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5 min readTo run Hadoop with an external JAR file, you can use the command line to include the JAR file in your Hadoop classpath. This can be done by specifying the JAR file using the "-libjars" option when running your Hadoop job. This will make sure that the external JAR file is available to all nodes in the Hadoop cluster when the job is executed.
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5 min readIn Julia, you can sum multi-dimensional vectors of type "any" by using the sum() function along with the broadcast() function. The sum() function calculates the sum of elements along a given dimension, while the broadcast() function applies a function element-wise to arrays.For example, if you have a multi-dimensional vector A of type "any", you can sum all the elements by using the following code: A = rand(1:10, (2, 3, 4)) total_sum = sum(broadcast(identity, A)...
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6 min readWhen dealing with split zip files in Hadoop, you can use the Hadoop Archive utility (hadoop archives) to unzip the files.First, you need to copy the split zip files into HDFS using the "hdfs dfs -copyFromLocal" command. Once the files are in HDFS, you can use the "hadoop archive" command to extract the contents of the split zip files.
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4 min readTo generate symmetric matrices in Julia, you can use the Symmetric type provided by the LinearAlgebra module. To create a symmetric matrix, you can simply pass a square matrix to the Symmetric constructor. For example, you can generate a random symmetric matrix of size 5x5 using the following code: using LinearAlgebra A = rand(5,5) symmetric_A = Symmetric(A) This will create a symmetric matrix symmetric_A based on the random matrix A.
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7 min readTo merge CSV files in Hadoop, you can use the Hadoop FileUtil class to copy the contents of multiple input CSV files into a single output CSV file. First, you need to create a MapReduce job that reads the input CSV files and writes the output to a single CSV file. In the map function, you can read each line of the input CSV files and write them to the output CSV file. In the reduce function, you can merge the output of the map function to create a single output CSV file.
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3 min readTo generate a random matrix of arbitrary rank in Julia, you can use the rand function along with the svd function. First, create a random matrix of any size using the rand function. Then, decompose this matrix using the svd function to get the singular value decomposition. Finally, modify the singular values to achieve the desired rank and reconstruct a new matrix using the modified singular values. This new matrix will have the desired rank while being random.
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4 min readTo stop a particular service in the Hadoop environment, you can use the command "hadoop-daemon.sh stop ". Replace "" with the name of the service you want to stop, such as namenode, datanode, ResourceManager, or NodeManager. This command will stop the specified service running on the Hadoop cluster.Additionally, you can use the Ambari web interface to stop services in a Hadoop cluster.
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7 min readTo install Hadoop on macOS, you first need to download the desired version of Hadoop from the Apache Hadoop website. After downloading the file, extract it to a location on your computer. Next, you will need to set up the environment variables in the .bash_profile file to point to the Hadoop installation directory.You will also need to configure the Hadoop configuration files such as core-site.xml, hdfs-site.xml, and mapred-site.xml to specify the required settings for your Hadoop setup.
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5 min readTo get thin QR decomposition in Julia, you can use the qr() function with the Thin=true parameter. This will compute the QR decomposition with only the essential part of the orthogonal matrix needed to represent the input matrix. The thin QR decomposition is useful for solving least squares problems and can be more efficient and memory-saving than the full QR decomposition.
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6 min readPhysical memory in a Hadoop cluster refers to the actual RAM (Random Access Memory) that is available on the individual nodes within the cluster. This memory is used by the Hadoop framework to store and process data during various operations such as map-reduce tasks, data storage, and computations. The amount of physical memory available on each node plays a crucial role in determining the performance and efficiency of the Hadoop cluster.
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5 min readTo delete a column in a matrix in Julia, you can use the hcat function to concatenate the desired columns before and after the column you want to delete. For example, if you have a matrix A and you want to delete the second column, you can do the following: A = [1 2 3; 4 5 6; 7 8 9] delete_column = 2 A = hcat(A[:,1:delete_column-1], A[:,delete_column+1:end]) This code snippet creates a new matrix A without the second column of the original matrix.