- What is checkpointing in Hadoop?
- Who created Hadoop?
- Is Hadoop a software?
- Why is K-means clustering used?
- What is name node in Hadoop?
- How can we improve the rack awareness algorithm in HDFS?
- What is a DataNode in Hadoop?
- Which type of data Hadoop can deal with?
- What are the main components of big data?
- Which clustering algorithm is best?
- What is cluster and its types?
- What is a rack in Hadoop?
- What is rack awareness algorithm?
- What is cluster in big data?
- What is NameNode and DataNode?
- What are the goals of HDFS?
- Which machine is NameNode?
- What data is stored in NameNode?
What is checkpointing in Hadoop?
Checkpointing is a process that takes an fsimage and edit log and compacts them into a new fsimage.
This way, instead of replaying a potentially unbounded edit log, the NameNode can load the final in-memory state directly from the fsimage.
This is a far more efficient operation and reduces NameNode startup time..
Who created Hadoop?
Doug CuttingApache HadoopOriginal author(s)Doug Cutting, Mike CafarellaDeveloper(s)Apache Software FoundationInitial releaseApril 1, 200610 more rows
Is Hadoop a software?
Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs.
Why is K-means clustering used?
The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the data. This can be used to confirm business assumptions about what types of groups exist or to identify unknown groups in complex data sets.
What is name node in Hadoop?
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. … The NameNode is a Single Point of Failure for the HDFS Cluster.
How can we improve the rack awareness algorithm in HDFS?
2. Minimize the cost of write and maximize the read speed. Rack awareness reduces write traffic in between different racks by placing write requests to replicas on the same rack or nearby rack, thus reducing the cost of write. Also, using the bandwidth of multiple racks increases the read performance.
What is a DataNode in Hadoop?
DataNodes store data in a Hadoop cluster and is the name of the daemon that manages the data. File data is replicated on multiple DataNodes for reliability and so that localized computation can be executed near the data. Within a cluster, DataNodes should be uniform.
Which type of data Hadoop can deal with?
Hadoop can handle not only structured data that fits well into relational tables and arrays but also unstructured data. A partial list of this type of data Hadoop can deal with are: Computer logs.
What are the main components of big data?
In this article, we discussed the components of big data: ingestion, transformation, load, analysis and consumption. We outlined the importance and details of each step and detailed some of the tools and uses for each.
Which clustering algorithm is best?
We shall look at 5 popular clustering algorithms that every data scientist should be aware of.K-means Clustering Algorithm. … Mean-Shift Clustering Algorithm. … DBSCAN – Density-Based Spatial Clustering of Applications with Noise. … EM using GMM – Expectation-Maximization (EM) Clustering using Gaussian Mixture Models (GMM)More items…•Oct 25, 2018
What is cluster and its types?
Clustering itself can be categorized into two types viz. Hard Clustering and Soft Clustering. In hard clustering, one data point can belong to one cluster only. But in soft clustering, the output provided is a probability likelihood of a data point belonging to each of the pre-defined numbers of clusters.
What is a rack in Hadoop?
A Rack is a collection nodes usually in 10 of nodes which are closely stored together and all nodes are connected to a same Switch. When an user requests for a read/write in a large cluster of Hadoop in order to improve traffic the namenode chooses a datanode that is closer this is called Rack Awareness .
What is rack awareness algorithm?
Rack Awareness in Hadoop is the concept that chooses closer Datanodes based on the rack information. … To improve network traffic while reading/writing HDFS files in large clusters of Hadoop. NameNode chooses data nodes, which are on the same rack or a nearby rock to read/ write requests (client node).
What is cluster in big data?
Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. … There are many different clustering models: Connectivity models based on connectivity distance.
What is NameNode and DataNode?
The NameNode keeps an image of the entire file system namespace and file Blockmap in memory. … The DataNode stores HDFS data in files in its local file system. The DataNode has no knowledge about HDFS files. It stores each block of HDFS data in a separate file in its local file system.
What are the goals of HDFS?
The goals of HDFSFast recovery from hardware failures. Because one HDFS instance may consist of thousands of servers, failure of at least one server is inevitable. … Access to streaming data. … Accommodation of large data sets. … Portability.
Which machine is NameNode?
Here is a recommended setup from the Hadoop setup guide. Typically one machine in the cluster is designated as the NameNode and another machine the as JobTracker, exclusively. These are the masters. The rest of the machines in the cluster act as both DataNode and TaskTracker.
What data is stored in NameNode?
NameNode is the centerpiece of HDFS. NameNode only stores the metadata of HDFS – the directory tree of all files in the file system, and tracks the files across the cluster. NameNode does not store the actual data or the dataset. The data itself is actually stored in the DataNodes.