1. What is Apache Hadoop?
Hadoop is an open source software framework for distributed storage and distributed processing of large data sets. Open source means it is freely available and even we can change its source code as per our requirements. Apache Hadoop makes it possible to run applications on the system with thousands of commodity hardware nodes. It’s distributed file system has the provision of rapid data transfer rates among nodes. It also allows the system to continue operating in case of node failure.
2. Main Components of Hadoop?
Storage layer – HDFS
Batch processing engine – MapReduce
Resource Management Layer – YARN
HDFS – HDFS (Hadoop Distributed File System) is the storage unit of Hadoop. It is responsible for storing different kinds of data as blocks in a distributed environment. It follows master and slave topology
Components of HDFS are NameNode and DataNode
MapReduce – For processing large data sets in parallel across a hadoop cluster, Hadoop MapReduce framework is used. Data analysis uses a two-step map and reduce process.
YARN – YARN (Yet Another Resource Negotiator) is the processing framework in Hadoop, which manages resources and provides an execution environment to the processes.
Main Components of YARN are Node Manager and Resource Manager
3. Why do we need Hadoop?
Storage – Since data is very large, so storing such huge amount of data is very difficult.
Security – Since the data is huge in size, keeping it secure is another challenge.
Analytics – In Big Data, most of the time we are unaware of the kind of data we are dealing with. So analyzing that data is even more difficult.
Data Quality – In the case of Big Data, data is very messy, inconsistent and incomplete.
Discovery – Using a powerful algorithm to find patterns and insights are very difficult.
4. What are the four characteristics of Big Data?
Volume: The volume represents the amount of data which is growing at an exponential rate i.e. in Petabytes and Exabytes.
Velocity: Velocity refers to the rate at which data is growing, which is very fast. Today, yesterday’s data are considered as old data. Nowadays, social media is a major contributor in the velocity of growing data.
Variety: Variety refers to the heterogeneity of data types. In another word, the data which are gathered has a variety of formats like videos, audios, csv, etc. So, these various formats represent the variety of data.
Value: It is all well and good to have access to big data but unless we can turn it into a value it is useless.
5. What are the modes in which Hadoop run?
Local (Standalone) Mode – Hadoop by default run in a single-node, non-distributed mode, as a single Java process.
Pseudo-Distributed Mode – Just like the Standalone mode, Hadoop also runs on a single-node in a Pseudo-distributed mode.
Fully-Distributed Mode – In this mode, all daemons execute in separate nodes forming a multi-node cluster. Thus, it allows separate nodes for Master and Slave.
6. Explain about the indexing process in HDFS.
Indexing process in HDFS depends on the block size. HDFS stores the last part of the data that further points to the address where the next part of data chunk is stored.
7. What happens to a NameNode that has no data?
There does not exist any NameNode without data. If it is a NameNode then it should have some sort of data in it.
8. What is Hadoop streaming?
Hadoop distribution has a generic application programming interface for writing Map and Reduce jobs in any desired programming language like Python, Perl, Ruby, etc. This is referred to as Hadoop Streaming. Users can create and run jobs with any kind of shell scripts or executable as the Mapper or Reducers.
9. What is a block and block scanner in HDFS?
Block – The minimum amount of data that can be read or written is generally referred to as a “block” in HDFS. The default size of a block in HDFS is 64MB.
Block Scanner – Block Scanner tracks the list of blocks present on a DataNode and verifies them to find any kind of checksum errors. Block Scanners use a throttling mechanism to reserve disk bandwidth on the datanode.
10. What is a checkpoint?
Checkpoint Node keeps track of the latest checkpoint in a directory that has same structure as that of NameNode’s directory. Checkpoint node creates checkpoints for the namespace at regular intervals by downloading the edits and fsimage file from the NameNode and merging it locally. The new image is then again updated back to the active NameNode.