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).

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.

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.

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.

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.

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.

Is map like a pointer?

No, Map is not like a pointer.

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 ‘Key value pair’ in HDFS?

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

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 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.

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 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 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.

Do we need to place 2nd and 3rd data in rack 2 only?

Yes, this is to avoid datanode failure.

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“.

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.

What is the communication channel between client and namenode/datanode?

The mode of communication is SSH.

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).

Who is a ‘user’ in HDFS?

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

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.

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.

Does hadoop always require digital data to process?

Yes.  Hadoop always require digital data to be processed.

When we send a data to a node, do we allow settling in time, before sending another data to that node?

Yes, we do.

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.

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.

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.

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 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.

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.

What is a ‘block’ in HDFS?

A ‘block’ is the minimum amount of data that can be read or written. In HDFS, the default block size is 64 MB as contrast to the block size of 8192 bytes in Unix/Linux. Files in HDFS are broken down into block-sized chunks, which are stored as independent units. HDFS blocks are large as compared to disk blocks, particularly to minimize the cost of seeks. If a particular file is 50 mb, will the HDFS block still consume 64 mb as the default size? No, not at all! 64 mb is just a unit where the data will be stored. In this particular situation, only 50 mb will be consumed by an HDFS block and 14 mb will be free to store something else. It is the MasterNode that does data allocation in an efficient manner.

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 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.

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 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.

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 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”.

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 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.

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.

Is Namenode also a commodity?

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

What is a 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 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 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.

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.

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.

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.

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 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 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 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.

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.

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.

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.

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. The link Why Hadoop gives you a detailed explanation about why Hadoop is gaining so much popularity!

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!

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.

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.

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.

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.

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.

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 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 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.

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.

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.

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.

How analysis of Big Data is useful for organizations?

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

How Big is ‘Big Data’?

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

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.

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.

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 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.

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!

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!

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.

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.

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.]

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.

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.

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.

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.

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 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.

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.

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 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.

Windows Server Security Recommendations

The following recommendations are meant as a guide to secure servers (a server being either a physical or virtual instance of an autonomous software system intended to connect with and provide services to other computers). Each and every recommendation will not be applicable to every server; therefore the system administrator should exercise their own judgment in conjunction with their department's own requirements and business needs. Deviations from the recommended guidelines should be documented according to each department's own procedures. The end goal is a secure server that meets the functional and business needs of each department.

Note that if a department is required to comply with PCI (Payment Card Industry) regulations, the specific recommendation has been labeled with "PCI/DSS" so that it may be employed. These are requirements for PCI certification, and therefore not recommendations, if you are subject to the PCI requirements. Also, the sections "Installation" and "Configuration" refer to those recommendations aimed at system administrators. The "Hosting" section is specific to data centers or those hosting a server and "Ongoing" is meant to apply to those individuals/departments maintaining servers.

Specific sections for the most common operating systems at Northwestern have been included (Windows, RedHat Linux, OS X and Solaris). Other operating systems (ie Debian, OpenBSD, etc) are addressed by the more general recommendations that would apply to the respective operating system regardless and further augmented by the hardening guidelines from CIS (Center for Internet Security).

Audience:

Department and group information technology support and information technology security staff.

Policy Statement:

Windows Server Security Recommendations

• Installation
• Configuration
• Networking
• Hosting
• Ongoing

Installation

Number	Recommendation/Description
1	Disable system restore (if applicable to the version of Windows)
2	Systems (servers) with a NetID password feed may not be used for multiple purposes. Exceptions require approval of NUIT-ISS/C.
3	(PCI/DSS) Implement only one primary function per server (for example, web servers, database servers, and DNS should be implemented on separate servers)*

Configuration

Number	Recommendation/Description
1	Remove, disable or change password of default accounts
2	Guest accounts disabled
3	All local and domain accounts with privileges above normal user level should have a minimum 15 character passphrase and must be changed at least once every quarter. To facilitate remembering such a password, wallet-sized cards may be created and carried by system administrators for reference.
4	Audit the use of all privileged accounts. This auditing should include the read and write access performed by these accounts.
5	Machines may not be connected to the network until they have had the latest OS and application updates applied, anti-viral software installed and activated, firewall enabled, AND a strong passphrase enabled on all accounts.
6	OS that is not older than one minor release, or service pack, from the current release, if business needs allow for it.
7	Software and OS patches installed as soon as practical for your environment.
8	(PCI/DSS) Ensure that all system components and software have the latest vendor-supplied security patches installed. Install relevant security patches within one month of release.
9	(PCI/DSS) Deploy anti-virus software on all systems commonly affected by viruses, ensure that anti-virus programs are capable of detecting, removing, and protecting against other forms of malicious software, including spyware and adware.
10	(PCI/DSS) Ensure that all anti-virus mechanisms are current, actively running, and capable of generating audit logs.
11	Hosts should either automatically disable local accounts or attacking hosts for a period of not less than two minutes after 15 authentication failures in a rolling five minute window.
12	Unused services should be disabled
13	Remove LM Hash
14	Clock must be automatically synchronized to a recognized time server (time.northwestern.edu).
15	Departments must limit information system access to authorized users, processes acting on behalf of authorized users, or devices (including other information systems) and to the types of transactions and functions that authorized users are permitted to exercise.
16	Departments must establish, maintain, and effectively implement plans for emergency response, backup operations, and post-disaster recovery for organizational information systems to ensure the availability of critical information resources and continuity of operations in emergency situations.
17	Departments must: (i) ensure that individuals occupying positions of responsibility within organizations (including third-party service providers) are trustworthy and meet established security criteria for those positions; (ii) ensure that organizational information and information systems are protected during and after personnel actions such as terminations and transfers; and (iii) employ formal sanctions for personnel failing to comply with organizational security policies and procedures.

Networking

Number	Recommendation/Description
1	Appliance based firewall required. If a host based firewall option is available, consider using it in addition to the appliance.
2	(PCI/DSS) Build a firewall configuration that restricts connections between publicly accessible servers and any system component storing cardholder data, including any connections from wireless networks.
3	(PCI/DSS) Prohibit direct public access between external networks and any system component that stores cardholder data (for example, databases, logs, trace files).
4	No open, non-authenticated, file sharing may be enabled.
5	(PCI/DSS) Encrypt all non-console administrative access. Use technologies such as SSH, VPN, or SSL/TLS (transport layer security) for web-based management and other non-console administrative access.
6	Remote access software must be disabled or restricted to specific IP addresses by default. It can be temporarily enabled on a case by case basis by authorized personnel. Only software that supports end to end encryption should be used for this purpose.

Hosting

Number	Recommendation/Description
1	Port Reporter or similar system installed and active.
2	Encrypted backups should be taken regularly, and all on/off site storage should be physically secure.
3	(PCI/DSS) – Clocks must be synchronized to two (2) internally hosted time servers (time.northwestern.edu) *
4	Housed at University data center or similar setup.

Ongoing

Number	Recommendation/Description
1	Mandatory audit log monitoring program or procedure by personnel of the department owning the logs or an approved subcontractor/vendor.
2	(PCI/DSS) Logs must be reviewed, or aggregated and then reviewed, daily.
3	(PCI/DSS) Logs must be available online (electronically) for three months, available on tape (or other removable media) for one year.
4	(PCI/DSS) Ensure that all system components and software have the latest vendor-supplied security patches installed. Install relevant security patches within one month of release.
5	(PCI/DSS) Establish a process to identify newly discovered security vulnerabilities (for example, subscribe to alert services freely available on the Internet). Update standards to address new vulnerability issues.
6	Encrypt sensitive data (Recommendations currently in development).
7	Defined process for approval, acceptable use, and removal of system privileges.
8	(PCI/DSS) Follow change control procedures for all system and software configuration changes.
9	(PCI/DSS) Identify all users with a unique user name with at least one authentication method (passphrase, token device and/or biometrics).
10	(PCI/DSS) Immediately revoke access for any terminated users.
11	Remove inactive user accounts at least every 90 days.
12	(PCI/DSS) Set first-time passwords to a unique value for each user and change immediately after the first use