Installing Additional Hadoop Services using Parcels in Cloudera Manager

To install additional Services using Parcel in Cloudera Manager, follow the following steps:

  1. Download Parcel file from internet, make sure the component version is supported by your Cloudera version & the Operating System
  2. Download corresponding manifest.json file, also create .sha file using command
     echo "*********************" > componentName.parcel.sha
  3.  Where this hash will be found in manifest.json file
  4. Scp these three files to Hadoop Master node at location like /opt/cloudera/parcel-local/newdir
  5. Run an httpserver in this dir to expose this a local parcel repository 
    python -m SimpleHTTPServer 8900 
  6. In Cloudera manager, go to parcel page and Check for New Parcels. You might have to configure parcel setting and add your server:8900 as one of the parcel repository.
  7. You should see your parcel listed here now
  8. Perform Download of this parcel in CM
  9. Perform Distribute, by clicking on Distribute button
  10. Perform Activate, by clicking on Activate button
  11. Check Parcel Usage
  12. Now you may need to perform 'Cluster Add Service' to actually install your component based on above Parcel

Comments

Post a Comment

Popular posts from this blog

Spark Cluster Mode - Too many open files

When running complex Spark Jobs in Cluster mode (Yarn-Client or Yarn-Cluster) mode, it is quite possible the default ulimit (number of open files) of 1024 is not sufficient and hence gets error "Too many open files". One way to address this is issue is by increasing the ulimit size. Steps (as root user): 1) vim /etc/sysctl.conf, append the file with:     fs.file-max = 65536 2) vim /etc/security/limits.conf, append the file with: * soft nproc 65535 * hard nproc 65535 * soft nofile 65535 * hard nofile 65535 3) ulimit -n 65535
Read more

Binary Data to Float using Spark SQL

Often when there is massive data communication among the enterprise applications, the data is converted to binary datatype, for reasons like: Faster I/O, Smaller Size, any type of data can be represented, etc. This brings in need to convert binary back to known/readable data type for further analysis/processing etc. For example, here floating point data is represented in Decimal Array[Byte] then stored in Binary format. Ex: [62, -10, 0, 0] Whole floating point equivalent is: 0.480 To achieve above conversion using Apache Spark Sql DataFrames: Voila!
Read more

HDFS filenames without rest of the file details

Get the list of file names and absolute path alone from HDFS: General hdfs list output would be: $ hdfs dfs -ls -rw-r--r--   3 foo bar    6346268 2016-12-28 02:52 /user/foo/data/file007.csv -rw-r--r--   3 foo bar    4397850 2016-12-28 02:52 /user/foo/data/file014.csv -rw-r--r--   3 foo bar   13297361 2016-12-28 02:52 /user/foo/data/file020.csv -rw-r--r--   3 foo bar   10400852 2016-12-28 02:53 /user/foo/data/file118.csv -rw-r--r--   3 foo bar   10184639 2016-12-28 02:52 /user/foo/data/file205.csv -rw-r--r--   3 foo bar    5542293 2016-12-28 02:53 /user/foo/data/file214.csv -rw-r--r--   3 foo bar    6085128 2016-12-28 02:53 /user/foo/data/file307.csv But we would need get just the absolute hdfs file paths especially in shell scripts to perform CRUD operations, like: /user/foo/data/file007.csv /user/foo/data/file014.csv /user/foo/data/file020.csv /user/foo/data/file118.csv /user/foo/data/file205.csv /user/foo/data/file214.csv One way to achieve this is using awk, like: $
Read more