Programming MapReduce Jobs with HDInsight Server for Windows

In a previous blog “Installing HDInsight Server for Windows”, I introduced you to the Microsoft HDInsight Server for Windows. Recall that HDInsight Server for Windows is a Windows-based Hadoop distribution that offers two main benefits for Big Data customers:

  • An officially supported Hadoop distribution on Windows server – Previously, you can set up Hadoop on Windows as an unsupported installation (via Cygwin) for development purposes. What this means for you is that you can now set up a Hadoop cluster on servers running Windows Server OS.
  • Extends the reach of the Hadoop ecosystem to .NET developers by allowing them to write MapReduce jobs in .NET code, such as C#.

And, in previous blogs, I’ve introduced you to Hadoop. Recall that there are two main reasons for using Hadoop for storing and processing Big Data:

  • Storage – You can store massive files in a distributed and fault-tolerant file system (HDFS) without worrying that hardware failure will result in a loss of data.
  • Distributed processing – When you outgrows the limitations of a single server, you can distribute job processing across the nodes in a Hadoop cluster. This allows you to perform crude data analysis directly on files stored in HDFS or execute any other type of jobs that can benefit from a parallel execution.

This blog continues the HDInsight Server for Windows journey. As many of you probably don’t have experience in Unix or Java, I’ll show you how HDInsight makes it easy to write MapReduce jobs on a Windows machine.

Note Writing MapReduce jobs can be complex. If all you need is performing some crude data analysis, you should consider an abstraction layer, such as Hive, which is capable for deriving the schema and generating the MapReduce jobs for you. This doesn’t mean that experience in MapReduce is not useful. When processing the files go beyond just imposing a schema on the data and querying the results , you might need programming logic, such as in The New York Times Archive case.

As a prerequisite, I installed HDInsight on my Windows 8 laptop. Because of its prerelease status, the CTP of HDInsight Server for Windows currently supports a single node only which is fine for development and testing. My task is to analyze the same dataset that I used in the MS BI Guy Does Hadoop (Part 2 – Taking Hadoop for a Spin) blog. The dataset (temp.txt) contains temperature readings from weather stations around the world and it represents the weather datasets kept by National Climatic Data Center (NCDC). You will find the sample dataset in the source code attached to this blog. It has the following content (the most important parts are highlighted in red: the year found in offset 15 and temperature found in offset 88).






Note that the data is stored in its raw format and no schema was imposed on the data. The schema will be derived at runtime by parsing the file content.

Installing Microsoft .NET SDK for Hadoop

The Microsoft .NET SDK for Hadoop facilitates the programming effort required to code MapReduce jobs in .NET. To install it:

  1. Install NuGet first. NuGet is a Visual Studio extension that makes it easy to add, remove, and update libraries and tools in Visual Studio projects that use the .NET Framework.
  2. Open Visual Studio (2010 or 2012) and create a new C# Class Library project.
  3. Go to Tools ð Library Package Manager ð Package Manager Console.
  4. In the Package Manager Console window that opens in the bottom of the screen, enter:
    install-package Microsoft.Hadoop.MapReduce –pre

    This command will download the required Hadoop binaries and add them as references in your project.

Coding the Map Job

The Map job is responsible for parsing the input (the weather dataset), deriving the schema from it, and generating a key-value pair for the data that we’re interested in. In our case, the key will be the year and the value will be the temperature measure for that year. The Map class derives from the MapperBase class defined in Microsoft.Hadoop.MapReduce.dll.


At runtime, HDInsight will parse the file content and invoke the Map method once for each line in the file. In our case, the Map job is simple. We parse the input and extract the temperature and year. If the parsing operation is successful, we return the key-value pair. The end result will look like this:

(1950, 0)

(1950, 22)

(1950, 11)

(1949, 111)

(1949, 78)

Coding the Reduce Job

Suppose that we want to get the maximum temperature for each year. Because each weather station might have multiple readings (lines in the input file) for the same year, we need to combine the results and find the maximum year. This is analogous to GROUP BY in SQL. The following Reduce job gets the work done:


The Reduce job is even simpler. The Hadoop framework pre-processed the output of the Map jobs before it’s sent to the Reduce function. This processing sorts and groups the key-value pairs by key, so the input to the Reduce job will look like this:

(1949, [111, 78])

(1950, [0, 22, −11])

In our case, the only thing left for the Reduce job is to loop through the values for a given key (year) and return the maximum value, so the final output will be:

(1949, 111)

(1950, 22)

Testing MapReduce

Instead of deploying to Hadoop each time you make a change during the development and testing lifecycle, you can add another project, such as a Console Application, and use it as a test harness to test the MapReduce code. For your convenience, Microsoft provides a StreamingUnit class in Microsoft.Hadoop.MapReduce.dll. Here is what our test harness code looks like:


The code uses a test input file. It reads the content of the file one line at the time and adds each line as a new element to an instance of ArrayList. Then, the code calls the StreamInsight.Execute method to initiate the MapReduce job.

Deploying to Hadoop

Once the code is tested, it’s time to deploy the dataset and MapReduce jobs to Hadoop.

  1. Deploy the file to the Hadoop HDFS file system.
    C:\Hadoop\hadoop-1.1.0-SNAPSHOT\bin>hadoop fs -copyFromLocal D:\MyApp\Hadoop\MapReduce\temp.txt input/Temp/input.txt

Note When you execute the hadoop command shell in the previous step, the file will be uploaded to your folder. However, if you use the JavaScript interactive console found in the HDInsight Dashboard, the file will be uploaded to the Hadoop folder in HDFS because the console runs under the hadoop user. Consequently, the MapReduce job won’t be able to find the file. So, you use the hadoop command prompt.

      2.   Browse the file system using the web interface (http://localhost:50070) to see that the file is in your folder.


3.     Finally, we need to execute the job with HadoopJobExecutor, which be called in various ways. The easiest way is to use MRRunner
D:\MyApp\Hadoop\MapReduce\FirstJob\bin\Debug>.\mrlib\mrrunner -dll FirstJob.dll

D:\MyApp\Hadoop\MapReduce\FirstJob\bin\Debug>.\mrlib\mrrunner -dll FirstJob.dll

File dependencies to include with job:[Auto-detected] D:\MyApp\Hadoop\MapReduce\FirstJob\bin\Debug\FirstJob.dll

[Auto-detected] D:\MyApp\Hadoop\MapReduce\FirstJob\bin\Debug\Microsoft.Hadoop.MapReduce.dll

[Auto-detected] D:\MyApp\Hadoop\MapReduce\FirstJob\bin\Debug\Newtonsoft.Json.dll

>>CMD: c:\hadoop\hadoop-1.1.0-SNAPSHOT\bin\hadoop.cmd jar c:\hadoop\hadoop-1.1.0-SNAPSHOT\lib\hadoop-streaming.jar -D “” -D “mapred.reduce.max.attempts=1” -input inpu

emp -mapper ..\..\jars\Microsoft.Hadoop.MapDriver.exe -reducer ..\..\jars\Microsoft.Hadoop.ReduceDriver.exe -file D:\MyApp\Hadoop\MapReduce\FirstJob\bin\Debug\MRLib\Microsoft.Hadoop.MapDriver.e

p\MapReduce\FirstJob\bin\Debug\MRLib\Microsoft.Hadoop.ReduceDriver.exe -file D:\MyApp\Hadoop\MapReduce\FirstJob\bin\Debug\MRLib\Microsoft.Hadoop.CombineDriver.exe -file “D:\MyApp\Hadoop\MapRedu

irstJob.dll” -file “D:\MyApp\Hadoop\MapReduce\FirstJob\bin\Debug\Microsoft.Hadoop.MapReduce.dll” -file “D:\MyApp\Hadoop\MapReduce\FirstJob\bin\Debug\Newtonsoft.Json.dll” -cmdenv “MSFT_HADOOP_MA

-cmdenv “MSFT_HADOOP_MAPPER_TYPE=FirstJob.TemperatureMapper” -cmdenv “MSFT_HADOOP_REDUCER_DLL=FirstJob.dll” -cmdenv “MSFT_HADOOP_REDUCER_TYPE=FirstJob.TemperatureReducer”

packageJobJar: [D:\MyApp\Hadoop\MapReduce\FirstJob\bin\Debug\MRLib\Microsoft.Hadoop.MapDriver.exe, D:\MyApp\Hadoop\MapReduce\FirstJob\bin\Debug\MRLib\Microsoft.Hadoop.ReduceDriver.exe, D:\MyApp

Job\bin\Debug\MRLib\Microsoft.Hadoop.CombineDriver.exe, D:\MyApp\Hadoop\MapReduce\FirstJob\bin\Debug\FirstJob.dll, D:\MyApp\Hadoop\MapReduce\FirstJob\bin\Debug\Microsoft.Hadoop.MapReduce.dll, D

e\FirstJob\bin\Debug\Newtonsoft.Json.dll] [/C:/Hadoop/hadoop-1.1.0-SNAPSHOT/lib/hadoop-streaming.jar] C:\Users\Teo\AppData\Local\Temp\streamjob7017247708817804198.jar tmpDir=null

12/12/28 12:35:20 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform… using builtin-java classes where applicable

log4j:ERROR Failed to rename [C:\Hadoop\hadoop-1.1.0-SNAPSHOT\logs/hadoop.log] to [C:\Hadoop\hadoop-1.1.0-SNAPSHOT\logs/hadoop.log.2012-12-27].

12/12/28 12:35:20 WARN snappy.LoadSnappy: Snappy native library not loaded

12/12/28 12:35:20 INFO mapred.FileInputFormat: Total input paths to process : 1

12/12/28 12:35:20 INFO streaming.StreamJob: getLocalDirs(): [c:\hadoop\hdfs\mapred\local]

12/12/28 12:35:20 INFO streaming.StreamJob: Running job: job_201212271510_0010

12/12/28 12:35:20 INFO streaming.StreamJob: To kill this job, run:

12/12/28 12:35:20 INFO streaming.StreamJob: C:\Hadoop\hadoop-1.1.0-SNAPSHOT/bin/hadoop job -Dmapred.job.tracker=localhost:50300 -kill job_201212271510_0010

12/12/28 12:35:20 INFO streaming.StreamJob: Tracking URL:

12/12/28 12:35:21 INFO streaming.StreamJob: map 0% reduce 0%

12/12/28 12:35:38 INFO streaming.StreamJob: map 100% reduce 0%

12/12/28 12:35:50 INFO streaming.StreamJob: map 100% reduce 100%

12/12/28 12:35:56 INFO streaming.StreamJob: Job complete: job_201212271510_0010

12/12/28 12:35:56 INFO streaming.StreamJob: Output: output/Temp

4.   Using the web interface or the JavaScript console, go to the output folder and view the part-00000 file to see the output (should match your testing results).


Happy Holidays!

As another year is winding down, it’s time to review and plan ahead. 2012 was a great year for both Prologika and BI. On the business side of things, we achieved Microsoft Gold BI and Silver Data Platform competencies. We added new customers and consultants. We completed several important projects with Microsoft acknowledging two of them.

2012 was an eventful year for Microsoft BI. SQL Server 2012 was released in March. It added important BI enhancements, including Power View, PowerPivot v2, Reporting Services End-User Alerting, Analysis Services in Tabular mode, Data Quality Services, Integration Services enhancements, MDS Add-In for Excel, Reporting in the Cloud, and self-service BI for Big Data with the Excel Hive add-in. The next BI wave came with Office 2013 and added important organizational and self-service BI features, including PowerPivot Integration in Excel 2013, Power View Integration in Excel 2013, Excel updatable web reports in SharePoint, productivity enhancements (Flash Fill, Quick Explore, Quick Analysis, and so on), PerformancePoint theming support and enhanced filtering, better mobile BI support, and self-service BI in Office 365.

Microsoft added support for Big Data and Hadoop both on cloud with HDInsight Service and on-premises with the CTP release of HDInsight Server. Finally, we got the public prerelease bits of DAXMD to connect Power View to multidimensional cubes.

As we witnessed, BI is a very important part of the Microsoft data strategy. Although overwhelming in times, I hope the trend will continue in 2013 and beyond. In the spirit of the season, here is my top 5 wish list:

  1. Continuing focus on integration and simplification – Fast-paced in nature, Microsoft BI has grown in complexity and redundancy through evolution and product acquisitions. Personally, I’d like to see further unification of the Multidimensional and Tabular models, so BI pros don’t have to choose which path to take and what compromise to make. Integration opportunities exist in other areas, such DQS and MDS, as well Tabular native support in the client tools (Excel and SSRS).
  2. Extending mobile BI reach – Customers are asking for it.
  3. Easier and simpler self-service BI – Excel 2013 has started the path but I think we can do a better job to simplify the user experience and compete more successfully with other self-service BI vendors.
  4. BI in the cloud – This will enable interesting scenarios and extend the reach of BI products and services.
  5. Enterprise lineage and change impact analysis – I think it’s about time to have this.

Most importantly, I hope to see Microsoft B having a renewed focus on customers in 2013. We should be listening more to our customers as sometimes as geeks we tend to be too much caught up in technology and we learn our lesson the hard way.

Stay happy and healthy in 2013!