Download Architecting HBase Applications: A Guidebook for Successful by Jean-Marc Spaggiari, Kevin O'Dell PDF

By Jean-Marc Spaggiari, Kevin O'Dell

Lots of HBase books, on-line HBase courses, and HBase mailing lists/forums can be found if you want to understand how HBase works. but when you must take a deep dive into use circumstances, gains, and troubleshooting, Architecting HBase functions is definitely the right resource for you.

With this booklet, you’ll examine a managed set of APIs that coincide with use-case examples and simply deployed use-case versions, in addition to sizing/best practices to aid leap begin your business software improvement and deployment.

  • Learn layout patterns—and not only components—necessary for a winning HBase deployment
  • Go extensive into the entire HBase shell operations and API calls required to enforce documented use cases
  • Become acquainted with the commonest matters confronted by means of HBase clients, establish the motives, and comprehend the consequences
  • Learn document-specific API calls which are difficult or vitally important for users
  • Get use-case examples for each subject presented

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Extra info for Architecting HBase Applications: A Guidebook for Successful Development and Design

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You can validate your tables splits and the content of each region by looking in HDFS has seen in “Bulk loading” on page 29 Impact on table parameters We have created our table using the parameters which are good for our current usecase. We recommend modifying the various parameters and re-running the process to measure the impact. Compression Try to use different types of compression and compare. If you used Snappy, which is fast, try to configure LZ4, which is slower but compress better, and compare the over‐ all time it takes to process everything vs.

To summarize, we have validated the size of the HFiles, their format, the num‐ bers of entries in the HFiles and in the table, and the table content itself. We can now confirm that our data has been correctly and fully loaded into the table. Data indexing The next and last step of the implementation consists of indexing the table we have just loaded, to be able to quickly search for any of the records using SOLR. Indexation is an incremental process. Indeed, Omneo receive new files daily. As seen in the pre‐ vious chapter, data from those files is loaded into a main table which contains data from the previous days, and an indexation table.

Both commands below will return all the columns for the row with rowkey value 000a: get 'sensors', '000a', {COLUMN => 'v'} scan 'sensors', {COLUMNS => ['v'], STARTROW => '000a', LIMIT => 1 } Now as you will see in the output, there might be many columns for each row. If you want to limit the output to a specific column qualifier, you need to specify it in both commands the following way: 32 | Chapter 2: Underlying storage engine - Implementation get 'sensors', '000a', {COLUMN => 'v:f92acb5b-079a-42bc-913a-657f270a3dc1'} scan 'sensors', { COLUMNS => ['v:f92acb5b-079a-42bc-913a-657f270a3dc1'], \ STARTROW => '000a', STOPROW => '000a' } The output of the get should then look like this: COLUMN CELL v:f9acb...

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