St Louis
-
4 min readTo unzip .gz files in a new directory in Hadoop, you can use the Hadoop FileSystem API to programmatically achieve this task. First, you need to create a new directory in Hadoop where you want to unzip the .gz files. Then, you can use the Hadoop FileSystem API to read the .gz files, unzip them, and write the uncompressed files to the new directory. You can also use shell commands or Hadoop command-line tools like hdfs dfs -copyToLocal to copy the .
-
5 min readTo use only one GPU for a TensorFlow session, you can set the environment variable CUDA_VISIBLE_DEVICES before running your Python script. This variable determines which GPU devices are visible to TensorFlow.For example, if you want to use only GPU 1, you can set CUDA_VISIBLE_DEVICES to 1 before running your script: export CUDA_VISIBLE_DEVICES=1 python your_script.py This will restrict TensorFlow to only use GPU 1 for the session, ignoring other available GPUs.
-
3 min readTo check the Hadoop server name, you can open the Hadoop configuration files located in the conf directory of your Hadoop installation. Look for core-site.xml or hdfs-site.xml files where the server name will be specified. Additionally, you can also use the command "hdfs getconf -nnRpcAddresses" in the Hadoop terminal to retrieve the server name. This command will display the hostname and port number of the Hadoop NameNode.
-
6 min readTo submit a Hadoop job from another Hadoop job, you can use the Hadoop JobControl class in the org.apache.hadoop.mapred.control package. This class allows you to control multiple job instances and their dependencies.You can create a JobControl object and add the jobs that you want to submit to it using the addJob() method. You can then use the run() method of the JobControl object to submit the jobs for execution. The run() method will wait for the jobs to complete before returning.
-
7 min readTo install Hadoop using Ambari setup, first ensure that all the prerequisites are met, such as having a compatible operating system and enough resources allocated to the servers. Then, download and install the Ambari server on a dedicated server.Next, access the Ambari web interface and start the installation wizard. Follow the prompts to specify the cluster name, select the services you want to install (including Hadoop components such as HDFS, YARN, MapReduce, etc.), and configure the cluster.
-
6 min readHadoop gives reducers the ability to perform aggregation and analysis on the output of the mappers. Reducers receive the intermediate key-value pairs from the mappers, which they then process and combine based on a common key. This allows for tasks such as counting, summing, averaging, and other types of data manipulation to be performed on large datasets efficiently.
-
3 min readTo change file permissions in the Hadoop file system, you can use the command "hadoop fs -chmod" followed by the desired permissions and the file path. The syntax for the command is as follows: hadoop fs -chmod <file_path>. Permissions can be specified using symbolic notation (e.g., u=rwx,g=rw,o=r) or octal notation (e.g., 755). This command will change the permissions of the specified file to the ones you provided.
-
4 min readTo increase the Hadoop Filesystem size, you can add more storage nodes to your Hadoop cluster, either by adding more disks to existing nodes or by adding more nodes to the cluster. This will increase the overall storage capacity available to Hadoop.You can also adjust the replication factor of your data in HDFS to consume more storage space. By increasing the replication factor, you can ensure that each block of data is replicated across more nodes, thereby consuming more storage space.
-
8 min readTo put a large text file in Hadoop HDFS, you can use the command line interface or the Hadoop File System API. First, make sure you have access to the Hadoop cluster and a text file that you want to upload.To upload the text file using the command line interface, you can use the hadoop fs -put command followed by the path to the file you want to upload and the destination directory in HDFS. For example, hadoop fs -put /path/to/localfile.txt /user/username/hdfsfile.txt.
-
6 min readTo remove a disk from a running Hadoop cluster, you first need to safely decommission the data node on the disk you want to remove. This involves marking the node as decommissioned and ensuring that the Hadoop cluster redistributes the blocks that were stored on the disk to other nodes in the cluster. Once the decommission process is completed and all data has been redistributed, you can physically remove the disk from the data node.
-
7 min readHadoop follows a memory allocation strategy that is based on the concept of containers. When a job is submitted, Hadoop divides the memory available on each node into equal-sized containers. These containers are then used to run various processes related to the job, such as map tasks, reduce tasks, and other operations.Hadoop also uses a concept called memory management units (MMUs) to allocate memory resources efficiently.