Hadoop Interview Questions – MapReduce
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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 hexa-decimal 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.
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.