Hadoop MapReduce Applications With Combiners
Apache Hadoop 2.9
| Intermediate
- 13 videos | 1h 23m 5s
- Includes Assessment
- Earns a Badge
In this Skillsoft Aspire course, explore the use of Combiners to make MapReduce applications more efficient by minimizing data transfers. Start by learning about the need for Combiners to optimize the execution of a MapReduce application by minimizing data transfers within a cluster. Recall the steps to process data in a MapReduce application, and look at using a Combiner to perform partial reduction of data output from the Mapper. Then create a new project to calculate average automobile prices using Maven for a MapReduce application. Next, develop the Mapper and Reducer to calculate the average price for automobile makes in the input data set. Create a driver program for the MapReduce application, run it, and check output to get the average price per automobile. Learn how to code up a Combiner for a MapReduce application, fix the bug in the application so it can be used to correctly calculate the average price, then run the fixed application to verify that the prices are being calculated correctly. The concluding exercise concerns optimizing MapReduce with Combiners.
WHAT YOU WILL LEARN
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recognize the need for combiners to optimize the execution of a MapReduce application by minimizing data transfers within a cluster
recall the steps involved in processing data in a MapReduce application
describe the working of a Combiner in performing a partial reduction of the data that is output from the Mapper
configure a Combiner to optimize a MapReduce application that calculates an average value
use Maven to create a new project for a MapReduce application and plan out the Map and Reduce phases by examining the auto prices dataset
develop the Mapper and Reducer for the application that will calculate the average price for each make of automobile in the input dataset -
create the driver program for the MapReduce application
run the MapReduce application and check the output to get the average price for each automobile make
code up a Combiner for the MapReduce application and configure the Driver to use it for a partial reduction on the Mapper nodes of the cluster
fix the bug in the previous application by defining a type that represents both the aggregate price and count of automobiles that can be used to correctly calculate the average price
compare the output of the modified application with the previous buggy version and verify that the average prices for the vehicles are being calculated correctly
identify the shortcomings of regular MapReduce operations which are addressed by Combiners, and how Combiners differ from Reducers
IN THIS COURSE
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2m 32s
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5m 19sUpon completion of this video, you will be able to recognize the need for combiners to optimize the execution of a MapReduce application by minimizing data transfers within a cluster. FREE ACCESS
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3. Revisiting MapReduce5m 2sUpon completion of this video, you will be able to recall the steps involved in processing data in a MapReduce application. FREE ACCESS
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4. Working with Combiners5m 47sAfter completing this video, you will be able to describe how a Combiner works in performing a partial reduction of the data that is output from the Mapper. FREE ACCESS
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5. Using Combiners for Calculating Averages8m 22sIn this video, find out how to configure a Combiner to optimize a MapReduce application that calculates an average value. FREE ACCESS
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6. Creating a Project to Calculate Averages7m 1sIn this video, you will use Maven to create a new project for a MapReduce application. You will also plan out the Map and Reduce phases by examining the auto prices dataset. FREE ACCESS
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7. Coding the Map and Reduce Phases8m 3sFind out how to develop the Mapper and Reducer for the application that will calculate the average price for each make of automobile in the input dataset. FREE ACCESS
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8. Configure the Application in the Driver3m 13sIn this video, you will create the driver program for the MapReduce application. FREE ACCESS
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9. Executing the Application and Examining the Output7m 54sLearn how to run the MapReduce application and check the output to get the average price for each automobile make. FREE ACCESS
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10. Adding a Combiner to a MapReduce Application9m 31sIn this video, you will code up a Combiner for the MapReduce application and configure the Driver to use it for a partial reduction on the Mapper nodes of the cluster. FREE ACCESS
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11. Conveying a Pair of Numbers from the Mapper9m 21sIn this video, learn how to fix the bug in the previous application by defining a type that represents both the aggregate price and count of automobiles. This will allow you to correctly calculate the average price. FREE ACCESS
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12. Running the Fixed Application5m 6sLearn how to compare the output of the modified application with the previous buggy version and verify that the average prices for the vehicles are being calculated correctly. FREE ACCESS
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13. Exercise: Optimizing MapReduce With Combiners5m 54sIn this video, you will identify the shortcomings of regular MapReduce operations which are addressed by Combiners, and how Combiners differ from Reducers. FREE ACCESS
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