1. What is BIG DATA?
Ans). 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.
2. Can you give some examples of Big Data?
Ans). 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 sensor data for every 30 minutes of flying time. All these are a day to day examples of Big Data!
3. Can you give a detailed overview of the Big Data being generated by Facebook?
Ans). Every day a large amount of unstructured data is getting dumped into our machines. The major challenge is not to store large datasets in our systems but to retrieve and analyze the big data in the organizations, that to 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! 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.
10. Give examples of some companies that are using Hadoop structure?
Ans). A lot of companies are using the Hadoop structure such as Cloudera, EMC, MapR, Hortonworks, Amazon, Facebook, eBay, Twitter, Google and so on.
11. What is the basic difference between traditional RDBMS and Hadoop?
Ans). 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.
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. 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 datasets. HDFS is a file system designed for storing very large files with streaming data access patterns, running clusters on commodity hardware. 12. What are the key features of HDFS?
Ans). 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.
13. What is Fault Tolerance?
Ans). 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.
14. Replication causes data redundancy then why is pursued in HDFS?
Ans). 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 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.
15. 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?
Ans). 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.
16. 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 the 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.
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.
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 supercomputers 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.
17. What is a Namenode?
Ans). Namenode is the master node on which job tracker runs and consists of the metadata. It maintains and manages the blocks which are present on the data nodes. It is a high-availability machine and single point of failure in HDFS.
18. Is Namenode also a commodity?
Ans). 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.
19. What is a metadata?
Ans). Metadata is the information about the data stored in data nodes such as the location of the file, size of the file and so on.
20. Why do we use HDFS for applications having large data sets and not when there are a lot of small files?
Ans). HDFS is more suitable for a large number 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 an 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.
21. What is a daemon?
Ans). Daemon is a process or service that runs in the background. In general, we use this word in the UNIX environment. The equivalent of Daemon in Windows is “services” and in Dos is ” TSR”.
22. What is a job tracker?
Ans). Job tracker is a daemon that runs on a name node 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 a heartbeat from task tracker based on which Job tracker decides whether the assigned task is completed or not.
Task tracker is also a daemon that runs on data nodes. Task Trackers manage the execution of individual tasks on the 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 the heartbeat. If the job tracker does not receive the heartbeat from task tracker within the specified time, then it will assume that task tracker has crashed and assigned that task to another task tracker in the cluster.
23. Is Namenode machine same as data node machine as in terms of hardware?
Ans). 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 data nodes are on different machines.
24. What is a heartbeat in HDFS?
Ans). A heartbeat is a signal indicating that it is alive. A data node sends heartbeat to Namenode and task tracker will send its heartbeat to job tracker. If the Namenode or job tracker does not receive heartbeat then they will decide that there is some problem in data node or task tracker is unable to perform the assigned task. No, in a practical environment, Namenode is on a separate host and job tracker is on a separate host.
25. What is a ‘block’ in HDFS?
Ans). A ‘block’ is the minimum amount of data that can be read or written. In HDFS, the default block size is 64 MB as a 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.
26. If a particular file is 50 MB, will the HDFS block still consume 64 MB as the default size?
Ans). 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 master node that does data allocation in an efficient manner.
27. What are the benefits of block transfer?
Ans). 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 ensure 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.
28. 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?
Ans). 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 blocks are being used, how much space is available, and it will allocate the blocks accordingly.
29. How indexing is done in HDFS?
Ans). 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.
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