What is a block pool in HDFS?

A Block Pool is a set of blocks that belong to a single namespace. Datanodes store blocks for all the block pools in the cluster. A Namenode failure does not prevent the Datanode from serving other Namenodes in the cluster. A Namespace and its block pool together are called Namespace Volume.

Also, what is the use of journal node in Hadoop?

The Active NameNode is responsible for all client operations in the cluster, while the Standby is simply acting as a slave, maintaining enough state to provide a fast failover if necessary. For standby NameNode to be synchronized with active NameNode, both communicate with each other through Journal Node.

What is yarn cluster?

YARN is a large-scale, distributed operating system for big data applications. The technology is designed for cluster management and is one of the key features in the second generation of Hadoop, the Apache Software Foundation’s open source distributed processing framework.

What is HDFS Federation?

HDFS Federation improves the existing HDFS architecture through a clear separation of namespace and storage, enabling generic block storage layer. It enables support for multiple namespaces in the cluster to improve scalability and isolation.

How many instances of Job Tracker can 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.

Who is the first Hadoop service provider in the market?

Cloudera. Cloudera Inc. was founded by big data geniuses from Facebook, Google, Oracle and Yahoo in 2008. It was the first company to develop and distribute Apache Hadoop-based software and still has the largest user base with most number of clients.

What are the characteristics of Hadoop?

Here are the prominent characteristics of Hadoop: Hadoop provides a reliable shared storage (HDFS) and analysis system (MapReduce). Hadoop is highly scalable and unlike the relational databases, Hadoop scales linearly. Due to linear scale, a Hadoop Cluster can contain tens, hundreds, or even thousands of servers.

What is Hadoop and what are its basic components?

Core Hadoop Components

  • 1) Hadoop Common-
  • 2) Hadoop Distributed File System (HDFS) –
  • HDFS Use Case-
  • MapReduce Use Case:
  • Key Benefits of Hadoop 2.0 YARN Component-
  • YARN Use Case:
  • Pig Use Case-
  • Hive Use Case-
  • What is raid in HDFS?

    Raid (redundant array of independent disks) is a way of storing the same data in different places (thus, redundantly) on multiple hard disks. The Hadoop Distributed File System (HDFS) is the primary storage system used by Hadoop applications.

    Why replication is done in HDFS?

    Files in HDFS are write-once and have strictly one writer at any time. The NameNode makes all decisions regarding replication of blocks. It 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.

    How the data is stored in HDFS?

    A single NameNode tracks where data is housed in the cluster of servers, known as DataNodes. Data is stored in data blocks on the DataNodes. HDFS replicates those data blocks, usually 128MB in size, and distributes them so they are replicated within multiple nodes across the cluster.

    What is a Datanode?

    A DataNode stores data in the [HadoopFileSystem]. A functional filesystem has more than one DataNode, with data replicated across them. It then responds to requests from the NameNode for filesystem operations. Client applications can talk directly to a DataNode, once the NameNode has provided the location of the data.

    What is meant by HDFS?

    The Hadoop Distributed File System (HDFS) is designed to store very large data sets reliably, and to stream those data sets at high bandwidth to user applications. In a large cluster, thousands of servers both host directly attached storage and execute user application tasks.

    How does map and reduce work?

    A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.

    What is Hadoop Mapreduce used for?

    Hadoop MapReduce (Hadoop Map/Reduce) is a software framework for distributed processing of large data sets on compute clusters of commodity hardware. It is a sub-project of the Apache Hadoop project. The framework takes care of scheduling tasks, monitoring them and re-executing any failed tasks.

    What is the use of spark in Hadoop?

    Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.

    What is the use of Cassandra?

    Apache Cassandra is a highly scalable, high-performance distributed database designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. It is a type of NoSQL database. Let us first understand what a NoSQL database does.

    What is spark vs Hadoop?

    Spark is a cluster-computing framework, which means that it competes more with MapReduce than with the entire Hadoop ecosystem. For example, Spark doesn’t have its own distributed filesystem, but can use HDFS. Spark uses memory and can use disk for processing, whereas MapReduce is strictly disk-based.

    Is Hadoop is a database?

    Hadoop is not a type of database, but rather a software ecosystem that allows for massively parallel computing. It is an enabler of certain types NoSQL distributed databases (such as HBase), which can allow for data to be spread across thousands of servers with little reduction in performance.

    What is Spark architecture?

    Apache Spark Architectural Overview. Spark is a top-level project of the Apache Software Foundation, designed to be used with a range of programming languages and on a variety of architectures.

    Is spark faster than Mapreduce?

    Apache Spark is setting the world of Big Data on fire. With a promise of speeds up to 100 times faster than Hadoop MapReduce and comfortable APIs, some think this could be the end of Hadoop MapReduce. The secret is that it runs in-memory on the cluster, and that it isn’t tied to Hadoop’s MapReduce two-stage paradigm.

    What is Hadoop architecture?

    Hadoop Architecture Overview. Apache Hadoop is an open-source software framework for storage and large-scale processing of data-sets on clusters of commodity hardware. the cluster is the set of host machines (nodes).

    What is the use of journal node?

    Think of it like this, zookeeper is a group of people, each assigned to watch over a factory and coordinate them, journal node is a place where all factory managers can check others status and coordinate. QJM is a combination of both to be used in HA for better coordination in case of fail over.

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