Bob has a Hadoop cluster with 50 machines under default setup (replication factor 3, 128MB input split size). Each machine has 100GB of HDFS disk space. The cluster is currently empty (no job, no data). Bob intends to upload 1 Terabyte of plain text (in 5 files of approximately 200GB each), followed by running Hadoop's standard job. Question 4a -Design the HDFS topology. Question 4b (-e)- Describe the components of the solution

Bob has a Hadoop cluster with 50 machines under default setup (replication factor 3, 128MB input split size). Each machine has 100GB of HDFS disk space. The cluster is currently empty (no job, no data). Bob intends to upload 1 Terabyte of plain text (in 5 files of approximately 200GB each), followed by running Hadoop's standard job. Question 4a -Design the HDFS topology. Question 4b (-e)- Describe the components of the solution

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Pls solve this question correctly in 5 min i will give u like for sure import seaborn as sns import pandas as pd import numpy as np from sklearn.cluster import KMeans from sklearn import datasets # download the iris datafile X,y = datasets.load_iris(return_X_y=True,) cols = ['s_length','s_width','p_length','p_width'] df = pd.DataFrame(X, columns=cols) df['class'] = y df sns.scatterplot(data=df, x='s_length', y='p_width', hue='class') from sklearn.model_selection import train_test_split train, test = train_test_split(df, test_size=0.4) X = train[['s_length', 'p_width']].to_numpy() y = train[['class']].to_numpy() k = 3 model = KMeans(n_clusters=k) model.fit_predict(X, y) s = model.score(X,y)
Consider applications “Q1”, “Q2”, “Q3”, and “Q4” as shown. These applications work on the table STUDENTS defined as “STUDENTS (RNO, NAME, ADDRESS, SEM, MARKS),” where RNO is the primary key column of the table. Q1: Select NAME from STUDENTS where MARKS = 100; Q2: Select MARKS from STUDENTS; Q3: Select SEM from STUDENTS where ADDRESS = ‘RWP’; Q4: Select NAME from STUDENTS where SEM = 6; Perform vertical fragmentation? S1 S2 S3 S4 Q1 1 0 2 0 Q2 0 4 3 0 Q3 0 0 4 0 Q4 3 0 0 0 frequency matrix Calculate usage matrix, Attribute affinity matrix, and Clustered affinity matrix? Calculate Split point?
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