed file system, a distributed file system with high throughput,
    • Hadoop MapReduce, a software framework for processing large data sets.
  • Explain The Use Of Tasktracker In The Hadoop Cluster?

    A Tasktracker is a slave node in the cluster which that accepts the tasks from JobTracker like Map, Reduce or shuffle operation. Tasktracker also runs in its own JVM Process.

    Every TaskTracker is configured with a set of slots; these indicate the number of tasks that it can accept. The TaskTracker starts a separate JVM processes to do the actual work (called as Task Instance) this is to ensure that process failure does not take down the task tracker.

    The Tasktracker monitors these task instances, capturing the output and exit codes. When the Task instances finish, successfully or not, the task tracker notifies the JobTracker.

    The TaskTrackers also send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These messages also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster work can be delegated.

  • What Do You Mean By Taskinstance?

    Task instances are the actual MapReduce jobs which run on each slave node. The TaskTracker starts a separate JVM processes to do the actual work (called as Task Instance) this is to ensure that process failure does not take down the entire task tracker.Each Task Instance runs on its own JVM process. There can be multiple processes of task instance running on a slave node. This is based on the number of slots configured on task tracker. By default a new task instance JVM process is spawned for a task.

  • How Many Daemon Processes Run On A Hadoop Cluster?

    Hadoop is comprised of five separate daemons. Each of these daemons runs in its own JVM.

    Following 3 Daemons run on Master nodes.

    NameNode : This daemon stores and maintains the metadata for HDFS.

    Secondary NameNode : Performs housekeeping functions for the NameNode.

    JobTracker : Manages MapReduce jobs, distributes individual tasks to machines running the Task Tracker. Following 2 Daemons run on each Slave nodes

    DataNode : Stores actual HDFS data blocks.

    TaskTracker : It is Responsible for instantiating and monitoring individual Map and Reduce tasks.

  • How Many Maximum Jvm Can Run On A Slave Node?

    One or Multiple instances of Task Instance can run on each slave node. Each task instance is run as a separate JVM process. The number of Task instances can be controlled by configuration. Typically a high end machine is configured to run more task instances.

  • What Is Nas?

    It is one kind of file system where data can reside on one centralized machine and all the cluster member will read write data from that shared database, which would not be as efficient as HDFS.

  • How Hdfa Differs With Nfs?

    Following are differences between HDFS and NAS

    • In HDFS Data Blocks are distributed across local drives of all machines in a cluster. Whereas in NAS data is stored on dedicated hardware.
    • HDFS is designed to work with MapReduce System, since computation is moved to data. NAS is not suitable for MapReduce since data is stored separately from the computations.
    • HDFS runs on a cluster of machines and provides redundancy using replication protocol. Whereas NAS is provided by a single machine therefore does not provide data redundancy.
  • How Does A Namenode Handle The Failure Of The Data Nodes?

    HDFS has master/slave architecture. An HDFS cluster consists of a single

    NameNode, a master server that manages the file system namespace and regulates access to files by clients.

    In addition, there are a number of DataNodes, usually one per node in the cluster, which manage storage attached to the nodes that they run on. The NameNode and DataNode are pieces of software designed to run on commodity machines.

    NameNode periodically receives a Heartbeat and a Block report from each of the DataNodes in the cluster. Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode. When NameNode notices that it has not received a heartbeat message from a data node after a certain amount of time, the data node is marked as dead. Since blocks will be under replicated the system begins replicating the blocks that were stored on the dead DataNode. The NameNode Orchestrates the replication of data blocks from one DataNode to another. The replication data transfer happens directly between DataNode and the data never passes through the NameNode.

  • Can Reducer Talk With Each Other?

    No, Reducer runs in isolation.

  • Where The Mapper's Intermediate Data Will Be Stored?

    The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each individual mapper nodes. This is typically a temporary directory location which can be setup in config by the Hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes.

  • What Is The Use Of Combiners In The Hadoop Framework?

    Combiners are used to increase the efficiency of a MapReduce program. They are used to aggregate intermediate map output locally on individual mapper outputs. Combiners can help you reduce the amount of data that needs to be transferred across to the reducers.

    You can use your reducer code as a combiner if the operation performed is commutative and associative.

    The execution of combiner is not guaranteed; Hadoop may or may not execute a combiner. Also, if required it may execute it more than 1 times. Therefore your MapReduce jobs should not depend on the combiners’ execution.

  • What Is The Hadoop Mapreduce Api Contract For A Key And Value Class?

    • The Key must implement the org.apache.hadoop.io.WritableComparable interface.
    • The value must implement the org.apache.hadoop.io.Writable interface.
  • What Is A Identitymapper And Identityreducer In Mapreduce?

    • org.apache.hadoop.mapred.lib.IdentityMapper: Implements the identity function, mapping inputs directly to outputs. If MapReduce programmer does not set the Mapper Class using JobConf.setMapperClass then IdentityMapper.class is used as a default value.
    • org.apache.hadoop.mapred.lib.IdentityReducer : Performs no reduction, writing all input values directly to the output. If MapReduce programmer does not set the Reducer Class using JobConf.setReducerClass then IdentityReducer.class is used as a default value.
  • What Is The Meaning Of Speculative Execution In Hadoop? Why Is It Important?

    Speculative execution is a way of coping with individual Machine performance. In large clusters where hundreds or thousands of machines are involved there may be machines which are not performing as fast as others.

    This may result in delays in a full job due to only one machine not performaing well. To avoid this, speculative execution in hadoop can run multiple copies of same map or reduce task on different slave nodes. The results from first node to finish are used.

  • When The Reducers Are Are Started In A Mapreduce Job?

    In a MapReduce job reducers do not start executing the reduce method until the all Map jobs have completed. Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The programmer defined reduce method is called only after all the mappers have finished.

    If reducers do not start before all mappers finish then why does the progress on MapReduce job shows something like Map(50%) Reduce(10%)? Why reducers progress percentage is displayed when mapper is not finished yet?

    Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The progress calculation also takes in account the processing of data transfer which is done by reduce process, therefore the reduce progress starts showing up as soon as any intermediate key-value pair for a mapper is available to be transferred to reducer.

    Though the reducer progress is updated still the programmer defined reduce method is called only after all the mappers have finished.

  • What Is Hdfs ? How It Is Different From Traditional File Systems?

    HDFS, the Hadoop Distributed File System, is responsible for storing huge data on the cluster. This is a distributed file system designed to run on commodity hardware.

    It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant.

    • HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware.
    • HDFS provides high throughput access to application data and is suitable for applications that have large data sets.
    • HDFS is designed to support very large files. Applications that are compatible with HDFS are those that deal with large data sets. These applications write their data only once but they read it one or more times and require these reads to be satisfied at streaming speeds. HDFS supports write-once-read-many semantics on files.
  • What Is Hdfs Block Size? How Is It Different From Traditional File System Block Size?

    In HDFS data is split into blocks and distributed across multiple nodes in the cluster. Each block is typically 64Mb or 128Mb in size. Each block is replicated multiple times. Default is to replicate each block three times. Replicas are stored on different nodes. HDFS utilizes the local file system to store each HDFS block as a separate file. HDFS Block size can not be compared with the traditional file system block size.

  • What Is A Namenode? How Many Instances Of Namenode Run On A Hadoop Cluster?

    The NameNode is the centerpiece of an HDFS file system. It keeps the directory tree of all files in the file system, and tracks where across the cluster the file data is kept. It does not store the data of these files itself.

    There is only One NameNode process run on any hadoop cluster. NameNode runs on its own JVM process. In a typical production cluster its run on a separate machine.

    The NameNode is a Single Point of Failure for the HDFS Cluster. When the NameNode goes down, the file system goes offline.

    Client applications talk to the NameNode whenever they wish to locate a file, or when they want to add /copy /move /delete a file. The NameNode responds the successful requests by returning a list of relevant DataNode servers where the data lives.

  • What Is A Datanode? How Many Instances Of Datanode Run On A Hadoop Cluster?

    A DataNode stores data in the Hadoop File System HDFS. There is only One DataNode process run on any hadoop slave node. DataNode runs on its own JVM process. On startup, a DataNode connects to the NameNode. DataNode instances can talk to each other, this is mostly during replicating data.

  • How The Client Communicates With Hdfs?

    The Client communication to HDFS happens using Hadoop HDFS API. Client applications talk to the NameNode whenever they wish to locate a file, or when they want to add/copy/move/delete a file on HDFS. The NameNode responds the successful requests by returning a list of relevant DataNode servers where the data lives. Client applications can talk directly to a DataNode, once the NameNode has provided the location of the data.

  • How The Hdfs Blocks Are Replicated?

    HDFS is designed to reliably store very large files across machines in a large cluster. It stores each file as a sequence of blocks; all blocks in a file except the last block are the same size. The blocks of a file are replicated for fault tolerance. The block size and replication factor are configurable per file. An application can specify the number of replicas of a file. The replication factor can be specified at file creation time and can be changed later. Files in HDFS are write-once and have strictly one writer at any time.

    The NameNode makes all decisions regarding replication of blocks. HDFS uses rack-aware replica placement policy. In default configuration there are total 3 copies of a datablock on HDFS, 2 copies are stored on datanodes on same rack and 3rd copy on a different rack.

  • What Is Hadoop Framework?

    Hadoop is a open source framework which is written in java by apche software foundation. This framework is used to wirite software application which requires to process vast amount of data (It could handle multi tera bytes of data). It works in-paralle on large clusters which could have 1000 of computers (Nodes) on the clusters. It also process data very reliably and fault-tolerant manner.

  • What Is Big Data?

    Big Data is nothing but an assortment of such a huge and complex data that it becomes very tedious to capture, store, process, retrieve and analyze it with the help of on-hand database management tools or traditional data processing techniques.

  • Can You Give Some Examples Of Big Data?

    There are many real life examples of Big Data! Facebook is generating 500+ terabytes of data per day, NYSE (New York Stock Exchange) generates about 1 terabyte of new trade data per day, a jet airline collects 10 terabytes of censor data for every 30 minutes of flying time. All these are day to day examples of Big Data!

  • Can You Give A Detailed Overview About The Big Data Being Generated By Facebook?

    As of December 31, 2012, there are 1.06 billion monthly active users on facebook and 680 million mobile users. On an average, 3.2 billion likes and comments are posted every day on Facebook. 72% of web audience is on Facebook. And why not! There are so many activities going on facebook from wall posts, sharing images, videos, writing comments and liking posts, etc. In fact, Facebook started using Hadoop in mid-2009 and was one of the initial users of Hadoop.

  • According To Ibm, What Are The Three Characteristics Of Big Data?

    According to IBM, the three characteristics of Big Data are:

    Volume: Facebook generating 500+ terabytes of data per day.

    Velocity: Analyzing 2 million records each day to identify the reason for losses.

    Variety: images, audio, video, sensor data, log files, etc.

  • How Analysis Of Big Data Is Useful For Organizations?

    Effective analysis of Big Data provides a lot of business advantage as organizations will learn which areas to focus on and which areas are less important. Big data analysis provides some early key indicators that can prevent the company from a huge loss or help in grasping a great opportunity with open hands! A precise analysis of Big Data helps in decision making! For instance, nowadays people rely so much on Facebook and Twitter before buying any product or service. All thanks to the Big Data explosion.

  • How Big Is 'big Data'?

    With time, data volume is growing exponentially. Earlier we used to talk about Megabytes or Gigabytes. But time has arrived when we talk about data volume in terms of terabytes, petabytes and also zettabytes! Global data volume was around 1.8ZB in 2011 and is expected to be 7.9ZB in 2015. It is also known that the global information doubles in every two years!

  • Who Are 'data Scientists'?

    Data scientists are soon replacing business analysts or data analysts. Data scientists are experts who find solutions to analyze data. Just as web analysis, we have data scientists who have good business insight as to how to handle a business challenge. Sharp data scientists are not only involved in dealing business problems, but also choosing the relevant issues that can bring value-addition to the organization.

  • Why The Name 'hadoop'?

    Hadoop doesn’t have any expanding version like ‘oops’. The charming yellow elephant you see is basically named after Doug’s son’s toy elephant!

  • Why Do We Need Hadoop?

    Everyday a large amount of unstructured data is getting dumped into our machines. The major challenge is not to store large data sets in our systems but to retrieve and analyze the big data in the organizations, that too data present in different machines at different locations. In this situation a necessity for Hadoop arises. Hadoop has the ability to analyze the data present in different machines at different locations very quickly and in a very cost effective way. It uses the concept of MapReduce which enables it to divide the query into small parts and process them in parallel. This is also known as parallel computing.

  • What Are Some Of The Characteristics Of Hadoop Framework?

    Hadoop framework is written in Java. It is designed to solve problems that involve analyzing large data (e.g. petabytes). The programming model is based on Google’s MapReduce. The infrastructure is based on Google’s Big Data and Distributed File System. Hadoop handles large files/data throughput and supports data intensive distributed applications. Hadoop is scalable as more nodes can be easily added to it.

  • Give A Brief Overview Of Hadoop History?

    In 2002, Doug Cutting created an open source, web crawler project.

    In 2004, Google published MapReduce, GFS papers.

    In 2006, Doug Cutting developed the open source, Mapreduce and HDFS project.

    In 2008, Yahoo ran 4,000 node Hadoop cluster and Hadoop won terabyte sort benchmark.

    In 2009, Facebook launched SQL support for Hadoop.

  • Give Examples Of Some Companies That Are Using Hadoop Structure?

    A lot of companies are using the Hadoop structure such as Cloudera, EMC, MapR, Hortonworks, Amazon, Facebook, eBay, Twitter, Google and so on.

  • What Is The Basic Difference Between Traditional Rdbms And Hadoop?

    Traditional RDBMS is used for transactional systems to report and archive the data, whereas Hadoop is an approach to store huge amount of data in the distributed file system and process it. RDBMS will be useful when you want to seek one record from Big data, whereas, Hadoop will be useful when you want Big data in one shot and perform analysis on that later.

  • What Is Structured And Unstructured Data?

    Structured data is the data that is easily identifiable as it is organized in a structure. The most common form of structured data is a database where specific information is stored in tables, that is, rows and columns. Unstructured data refers to any data that cannot be identified easily. It could be in the form of images, videos, documents, email, logs and random text. It is not in the form of rows and columns.

  • What Are The Core Components Of Hadoop?

    Core components of Hadoop are HDFS and MapReduce. HDFS is basically used to store large data sets and MapReduce is used to process such large data sets.

  • What Is Hdfs?

    HDFS is a file system designed for storing very large files with streaming data access patterns, running clusters on commodity hardware.

  • What Are The Key Features Of Hdfs?

    HDFS is highly fault-tolerant, with high throughput, suitable for applications with large data sets, streaming access to file system data and can be built out of commodity hardware.

  • What Is Fault Tolerance?

    Suppose you have a file stored in a system, and due to some technical problem that file gets destroyed. Then there is no chance of getting the data back present in that file. To avoid such situations, Hadoop has introduced the feature of fault tolerance in HDFS. In Hadoop, when we store a file, it automatically gets replicated at two other locations also. So even if one or two of the systems collapse, the file is still available on the third system.

  • Replication Causes Data Redundancy Then Why Is Is Pursued In Hdfs?

    HDFS works with commodity hardware (systems with average configurations) that has high chances of getting crashed any time. Thus, to make the entire system highly fault-tolerant, HDFS replicates and stores data in different places. Any data on HDFS gets stored at at least 3 different locations. So, even if one of them is corrupted and the other is unavailable for some time for any reason, then data can be accessed from the third one. Hence, there is no chance of losing the data. This replication factor helps us to attain the feature of Hadoop called Fault Tolerant.

  • Since The Data Is Replicated Thrice In Hdfs, Does It Mean That Any Calculation Done On One Node Will Also Be Replicated On The Other Two?

    Since there are 3 nodes, when we send the MapReduce programs, calculations will be done only on the original data. The master node will know which node exactly has that particular data. In case, if one of the nodes is not responding, it is assumed to be failed. Only then, the required calculation will be done on the second replica.

  • What Is Throughput? How Does Hdfs Get A Good Throughput?

    Throughput is the amount of work done in a unit time. It describes how fast the data is getting accessed from the system and it is usually used to measure performance of the system. In HDFS, when we want to perform a task or an action, then the work is divided and shared among different systems. So all the systems will be executing the tasks assigned to them independently and in parallel. So the work will be completed in a very short period of time. In this way, the HDFS gives good throughput. By reading data in parallel, we decrease the actual time to read data tremendously.

  • What Is Streaming Access?

    As HDFS works on the principle of ‘Write Once, Read Many‘, the feature of streaming access is extremely important in HDFS. HDFS focuses not so much on storing the data but how to retrieve it at the fastest possible speed, especially while analyzing logs. In HDFS, reading the complete data is more important than the time taken to fetch a single record from the data.

  • What Is A Commodity Hardware? Does Commodity Hardware Include Ram?

    Commodity hardware is a non-expensive system which is not of high quality or high-availability. Hadoop can be installed in any average commodity hardware. We don’t need super computers or high-end hardware to work on Hadoop. Yes, Commodity hardware includes RAM because there will be some services which will be running on RAM.

  • Is Namenode Also A Commodity?

    No. Namenode can never be a commodity hardware because the entire HDFS rely on it. It is the single point of failure in HDFS. Namenode has to be a high-availability machine.

  • What Is A Metadata?

    Metadata is the information about the data stored in data nodes such as location of the file, size of the file and so on.

  • What Is A Daemon?

    Daemon is a process or service that runs in background. In general, we use this word in UNIX environment. The equivalent of Daemon in Windows is “services” and in Dos is ” TSR”.

  • What Is A Job Tracker?

    Job tracker is a daemon that runs on a namenode for submitting and tracking MapReduce jobs in Hadoop. It assigns the tasks to the different task tracker. In a Hadoop cluster, there will be only one job tracker but many task trackers. It is the single point of failure for Hadoop and MapReduce Service. If the job tracker goes down all the running jobs are halted. It receives heartbeat from task tracker based on which Job tracker decides whether the assigned task is completed or not.

  • What Is A Task Tracker?

    Task tracker is also a daemon that runs on datanodes. Task Trackers manage the execution of individual tasks on slave node. When a client submits a job, the job tracker will initialize the job and divide the work and assign them to different task trackers to perform MapReduce tasks. While performing this action, the task tracker will be simultaneously communicating with job tracker by sending heartbeat. If the job tracker does not receive heartbeat from task tracker within specified time, then it will assume that task tracker has crashed and assign that task to another task tracker in the cluster.

  • Is Namenode Machine Same As Datanode Machine As In Terms Of Hardware?

    It depends upon the cluster you are trying to create. The Hadoop VM can be there on the same machine or on another machine. For instance, in a single node cluster, there is only one machine, whereas in the development or in a testing environment, Namenode and data nodes are on different machines.

  • What Is A Heartbeat In Hdfs?

    A heartbeat is a signal indicating that it is alive. A datanode sends heartbeat to Namenode and task tracker will send its heart beat to job tracker. If the Namenode or job tracker does not receive heart beat then they will decide that there is some problem in datanode or task tracker is unable to perform the assigned task.

  • Are Namenode And Job Tracker On The Same Host?

    No, in practical environment, Namenode is on a separate host and job tracker is on a separate host.

  • What Is A 'block' In Hdfs?

    A ‘block’ is the minimum amount of data that can be read or written. In HDFS, the default block size is 64 MB as contrast to the block size of 8192 bytes in Unix/Linux. Files in HDFS are broken down into block-sized chunks, which are stored as independent units. HDFS blocks are large as compared to disk blocks, particularly to minimize the cost of seeks.

  • If A Particular File Is 50 Mb, Will The Hdfs Block Still Consume 64 Mb As The Default Size?

    No, not at all! 64 mb is just a unit where the data will be stored. In this particular situation, only 50 mb will be consumed by an HDFS block and 14 mb will be free to store something else. It is the MasterNode that does data allocation in an efficient manner.

  • What Are The Benefits Of Block Transfer?

    A file can be larger than any single disk in the network. There’s nothing that requires the blocks from a file to be stored on the same disk, so they can take advantage of any of the disks in the cluster. Making the unit of abstraction a block rather than a file simplifies the storage subsystem. Blocks provide fault tolerance and availability. To insure against corrupted blocks and disk and machine failure, each block is replicated to a small number of physically separate machines (typically three). If a block becomes unavailable, a copy can be read from another location in a way that is transparent to the client.

  • If We Want To Copy 10 Blocks From One Machine To Another, But Another Machine Can Copy Only 8.5 Blocks, Can The Blocks Be Broken At The Time Of Replication?

    In HDFS, blocks cannot be broken down. Before copying the blocks from one machine to another, the Master node will figure out what is the actual amount of space required, how many block are being used, how much space is available, and it will allocate the blocks accordingly.

  • How Indexing Is Done In Hdfs?

    Hadoop has its own way of indexing. Depending upon the block size, once the data is stored, HDFS will keep on storing the last part of the data which will say where the next part of the data will be. In fact, this is the base of HDFS.

  • If A Data Node Is Full How It's Identified?

    When data is stored in datanode, then the metadata of that data will be stored in the Namenode. So Namenode will identify if the data node is full.

  • If Datanodes Increase, Then Do We Need To Upgrade Namenode?

    While installing the Hadoop system, Namenode is determined based on the size of the clusters. Most of the time, we do not need to upgrade the Namenode because it does not store the actual data, but just the metadata, so such a requirement rarely arise.

  • Are Job Tracker And Task Trackers Present In Separate Machines?

    Yes, job tracker and task tracker are present in different machines. The reason is job tracker is a single point of failure for the Hadoop MapReduce service. If it goes down, all running jobs are halted.

  • When We Send A Data To A Node, Do We Allow Settling In Time, Before Sending Another Data To That Node?

    Yes, we do.

  • Does Hadoop Always Require Digital Data To Process?

    Yes. Hadoop always require digital data to be processed.

  • On What Basis Namenode Will Decide Which Datanode To Write On?

    As the Namenode has the metadata (information) related to all the data nodes, it knows which datanode is free.

  • Doesn't Google Have Its Very Own Version Of Dfs?

    Yes, Google owns a DFS known as “Google File System (GFS)” developed by Google Inc. for its own use.

  • Who Is A 'user' In Hdfs?

    A user is like you or me, who has some query or who needs some kind of data.

  • Is Client The End User In Hdfs?

    No, Client is an application which runs on your machine, which is used to interact with the Namenode (job tracker) or datanode (task tracker).

  • What Is The Communication Channel Between Client And Namenode/datanode?

    The mode of communication is SSH.

  • What Is A Rack?

    Rack is a storage area with all the datanodes put together. These datanodes can be physically located at different places. Rack is a physical collection of datanodes which are stored at a single location. There can be multiple racks in a single location.

  • On What Basis Data Will Be Stored On A Rack?

    When the client is ready to load a file into the cluster, the content of the file will be divided into blocks. Now the client consults the Namenode and gets 3 datanodes for every block of the file which indicates where the block should be stored. While placing the datanodes, the key rule followed is “for every block of data, two copies will exist in one rack, third copy in a different rack“. This rule is known as “Replica Placement Policy“.

  • Do We Need To Place 2nd And 3rd Data In Rack 2 Only?

    Yes, this is to avoid datanode failure.

  • What If Rack 2 And Datanode Fails?

    If both rack2 and datanode present in rack 1 fails then there is no chance of getting data from it. In order to avoid such situations, we need to replicate that data more number of times instead of replicating only thrice. This can be done by changing the value in replication factor which is set to 3 by default.

  • What Is A Secondary Namenode? Is It A Substitute To The Namenode?

    The secondary Namenode constantly reads the data from the RAM of the Namenode and writes it into the hard disk or the file system. It is not a substitute to the Namenode, so if the Namenode fails, the entire Hadoop system goes down.

  • What Is The Difference Between Gen1 And Gen2 Hadoop With Regards To The Namenode?

    In Gen 1 Hadoop, Namenode is the single point of failure. In Gen 2 Hadoop, we have what is known as Active and Passive Namenodes kind of a structure. If the active Namenode fails, passive Namenode takes over the charge.

  • Can You Explain How Do 'map' And 'reduce' Work?

    Namenode takes the input and divide it into parts and assign them to data nodes. These datanodes process the tasks assigned to them and make a key-value pair and returns the intermediate output to the Reducer. The reducer collects this key value pairs of all the datanodes and combines them and generates the final output.

  • What Is 'key Value Pair' In Hdfs?

    Key value pair is the intermediate data generated by maps and sent to reduces for generating the final output.

  • What Is The Difference Between Mapreduce Engine And Hdfs Cluster?

    HDFS cluster is the name given to the whole configuration of master and slaves where data is stored. Map Reduce Engine is the programming module which is used to retrieve and analyze data.

  • Is Map Like A Pointer?

    No, Map is not like a pointer.

  • Do We Require Two Servers For The Namenode And The Datanodes?

    Yes, we need two different servers for the Namenode and the datanodes. This is because Namenode requires highly configurable system as it stores information about the location details of all the files stored in different datanodes and on the other hand, datanodes require low configuration system.

  • Why Are The Number Of Splits Equal To The Number Of Maps?

    The number of maps is equal to the number of input splits because we want the key and value pairs of all the input splits.

  • Is A Job Split Into Maps?

    No, a job is not split into maps. Spilt is created for the file. The file is placed on datanodes in blocks. For each split, a map is needed.

  • Which Are The Two Types Of 'writes' In Hdfs?

    There are two types of writes in HDFS: posted and non-posted write. Posted Write is when we write it and forget about it, without worrying about the acknowledgement. It is similar to our traditional Indian post. In a Non-posted Write, we wait for the acknowledgement. It is similar to the today’s courier services. Naturally, non-posted write is more expensive than the posted write. It is much more expensive, though both writes are asynchronous.

  • Why 'reading' Is Done In Parallel And 'writing' Is Not In Hdfs?

    Reading is done in parallel because by doing so we can access the data fast. But we do not perform the write operation in parallel. The reason is that if we perform the write operation in parallel, then it might result in data inconsistency. For example, you have a file and two nodes are trying to write data into the file in parallel, then the first node does not know what the second node has written and vice-versa. So, this makes it confusing which data to be stored and accessed.

  • Can Hadoop Be Compared To Nosql Database Like Cassandra?

    Though NOSQL is the closet technology that can be compared to Hadoop, it has its own pros and cons. There is no DFS in NOSQL. Hadoop is not a database. It’s a file system (HDFS) and distributed programming framework (MapReduce).

  • How Can I Install Cloudera Vm In My System?

    When you enrol for the hadoop course at Edureka, you can download the Hadoop Installation steps.pdf file from our dropbox.

  • How Jobtracker Schedules A Task?

    The TaskTrackers send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These message also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster work can be delegated. When the JobTracker tries to find somewhere to schedule a task within the MapReduce operations, it first looks for an empty slot on the same server that hosts the DataNode containing the data, and if not, it looks for an empty slot on a machine in the same rack.

  • What Is A Task Tracker In Hadoop? How Many Instances Of Tasktracker Run On A Hadoop Cluster

    A TaskTracker is a slave node daemon in the cluster that accepts tasks (Map, Reduce and Shuffle operations) from a JobTracker. There is only One Task Tracker process run on any hadoop slave node. Task Tracker runs on its own JVM process. Every TaskTracker is configured with a set of slots, these indicate the number of tasks that it can accept. The TaskTracker starts a separate JVM processes to do the actual work (called as Task Instance) this is to ensure that process failure does not take down the task tracker. The TaskTracker monitors these task instances, capturing the output and exit codes. When the Task instances finish, successfully or not, the task tracker notifies the JobTracker. The TaskTrackers also send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These message also inform the JobTracker of the number of available slots, so the JobTracker can stay up to date with where in the cluster work can be delegated.

  • What Is A Task Instance In Hadoop? Where Does It Run?

    Task instances are the actual MapReduce jobs which are run on each slave node. The TaskTracker starts a separate JVM processes to do the actual work (called as Task Instance) this is to ensure that process failure does not take down the task tracker. Each Task Instance runs on its own JVM process. There can be multiple processes of task instance running on a slave node. This is based on the number of slots configured on task tracker. By default a new task instance JVM process is spawned for a task.

  • How Many Daemon Processes Run On A Hadoop System?

    Hadoop is comprised of five separate daemons. Each of these daemon run in its own JVM.Following 3 Daemons run on Master nodes

    NameNode : This daemon stores and maintains the metadata for HDFS.

    Secondary NameNode : Performs housekeeping functions for the NameNode.

    JobTracker : Manages MapReduce jobs, distributes individual tasks to machines running the Task Tracker.

    Following 2 Daemons run on each Slave nodes

    DataNode : Stores actual HDFS data blocks.

    TaskTracker : Responsible for instantiating and monitoring individual Map and Reduce tasks.

  • What Is Configuration Of A Typical Slave Node On Hadoop Cluster? How Many Jvms Run On A Slave Node?

    • Single instance of a Task Tracker is run on each Slave node. Task tracker is run as a separate JVM process.
    • Single instance of a DataNode daemon is run on each Slave node. DataNode daemon is run as a separate JVM process.
    • One or Multiple instances of Task Instance is run on each slave node. Each task instance is run as a separate JVM process. The number of Task instances can be controlled by configuration. Typically a high end machine is configured to run more task instances.
  • What Is The Difference Between Hdfs And Nas ?

    The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant.

    Following are differences between HDFS and NAS

    • In HDFS Data Blocks are distributed across local drives of all machines in a cluster. Whereas in NAS data is stored on dedicated hardware.
    • HDFS is designed to work with MapReduce System, since computation are moved to data. NAS is not suitable for MapReduce since data is stored separately from the computations.
    • HDFS runs on a cluster of machines and provides redundancy using a replication protocol. Whereas NAS is provided by a single machine therefore does not provide data redundancy.
  • How Namenode Handles Data Node Failures?

    NameNode periodically receives a Heartbeat and a Blockreport from each of the DataNodes in the cluster. Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode. When NameNode notices that it has not recieved a hearbeat message from a data node after a certain amount of time, the data node is marked as dead. Since blocks will be under replicated the system begins replicating the blocks that were stored on the dead datanode. The NameNode Orchestrates the replication of data blocks from one datanode to another. The replication data transfer happens directly between datanodes and the data never passes through the namenode.

  • Does Mapreduce Programming Model Provide A Way For Reducers To Communicate With Each Other? In A Mapreduce Job Can A Reducer Communicate With Another Reducer?

    Nope, MapReduce programming model does not allow reducers to communicate with each other. Reducers run in isolation.

  • Can I Set The Number Of Reducers To Zero?

    Yes, Setting the number of reducers to zero is a valid configuration in Hadoop. When you set the reducers to zero no reducers will be executed, and the output of each mapper will be stored to a separate file on HDFS. [This is different from the condition when reducers are set to a number greater than zero and the Mappers output (intermediate data) is written to the Local file system(NOT HDFS) of each mappter slave node.]

  • Where Is The Mapper Output (intermediate Kay-value Data) Stored ?

    The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each individual mapper nodes. This is typically a temporary directory location which can be setup in config by the hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes.

  • What Are Combiners? When Should I Use A Combiner In My Mapreduce Job?

    Combiners are used to increase the efficiency of a MapReduce program. They are used to aggregate intermediate map output locally on individual mapper outputs. Combiners can help you reduce the amount of data that needs to be transferred across to the reducers. You can use your reducer code as a combiner if the operation performed is commutative and associative. The execution of combiner is not guaranteed, Hadoop may or may not execute a combiner. Also, if required it may execute it more then 1 times. Therefore your MapReduce jobs should not depend on the combiners execution.

  • What Is Writable & Writablecomparable Interface?

    • org.apache.hadoop.io.Writable is a Java interface. Any key or value type in the Hadoop Map-Reduce framework implements this interface. Implementations typically implement a static read(DataInput) method which constructs a new instance, calls readFields(DataInput) and returns the instance.
    • org.apache.hadoop.io.WritableComparable is a Java interface. Any type which is to be used as a key in the Hadoop Map-Reduce framework should implement this interface. WritableComparable objects can be compared to each other using Comparators.
  • What Is A Identitymapper And Identityreducer In Mapreduce ?

    • org.apache.hadoop.mapred.lib.IdentityMapper Implements the identity function, mapping inputs directly to outputs. If MapReduce programmer do not set the Mapper Class using JobConf.setMapperClass then IdentityMapper.class is used as a default value.
    • org.apache.hadoop.mapred.lib.IdentityReducer Performs no reduction, writing all input values directly to the output. If MapReduce programmer do not set the Reducer Class using JobConf.setReducerClass then IdentityReducer.class is used as a default value.
  • When Is The Reducers Are Started In A Mapreduce Job?

    In a MapReduce job reducers do not start executing the reduce method until the all Map jobs have completed. Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The programmer defined reduce method is called only after all the mappers have finished.

  • If Reducers Do Not Start Before All Mappers Finish Then Why Does The Progress On Mapreduce Job Shows Something Like Map(50%) Reduce(10%)? Why Reducers Progress Percentage Is Displayed When Mapper Is Not Finished Yet?

    Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The progress calculation also takes in account the processing of data transfer which is done by reduce process, therefore the reduce progress starts showing up as soon as any intermediate key-value pair for a mapper is available to be transferred to reducer. Though the reducer progress is updated still the programmer defined reduce method is called only after all the mappers have finished.