Interview Questions on Big Data and Hadoop MapReduce

Interview Questions on Big Data and Hadoop MapReduce. Big Data Questions asked in companies like IBM, Amazon, HP, Google.

What is a JobTracker in Hadoop? How many instances of JobTracker run on a Hadoop Cluster? JobTracker is the daemon service for submitting and tracking MapReduce jobs in Hadoop. There is only One Job Tracker process run on any hadoop cluster. Job Tracker runs on its own JVM process. In a typical production cluster its run on a separate machine. 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(from Hadoop Wiki:) 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. 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 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 seperately from the computations. HDFS runs on a cluster of machines and provides redundancy usinga replication protocal. 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 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 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. 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 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. 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.

Apache Hadoop Cluster Interview Questions What is the Hadoop-core configuration? Hadoop core is configured by two xml files: 1. hadoop-default.xml which was renamed to 2. hadoop-site.xml. These files are written in xml format. We have certain properties in these xml files, which consist of name and value. But these files do not exist now. Which are the three modes in which Hadoop can be run? The three modes in which Hadoop can be run are: 1. standalone (local) mode 2. Pseudo-distributed mode 3. Fully distributed mode Explain what are the features of Stand alone (local) mode? In stand-alone mode there are no daemons, everything runs on a single JVM. It has no DFS and utilizes the local file system. Stand-alone mode is suitable only for running MapReduce programs during development. It is one of the most least used environments. What are the features of Pseudo mode? Pseudo mode is used both for development and in the QA environment. In the Pseudo mode all the daemons run on the same machine. Can we call VMs as pseudos? No, VMs are not pseudos because VM is something different and pesudo is very specific to Hadoop. What are the features of Fully Distributed mode? Fully Distributed mode is used in the production environment, where we have „n‟ number of machines forming a Hadoop cluster. Hadoop daemons run on a cluster of machines.

There is one host onto which Namenode is running and another host on which datanode is running and then there are machines on which task tracker is running. We have separate masters and separate slaves in this distribution. Does Hadoop follows the UNIX pattern? Yes, Hadoop closely follows the UNIX pattern. Hadoop also has the „conf„ directory as in the case of UNIX. In which directory Hadoop is installed? Cloudera and Apache has the same directory structure. Hadoop is installed in cd /usr/lib/hadoop-0.20/. What are the port numbers of Namenode, job tracker and task tracker? The port number for Namenode is ‟70′, for job tracker is ‟30′ and for task tracker is ‟60′. What are the Hadoop configuration files at present? There are 3 configuration files in Hadoop: 1. core-site.xml 2. hdfs-site.xml 3. mapred-site.xml These files are located in the conf/ subdirectory. How to exit the Vi editor? To exit the Vi Editor, press ESC and type :q and then press enter. What is a spill factor with respect to the RAM? Spill factor is the size after which your files move to the temp file. Hadoop-temp directory is used for this. Is fs.mapr.working.dir a single directory? Yes, fs.mapr.working.dir it is just one directory. Which are the three main hdfs-site.xml properties? The three main hdfs-site.xml properties are: 1. dfs.name.dir which gives you the location on which metadata will be stored and where DFS is located – on disk or onto the remote. 2. dfs.data.dir which gives you the location where the data is going to be stored. 3. fs.checkpoint.dir which is for secondary Namenode. How to come out of the insert mode? To come out of the insert mode, press ESC, type :q (if you have not written anything) OR type :wq (if you have written anything in the file) and then press ENTER. What is Cloudera and why it is used? Cloudera is the distribution of Hadoop. It is a user created on VM by default. Cloudera belongs to Apache and is used for data processing. What happens if you get a ‘connection refused java exception’ when you type hadoop fsck /?

It could mean that the Namenode is not working on your VM. We are using Ubuntu operating system with Cloudera, but from where we can download Hadoop or does it come by default with Ubuntu? This is a default configuration of Hadoop that you have to download from Cloudera or from Edureka‟s dropbox and the run it on your systems. You can also proceed with your own configuration but you need a Linux box, be it Ubuntu or Red hat. There are installation steps present at the Cloudera location or in Edureka‟s Drop box. You can go either ways. What does ‘jps’ command do? This command checks whether your Namenode, datanode, task tracker, job tracker, etc are working or not. How can I restart Namenode? 1. Click on stop-all.sh and then click on start-all.sh OR 2. Write sudo hdfs (press enter), su-hdfs (press enter), /etc/init.d/ha (press enter) and then /etc/init.d/hadoop-0.20-namenode start (press enter). What is the full form of fsck? Full form of fsck is File System Check. How can we check whether Namenode is working or not? To check whether Namenode is working or not, use the command /etc/init.d/hadoop0.20-namenode status or as simple as jps. What does the command mapred.job.tracker do? The command mapred.job.tracker lists out which of your nodes is acting as a job tracker. What does /etc /init.d do? /etc /init.d specifies where daemons (services) are placed or to see the status of these daemons. It is very LINUX specific, and nothing to do with Hadoop. How can we look for the Namenode in the browser? If you have to look for Namenode in the browser, you don‟t have to give localhost:8021, the port number to look for Namenode in the brower is 50070. How to change from SU to Cloudera? To change from SU to Cloudera just type exit. Which files are used by the startup and shutdown commands? Slaves and Masters are used by the startup and the shutdown commands. What do slaves consist of? Slaves consist of a list of hosts, one per line, that host datanode and task tracker servers. What do masters consist of? Masters contain a list of hosts, one per line, that are to host secondary namenode servers. What does hadoop-env.sh do?

hadoop-env.sh provides the environment for Hadoop to run. JAVA_HOME is set over here. Can we have multiple entries in the master files? Yes, we can have multiple entries in the Master files. Where is hadoop-env.sh file present? hadoop-env.sh file is present in the conf location. In Hadoop_PID_DIR, what does PID stands for? PID stands for „Process ID‟. What does /var/hadoop/pids do? It stores the PID. What does hadoop-metrics.properties file do? hadoop-metrics.properties is used for „Reporting„ purposes. It controls the reporting for Hadoop. The default status is „not to report„. What are the network requirements for Hadoop? The Hadoop core uses Shell (SSH) to launch the server processes on the slave nodes. It requires password-less SSH connection between the master and all the slaves and the secondary machines. Why do we need a password-less SSH in Fully Distributed environment? We need a password-less SSH in a Fully-Distributed environment because when the cluster is LIVE and running in Fully Distributed environment, the communication is too frequent. The job tracker should be able to send a task to task tracker quickly. Does this lead to security issues? No, not at all. Hadoop cluster is an isolated cluster. And generally it has nothing to do with an internet. It has a different kind of a configuration. We needn‟t worry about that kind of a security breach, for instance, someone hacking through the internet, and so on. Hadoop has a very secured way to connect to other machines to fetch and to process data. On which port does SSH work? SSH works on Port No. 22, though it can be configured. 22 is the default Port number. Can you tell us more about SSH? SSH is nothing but a secure shell communication, it is a kind of a protocol that works on a Port No. 22, and when you do an SSH, what you really require is a password. Why password is needed in SSH localhost? Password is required in SSH for security and in a situation where passwordless communication is not set. Do we need to give a password, even if the key is added in SSH? Yes, password is still required even if the key is added in SSH.

What if a Namenode has no data? If a Namenode has no data it is not a Namenode. Practically, Namenode will have some data. What happens to job tracker when Namenode is down? When Namenode is down, your cluster is OFF, this is because Namenode is the single point of failure in HDFS. What happens to a Namenode, when job tracker is down? When a job tracker is down, it will not be functional but Namenode will be present. So, cluster is accessible if Namenode is working, even if the job tracker is not working. Can you give us some more details about SSH communication between Masters and the Slaves? SSH is a password-less secure communication where data packets are sent across the slave. It has some format into which data is sent across. SSH is not only between masters and slaves but also between two hosts. What is formatting of the DFS? Just like we do for Windows, DFS is formatted for proper structuring. It is not usually done as it formats the Namenode too. Does the HDFS client decide the input split or Namenode? No, the Client does not decide. It is already specified in one of the configurations through which input split is already configured. In Cloudera there is already a cluster, but if I want to form a cluster on Ubuntu can we do it? Yes, you can go ahead with this! There are installation steps for creating a new cluster. You can uninstall your present cluster and install the new cluster. Can we create a Hadoop cluster from scratch? Yes we can do that also once we are familiar with the Hadoop environment. Can we use Windows for Hadoop? Actually, Red Hat Linux or Ubuntu are the best Operating Systems for Hadoop. Windows is not used frequently for installing Hadoop as there are many support problems attached with Windows. Thus, Windows is not a preferred environment for Hadoop.

Big Data Hadoop Interview Questions and Answers on Apache Pig.

If you are planning to pursue a career in Hadoop, then you can expect some PIG interview Questions.

Explain what is PIG in Big Data? PIG is nothing but a platform for analyzing large data sets that consist of high level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. PIG‟s infrastructure layer consists of a compiler that produces sequence of MapReduce Programs. Pig was originally developed at Yahoo Research around 2006 for researchers to have an ad-hoc way of creating and executing map-reduce jobs on very large data sets. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets Explain PIG's language layer an its properties? Pig‟s language layer currently consists of a textual language called Pig Latin, which has the following key properties: Ease of programming. Pig is intended to make complex tasks comprised of multiple interrelated data transformations that are explicitly encoded as data flow sequences easy to write, understand, and maintain. Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing the user to focus on semantics rather than efficiency. Extensible. Users can create their own functions to do special-purpose processing. Explain what is the difference between logical and physical plans in PIG? Pig undergoes some steps when a Pig Latin Script is converted into MapReduce jobs. After performing the basic parsing and semantic checking, it produces a logical plan. The logical plan describes the logical operators that have to be executed by Pig during execution. After this, Pig produces a physical plan. The physical plan describes the physical operators that are needed to execute the script. What is the difference between PIG and SQL The differences between Pig and SQL include Pig's usage of lazy evaluation, Pig's usage for ETL, Pig's ability to store data at any point during a pipeline, Pig's explicit declaration of execution plans, and Pig's support for pipeline splits. Whereas, it has been argued DBMSs are substantially faster than the MapReduce system once the data is loaded, but that loading the data takes considerably longer in the database systems. It has also been argued RDBMSs offer out of the box support for column-storage, working with compressed data, indexes for efficient random data access, and transaction- level fault tolerance. Pig Latin is procedural and fits very naturally in the pipeline paradigm while SQL is instead declarative. In SQL users can specify that data from two tables must be joined, but not what join implementation to use. Pig Latin allows users to specify an implementation or aspects of an implementation to be used in executing a script in several ways. In effect, Pig Latin programming is similar to specifying a query execution plan, making it easier for programmers to explicitly control the flow of their data processing task. SQL is oriented around queries that produce a single result. SQL handles trees naturally, but has no built in mechanism for splitting a data processing stream and applying different operators to each sub-stream.

Pig Latin script describes a directed acyclic graph (DAG) rather than a pipeline. Pig Latin's ability to include user code at any point in the pipeline is useful for pipeline development. If SQL is used, data must first be imported into the database, and then the cleansing and transformation process can begin. Can you give us some examples how Hadoop is used in real time environment? Let us consider a scenario that the we have an exam consisting of 10 Multiple-choice questions and 20 students appear for that exam. Every student will attempt each question. For each question and each answer option, a key will be generated. So we have a set of key-value pairs for all the questions and all the answer options for every student. Based on the options that the students have selected, you have to analyze and find out how many students have answered correctly. This isn‟t an easy task. Here Hadoop comes into picture! Hadoop helps you in solving these problems quickly and without much effort. You may also take the case of how many students have wrongly attempted a particular question. What is BloomMapFile used for in PIG? The BloomMapFile is a class that extends MapFile. So its functionality is similar to MapFile. BloomMapFile uses dynamic Bloom filters to provide quick membership test for the keys. It is used in Hbase table format. What is bag in PIG? A bag is one of the data models present in Pig. It is an unordered collection of tuples with possible duplicates. Bags are used to store collections while grouping. The size of bag is the size of the local disk, this means that the size of the bag is limited. When the bag is full, then Pig will spill this bag into local disk and keep only some parts of the bag in memory. There is no necessity that the complete bag should fit into memory. We represent bags with “{}” Does ‘ILLUSTRATE’ run MR job? No, illustrate will not pull any MR, it will pull the internal data. On the console, illustrate will not do any job. It just shows output of each stage and not the final output. Is the keyword ‘DEFINE’ like a function name? Yes, the keyword „DEFINE‟ is like a function name. Once you have registered, you have to define it. Whatever logic you have written in Java program, you have an exported jar and also a jar registered by you. Now the compiler will check the function in exported jar. When the function is not present in the library, it looks into your jar. Is the keyword ‘FUNCTIONAL’ a User Defined Function (UDF)? No, the keyword „FUNCTIONAL‟ is not a User Defined Function (UDF). While using UDF, we have to override some functions. Certainly you have to do your job with the help of these functions only. But the keyword „FUNCTIONAL‟ is a built-in function i.e a pre-defined function, therefore it does not work as a UDF. Explain why do we need MapReduce during Pig programming? Pig is a high-level platform that makes many Hadoop data analysis issues easier to execute. The language we use for this platform is: Pig Latin. A program written in Pig Latin is like a query written in SQL, where we need an execution engine to execute the query. So, when a program is written in Pig Latin, Pig compiler will convert the program into MapReduce jobs. As such, MapReduce acts as the execution engine.

What does FOREACH do? FOREACH is used to apply transformations to the data and to generate new data items. The name itself is indicating that for each element of a data bag, the respective action will be performed. Syntax : FOREACH bagname GENERATE expression1, expression2, ….. The meaning of this statement is that the expressions mentioned after GENERATE will be applied to the current record of the data bag. Does Pig give any warning when there is a type mismatch or missing field? No, Pig will not show any warning if there is no matching field or a mismatch. If you assume that Pig gives such a warning, then it is difficult to find in log file. If any mismatch is found, it assumes a null value in Pig. Explain what co-group does in Pig? Co-group joins the data set by grouping one particular data set only. It groups the elements by their common field and then returns a set of records containing two separate bags. The first bag consists of the record of the first data set with the common data set and the second bag consists of the records of the second data set with the common data set. Can we say cogroup is a group of more than 1 data set? Cogroup is a group of one data set. But in the case of more than one data sets, cogroup will group all the data sets and join them based on the common field. Hence, we can say that cogroup is a group of more than one data set and join of that data set as well. Are there any problems which can only be solved by MapReduce and cannot be solved by PIG? In which kind of scenarios MR jobs will be more useful than PIG? Let us take a scenario where we want to count the population in two cities. I have a data set and sensor list of different cities. I want to count the population by using one mapreduce for two cities. Let us assume that one is Bangalore and the other is Noida. So I need to consider key of Bangalore city similar to Noida through which I can bring the population data of these two cities to one reducer. The idea behind this is some how I have to instruct map reducer program – whenever you find city with the name „Bangalore„ and city with the name „Noida‟, you create the alias name which will be the common name for these two cities so that you create a common key for both the cities and it get passed to the same reducer. For this, we have to write custom partitioner. In mapreduce when you create a „key‟ for city, you have to consider ‟city‟ as the key. So, whenever the framework comes across a different city, it considers it as a different key. Hence, we need to use customized partitioner. There is a provision in Mapreduce only, where you can write your custom partitioner and mention if city = Bangalore or Noida then pass similar hashcode. However, we cannot create custom partitioner in Pig. As Pig is not a framework, we cannot direct execution engine to customize the partitioner. In such scenarios, MapReduce works better than Pig.

A complete list of Hadoop Interview Questions and Answers on HDFS.

The below list of Big Data and Hadoop Interview Questions will be helpful in clearing a Big Data Interview.

Please give a detailed overview about the Big Data being generated by social networking website Facebook? As of January 31, 2013, there are 1.08 billion monthly active users on Facebook and 685 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. There are so many activities going on facebook from wall posts, sharing images, videos, writing comments and liking posts, etc. Facebook started using Hadoop in mid-2009 and was one of the initial users of Hadoop. Explain what are the three characteristics of Big Data according to IBM ? The three characteristics of Big Data are: Velocity: Analyzing 2 million records each day to identify the reason for losses. Variety: text, images, audio, video, sensor data, log files, etc. Volume: Twitter and Facebook generating 550+ terabytes of data per day.

Explain why do we need Hadoop? Everyday we are witnessing 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 data which is present in different machines at different locations. This is where Hadoop arises and addresses the problem. 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 very concept of MapReduce which enables it to divide the query into small parts and processing them in parallel. This is also known as parallel computing. What are some of the characteristics of Hadoop framework? Hadoop framework is written in core Java. It is designed to solve problems that involve analyzing large data (e.g. petabytes). The programming model is based on Google‟s Open Source 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 without any effect. Can you give examples of some companies that are using Hadoop structure? Almost all social networking companies use Hadoop. Companies using the Hadoop structure are Facebook, eBay, Twitter, Cloudera, EMC, MapR, Hortonworks, Amazon, Google and so on. Explain the basic difference between Hadoop and a traditional RDBMS ? 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. Explain what do you mean by structured and unstructured data? Structured data is the data that is easily identifiable as it is organized in the form of 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 videos, images, 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. MapReduce is used to process such large data sets and HDFS is basically used to store large data sets. What is HDFS and what are the key features of HDFS? HDFS is a file system designed for storing very large files with streaming data access patterns, running clusters on commodity hardware. 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? Consider a scenario - 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 and to retrieve the file back, 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 as backup. Since replication causes data redundancy it should have been discouraged but it pursued in HDFS. Explain HDFS works with hardware systems with average configurations which has high chances of getting crashed or damaged any time. In order to make the entire system highly fault-tolerant, HDFS replicates and stores data in different places at least in three 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. As such, there is no chance of losing the data. This replication factor helps us to attain the feature of Hadoop called Fault Tolerance. Data is replicated 3 times in HDFS, does it mean that any calculation done on one node will also be replicated on the other two? Since there are three nodes, when we send the MapReduce programs, calculations will be done only on the original data and not on the other two. The master node will only know which node exactly has that particular data. In case, if one of the nodes is not responding, it is considered to be failed. Only then, the required calculation will be done on the second replica. What do you mean by throughput? How does HDFS get a good throughput? Throughput is nothing but the amount of work done in a unit time. It is used to describe how fast the data is getting accessed from the system. 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 in the network. Hence all the systems will be executing the tasks assigned to them independently and in parallel. As such, the work will be completed in a very short period of time thereby ensuring the HDFS gives good throughput. By reading data in parallel, we decrease the actual time to read data in a significant way. Explain what is streaming access? HDFS works on the principle of „Write Once, Read Many„, the feature of streaming access is a very important feature 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 do you mean by commodity hardware? Does commodity hardware include RAM? Commodity hardware is a not-so-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 highend hardware to work on Hadoop. Commodity hardware must includes RAM because there will be some process or services which will be running on RAM in each of these systems. Explain what is a Namenode? Is Namenode also a commodity? Namenode is the master node on which job tracker runs and consists of the metadata. It maintains and manages the blocks which are present on the datanodes. It is a high-availability machine and single point of failure in HDFS.

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 do you mean by metadata? Metadata is "data about data". It is the information about the data stored in datanodes such as location of the file, size of the file and so on. What is a Datanode? Datanodes are the slaves which are deployed on each machine and provide the actual storage. These are responsible for serving read and write requests for the clients in the network. Why do we use HDFS for applications having large data sets and not when there are lot of small files? HDFS is more suitable for large amount of data sets in a single file as compared to small amount of data spread across multiple files. The reason is Namenode is a very expensive high performance system, so it is not prudent to occupy the space in the Namenode by unnecessary amount of metadata that is generated for multiple small files. So, when there is a large amount of data in a single file, name node will occupy less space. Hence for getting optimized performance, HDFS supports large data sets instead of multiple small files.

What is a daemon process? 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”. Explain 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 datanodes 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 blocksized 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.

Explain 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? Is client the end user in HDFS? A user has some query or who needs some kind of data. Client is an application which runs on your machine, which is used to interact with the Namenode (job tracker) or datanode (task tracker). Hence the answer is no. What is the communication channel between client and namenode/datanode? The mode of communication is SSH(Secure Shell). What is a rack? On what basis data will be stored on 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. 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 we have to place it so as 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. What is MapReduce? Can you explain how do ‘map’ and ‘reduce’ work? Map Reduce is the „heart„ of Hadoop that consists of two parts – „map‟ and „reduce‟. Maps and reduces are programs for processing data. „Map‟ processes the data first to give some intermediate output which is further processed by „Reduce‟ to generate the final output. Thus, MapReduce allows for distributed processing of the map and reduction operations. 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 to have two different servers for the Namenode and the datanodes. The reason for that 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.

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 filesystem (HDFS) and distributed programming framework (MapReduce). 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 very 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 always done in parallel because by doing so we can access the data fast. But we never 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 which is really not acceptable. 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. As such, this makes it confusing which data to be stored and accessed.

MapReduce Questions by Ashok Talukdar. These questions are frequently asked in a Big Data, Data Scientist Interview in top companies like IBM, Google, HP, Cisco. What exactly is MapReduce?MapReduce is the system used to process data in the Hadoop cluster. It consists of two phases: Map, and then Reduce. Between the two is a stage known as the shuffle and sort. Each Map task operates on a discrete portion of the overall dataset. Typically one HDFS block of data. After all Maps are complete, the MapReduce system distributes the intermediate data to nodes which perform the Reduce phase. Each node processes data stored on that node where possible. Can I write a MapReduce program with any language other than Java? Yes, MapReduce can be written in many programming languages Java, R, C++, scripting languages (Python, PHP). Any language able to read from stadin and write to stdout and parse tab and new line characters should work. Hadoop streaming (a Hadoop utility) allows you to create and run Map/Reduce jobs with any executable or script as the mapper and/or the reducer. Is the MapReduce infrastructure on the BDA open source?Yes, the core Hadoop HDFS storage and MapReduce compute infrastructure is 100% open source. Since $HADOOP_HOME is deprecated on CDH4.1.2 / BDA V2.0.1 what environment variable should be used? On BDA V2.0.1 with CDH 4.1.2, use $HADOOP_MAPRED_HOME=/usr/lib/hadoop-0.20-mapreduce.

What is the impact of shutting down a server for maintenance on MapReduce jobs? In the general case for a non-critical server (i.e. not node 1, 2, or 3) HDFS should redistribute jobs to other nodes. There should be no noticeable impact. Can standard R code be translated into MapReduce? ORCH V2.0 can auto generate Hive queries for R Language constructs to aid in data analysis and data preparation. The Hive queries in turn are executed as map-reduce code. This is accomplished through the ore API (ore.connect(type="HIVE")).

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Hadoop MapReduce Interview Questions What is MapReduce? It is a framework or a programming model that is used for processing large data sets over clusters of computers using distributed programming. What are 'maps' and 'reduces'? 'Maps' and 'Reduces' are two phases of solving a query in HDFS. 'Map' is responsible to read data from input location, and based on the input type, it will generate a key value pair,that is, an intermediate output in local machine.'Reducer' is responsible to process the intermediate output received from the mapper and generate the final output. What are the four basic parameters of a mapper? The four basic parameters of a mapper are LongWritable, text, text and IntWritable. The first two represent input parameters and the second two represent intermediate output parameters. What are the four basic parameters of a reducer? The four basic parameters of a reducer are Text, IntWritable, Text, IntWritable.The first two represent intermediate output parameters and the second two represent final output parameters. What do the master class and the output class do? Master is defined to update the Master or the job tracker and the output class is defined to write data onto the output location. What is the input type/format in MapReduce by default? By default the type input type in MapReduce is 'text'. Is it mandatory to set input and output type/format in MapReduce? No, it is not mandatory to set the input and output type/format in MapReduce. By default, the cluster takes the input and the output type as 'text'. What does the text input format do? In text input format, each line will create a line object, that is an hexadecimal number. Key is considered as a line object and value is considered as a whole line text. This is how the data gets processed by a mapper. The mapper will receive the 'key' as a 'LongWritable' parameter and value as a 'Text' parameter.

What does job conf class do? MapReduce needs to logically separate different jobs running on the same cluster. 'Job conf class' helps to do job level settings such as declaring a job in real environment. It is recommended that Job name should be descriptive and represent the type of job that is being executed. What does conf.setMapper Class do? Conf.setMapperclass sets the mapper class and all the stuff related to map job such as reading a data and generating a key-value pair out of the mapper. What do sorting and shuffling do? Sorting and shuffling are responsible for creating a unique key and a list of values.Making similar keys at one location is known as Sorting. And the process by which the intermediate output of the mapper is sorted and sent across to the reducers is known as Shuffling. What does a split do? Before transferring the data from hard disk location to map method, there is a phase or method called the 'Split Method'. Split method pulls a block of data from HDFS to the framework. The Split class does not write anything, but reads data from the block and pass it to the mapper.Be default, Split is taken care by the framework. Split method is equal to the block size and is used to divide block into bunch of splits. How can we change the split size if our commodity hardware has less storage space? If our commodity hardware has less storage space, we can change the split size by writing the 'custom splitter'. There is a feature of customization in Hadoop which can be called from the main method. What does a MapReduce partitioner do? A MapReduce partitioner makes sure that all the value of a single key goes to the same reducer, thus allows evenly distribution of the map output over the reducers. It redirects the mapper output to the reducer by determining which reducer is responsible for a particular key. How is Hadoop different from other data processing tools? In Hadoop, based upon your requirements, you can increase or decrease the number of mappers without bothering about the volume of data to be processed. this is the beauty of parallel processing in contrast to the other data processing tools available.

Can we rename the output file? Yes we can rename the output file by implementing multiple format output class. Why we cannot do aggregation (addition) in a mapper? Why we require reducer for that? We cannot do aggregation (addition) in a mapper because, sorting is not done in a mapper. Sorting happens only on the reducer side. Mapper method initialization depends upon each input split. While doing aggregation, we will lose the value of the previous instance. For each row, a new mapper will get initialized. For each row, inputsplit again gets divided into mapper, thus we do not have a track of the previous row value. What is Streaming? Streaming is a feature with Hadoop framework that allows us to do programming using MapReduce in any programming language which can accept standard input and can produce standard output. It could be Perl, Python, Ruby and not necessarily be Java. However, customization in MapReduce can only be done using Java and not any other programming language. What is a Combiner? A 'Combiner' is a mini reducer that performs the local reduce task. It receives the input from the mapper on a particular node and sends the output to the reducer. Combiners help in enhancing the efficiency of MapReduce by reducing the quantum of data that is required to be sent to the reducers. What is the difference between an HDFS Block and Input Split? HDFS Block is the physical division of the data and Input Split is the logical division of the data. What happens in a TextInputFormat? In TextInputFormat, each line in the text file is a record. Key is the byte offset of the line and value is the content of the line. For instance,Key: LongWritable, value: Text. What do you know about KeyValueTextInputFormat? In KeyValueTextInputFormat, each line in the text file is a 'record'. The first separator character divides each line. Everything before the separator is the key and everything after the separator is the value. For instance,Key: Text, value: Text. What do you know about SequenceFileInputFormat?

SequenceFileInputFormat is an input format for reading in sequence files. Key and value are user defined. It is a specific compressed binary file format which is optimized for passing the data between the output of one MapReduce job to the input of some other MapReduce job. What do you know about NLineOutputFormat? NLineOutputFormat splits 'n' lines of input as one split.

Hive Interview Questions What is Hive? Hive is a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems. Hive was originally developed at Facebook. It‟s now a Hadoop subproject with many contributors. Users need to concentrate only on the top level hive language rather than java map reduce programs. One of the main advantages of Hive is its SQLish nature. Thus it leverages the usability to a higher extend. A hive program will be automatically compiled into map-reduce jobs executed on Hadoop. In addition, HiveQL supports custom map-reduce scripts to be plugged into queries. Hive example: selecting the employee names whose salary more than 100 dollars from a hive table called tbl_employee. SELECT employee_name FROM tbl_employee WHERE salary > 100; Users are excited to use Hive since it is very similar to SQL. What are the types of tables in Hive? There are two types of tables. 1. Managed tables. 2. External tables. Only the drop table command differentiates managed and external tables. Otherwise, both type of tables are very similar. Does Hive support record level Insert, delete or update? Hive does not provide record-level update, insert, or delete. Henceforth, Hive does not provide transactions too. However, users can go with CASE statements and built in functions of Hive to satisfy the above DML operations. Thus, a complex update query in a RDBMS may need many lines of code in Hive. What kind of datawarehouse application is suitable for Hive?

Hive is not a full database. The design constraints and limitations of Hadoop and HDFS impose limits on what Hive can do. Hive is most suited for data warehouse applications, where 1) Relatively static data is analyzed, 2) Fast response times are not required, and 3) When the data is not changing rapidly. Hive doesn‟t provide crucial features required for OLTP, Online Transaction Processing. It‟s closer to being an OLAP tool, Online Analytic Processing.So, Hive is best suited for data warehouse applications, where a large data set is maintained and mined for insights, reports, etc. How can the columns of a table in hive be written to a file? By using awk command in shell, the output from HiveQL (Describe) can be written to a file. hive -S -e "describe table_name;" | awk -F" " ‟{print 1}‟ > ~/output.

CONCAT function in Hive with Example? CONCAT function will concat the input strings. You can specify any number of strings separated by comma. Example: CONCAT ('Hive','-','performs','-','good','-','in','-','Hadoop'); Output: Hive-performs-good-in-Hadoop So, every time you delimit the strings by '-'. If it is common for all the strings, then Hive provides another command CONCAT_WS. Here you have to specify the delimit operator first. CONCAT_WS ('-','Hive','performs','good','in','Hadoop'); Output: Hive-performs-good-in-Hadoop REPEAT function in Hive with example? REPEAT function will repeat the input string n times specified in the command. Example: REPEAT('Hadoop',3); Output: HadoopHadoopHadoop. Note: You can add a space with the input string also.

TRIM function in Hive with example? TRIM function will remove the spaces associated with a string. Example: TRIM(' Hadoop '); Output: Hadoop. Note: If you want to remove only leading or trialing spaces then you can specify the below commands respectively. LTRIM(' Hadoop'); RTRIM('Hadoop '); REVERSE function in Hive with example? REVERSE function will reverse the characters in a string. Example: REVERSE('Hadoop'); Output: poodaH LOWER or LCASE function in Hive with example? LOWER or LCASE function will convert the input string to lower case characters. Example: LOWER('Hadoop'); LCASE('Hadoop'); Output: hadoop Note: If the characters are already in lower case then they will be preserved. UPPER or UCASE function in Hive with example? UPPER or UCASE function will convert the input string to upper case characters. Example: UPPER('Hadoop'); UCASE('Hadoop');

Output: HADOOP Note: If the characters are already in upper case then they will be preserved. Double type in Hive – Important points? It is important to know about the double type in Hive. Double type in Hive will present the data differently unlike RDBMS. See the double type data below: 24624.0 32556.0 3.99893E5 4366.0 E5 represents 10^5 here. So, the value 3.99893E5 represents 399893. All the calculations will be accurately performed using double type. The maximum value for a IEEE 754 double is about 2.22E308. It is crucial while exporting the double type data to any RDBMS since the type may be wrongly interpreted. So, it is advised to cast the double type into appropriate type before exporting. Rename a table in Hive – How to do it? Using ALTER command, we can rename a table in Hive. ALTER TABLE hive_table_name RENAME TO new_name; There is another way to rename a table in Hive. Sometimes, ALTER may take more time if the underlying table has more partitions/functions. In that case, Import and export options can be utilized. Here you are saving the hive data into HDFS and importing back to new table like below. EXPORT TABLE tbl_name TO 'HDFS_location'; IMPORT TABLE new_tbl_name FROM 'HDFS_location'; If you prefer to just preserve the data, you can create a new table from old table like below. CREATE TABLE new_tbl_name AS SELECT * FROM old_tbl_name; DROP TABLE old_tbl_name; How to change a column data type in Hive? ALTER TABLE table_name CHANGE column_name column_name new_datatype; Example: If you want to change the data type of ID column from integer to bigint in a table called employee. ALTER TABLE employee CHANGE id id BIGINT;

Difference between order by and sort by in hive? SORT BY will sort the data within each reducer. You can use any number of reducers for SORT BY operation. ORDER BY will sort all of the data together, which has to pass through one reducer. Thus, ORDER BY in hive uses single reducer. ORDER BY guarantees total order in the output while SORT BY only guarantees ordering of the rows within a reducer. If there is more than one reducer, SORT BY may give partially ordered final results RLIKE in Hive? RLIKE (Right-Like) is a special function in Hive where if any substring of A matches with B then it evaluates to true. It also obeys Java regular expression pattern. Users don't need to put % symbol for a simple match in RLIKE. Examples: 'Express' RLIKE 'Exp' --> True 'Express' RLIKE '^E.*' --> True (Regular expression) Moreover, RLIKE will come handy when the string has some spaces. Without using TRIM function, RLIKE satisfies the required scenario. Suppose if A has value 'Express ' (2 spaces additionally) and B has value 'Express' RLIKE will work better without using TRIM. 'Express' RLIKE 'Express' --> True Note: RLIKE evaluates to NULL if A or B is NULL.

Hadoop interview questions Name the most common InputFormats defined in Hadoop? Which one is default ? Following 3 are most common InputFormats defined in Hadoop - TextInputFormat - KeyValueInputFormat - SequenceFileInputFormat TextInputFormat is the hadoop default. What is the difference between TextInputFormat and KeyValueInputFormat class? TextInputFormat: It reads lines of text files and provides the offset of the line as key to the Mapper and actual line as Value to the mapper KeyValueInputFormat: Reads text file and parses lines into key, val pairs.

Everything up to the first tab character is sent as key to the Mapper and the remainder of the line is sent as value to the mapper. What is InputSplit in Hadoop? When a hadoop job is run, it splits input files into chunks and assign each split to a mapper to process. This is called Input Split How is the splitting of file invoked in Hadoop Framework ? It is invoked by the Hadoop framework by running getInputSplit() method of the Input format class (like FileInputFormat) defined by the user Consider case scenario: In M/R system, - HDFS block size is 64 MB - Input format is FileInputFormat - We have 3 files of size 64K, 65Mb and 127Mb then how many input splits will be made by Hadoop framework? Hadoop will make 5 splits as follows - 1 split for 64K files - 2 splits for 65Mb files - 2 splits for 127Mb file What is the purpose of RecordReader in Hadoop? The InputSplit has defined a slice of work, but does not describe how to access it. The RecordReader class actually loads the data from its source and converts it into (key, value) pairs suitable for reading by the Mapper. The RecordReader instance is defined by the InputFormat After the Map phase finishes, the hadoop framework does "Partitioning, Shuffle and sort". Explain what happens in this phase? - Partitioning Partitioning is the process of determining which reducer instance will receive which intermediate keys and values. Each mapper must determine for all of its output (key, value) pairs which reducer will receive them. It is necessary that for any key, regardless of which mapper instance generated it, the destination partition is the same - Shuffle After the first map tasks have completed, the nodes may still be performing several more map tasks each. But they also begin exchanging the intermediate outputs from the map tasks to where they are required by the reducers. This process of moving map outputs to the reducers is known as shuffling.

- Sort Each reduce task is responsible for reducing the values associated with several intermediate keys. The set of intermediate keys on a single node is automatically sorted by Hadoop before they are presented to the Reducer If no custom partitioner is defined in the hadoop then how is data partitioned before its sent to the reducer? The default partitioner computes a hash value for the key and assigns the partition based on this result What is a Combiner? The Combiner is a "mini-reduce" process which operates only on data generated by a mapper. The Combiner will receive as input all data emitted by the Mapper instances on a given node. The output from the Combiner is then sent to the Reducers, instead of the output from the Mappers. What is job tracker? Job Tracker is the service within Hadoop that runs Map Reduce jobs on the cluster What are some typical functions of Job Tracker? The following are some typical tasks of Job Tracker - Accepts jobs from clients - It talks to the NameNode to determine the location of the data - It locates TaskTracker nodes with available slots at or near the data - It submits the work to the chosen Task Tracker nodes and monitors progress of each task by receiving heartbeat signals from Task tracker What is task tracker? Task Tracker is a node in the cluster that accepts tasks like Map, Reduce and Shuffle operations - from a JobTracker Whats the relationship between Jobs and Tasks in Hadoop? One job is broken down into one or many tasks in Hadoop. Suppose Hadoop spawned 100 tasks for a job and one of the task failed. What will hadoop do ? It will restart the task again on some other task tracker and only if the task fails more than 4 (default setting and can be changed) times will it kill the job Hadoop achieves parallelism by dividing the tasks across many nodes, it is possible for a few slow nodes to rate-limit the rest of the program and slow down the program. What mechanism Hadoop provides to combat this ?

Speculative Execution How does speculative execution works in Hadoop ? Job tracker makes different task trackers process same input. When tasks complete, they announce this fact to the Job Tracker. Whichever copy of a task finishes first becomes the definitive copy. If other copies were executing speculatively, Hadoop tells the Task Trackers to abandon the tasks and discard their outputs. The Reducers then receive their inputs from whichever Mapper completed successfully, first. Using command line in Linux, how will you - see all jobs running in the hadoop cluster - kill a job - hadoop job -list - hadoop job -kill jobid What is Hadoop Streaming ? Streaming is a generic API that allows programs written in virtually any language to be used as Hadoop Mapper and Reducer implementations

What is the characteristic of streaming API that makes it flexible run map reduce jobs in languages like perl, ruby, awk etc. ? Hadoop Streaming allows to use arbitrary programs for the Mapper and Reducer phases of a Map Reduce job by having both Mappers and Reducers receive their input on stdin and emit output (key, value) pairs on stdout.

Whats is Distributed Cache in Hadoop ? Distributed Cache is a facility provided by the Map/Reduce framework to cache files (text, archives, jars and so on) needed by applications during execution of the job. The framework will copy the necessary files to the slave node before any tasks for the job are executed on that node.

What is the benifit of Distributed cache, why can we just have the file in HDFS and have the application read it ? This is because distributed cache is much faster. It copies the file to all trackers at the start of the job. Now if the task tracker runs 10 or 100 mappers or reducer, it will use the same copy of distributed cache. On the other hand, if you put code in file to read it from HDFS in the MR job then every mapper will try to access it from HDFS hence if a task tracker run 100 map jobs then it will try to read this file 100

times from HDFS. Also HDFS is not very efficient when used like this.

What mechanism does Hadoop framework provides to synchronize changes made in Distribution Cache during runtime of the application ? This is a trick questions. There is no such mechanism. Distributed Cache by design is read only during the time of Job execution

Have you ever used Counters in Hadoop. Give us an example scenario ? Anybody who claims to have worked on a Hadoop project is expected to use counters Is it possible to provide multiple input to Hadoop? If yes then how can you give multiple directories as input to the Hadoop job ? Yes, The input format class provides methods to add multiple directories as input to a Hadoop job

Is it possible to have Hadoop job output in multiple directories. If yes then how ? Yes, by using Multiple Outputs class

What will a hadoop job do if you try to run it with an output directory that is already present? Will it - overwrite it - warn you and continue - throw an exception and exit The hadoop job will throw an exception and exit.

How can you set an arbitrary number of mappers to be created for a job in Hadoop ? This is a trick question. You cannot set it

How can you set an arbitary number of reducers to be created for a job in Hadoop ? You can either do it progamatically by using method setNumReduceTasksin the JobConfclass or set it up as a configuration setting

How will you write a custom partitioner for a Hadoop job ?

To have hadoop use a custom partitioner you will have to do minimum the following three - Create a new class that extends Partitioner class - Override method getPartition - In the wrapper that runs the Map Reducer, either - add the custom partitioner to the job programtically using method setPartitionerClass or - add the custom partitioner to the job as a config file (if your wrapper reads from config file or oozie)

How did you debug your Hadoop code ? There can be several ways of doing this but most common ways are - By using counters - The web interface provided by Hadoop framework

Did you ever built a production process in Hadoop ? If yes then what was the process when your hadoop job fails due to any reason? Its an open ended question but most candidates, if they have written a production job, should talk about some type of alert mechanisn like email is sent or there monitoring system sends an alert. Since Hadoop works on unstructured data, its very important to have a good alerting system for errors since unexpected data can very easily break the job.

Did you ever ran into a lop sided job that resulted in out of memory error, if yes then how did you handled it ? This is an open ended question but a candidate who claims to be an intermediate developer and has worked on large data set (10-20GB min) should have run into this problem. There can be many ways to handle this problem but most common way is to alter your algorithm and break down the job into more map reduce phase or use a combiner if possible.

What is HDFS? HDFS, the Hadoop Distributed File System, is a distributed file system designed to hold very large amounts of data (terabytes or even petabytes), and provide highthroughput access to this information. Files are stored in a redundant fashion across multiple machines to ensure their durability to failure and high availability to very parallel applications

What does the statement "HDFS is block structured file system" means? It means that in HDFS individual files are broken into blocks of a fixed size. These blocks are stored across a cluster of one or more machines with data storage capacity What does the term "Replication factor" mean? Replication factor is the number of times a file needs to be replicated in HDFS What is the default replication factor in HDFS? 3 What is the default block size of an HDFS block? 64Mb What is the benefit of having such big block size (when compared to block size of linux file system like ext)? It allows HDFS to decrease the amount of metadata storage required per file (the list of blocks per file will be smaller as the size of individual blocks increases). Furthermore, it allows for fast streaming reads of data, by keeping large amounts of data sequentially laid out on the disk Why is it recommended to have few very large files instead of a lot of small files in HDFS? This is because the Name node contains the meta data of each and every file in HDFS and more files means more metadata and since namenode loads all the metadata in memory for speed hence having a lot of files may make the metadata information big enough to exceed the size of the memory on the Name node True/false question. What is replication factor in HDFS - You can choose replication - You can choose replication - You can choose replication - True - True - False

the lowest granularity at which you can apply factor per directory factor per file in a directory factor per block of a file

What is a datanode in HDFS? Individual machines in the HDFS cluster that hold blocks of data are called datanodes What is a Namenode in HDFS?

The Namenode stores all the metadata for the file system What alternate way does HDFS provides to recover data in case a Namenode, without backup, fails and cannot be recovered? There is no way. If Namenode dies and there is no backup then there is no way to recover data Describe how a HDFS client will read a file in HDFS, like will it talk to data node or namenode ... how will data flow etc? To open a file, a client contacts the Name Node and retrieves a list of locations for the blocks that comprise the file. These locations identify the Data Nodes which hold each block. Clients then read file data directly from the Data Node servers, possibly in parallel. The Name Node is not directly involved in this bulk data transfer, keeping its overhead to a minimum. Using linux command line. how will you - List the the number of files in a HDFS directory - Create a directory in HDFS - Copy file from your local directory to HDFS hadoop fs -ls hadoop fs -mkdir hadoop fs -put localfile hdfsfile

Advantages of Hadoop? • Bringing compute and storage together on commodity hardware: The result is blazing speed at low cost. • Price performance: The Hadoop big data technology provides significant cost savings (think a factor of approximately 10) with significant performance improvements (again, think factor of 10). Your mileage may vary. If the existing technology can be so dramatically trounced, it is worth examining if Hadoop can complement or replace aspects of your current architecture. • Linear Scalability: Every parallel technology makes claims about scale up.Hadoop has genuine scalability since the latest release is expanding the limit on the number of nodes to beyond 4,000. • Full access to unstructured data: A highly scalable data store with a good parallel programming model, MapReduce, has been a challenge for the industry for some time. Hadoop programming model does not solve all problems, but it is a strong solution for many tasks. Definition of Big data? According to Gartner, Big data can be defined as high volume, velocity and variety

information requiring innovative and cost effective forms of information processing for enhanced decision making. How Big data differs from database ? Datasets which are beyond the ability of the database to store, analyze and manage can be defined as Big. The technology extracts required information from large volume whereas the storage area is limited for a database. Who are all using Hadoop? Give some examples? • A9.com • Amazon • Adobe • AOL • Baidu • Cooliris • Facebook • NSF-Google • IBM • LinkedIn • Ning • PARC • Rackspace • StumbleUpon • Twitter • Yahoo! Pig for Hadoop - Give some points? Pig is Data-flow oriented language for analyzing large data sets. It is a platform for analyzing large data sets that consists of a high-level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. The salient property of Pig programs is that their structure is amenable to substantial parallelization, which in turns enables them to handle very large data sets. At the present time, Pig infrastructure layer consists of a compiler that produces sequences of Map-Reduce programs, for which large-scale parallel implementations already exist (e.g., the Hadoop subproject). Pig language layer currently consists of a textual language called Pig Latin, which has the following key properties: Ease of programming. It is trivial to achieve parallel execution of simple, "embarrassingly parallel" data analysis tasks. Complex tasks comprised of multiple interrelated data

transformations are explicitly encoded as data flow sequences, making them easy to write, understand, and maintain. Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing the user to focus on semantics rather than efficiency. Extensibility. Users can create their own functions to do special-purpose processing. Features of Pig: – data transformation functions – datatypes include sets, associative arrays, tuples – high-level language for marshalling data - developed at yahoo!

Hive for Hadoop - Give some points? Hive is a data warehouse system for Hadoop that facilitates easy data summarization, ad-hoc queries, and the analysis of large datasets stored in Hadoop compatible file systems. Hive provides a mechanism to project structure onto this data and query the data using a SQL-like language called HiveQL. At the same time this language also allows traditional map/reduce programmers to plug in their custom mappers and reducers when it is inconvenient or inefficient to express this logic in HiveQL. Keypoints: • SQL-based data warehousing application – features similar to Pig – more strictly SQL-type • Supports SELECT, JOIN, GROUP BY,etc • Analyzing very large data sets – log processing, text mining, document indexing • Developed at Facebook Map Reduce in Hadoop? Map reduce : it is a framework for processing in parallel across huge datasets usning large no. of computers referred to cluster, it involves two processes namely Map and reduce. Map Process: In this process input is taken by the master node,which divides it into smaller tasks

and distribute them to the workers nodes. The workers nodes process these sub tasks and pass them back to the master node. Reduce Process : In this the master node combines all the answers provided by the worker nodes to get the results of the original task. The main advantage of Map reduce is that the map and reduce are performed in distributed mode. Since each operation is independent, so each map can be performed in parallel and hence reducing the net computing time. What is a heartbeat in HDFS? A heartbeat is a signal indicating that it is alive. A data node sends heartbeat to Name node and task tracker will send its heart beat to job tracker. If the Name node or job tracker does not receive heart beat then they will decide that there is some problem in data node or task tracker is unable to perform the assigned task. 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. 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. 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 filesystem (HDFS) and distributed programming framework (MapReduce). 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. 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. 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. History of Hadoop? Hadoop was created by Doug Cutting, the creator of Apache Lucene, the widely used text search library. Hadoop has its origins in Apache Nutch, an open source web search engine, itself a part of the Lucene project. The name Hadoop is not an acronym; it‟s a made-up name. The project‟s creator, Doug Cutting, explains how the name came about: The name my kid gave a stuffed yellow elephant. Short, relatively easy to spell and pronounce, meaningless, and not used elsewhere: those are my naming criteria. Subprojects and “contrib” modules in Hadoop also tend to have names that are unrelated to their function, often with an elephant or other animal theme (“Pig,” for example). Smaller components are given more descriptive (and therefore more mundane) names. This is a good principle, as it means you can generally work out what something does from its name. For example, the jobtracker keeps track of MapReduce jobs. What is meant by Volunteer Computing? Volunteer computing projects work by breaking the problem they are trying to solve into chunks called work units, which are sent to computers around the world to be analyzed. SETI@home is the most well-known of many volunteer computing projects. How Hadoop differs from SETI (Volunteer computing)? Although SETI (Search for Extra-Terrestrial Intelligence) may be superficially similar to MapReduce (breaking a problem into independent pieces to be worked on in parallel), there are some significant differences. The SETI@home problem is very CPU-intensive, which makes it suitable for running on hundreds of thousands of computers across the world. Since the time to transfer the work unit is dwarfed by the time to run the computation on it. Volunteers are donating CPU cycles, not bandwidth. MapReduce is designed to run jobs that last minutes or hours on trusted, dedicated

hardware running in a single data center with very high aggregate bandwidth interconnects. By contrast, SETI@home runs a perpetual computation on untrusted machines on the Internet with highly variable connection speeds and no data locality. Compare RDBMS and MapReduce? Data size: RDBMS - Gigabytes MapReduce - Petabytes Access: RDBMS - Interactive and batch MapReduce - Batch Updates: RDBMS - Read and write many times MapReduce - Write once, read many times Structure: RDBMS - Static schema MapReduce - Dynamic schema Integrity: RDBMS - High MapReduce - Low Scaling: RDBMS - Nonlinear MapReduce - Linear What is HBase? A distributed, column-oriented database. HBase uses HDFS for its underlying storage, and supports both batch-style computations using MapReduce and point queries (random reads). What is ZooKeeper? A distributed, highly available coordination service. ZooKeeper provides primitives such as distributed locks that can be used for building distributed applications. What is Chukwa? A distributed data collection and analysis system. Chukwa runs collectors that store data in HDFS, and it uses MapReduce to produce reports. (At the time of this writing, Chukwa had only recently graduated from a “contrib” module in Core to its own subproject.) What is Avro? A data serialization system for efficient, cross-language RPC, and persistent data

storage. (At the time of this writing, Avro had been created only as a new subproject, and no other Hadoop subprojects were using it yet.) core subproject in Hadoop - What is it? A set of components and interfaces for distributed filesystems and general I/O (serialization, Java RPC, persistent data structures). What are all Hadoop subprojects? Pig, Chukwa, Hive, HBase, MapReduce, HDFS, ZooKeeper, Core, Avro What is a split? Hadoop divides the input to a MapReduce job into fixed-size pieces called input splits, or just splits. Hadoop creates one map task for each split, which runs the userdefined map function for each record in the split. Having many splits means the time taken to process each split is small compared to the time to process the whole input. So if we are processing the splits in parallel, the processing is better load-balanced. On the other hand, if splits are too small, then the overhead of managing the splits and of map task creation begins to dominate the total job execution time. For most jobs, a good split size tends to be the size of a HDFS block, 64 MB by default, although this can be changed for the cluster Map tasks write their output to local disk, not to HDFS. Why is this? Map output is intermediate output: it‟s processed by reduce tasks to produce the final output, and once the job is complete the map output can be thrown away. So storing it in HDFS, with replication, would be overkill. If the node running the map task fails before the map output has been consumed by the reduce task, then Hadoop will automatically rerun the map task on another node to recreate the map output. MapReduce data flow with a single reduce task- Explain? The input to a single reduce task is normally the output from all mappers. The sorted map outputs have to be transferred across the network to the node where the reduce task is running, where they are merged and then passed to the user-defined reduce function. The output of the reduce is normally stored in HDFS for reliability. For each HDFS block of the reduce output, the first replica is stored on the local node, with other replicas being stored on off-rack nodes. MapReduce data flow with multiple reduce tasks- Explain?

When there are multiple reducers, the map tasks partition their output, each creating one partition for each reduce task. There can be many keys (and their associated values) in each partition, but the records for every key are all in a single partition. The partitioning can be controlled by a user-defined partitioning function, but normally the default partitioner. MapReduce data flow with no reduce tasks- Explain? It‟s also possible to have zero reduce tasks. This can be appropriate when you don‟t need the shuffle since the processing can be carried out entirely in parallel. In this case, the only off-node data transfer is used when the map tasks write to HDFS What is a block in HDFS? Filesystems deal 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. 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. File permissions in HDFS? HDFS has a permissions model for files and directories. There are three types of permission: the read permission (r), the write permission (w) and the execute permission (x). The read permission is required to read files or list the contents of a directory. The write permission is required to write a file, or for a directory, to create or delete files or directories in it. The execute permission is ignored for a file since you can‟t execute a file on HDFS. What is Thrift in HDFS? The Thrift API in the “thriftfs” contrib module exposes Hadoop filesystems as an Apache Thrift service, making it easy for any language that has Thrift bindings to interact with a Hadoop filesystem, such as HDFS. To use the Thrift API, run a Java server that exposes the Thrift service, and acts as a proxy to the Hadoop filesystem. Your application accesses the Thrift service, which is typically running on the same machine as your application. How Hadoop interacts with C? Hadoop provides a C library called libhdfs that mirrors the Java FileSystem

interface. It works using the Java Native Interface (JNI) to call a Java filesystem client. The C API is very similar to the Java one, but it typically lags the Java one, so newer features may not be supported. You can find the generated documentation for the C API in the libhdfs/docs/api directory of the Hadoop distribution. What is FUSE in HDFS Hadoop? Filesystem in Userspace (FUSE) allows filesystems that are implemented in user space to be integrated as a Unix filesystem. Hadoop‟s Fuse-DFS contrib module allows any Hadoop filesystem (but typically HDFS) to be mounted as a standard filesystem. You can then use Unix utilities (such as ls and cat) to interact with the filesystem. Fuse-DFS is implemented in C using libhdfs as the interface to HDFS. Documentation for compiling and running Fuse-DFS is located in the src/contrib/fuse-dfs directory of the Hadoop distribution. Explain WebDAV in Hadoop? WebDAV is a set of extensions to HTTP to support editing and updating files. WebDAV shares can be mounted as filesystems on most operating systems, so by exposing HDFS (or other Hadoop filesystems) over WebDAV, it‟s possible to access HDFS as a standard filesystem. What is Sqoop in Hadoop? It is a tool design to transfer the data between Relational database management system(RDBMS) and Hadoop HDFS. Thus, we can sqoop the data from RDBMS like mySql or Oracle into HDFS of Hadoop as well as exporting data from HDFS file to RDBMS. Sqoop will read the table row-by-row and the import process is performed in Parallel. Thus, the output may be in multiple files. Example: sqoop INTO "directory"; (SELECT * FROM database.table WHERE condition;)

Pig Interview Questions Can you give us some examples how Hadoop is used in real time environment? Let us assume that the we have an exam consisting of 10 Multiple-choice questions and 20 students appear for that exam. Every student will attempt each question. For each question and each answer option, a key will be generated. So we have a set of key-value pairs for all the questions and all the answer options for every

student. Based on the options that the students have selected, you have to analyze and find out how many students have answered correctly. This isn‟t an easy task. Here Hadoop comes into picture! Hadoop helps you in solving these problems quickly and without much effort. You may also take the case of how many students have wrongly attempted a particular question.

What is BloomMapFile used for? The BloomMapFile is a class that extends MapFile. So its functionality is similar to MapFile. BloomMapFile uses dynamic Bloom filters to provide quick membership test for the keys. It is used in Hbase table format.

What is PIG? PIG is a platform for analyzing large data sets that consist of high level language for expressing data analysis programs, coupled with infrastructure for evaluating these programs. PIG‟s infrastructure layer consists of a compiler that produces sequence of MapReduce Programs.

What is the difference between logical and physical plans? Pig undergoes some steps when a Pig Latin Script is converted into MapReduce jobs. After performing the basic parsing and semantic checking, it produces a logical plan. The logical plan describes the logical operators that have to be executed by Pig during execution. After this, Pig produces a physical plan. The physical plan describes the physical operators that are needed to execute the script.

Does „ILLUSTRATE‟ run MR job? No, illustrate will not pull any MR, it will pull the internal data. On the console, illustrate will not do any job. It just shows output of each stage and not the final output.

Is the keyword „DEFINE‟ like a function name? Yes, the keyword „DEFINE‟ is like a function name. Once you have registered, you have to define it. Whatever logic you have written in Java program, you have an

exported jar and also a jar registered by you. Now the compiler will check the function in exported jar. When the function is not present in the library, it looks into your jar.

Is the keyword „FUNCTIONAL‟ a User Defined Function (UDF)? No, the keyword „FUNCTIONAL‟ is not a User Defined Function (UDF). While using UDF, we have to override some functions. Certainly you have to do your job with the help of these functions only. But the keyword „FUNCTIONAL‟ is a built-in function i.e a pre-defined function, therefore it does not work as a UDF.

Why do we need MapReduce during Pig programming? Pig is a high-level platform that makes many Hadoop data analysis issues easier to execute. The language we use for this platform is: Pig Latin. A program written in Pig Latin is like a query written in SQL, where we need an execution engine to execute the query. So, when a program is written in Pig Latin, Pig compiler will convert the program into MapReduce jobs. Here, MapReduce acts as the execution engine.

Are there any problems which can only be solved by MapReduce and cannot be solved by PIG? In which kind of scenarios MR jobs will be more useful than PIG? Let us take a scenario where we want to count the population in two cities. I have a data set and sensor list of different cities. I want to count the population by using one mapreduce for two cities. Let us assume that one is Bangalore and the other is Noida. So I need to consider key of Bangalore city similar to Noida through which I can bring the population data of these two cities to one reducer. The idea behind this is some how I have to instruct map reducer program – whenever you find city with the name „Bangalore„ and city with the name „Noida‟, you create the alias name which will be the common name for these two cities so that you create a common key for both the cities and it get passed to the same reducer. For this, we have to write custom partitioner. In mapreduce when you create a „key‟ for city, you have to consider ‟city‟ as the key. So, whenever the framework comes across a different city, it considers it as a different key. Hence, we need to use customized partitioner. There is a provision in mapreduce only, where you can write your custom partitioner and mention if city = bangalore or noida then pass similar hashcode. However, we cannot create custom partitioner in Pig. As Pig is not a framework, we cannot direct execution engine to customize the partitioner. In such scenarios, MapReduce works better than Pig.

Does Pig give any warning when there is a type mismatch or missing field? No, Pig will not show any warning if there is no matching field or a mismatch. If you assume that Pig gives such a warning, then it is difficult to find in log file. If any mismatch is found, it assumes a null value in Pig.

What co-group does in Pig? Co-group joins the data set by grouping one particular data set only. It groups the elements by their common field and then returns a set of records containing two separate bags. The first bag consists of the record of the first data set with the common data set and the second bag consists of the records of the second data set with the common data set.

Can we say cogroup is a group of more than 1 data set? Cogroup is a group of one data set. But in the case of more than one data sets, cogroup will group all the data sets and join them based on the common field. Hence, we can say that cogroup is a group of more than one data set and join of that data set as well.

What does FOREACH do? FOREACH is used to apply transformations to the data and to generate new data items. The name itself is indicating that for each element of a data bag, the respective action will be performed. Syntax : FOREACH bagname GENERATE expression1, expression2, ….. The meaning of this statement is that the expressions mentioned after GENERATE will be applied to the current record of the data bag.

What is bag? A bag is one of the data models present in Pig. It is an unordered collection of tuples with possible duplicates. Bags are used to store collections while grouping. The size of bag is the size of the local disk, this means that the size of the bag is limited. When the bag is full, then Pig will spill this bag into local disk and keep only some parts of the bag in memory. There is no necessity that the complete bag should fit into memory. We represent bags with “{}”.

Hadoop Cluster Interview Questions Which are the three modes in which Hadoop can be run? The three modes in which Hadoop can be run are: 1. standalone (local) mode 2. Pseudo-distributed mode 3. Fully distributed mode What are the features of Stand alone (local) mode? In stand-alone mode there are no daemons, everything runs on a single JVM. It has no DFS and utilizes the local file system. Stand-alone mode is suitable only for running MapReduce programs during development. It is one of the most least used environments. What are the features of Pseudo mode? Pseudo mode is used both for development and in the QA environment. In the Pseudo mode all the daemons run on the same machine. Can we call VMs as pseudos? No, VMs are not pseudos because VM is something different and pesudo is very specific to Hadoop. What are the features of Fully Distributed mode? Fully Distributed mode is used in the production environment, where we have „n‟ number of machines forming a Hadoop cluster. Hadoop daemons run on a cluster of machines. There is one host onto which Namenode is running and another host on which datanode is running and then there are machines on which task tracker is running. We have separate masters and separate slaves in this distribution. Does Hadoop follows the UNIX pattern? Yes, Hadoop closely follows the UNIX pattern. Hadoop also has the „conf„ directory as in the case of UNIX. In which directory Hadoop is installed? Cloudera and Apache has the same directory structure. Hadoop is installed in cd /usr/lib/hadoop/ What are the port numbers of Namenode, job tracker and task tracker? The port number for Namenode is ‟50070′, for job tracker is ‟50030′ and for task tracker is ‟50060′. What is the Hadoop-core configuration?

Hadoop core is configured by two xml files: 1. hadoop-default.xml which was renamed to 2. hadoop-site.xml. These files are written in xml format. We have certain properties in these xml files, which consist of name and value. What are the Hadoop configuration files at present? There are 3 configuration files in Hadoop: 1. core-site.xml 2. hdfs-site.xml 3. mapred-site.xml These files are located in the hadoop/conf/ subdirectory. How to exit the Vi editor? To exit the Vi Editor, press ESC and type :q and then press enter. What is a spill factor with respect to the RAM? Spill factor is the size after which your files move to the temp file. Hadoop-temp directory is used for this. Is fs.mapr.working.dir a single directory? Yes, fs.mapr.working.dir it is just one directory. Which are the three main hdfs-site.xml properties? The three main hdfs-site.xml properties are: 1. dfs.name.dir which gives you the location on which metadata will be stored and where DFS is located – on disk or onto the remote. 2. dfs.data.dir which gives you the location where the data is going to be stored. 3. fs.checkpoint.dir which is for secondary Namenode. How to come out of the insert mode? To come out of the insert mode, press ESC, type :q (if you have not written anything) OR type :wq (if you have written anything in the file) and then press ENTER. What is Cloudera and why it is used? Cloudera is the distribution of Hadoop. It is a user created on VM by default. Cloudera belongs to Apache and is used for data processing. What happens if you get a „connection refused java exception‟ when you type hadoop fsck /? It could mean that the Namenode is not working on your VM. We are using Ubuntu operating system with Cloudera, but from where we can

What does „jps‟ command do? This command checks whether your Namenode, datanode, task tracker, job tracker, etc are working or not. How can I restart Namenode? 1. Click on stop-all.sh and then click on start-all.sh OR 2. Write sudo hdfs (press enter), su-hdfs (press enter), /etc/init.d/ha (press enter) and then /etc/init.d/hadoop-namenode start (press enter). What is the full form of fsck? Full form of fsck is File System Check. How can we check whether Namenode is working or not? To check whether Namenode is working or not, use the command /etc/init.d/hadoop-namenode status or as simple as jps. What does the command mapred.job.tracker do? The command mapred.job.tracker lists out which of your nodes is acting as a job tracker. What does /etc /init.d do? /etc /init.d specifies where daemons (services) are placed or to see the status of these daemons. It is very LINUX specific, and nothing to do with Hadoop. How can we look for the Namenode in the browser? If you have to look for Namenode in the browser, you don‟t have to give localhost:8021, the port number to look for Namenode in the brower is 50070. How to change from SU to Cloudera? To change from SU(super user) to Cloudera just type exit. Which files are used by the startup and shutdown commands? Slaves and Masters are used by the startup and the shutdown commands. What do slaves consist of? Slaves consist of a list of hosts, one per line, that host datanode and task tracker servers. What do masters consist of? Masters contain a list of hosts, one per line, that are to host secondary namenode

servers. What does hadoop-env.sh do? hadoop-env.sh provides the environment for Hadoop to run. JAVA_HOME is set over here. Can we have multiple entries in the master files? Yes, we can have multiple entries in the Master files. Where is hadoop-env.sh file present? hadoop-env.sh file is present in the conf location. In Hadoop_PID_DIR, what does PID stands for? PID stands for „Process ID‟. What does /var/hadoop/pids do? It stores the PID. What does hadoop-metrics.properties file do? hadoop-metrics.properties is used for „Reporting„ purposes. It controls the reporting for Hadoop. The default status is „not to report„. What are the network requirements for Hadoop? The Hadoop core uses Shell (SSH) to launch the server processes on the slave nodes. It requires password-less SSH connection between the master and all the slaves and the secondary machines. Why do we need a password-less SSH in Fully Distributed environment? We need a password-less SSH in a Fully-Distributed environment because when the cluster is LIVE and running in Fully Distributed environment, the communication is too frequent. The job tracker should be able to send a task to task tracker quickly. Does this lead to security issues? No, not at all. Hadoop cluster is an isolated cluster. And generally it has nothing to do with an internet. It has a different kind of a configuration. We needn‟t worry about that kind of a security breach, for instance, someone hacking through the internet, and so on. Hadoop has a very secured way to connect to other machines to fetch and to process data. On which port does SSH work? SSH works on Port No. 22, though it can be configured. 22 is the default Port number.

Can you tell us more about SSH? SSH is nothing but a secure shell communication, it is a kind of a protocol that works on a Port No. 22, and when you do an SSH, what you really require is a password. Why password is needed in SSH localhost? Password is required in SSH for security and in a situation where passwordless communication is not set. Do we need to give a password, even if the key is added in SSH? Yes, password is still required even if the key is added in SSH. What if a Namenode has no data? If a Namenode has no data it is not a Namenode. Practically, Namenode will have some data. What happens to job tracker when Namenode is down? When Namenode is down, your cluster is OFF, this is because Namenode is the single point of failure in HDFS. What happens to a Namenode, when job tracker is down? When a job tracker is down, it will not be functional but Namenode will be present. So, cluster is accessible if Namenode is working, even if the job tracker is not working. Can you give us some more details about SSH communication between Masters and the Slaves? SSH is a password-less secure communication where data packets are sent across the slave. It has some format into which data is sent across. SSH is not only between masters and slaves but also between two hosts. What is formatting of the DFS? Just like we do for Windows, DFS is formatted for proper structuring. It is not usually done as it formats the Namenode too. Does the HDFS client decide the input split or Namenode? No, the Client does not decide. It is already specified in one of the configurations through which input split is already configured. In Cloudera there is already a cluster, but if I want to form a cluster on Ubuntu can we do it?

Yes, you can go ahead with this! There are installation steps for creating a new cluster. You can uninstall your present cluster and install the new cluster. Can we create a Hadoop cluster from scratch? Yes we can do that also once we are familiar with the Hadoop environment. Can we use Windows for Hadoop? Actually, Red Hat Linux or Ubuntu are the best Operating Systems for Hadoop. Windows is not used frequently for installing Hadoop as there are many support problems attached with Windows. Thus, Windows is not a preferred environment for Hadoop.

Hadoop HDFS Interview Questions 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. To know more about BIG DATA, browse through The Hype Behind Big Data!

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 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!

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.

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.

What is Hadoop? Hadoop is a framework that allows for distributed processing of large data sets across clusters of commodity computers using a simple programming model.

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 atleast 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.

What is a Namenode? Namenode is the master node on which job tracker runs and consists of the metadata. It maintains and manages the blocks which are present on the datanodes. It is a high-availability machine and single point of failure in HDFS.

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 highavailability machine.

What is a metadata? Metadata is the information about the data stored in datanodes such as location of the file, size of the file and so on.

What is a Datanode? Datanodes are the slaves which are deployed on each machine and provide the actual storage. These are responsible for serving read and write requests for the clients.

Why do we use HDFS for applications having large data sets and not when there are lot of small files?

HDFS is more suitable for large amount of data sets in a single file as compared to small amount of data spread across multiple files. This is because Namenode is a very expensive high performance system, so it is not prudent to occupy the space in the Namenode by unnecessary amount of metadata that is generated for multiple small files. So, when there is a large amount of data in a single file, name node will occupy less space. Hence for getting optimized performance, HDFS supports large data sets instead of multiple small files.

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

as the default this particular will be free to in an efficient

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. This is called Hadoop Single Point Of Failure (SPOF)

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.

What is MapReduce? Map Reduce is the „heart„ of Hadoop that consists of two parts – „map‟ and „reduce‟. Maps and reduces are programs for processing data. „Map‟ processes the data first to give some intermediate output which is further processed by „Reduce‟ to generate the final output. Thus, MapReduce allows for distributed processing of the map and reduction operations.

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 filesystem (HDFS) and distributed programming framework (MapReduce).

Java Interview Questions What is the most important feature of Java? Java is a platform independent language. What do you mean by platform independence? Platform independence means that we can write and compile the java code in one platform (eg Windows) and can execute the class in any other supported platform eg (Linux,Solaris,etc). What is a JVM? JVM is Java Virtual Machine which is a run time environment for the compiled java

class files. Are JVM's platform independent? JVM's are not platform independent. JVM's are platform specific run time implementation provided by the vendor. What is the difference between a JDK and a JVM? JDK is Java Development Kit which is for development purpose and it includes execution environment also. But JVM is purely a run time environment and hence you will not be able to compile your source files using a JVM. What is a pointer and does Java support pointers? Pointer is a reference handle to a memory location. Improper handling of pointers leads to memory leaks and reliability issues hence Java doesn't support the usage of pointers. What is the base class of all classes? java.lang.Object Does Java support multiple inheritance? Java doesn't support multiple inheritance. Is Java a pure object oriented language? Java uses primitive data types and hence is not a pure object oriented language. Are arrays primitive data types? In Java, Arrays are objects. What is difference between Path and Classpath? Path and Classpath are operating system level environment variales. Path is used define where the system can find the executables(.exe) files and classpath is used to specify the location .class files. What are local variables? Local varaiables are those which are declared within a block of code like methods. Local variables should be initialised before accessing them. What are instance variables? Instance variables are those which are defined at the class level. Instance variables need not be initialized before using them as they are automatically initialized to their default values.

How to define a constant variable in Java? The variable should be declared as static and final. So only one copy of the variable exists for all instances of the class and the value can't be changed also. static final int PI = 2.14; is an example for constant. Should a main() method be compulsorily declared in all java classes? No not required. main() method should be defined only if the source class is a java application. What is the return type of the main() method? Main() method doesn't return anything hence declared void. Why is the main() method declared static? main() method is called by the JVM even before the instantiation of the class hence it is declared as static. What is the arguement of main() method? main() method accepts an array of String object as arguement. Can a main() method be overloaded? Yes. You can have any number of main() methods with different method signature and implementation in the class. Can a main() method be declared final? Yes. Any inheriting class will not be able to have it's own default main() method. Does the order of public and static declaration matter in main() method? No. It doesn't matter but void should always come before main(). Can a source file contain more than one class declaration? Yes a single source file can contain any number of Class declarations but only one of the class can be declared as public. What is a package? Package is a collection of related classes and interfaces. package declaration should be first statement in a java class. Which package is imported by default? java.lang package is imported by default even without a package declaration. Can a class declared as private be accessed outside it's package? Not possible.

Can a class be declared as protected? A class can't be declared as protected. only methods can be declared as protected. What is the access scope of a protected method? A protected method can be accessed by the classes within the same package or by the subclasses of the class in any package. What is the purpose of declaring a variable as final? A final variable's value can't be changed. final variables should be initialized before using them. What is the impact of declaring a method as final? A method declared as final can't be overridden. A sub-class can't have the same method signature with a different implementation. I don't want my class to be inherited by any other class. What should i do? You should declared your class as final. But you can't define your class as final, if it is an abstract class. A class declared as final can't be extended by any other class. Can you give few examples of final classes defined in Java API? java.lang.String, java.lang.Math are final classes. How is final different from finally and finalize()? final is a modifier which can be applied to a class or a method or a variable. final class can't be inherited, final method can't be overridden and final variable can't be changed. finally is an exception handling code section which gets executed whether an exception is raised or not by the try block code segment. finalize() is a method of Object class which will be executed by the JVM just before garbage collecting object to give a final chance for resource releasing activity. Can a class be declared as static? We can not declare top level class as static, but only inner class can be declared static. public class Test { static class InnerClass { public static void InnerMethod() { System.out.println("Static Inner Class!"); } } public static void main(String args[])

{ Test.InnerClass.InnerMethod(); } } //output: Static Inner Class! When will you define a method as static? When a method needs to be accessed even before the creation of the object of the class then we should declare the method as static. What are the restriction imposed on a static method or a static block of code? A static method should not refer to instance variables without creating an instance and cannot use "this" operator to refer the instance. I want to print "Hello" even before main() is executed. How will you acheive that? Print the statement inside a static block of code. Static blocks get executed when the class gets loaded into the memory and even before the creation of an object. Hence it will be executed before the main() method. And it will be executed only once. What is the importance of static variable? static variables are class level variables where all objects of the class refer to the same variable. If one object changes the value then the change gets reflected in all the objects. Can we declare a static variable inside a method? Static varaibles are class level variables and they can't be declared inside a method. If declared, the class will not compile. What is an Abstract Class and what is it's purpose? A Class which doesn't provide complete implementation is defined as an abstract class. Abstract classes enforce abstraction. Can a abstract class be declared final? Not possible. An abstract class without being inherited is of no use and hence will result in compile time error. What is use of a abstract variable? Variables can't be declared as abstract. only classes and methods can be declared as abstract. Can you create an object of an abstract class?

Not possible. Abstract classes can't be instantiated. Can a abstract class be defined without any abstract methods? Yes it's possible. This is basically to avoid instance creation of the class. Class C implements Interface I containing method m1 and m2 declarations. Class C has provided implementation for method m2. Can i create an object of Class C? No not possible. Class C should provide implementation for all the methods in the Interface I. Since Class C didn't provide implementation for m1 method, it has to be declared as abstract. Abstract classes can't be instantiated. Can a method inside a Interface be declared as final? No not possible. Doing so will result in compilation error. public and abstract are the only applicable modifiers for method declaration in an interface. Can an Interface implement another Interface? Intefaces doesn't provide implementation hence a interface cannot implement another interface. Can an Interface extend another Interface? Yes an Interface can inherit another Interface, for that matter an Interface can extend more than one Interface. Can a Class extend more than one Class? Not possible. A Class can extend only one class but can implement any number of Interfaces. Why is an Interface be able to extend more than one Interface but a Class can't extend more than one Class? Basically Java doesn't allow multiple inheritance, so a Class is restricted to extend only one Class. But an Interface is a pure abstraction model and doesn't have inheritance hierarchy like classes(do remember that the base class of all classes is Object). So an Interface is allowed to extend more than one Interface. Can an Interface be final? Not possible. Doing so so will result in compilation error. Can a class be defined inside an Interface? Yes it's possible. Can an Interface be defined inside a class? Yes it's possible.

What is a Marker Interface? An Interface which doesn't have any declaration inside but still enforces a mechanism. Which object oriented Concept is achieved by using overloading and overriding? Polymorphism. Why does Java not support operator overloading? Operator overloading makes the code very difficult to read and maintain. To maintain code simplicity, Java doesn't support operator overloading. Can we define private and protected modifiers for variables in interfaces? No. What is Externalizable? Externalizable is an Interface that extends Serializable Interface. And sends data into Streams in Compressed Format. It has two methods, writeExternal(ObjectOuput out) and readExternal(ObjectInput in) What modifiers are allowed for methods in an Interface? Only public and abstract modifiers are allowed for methods in interfaces. What is a local, member and a class variable? Variables declared within a method are "local" variables. Variables declared within the class i.e not within any methods are "member" variables (global variables). Variables declared within the class i.e not within any methods and are defined as "static" are class variables. What is an abstract method? An abstract method is a method whose implementation is deferred to a subclass. What value does read() return when it has reached the end of a file? The read() method returns -1 when it has reached the end of a file. Can a Byte object be cast to a double value? No, an object cannot be cast to a primitive value. What is the difference between a static and a non-static inner class? A non-static inner class may have object instances that are associated with instances of the class's outer class. A static inner class does not have any object

instances. What is an object's lock and which object's have locks? An object's lock is a mechanism that is used by multiple threads to obtain synchronized access to the object. A thread may execute a synchronized method of an object only after it has acquired the object's lock. All objects and classes have locks. A class's lock is acquired on the class's Class object. What is the % operator? It is referred to as the modulo or remainder operator. It returns the remainder of dividing the first operand by the second operand. When can an object reference be cast to an interface reference? An object reference be cast to an interface reference when the object implements the referenced interface. Which class is extended by all other classes? The Object class is extended by all other classes. Which non-Unicode letter characters may be used as the first character of an identifier? The non-Unicode letter characters $ and _ may appear as the first character of an identifier What restrictions are placed on method overloading? Two methods may not have the same name and argument list but different return types. What is casting? There are two types of casting, casting between primitive numeric types and casting between object references. Casting between numeric types is used to convert larger values, such as double values, to smaller values, such as byte values. Casting between object references is used to refer to an object by a compatible class, interface, or array type reference. What is the return type of a program's main() method? void. If a variable is declared as private, where may the variable be accessed? A private variable may only be accessed within the class in which it is declared. What do you understand by private, protected and public?

These are accessibility modifiers. Private is the most restrictive, while public is the least restrictive. There is no real difference between protected and the default type (also known as package protected) within the context of the same package, however the protected keyword allows visibility to a derived class in a different package. What is Downcasting ? Downcasting is the casting from a general to a more specific type, i.e. casting down the hierarchy What modifiers may be used with an inner class that is a member of an outer class? A (non-local) inner class may be declared as public, protected, private, static, final, or abstract. How many bits are used to represent Unicode, ASCII, UTF-16, and UTF-8 characters? Unicode requires 16 bits and ASCII require 7 bits Although the ASCII character set uses only 7 bits, it is usually represented as 8 bits. UTF-8 represents characters using 8, 16, and 18 bit patterns. UTF-16 uses 16-bit and larger bit patterns. What restrictions are placed on the location of a package statement within a source code file? A package statement must appear as the first line in a source code file (excluding blank lines and comments). What is a native method? A native method is a method that is implemented in a language other than Java. What are order of precedence and associativity, and how are they used? Order of precedence determines the order in which operators are evaluated in expressions. Associatity determines whether an expression is evaluated left-to-right or right-to-left. Can an anonymous class be declared as implementing an interface and extending a class? An anonymous class may implement an interface or extend a superclass, but may not be declared to do both. What is the range of the char type? The range of the char type is 0 to 216 - 1 (i.e. 0 to 65535.)

What is the range of the short type? The range of the short type is -(215) to 215 - 1. (i.e. -32,768 to 32,767) Why isn't there operator overloading? Because C++ has proven by example that operator overloading makes code almost impossible to maintain. What does it mean that a method or field is "static"? Static variables and methods are instantiated only once per class. In other words they are class variables, not instance variables. If you change the value of a static variable in a particular object, the value of that variable changes for all instances of that class. Static methods can be referenced with the name of the class rather than the name of a particular object of the class (though that works too). That's how library methods likeSystem.out.println() work. out is a static field in the java.lang.System class. Is null a keyword? The null value is not a keyword. Which characters may be used as the second character of an identifier, but not as the first character of an identifier? The digits 0 through 9 may not be used as the first character of an identifier but they may be used after the first character of an identifier. Is the ternary operator written x : y ? z or x ? y : z ? It is written x ? y : z. How is rounding performed under integer division? The fractional part of the result is truncated. This is known as rounding toward zero. If a class is declared without any access modifiers, where may the class be accessed? A class that is declared without any access modifiers is said to have package access. This means that the class can only be accessed by other classes and interfaces that are defined within the same package. Does a class inherit the constructors of its superclass? A class does not inherit constructors from any of its superclasses. Name the eight primitive Java types. The eight primitive types are byte, char, short, int, long, float, double, and boolean.

What restrictions are placed on the values of each case of a switch statement? During compilation, the values of each case of a switch statement must evaluate to a value that can be promoted to an int value. What is the difference between a while statement and a do while statement? A while statement checks at the beginning of a loop to see whether the next loop iteration should occur. A do while statement checks at the end of a loop to see whether the next iteration of a loop should occur. The do whilestatement will always execute the body of a loop at least once. What modifiers can be used with a local inner class? A local inner class may be final or abstract. When does the compiler supply a default constructor for a class? The compiler supplies a default constructor for a class if no other constructors are provided. If a method is declared as protected, where may the method be accessed? A protected method may only be accessed by classes or interfaces of the same package or by subclasses of the class in which it is declared. What are the legal operands of the instanceof operator? The left operand is an object reference or null value and the right operand is a class, interface, or array type. Are true and false keywords? The values true and false are not keywords. What happens when you add a double value to a String? The result is a String object. What is the diffrence between inner class and nested class? When a class is defined within a scope od another class, then it becomes inner class. If the access modifier of the inner class is static, then it becomes nested class. Can an abstract class be final? An abstract class may not be declared as final. What is numeric promotion? Numeric promotion is the conversion of a smaller numeric type to a larger numeric

type, so that integer and floating-point operations may take place. In numerical promotion, byte, char, and short values are converted to int values. The int values are also converted to long values, if necessary. The long and float values are converted to double values, as required. What is the difference between a public and a non-public class? A public class may be accessed outside of its package. A non-public class may not be accessed outside of its package. To what value is a variable of the boolean type automatically initialized? The default value of the boolean type is false. What is the difference between the prefix and postfix forms of the ++ operator? The prefix form performs the increment operation and returns the value of the increment operation. The postfix form returns the current value all of the expression and then performs the increment operation on that value. What restrictions are placed on method overriding? Overridden methods must have the same name, argument list, and return type. The overriding method may not limit the access of the method it overrides. The overriding method may not throw any exceptions that may not be thrown by the overridden method. What is a Java package and how is it used? A Java package is a naming context for classes and interfaces. A package is used to create a separate name space for groups of classes and interfaces. Packages are also used to organize related classes and interfaces into a single API unit and to control accessibility to these classes and interfaces. What modifiers may be used with a top-level class? A top-level class may be public, abstract, or final. What is the difference between an if statement and a switch statement? The if statement is used to select among two alternatives. It uses a boolean expression to decide which alternative should be executed. The switch statement is used to select among multiple alternatives. It uses an int expression to determine which alternative should be executed. What are the practical benefits, if any, of importing a specific class rather than an entire package (e.g. import java.net.* versus import java.net.Socket)? It makes no difference in the generated class files since only the classes that are

actually used are referenced by the generated class file. There is another practical benefit to importing single classes, and this arises when two (or more) packages have classes with the same name. Take java.util.Timer and javax.swing.Timer, for example. If I import java.util.* and javax.swing.* and then try to use "Timer", I get an error while compiling (the class name is ambiguous between both packages). Let's say what you really wanted was the javax.swing.Timer class, and the only classes you plan on using in java.util are Collection and HashMap. In this case, some people will prefer to import java.util.Collection and import java.util.HashMap instead of importing java.util.*. This will now allow them to use Timer, Collection, HashMap, and other javax.swing classes without using fully qualified class names in. Can a method be overloaded based on different return type but same argument type ? No, because the methods can be called without using their return type in which case there is ambiquity for the compiler. What happens to a static variable that is defined within a method of a class ? Can't do it. You'll get a compilation error. How many static initializers can you have ? As many as you want, but the static initializers and class variable initializers are executed in textual order and may not refer to class variables declared in the class whose declarations appear textually after the use, even though these class variables are in scope. What is the difference between method overriding and overloading? Overriding is a method with the same name and arguments as in a parent, whereas overloading is the same method name but different arguments What is constructor chaining and how is it achieved in Java ? A child object constructor always first needs to construct its parent (which in turn calls its parent constructor.). In Java it is done via an implicit call to the no-args constructor as the first statement. What is the difference between the Boolean & operator and the && operator? If an expression involving the Boolean & operator is evaluated, both operands are evaluated. Then the & operator is applied to the operand. When an expression involving the && operator is evaluated, the first operand is evaluated. If the first operand returns a value of true then the second operand is evaluated. The && operator is then applied to the first and second operands. If the first operand

evaluates to false, the evaluation of the second operand is skipped. Which Java operator is right associative? The = operator is right associative. Can a double value be cast to a byte? Yes, a double value can be cast to a byte. What is the difference between a break statement and a continue statement? A break statement results in the termination of the statement to which it applies (switch, for, do, or while). A continue statement is used to end the current loop iteration and return control to the loop statement. How are this() and super() used with constructors? a for statement loop indefinitely? Yes, a for statement can loop indefinitely. For example, consider the following: for(;;); To what value is a variable of the String type automatically initialized? The default value of an String type is null. What is the difference between a field variable and a local variable? A field variable is a variable that is declared as a member of a class. A local variable is a variable that is declared local to a method. this() is used to invoke a constructor of the same class. super() is used to invoke asuperclass constructor. What does it mean that a class or member is final? A final class cannot be inherited. A final method cannot be overridden in a subclass. A final field cannot be changed after it's initialized, and it must include an initializer statement where it's declared. What does it mean that a method or class is abstract? An abstract class cannot be instantiated. Abstract methods may only be included in abstract classes. However, an abstract class is not required to have any abstract methods, though most of them do. Each subclass of an abstract class must override the abstract methods of its superclasses or it also should be declared abstract. What is a transient variable? Transient variable is a variable that may not be serialized. How does Java handle integer overflows and underflows? It uses those low order bytes of the result that can fit into the size of the type

allowed by the operation. What is the difference between the >> and >>> operators? The >> operator carries the sign bit when shifting right. The >>> zero-fills bits that have been shifted out. Is sizeof a keyword? The sizeof operator is not a keyword.

UNIX INTERVIEW QUESTIONS UNIX INTERVIEW QUESTIONS ON AWK COMMAND Awk is powerful tool in Unix. Awk is an excellent tool for processing the files which have data arranged in rows and columns format. It is a good filter and report writer. 1. How to run awk command specified in a file? awk -f filename 2. Write a command to print the squares of numbers from 1 to 10 using awk command awk 'BEGIN { for(i=1;i0;i--) print $i," "; print "\n"}' filename 10. Write a command to find the total number of lines in a file without using NR

awk 'BEGIN {sum=0} {sum=sum+1} END {print sum}' filename Another way to print the number of lines is by using the NR. The command is awk 'END{print NR}' filename

UNIX INTERVIEW QUESTIONS ON GREP COMMAND The grep is one of the powerful tools in unix. Grep stands for "global search for regular expressions and print". The power of grep lies in using regular expressions mostly. The general syntax of grep command is grep [options] pattern [files] 1. Write a command to print the lines that has the the pattern "july" in all the files in a particular directory? grep july * This will print all the lines in all files that contain the word “july” along with the file name. If any of the files contain words like "JULY" or "July", the above command would not print those lines. 2. Write a command to print the lines that has the word "july" in all the files in a directory and also suppress the filename in the output. grep -h july * 3. Write a command to print the lines that has the word "july" while ignoring the case. grep -i july * The option i make the grep command to treat the pattern as case insensitive. 4. When you use a single file as input to the grep command to search for a pattern, it won't print the filename in the output. Now write a grep command to print the filename in the output without using the '-H' option. grep pattern filename /dev/null The /dev/null or null device is special file that discards the data written to it. So, the /dev/null is always an empty file. Another way to print the filename is using the '-H' option. The grep command for this is grep -H pattern filename 5. Write a Unix command to display the lines in a file that do not contain the word "july"? grep -v july filename The '-v' option tells the grep to print the lines that do not contain the specified pattern.

6. Write a command to print the file names in a directory that has the word "july"? grep -l july * The '-l' option make the grep command to print only the filename without printing the content of the file. As soon as the grep command finds the pattern in a file, it prints the pattern and stops searching other lines in the file. 7. Write a command to print the file names in a directory that does not contain the word "july"? grep -L july * The '-L' option makes the grep command to print the filenames that do not contain the specified pattern. 8. Write a command to print the line numbers along with the line that has the word "july"? grep -n july filename The '-n' option is used to print the line numbers in a file. The line numbers start from 1 9. Write a command to print the lines that starts with the word "start"? grep '^start' filename The '^' symbol specifies the grep command to search for the pattern at the start of the line. 10. Write a command to print the lines which end with the word "end"? grep 'end$' filename The '$' symbol specifies the grep command to search for the pattern at the end of the line. 11. Write a command to select only those lines containing "july" as a whole word? grep -w july filename The '-w' option makes the grep command to search for exact whole words. If the specified pattern is found in a string, then it is not considered as a whole word. For example: In the string "mikejulymak", the pattern "july" is found. However "july" is not a whole word in that string.

UNIX INTERVIEW QUESTIONS ON SED COMMAND SED is a special editor used for modifying files automatically. 1. Write a command to replace the word "bad" with "good" in file? sed s/bad/good/ < filename 2. Write a command to replace the word "bad" with "good" globally in a file? sed s/bad/good/g < filename 3. Write a command to replace the character '/' with ',' in a file? sed 's/\//,/' < filename sed 's|/|,|' < filename 4. Write a command to replace the word "apple" with "(apple)" in a file?

sed s/apple/(&)/ < filename 5. Write a command to switch the two consecutive words "apple" and "mango" in a file? sed 's/\(apple\) \(mango\)/\2 \1/' < filename 6. Write a command to replace the second occurrence of the word "bat" with "ball" in a file? sed 's/bat/ball/2' < filename 7. Write a command to remove all the occurrences of the word "jhon" except the first one in a line with in the entire file? sed 's/jhon//2g' < filename 8. Write a command to remove the first number on line 5 in file? sed '5 s/[0-9][0-9]*//' < filename 9. Write a command to remove the first number on all lines that start with "@"? sed '\,^@, s/[0-9][0-9]*//' < filename 10. Write a command to replace the word "gum" with "drum" in the first 100 lines of a file? sed '1,00 s/gum/drum/' < filename 11. write a command to replace the word "lite" with "light" from 100th line to last line in a file? sed '100,$ s/lite/light/' < filename 12. Write a command to remove the first 10 lines from a file? sed '1,10 d' < filename 13. Write a command to duplicate each line in a file? sed 'p' < filename 14. Write a command to duplicate empty lines in a file? sed '/^$/ p' < filename 15. Write a sed command to print the lines that do not contain the word "run"? sed -n '/run/!p' < filename

UNIX INTERVIEW QUESTIONS ON CUT COMMAND The cut command is used to used to display selected columns or fields from each line of a file. Cut command works in two modes: 

Delimited selection: The fields in the line are delimited by a single character like blank,comma etc.  Range selection: Each field starts with certain fixed offset defined as range.

1. Write a command to display the third and fourth character from each line of a file? cut -c 3,4 filename

2. Write a command to display the characters from 10 to 20 from each line of a file? cut -c 10-20 filename

3. Write a command to display the first 10 characters from each line of a file? cut -c -10 filename

4. Write a comamnd to display from the 10th character to the end of the line? cut -c 10- filename

5. The fields in each line are delimited by comma. Write a command to display third field from each line of a file? cut -d',' -f2 filename

6. Write a command to print the fields from 10 to 20 from each line of a file? cut -d',' -f10-20 filename

7. Write a command to print the first 5 fields from each line? cut -d',' -f-5 filename

8. Write a command to print the fields from 10th to the end of the line? cut -d',' -f10- filename

9. By default the cut command displays the entire line if there is no delimiter in it. Which cut option is used to supress these kind of lines? The -s option is used to supress the lines that do not contain the delimiter.

10. Write a cut command to extract the username from 'who am i' comamnd? who am i | cut -f1 -d' '

UNIX INTERVIEW QUESTIONS ON FIND COMMAND Find utility is used for searching files using the directory information. 1. Write a command to search for the file 'test' in the current directory? find -name test -type f 2. Write a command to search for the file 'temp' in '/usr' directory? find /usr -name temp -type f 3. Write a command to search for zero byte size files in the current directory? find -size 0 -type f 4. Write a command to list the files that are accessed 5 days ago in the current directory? find -atime 5 -type f 5. Write a command to list the files that were modified 5 days ago in the current directory? find -mtime 5 -type f 6. Write a command to search for the files in the current directory which are not owned by any user in the /etc/passwd file? find . -nouser -type f 7. Write a command to search for the files in '/usr' directory that start with 'te'? find /usr -name 'te*' -type f 8. Write a command to search for the files that start with 'te' in the current directory and then display the contents of the file? find . -name 'te*' -type f -exec cat {} \; 9. Write a command to list the files whose status is changed 5 days ago in the current directory? find -ctime 5 -type f 10. Write a command to list the files in '/usr' directory that start with 'ch' and then display the number of lines in each file? find /usr -name 'ch*' -type f -exec wc -l {} \;

TOP UNIX INTERVIEW QUESTIONS - PART 1 1. How to display the 10th line of a file?

head -10 filename | tail -1

2. How to remove the header from a file?

sed -i '1 d' filename

3. How to remove the footer from a file?

sed -i '$ d' filename

4. Write a command to find the length of a line in a file? The below command can be used to get a line from a file.

sed –n ' p' filename

We will see how to find the length of 10th line in a file

sed -n '10 p' filename|wc -c

5. How to get the nth word of a line in Unix?

cut –f -d' '

6. How to reverse a string in unix?

echo "java" | rev

7. How to get the last word from a line in Unix file?

echo "unix is good" | rev | cut -f1 -d' ' | rev

8. How to replace the n-th line in a file with a new line in Unix?

sed -i'' '10 d' filename

# d stands for delete

sed -i'' '10 i new inserted line' filename

# i stands for insert

9. How to check if the last command was successful in Unix?

echo $?

10. Write command to list all the links from a directory?

ls -lrt | grep "^l"

11. How will you find which operating system your system is running on in UNIX?

uname -a

12. Create a read-only file in your home directory?

touch file; chmod 400 file

13. How do you see command line history in UNIX?

The 'history' command can be used to get the list of commands that we are executed. 14. How to display the first 20 lines of a file? By default, the head command displays the first 10 lines from a file. If we change the option of head, then we can display as many lines as we want.

head -20 filename

An alternative solution is using the sed command

sed '21,$ d' filename

The d option here deletes the lines from 21 to the end of the file 15. Write a command to print the last line of a file? The tail command can be used to display the last lines from a file.

tail -1 filename

Alternative solutions are:

sed -n '$ p' filename awk 'END{print $0}' filename

TOP UNIX INTERVIEW QUESTIONS - PART 2

1. How do you rename the files in a directory with _new as suffix?

ls -lrt|grep '^-'| awk '{print "mv "$9" "$9".new"}' | sh

2. Write a command to convert a string from lower case to upper case?

echo "apple" | tr [a-z] [A-Z]

3. Write a command to convert a string to Initcap.

echo apple | awk '{print toupper(substr($1,1,1)) tolower(substr($1,2))}'

4. Write a command to redirect the output of date command to multiple files? The tee command writes the output to multiple files and also displays the output on the terminal.

date | tee -a file1 file2 file3

5. How do you list the hidden files in current directory?

ls -a | grep '^\.'

6. List out some of the Hot Keys available in bash shell?      

Ctrl+l - Clears the Screen. Ctrl+r - Does a search in previously given commands in shell. Ctrl+u - Clears the typing before the hotkey. Ctrl+a - Places cursor at the beginning of the command at shell. Ctrl+e - Places cursor at the end of the command at shell. Ctrl+d - Kills the shell.



Ctrl+z - Places the currently running process into background.

7. How do you make an existing file empty?

cat /dev/null >

filename

8. How do you remove the first number on 10th line in file?

sed '10 s/[0-9][0-9]*//' < filename

9. What is the difference between join -v and join -a?

join -v : outputs only matched lines between two files. join -a : In addition to the matched lines, this will output unmatched lines also.

10. How do you display from the 5th character to the end of the line from a file?

cut -c 5- filename

TOP UNIX INTERVIEW QUESTIONS - PART 3 1. Display all the files in current directory sorted by size?

ls -l | grep '^-' | awk '{print $5,$9}' |sort -n|awk '{print $2}'

2. Write a command to search for the file 'map' in the current directory?

find -name map -type f

3. How to display the first 10 characters from each line of a file?

cut -c -10 filename

4. Write a command to remove the first number on all lines that start with "@"?

sed '\,^@, s/[0-9][0-9]*//' < filename

5. How to print the file names in a directory that has the word "term"?

grep -l term *

The '-l' option make the grep command to print only the filename without printing the content of the file. As soon as the grep command finds the pattern in a file, it prints the pattern and stops searching other lines in the file. 6. How to run awk command specified in a file?

awk -f filename

7. How do you display the calendar for the month march in the year 1985? The cal command can be used to display the current month calendar. You can pass the month and year as arguments to display the required year, month combination calendar.

cal 03 1985

This will display the calendar for the March month and year 1985. 8. Write a command to find the total number of lines in a file?

wc -l filename

Other ways to print the total number of lines are

awk 'BEGIN {sum=0} {sum=sum+1} END {print sum}' filename awk 'END{print NR}' filename

9. How to duplicate empty lines in a file?

sed '/^$/ p' < filename

10. Explain iostat, vmstat and netstat?   

Iostat: reports on terminal, disk and tape I/O activity. Vmstat: reports on virtual memory statistics for processes, disk, tape and CPU activity. Netstat: reports on the contents of network data structures.

TOP UNIX INTERVIEW QUESTIONS - PART 4 1. How do you write the contents of 3 files into a single file?

cat file1 file2 file3 > file

2. How to display the fields in a text file in reverse order?

awk 'BEGIN {ORS=""} { for(i=NF;i>0;i--) print $i," "; print "\n"}' filename

3. Write a command to find the sum of bytes (size of file) of all files in a directory.

ls -l | grep '^-'| awk 'BEGIN {sum=0} {sum = sum + $5} END {print sum}'

4. Write a command to print the lines which end with the word "end"?

grep 'end$' filename

The '$' symbol specifies the grep command to search for the pattern at the end of the line. 5. Write a command to select only those lines containing "july" as a whole word?

grep -w july filename

The '-w' option makes the grep command to search for exact whole words. If the specified pattern is found in a string, then it is not considered as a whole word. For example: In the string "mikejulymak", the pattern "july" is found. However "july" is not a whole word in that string. 6. How to remove the first 10 lines from a file?

sed '1,10 d' < filename

7. Write a command to duplicate each line in a file?

sed 'p' < filename

8. How to extract the username from 'who am i' comamnd?

who am i | cut -f1 -d' '

9. Write a command to list the files in '/usr' directory that start with 'ch' and then display the number of lines in each file?

wc -l /usr/ch*

Another way is

find /usr -name 'ch*' -type f -exec wc -l {} \;

10. How to remove blank lines in a file ?

grep -v ‘^$’ filename > new_filename

TOP UNIX INTERVIEW QUESTIONS - PART 5 1. How to display the processes that were run by your user name ?

ps -aef | grep

2. Write a command to display all the files recursively with path under current directory?

find . -depth -print

3. Display zero byte size files in the current directory?

find -size 0 -type f

4. Write a command to display the third and fifth character from each line of a file?

cut -c 3,5 filename

5. Write a command to print the fields from 10th to the end of the line. The fields in the line are delimited by a comma?

cut -d',' -f10- filename

6. How to replace the word "Gun" with "Pen" in the first 100 lines of a file?

sed '1,00 s/Gun/Pen/' < filename

7. Write a Unix command to display the lines in a file that do not contain the word "RAM"?

grep -v RAM filename

The '-v' option tells the grep to print the lines that do not contain the specified pattern. 8. How to print the squares of numbers from 1 to 10 using awk command

awk 'BEGIN { for(i=1;i