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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.

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.

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.

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.

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”.

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”.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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.

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.

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.

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.

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.

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!

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!

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.

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.

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!

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!

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.

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.

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.

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.

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.

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.

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!

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!

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.

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.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

• org.apache.hadoop.mapred.lib.IdentityMapper: Implements the identity FUNCTION, mapping inputs directly to outputs. If MAPREDUCE PROGRAMMER does not set the Mapper Class using JobConf.setMapperClass then IdentityMapper.class is USED as a default value.
• org.apache.hadoop.mapred.lib.IdentityReducer : Performs no reduction, writing all input values directly to the output. If MapReduce programmer does not set the Reducer Class using JobConf.setReducerClass then IdentityReducer.class is used as a default value.

• The KEY MUST IMPLEMENT the org.apache.hadoop.io.WritableComparable INTERFACE.
• The VALUE must implement the org.apache.hadoop.io.Writable interface.

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

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

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

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

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

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

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.

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.

No, REDUCER RUNS in ISOLATION.

No, Reducer runs in isolation.

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

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

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

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

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

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

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

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

Following are differences between HDFS and NAS

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

Following are differences between HDFS and NAS

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

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

ONE or Multiple instances of Task Instance can RUN on each SLAVE node. Each task instance is run as a SEPARATE JVM process. The number of Task instances can be controlled by configuration. Typically a high END machine is configured to run more task instances.

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

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

Following 3 Daemons run on Master nodes.

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

Secondary NameNode : Performs housekeeping functions for the NameNode.

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

DataNode : Stores actual HDFS DATA blocks.

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

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

Following 3 Daemons run on Master nodes.

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

Secondary NameNode : Performs housekeeping functions for the NameNode.

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

DataNode : Stores actual HDFS data blocks.

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

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

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

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

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

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

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

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

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

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

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

Hadoop CONSISTS of two main parts.

• Hadoop distributed file system, a distributed file system with high throughput,
• Hadoop MAPREDUCE, a SOFTWARE framework for PROCESSING large data SETS.

Hadoop consists of two main parts.

There is one DAEMON TASKTRACKER process for each slave node in the HADOOP cluster.

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

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

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