What is Hadoop and Why is it Important for Big Data?
- Answer: Hadoop is an open-source framework designed to store and process big data in a distributed environment. It's crucial for big data because it offers massive storage, enormous processing power, and the ability to handle virtually limitless concurrent tasks.
Can You Explain Hadoop's Core Components?
- Answer: Hadoop's core components are the Hadoop Distributed File System (HDFS) for storage and the Yet Another Resource Negotiator (YARN) for resource management and job scheduling.
How Does Hadoop Perform Data Replication?
- Answer: Hadoop performs data replication by distributing data across multiple nodes in the cluster. This ensures high availability and fault tolerance. By default, each data block is replicated thrice in the cluster.
Difference Between HDFS and NAS?
- Answer: HDFS is a distributed file system that stores data across a Hadoop cluster, whereas NAS (Network Attached Storage) is a file-level data storage connected to a network providing data access to a group of clients. HDFS is designed for high throughput and is better suited for large data sets.
How Does MapReduce Work in Hadoop?
- Answer: MapReduce in Hadoop is a programming model for processing large datasets. It works in two phases: the Map phase, which processes and converts input data into a set of intermediate key-value pairs, and the Reduce phase, which then combines and reduces these pairs into a smaller set of aggregated results.
What is a NameNode in Hadoop?
- Answer: The NameNode in Hadoop is the master node that manages the metadata of the Hadoop Distributed File System (HDFS). It keeps track of the file system tree and the metadata for all the files and directories.
Explain DataNode in Hadoop.
- Answer: DataNodes in Hadoop are the slave nodes that store the actual data in HDFS. They are responsible for serving read and write requests from the file system’s clients.
What is a Rack Awareness Algorithm in Hadoop?
- Answer: Rack awareness is an algorithm used by Hadoop to determine how data is stored in clusters. It ensures data replication across different racks to minimize data loss in case of rack failure.
How Would You Optimize a Hadoop Cluster?
- Answer: To optimize a Hadoop cluster, one should consider factors like configuring the right number of mappers and reducers, choosing the appropriate data storage format, ensuring efficient data serialization and deserialization, and tuning the cluster configuration to match the specific workload.
What Are Some Common Challenges in Working with Hadoop?
- Answer: Common challenges include managing large data volumes, ensuring data security and privacy, dealing with hardware failures, and optimizing for performance and resource utilization.
What is the significance of the Hadoop daemons and list them?
- Answer: Hadoop daemons are background processes that run on the Hadoop cluster. The primary daemons are NameNode, DataNode, ResourceManager, NodeManager, and Secondary NameNode.
Explain the role of the JobTracker in Hadoop.
- Answer: JobTracker in Hadoop is responsible for resource management, tracking resource availability, and job scheduling and monitoring.
What is Hadoop Streaming?
- Answer: Hadoop Streaming allows users to create and run Map/Reduce jobs with any executable or script as the mapper and/or the reducer.
How is Hadoop different from Spark?
- Answer: Hadoop is primarily a distributed data infrastructure, whereas Spark is a data processing tool that operates on distributed data collections and is generally faster than Hadoop's MapReduce.
What are the types of schedulers in Hadoop?
- Answer: The primary types of schedulers in Hadoop are the FIFO Scheduler, Fair Scheduler, and Capacity Scheduler.
What is HBase?
- Answer: HBase is a distributed, scalable, NoSQL database built on top of Hadoop. It stores data in tables and is ideal for read/write heavy applications.
Explain the concept of a “block” in HDFS.
- Answer: A block in HDFS is a unit of data storage. HDFS stores each file as blocks, and these blocks are distributed across the cluster’s nodes.
How does HDFS ensure fault tolerance?
- Answer: HDFS ensures fault tolerance by replicating data across multiple nodes. If a node fails, data can be retrieved from another node that has a copy of the same data.
Describe the process of data replication in HDFS.
- Answer: Data replication in HDFS involves copying data blocks to multiple nodes throughout the cluster to ensure reliability and fault tolerance.
What is a Combiner in Hadoop?
- Answer: A Combiner in Hadoop is a mini-reducer that performs the local reduce task. It helps in reducing the amount of data transferred between the Map and Reduce phases.
Explain speculative execution in Hadoop.
- Answer: Speculative execution in Hadoop is a mechanism where the system initiates a duplicate task for a slow-running task. Whichever task finishes first, the other is killed to optimize the processing time.
What is ZooKeeper in Hadoop?
- Answer: ZooKeeper is a centralized service for maintaining configuration information, naming, and providing distributed synchronization in Hadoop.
Define Hive in Hadoop.
- Answer: Hive is a data warehouse system for Hadoop, facilitating easy data summarization, query, and analysis.
What is Pig in Hadoop?
- Answer: Pig is a high-level scripting language that is used with Hadoop. It is used for data transformation and analysis.
How does the Hadoop Distributed Cache work?
- Answer: Hadoop Distributed Cache is used to distribute large data sets across all the nodes in the cluster for faster data processing.
What is a SequenceFile in Hadoop?
- Answer: A SequenceFile is a flat file consisting of binary key/value pairs, widely used for passing data between the output of one MapReduce job to the input of another.
Explain the role of RecordReader in Hadoop.
- Answer: RecordReader in Hadoop converts the input data into a key-value pair suitable for reading by the Mapper.
What is the purpose of the YARN ResourceManager?
- Answer: The ResourceManager in YARN is responsible for allocating resources to the various running applications and scheduling tasks.
Define Flume in Hadoop.
- Answer: Flume is a distributed service for efficiently collecting, aggregating, and moving large amounts of log data to the Hadoop Distributed File System (HDFS).
How does the Checkpointing process work in Hadoop?
- Answer: Checkpointing is a process where the current state of the Hadoop file system namespace is saved periodically to prevent data loss in case of failure.
What is a Replication Factor in Hadoop?
- Answer: Replication factor refers to the number of times data is replicated in HDFS for fault tolerance. By default, it is set to three.
How do you configure a Hadoop cluster?
- Answer: Configuring a Hadoop cluster involves setting up Hadoop configuration files like core-site.xml, hdfs-site.xml, mapred-site.xml, and yarn-site.xml.
What is a Heartbeat in Hadoop?
- Answer: A Heartbeat is a signal used in Hadoop to check the status of DataNode by the NameNode. If a heartbeat is not received for a specified time, the DataNode is considered dead.
Explain the Shuffle and Sort phase of MapReduce.
- Answer: In the Shuffle phase, data output by the Mapper is sorted and shuffled across Reducers. In the Sort phase, the data is sorted based on the keys.
What is Ambari in Hadoop?
- Answer: Ambari is a web-based tool for provisioning, managing, and monitoring Apache Hadoop clusters.
What is the significance of the Hadoop fsck command?
- Answer: The 'fsck' command in Hadoop is used for checking the health of the file system and to find any corrupt or missing files.
How is data integrity ensured in HDFS?
- Answer: Data integrity in HDFS is ensured through checksums. Each block of data is associated with a checksum that is used to verify the integrity of data during transfers.
What is a Split in Hadoop?
- Answer: A split in Hadoop is a portion of the input data which is processed by a single Mapper in a MapReduce job.
Can you explain the concept of InputFormat in Hadoop?
- Answer: InputFormat in Hadoop defines how input files are split and read. It determines the key/value pairs used by the Mapper.
What is a rack-aware replica placement policy?
- Answer: Rack-aware replica placement policy is a policy used in Hadoop to place replicas of blocks in different racks to improve data reliability and network bandwidth utilization.
How can you improve the performance of a Hadoop application?
- Answer: Performance of a Hadoop application can be improved by optimizing the MapReduce algorithm, using a combiner, choosing the right data formats, and tuning the Hadoop cluster configuration.
What is Oozie in Hadoop?
- Answer: Oozie is a workflow scheduler system that manages Hadoop jobs in a distributed environment.