Top 50 Hadoop Mapreduce Interview Questions You Must Prepare 14.Jun.2024

  • Map method is called separately for each key/value have been processed. It process input key/value pairs and emits intermediate key/value pairs.
  • Reduce method is called separately for each key/values list pair. It process intermediate key/value pairs and emits final key/value pairs.
  • Both are initialize and called before any other method is called. Both don’t have any parameters and no output.

Hadoop can process key level comparison only but not in the value level comparison. 

The MapReduce programmers need to specify following configuration parameters to perform the map and reduce jobs:

  • The input location of the job in HDFs.
  • The output location of the job in HDFS.
  • The input’s and output’s format.
  • The classes containing map and reduce functions, respectively.
  • The .jar file for mapper, reducer and driver classes

  • Main Driver Class: providing job configuration parameters
  • Mapper Class: must extend org.apache.hadoop.mapreduce.Mapper class and performs execution of map() method
  • Reducer Class: must extend org.apache.hadoop.mapreduce.Reducer class

It’s a logical aggregation of key and value pair produced by mapper. It’s reduces a lot amount of duplicated data trfer between nodes, so eventually optimize the job performance. The framework decides whether combiner runs zero or multiple times. It’s not suitable where mean function occurs.

Compute nodes are logical processing units, Storage nodes are physical storage units (Nodes). Both are running in the same node because of “data locality” issue. As a result Hadoop minimize the data network wastage and allows to process quickly.

If you don’t no what is starting and ending point/lines, it’s much difficult to solve those problems. Setup and clean up can resolve it. N number of blocks, by default 1 mapper called to each split. each split has one start and clean up methods. N number of methods, number of lines. Setup is initialize job resources.

The purpose of clean up is close the job resources. Map is process the data. once last map is completed, cleanup is initialized. It Improves the data trfer performance. All these block size comparison can do in reducer as well. If you have any key and value, compare one key value to another key value use it. If you compare record level used these setup and cleanup. It open once and process many times and close once. So it save a lot of network wastage during process. 

Mappers and reducers are independent they dont talk each other. When the functions that are commutative(a.b = b.a) and associative {a.(b.c) = (a.b).c} we goes to combiner to optimize the mapreduce process. Many mapreduce jobs are limited by the bandwidth, so by default Hadoop framework minimizes the data bandwidth network wastage. To achieve it’s goal, Mapreduce allows user defined “Cominer function” to run on the map output. It’s an MapReduce optimization technique, but it’s optional.

  • Block: How much chunk data to stored in the memory called block. 
  • Split: how much data to process the data called split. 

Data locality. Move competition is cheaper than moving Data. So logic/ competition in Jar file and splits. So Where the data available, in File System Datanodes. So every resources copy where the data available.

First Hadoop programmer submit Mpareduce program to JobClient.

Job Client request the JobTracker to get Job id, Job tracker provide JobID, its’s in the form of Job_HadoopStartedtime_000@It’s unique ID.

Once JobClient receive received Job ID copy the Job resources (job.xml, job.jar) to File System (HDFS) and submit job to JobTracker. JobTracker initiate Job and schedule the job.

Based on configuration, job split the input splits and submit to HDFS. TaskTracker retrive the job resources from HDFS and launch Child JVM. In this Child JVM, run the map and reduce tasks and notify to the Job tracker the job status.

Compute nodes for processing the data, Storage nodes for storing the data. By default Hadoop framework tries to minimize the network wastage, to achieve that goal Framework follows the Data locality concept. The Compute code execute where the data is stored, so the data node and compute node are the same. 

RecordReader is used to read key/value pairs form the InputSplit by converting the byte-oriented view  and presenting record-oriented view to Mapper.

A compressed binary output file format to read in sequence files and extends the FileInputFormat.It passes data between output-input (between output of one MapReduce job to input of another MapReduce job)phases of MapReduce jobs.

JobTracker is a Hadoop service used for the processing of MapReduce jobs  in the cluster. It submits and tracks the jobs to specific nodes having data. Only one JobTracker runs on single Hadoop cluster on its own JVM process. if JobTracker goes down, all the jobs halt.

It is a primary interface to define a map-reduce job in the Hadoop for job execution. JobConf specifies mapper, Combiner, partitioner, Reducer,InputFormat , OutputFormat implementations and other advanced job faets liek Comparators.

Users can configure JobConf variable to set number of mappers and reducers.

  • job.setNumMaptasks()
  • job.setNumreduceTasks()

Partitioner is yet another important phase that controls the partitioning of the intermediate map-reduce output keys using a hash function. The process of partitioning determines in what reducer, a key-value pair (of the map output) is sent. The number of partitions is equal to the total number of reduce jobs for the process.

Hash Partitioner is the default class available in Hadoop , which implements the following function.int getPartition(K key, V value, int numReduceTasks)

The function returns the partition number using the numReduceTasks is the number of fixed reducers.

Even you have changed block size not effect existent data. After changed the block size, every file chunked after 128 MB of block size. It me old data is in 64 MB chunks, but new data stored in 128 MB blocks.

job.xml and job.jar are core resources to process the Job. Job Client copy the resources to the HDFS. 

By default each task has 2 slots for mapper and 2 slots for reducer. So each node has 4 slots to process the data. 

By default Hive reads entire dataset even the application have a slice of data. It’s a bottleneck for mapreduce jobs. So Hive allows special option called partitions. When you are creating table, hive partitioning the table based on requirement. 

A chunk of data is stored in different nodes with in a cluster called replication factor. By default replication value is 3, but it’s possible to change it. Automatically each file is split into blocks and spread across the cluster. 

To retrieve data faster, Hadoop reads data parallel, the main reason it can access data faster. While, writes in sequence, but not parallel, the main reason it might result one node can be overwritten by other and where the second node. Parallel processing is independent, so there is no relation between two nodes, if writes data in parallel, it’s not possible where the next chunk of data has. For example 100 MB data write parallel, 64 MB one block another block 36, if data writes parallel first block doesn’t know where the remaining data. So Hadoop reads parallel and write sequentially.

Based on the configuration, the MapReduce Job first splits the input data into independent chunks called Blocks. These blocks processed by Map() and Reduce() functions. First Map function process the data, then processed by reduce function. The Framework takes care of sorts the Map outputs, scheduling the tasks. 

OutPutCommitter describes the commit of MapReduce task. FileOutputCommitter is the default available class available for OutputCommitter in MapReduce. It performs the following operations:

  • Create temporary output directory for the job during initialization.
  • Then, it cle the job as in removes temporary output directory post job completion.
  • Sets up the task temporary output.
  • Identifies whether a task needs commit. The commit is applied if required.
  • JobSetup, JobCleanup and TaskCleanup are important tasks during output commit.

Identity Mapper is the default Mapper class provided by Hadoop. when no other Mapper class is defined, Identify will be executed. It only writes the input data into output and do not perform and computations and calculations on the input data. The class name is org.apache.hadoop.mapred.lib.IdentityMapper.

A chunk of data processed by a single mapper called InputSplit. In another words logical chunk of data which processed by a single mapper called Input split, by default inputSplit = block Size.

Chain mapper class is a special mapper class sets which run in a chain fashion within a single map task. It me, one mapper input acts as another mapper’s input, in this way n number of mapper connected in chain fashion.

Number of reducer = 0 also valid configuration in MapReduce. In this scenario, No reducer will execute, so mapper output consider as output, Hadoop store this information in separate folder.

To combine multiple mapper’s output used reducer. Reducer has 3 primary phases sort, shuffle and reduce. It’s possible to process data without reducer, but used when the shuffle and sort is required. 

The four parameters for mappers are:

  • LongWritable (input)
  • text (input)
  • text (intermediate output)
  • IntWritable (intermediate output)

The four parameters for reducers are:

  • Text (intermediate output)
  • IntWritable (intermediate output)
  • Text (final output)
  • IntWritable (final output)

It’s used to set/get of parameter name & value pairs in XML file.It’s used to initialize values, read from external file and set as a value parameter.Parameter values in the program always overwrite with new values which are coming from external configure files.Parameter values received from Hadoop’s default values.

Sometime child threads currupt parent threads. It me because of programmer mistake entired MapReduce task distruped. So task tracker launch a child JVM to process individual mapper or tasker. If tasktracker use existent JVM, it might damage main JVM. If any bugs occur, tasktracker kill the child process and relaunch another child JVM to do the same task. Usually task tracker relaunch and retry the task 4 times.

By default this value is true. It is used to split the data in the input format. if un­structured data, it’s not recommendable to split the data, so process entire file as a one split. to do it first change isSplitable() to false.

Whereever the data is there process the data, computation/process the data where the data available, this process called data locality. “Moving Computation is Cheaper than Moving Data” to achieve this goal follow data locality. It’s possible when the data is splittable, by default it’s true. 

Most frequently, hadoop developer mistakenly submit wrong jobs or having bugs. If Task Tracker use existent JVM, it may interrupt the main JVM, so other tasks may influenced. Where as child JVM if it’s trying to damage existent resources, TaskTracker kill that child JVM and retry or relaunch new child JVM. 

  • Minimum: 512 bytes. It’s local OS file system block size. No one can decrease fewer than block size.
  • Maximum: Depends on environment. There is no upper­bound. 

JobTracker communicates with NameNode to identify data location and submits the work to TaskTracker node. The TaskTracker plays a major role as it notifies the JobTracker for any job failure. It actually is referred to the heartbeat reporter reassuring the JobTracker that it is still alive. Later, the JobTracker is responsible for the actions as in it may either resubmit the job or mark a specific record as unreliable or blacklist it.

After Mapper generate the output temporary store the intermediate data on the local File System. Usually this temporary file configured at core­site.xml in the Hadoop file. Hadoop Framework aggregate and sort this intermediate data, then update into Hadoop to be processed by the Reduce function. The Framework deletes this temporary data in the local system after Hadoop completes the job.

Both Combiner and Reducer are optional, but most frequently used in MapReduce. There are three main differences such as:

  • combiner will get only one input from one Mapper. While Reducer will get multiple mappers from different mappers.
  • If aggregation required used reducer, but if the function follows commutative (a.b=b.a) and associative a.(b.c)= (a.b).c law, use combiner.
  • Input and output keys and values types must same in combiner, but reducer can follows any type input, any output format. 

No, By default Hadoop implemented in JavaTM, but MapReduce applications need not be written in Java. Hadoop support Python, Ruby, C++ and other Programming languages. Hadoop Streaming API allows to create and run Map/Reduce jobs with any executable or script as the mapper and/or the reducer.Hadoop Pipes allows programmers to implement MapReduce applications by using C++ programs.

MapReduce is a set of programs used to process or analyze vast of data over a Hadoop cluster. It process the vast amount of the datasets parallelly across the clusters in a fault­tolerant manner across the Hadoop framework.

Mapreduce is not recommended for Iterative kind of processing. It me repeat the output in a loop manner.To process Series of Mapreduce jobs, MapReduce not suitable. each job persists data in local disk, then again load to another job. It’s costly operation and not recommended.

Leg assume. 64 block size, system take 2 mappers, 2 reducers, so 64*4 = 256 MB memory and OS take atleast 30% extra space so atleast 256 + 80 = 326MB Ram required to process a chunk of data.So in this way required more memory to process un­structured process. 

Join multple tables in mapper side, called map side join. Please note mapside join should has strict format and sorted properly. If dataset is smaller tables, goes through reducer phrase. Data should partitioned properly.

Join the multiple tables in reducer side called reduce side join. If you have large amount of data tables, planning to join both tables. One table is large amount of rows and columns, another one has few number of tables only, goes through Rreduce side join. It’s the best way to join the multiple tables

When sort and shuffle is required then only goes to reducers otherwise no need partition. If filter, no need to sort and shuffle. So without reducer its possible to do this operation.

Scheduling is a systematic procedure of allocating resources in the best possible way among multiple tasks. Hadoop task tracker performing many procedures, sometime a particular procedure should finish quickly and provide more prioriety, to do it few job schedulers come into the picture. Default Schedule is FIFO. Fair scheduling, FIFO and CapacityScheduler are most popular hadoop scheduling in hadoop. 

MapReduce Framework consists of a single Job Tracker per Cluster, one Task Tracker per node. Usually A cluster has multiple nodes, so each cluster has single Job Tracker and multiple TaskTrackers.JobTracker can schedule the job and monitor the Task Trackers. If Task Tracker failed to execute tasks, try to re-execute the failed tasks.

TaskTracker follow the JobTracker’s instructions and execute the tasks. As a slave node, it report the job status to Master JobTracker in the form of Heartbeat. 

By default Hadoop can run 2 mappers and 2 reducers in one datanode. also each node has 2 map slots and 2 reducer slots. It’s possible to change this default values in Mapreduce.xml in conf file.