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Importing Windows Azure Marketplace DataMarket DataSets to Apache Hadoop on Windows Azure’s Hive Databases

04.18.2012
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Introduction

From the Apache Hive documentation:

imageThe Apache HiveTM data warehouse software facilitates querying and managing large datasets residing in distributed storage. Built on top of Apache HadoopTM, it provides

  • Tools to enable easy data extract/transform/load (ETL)
  • A mechanism to impose structure on a variety of data formats
  • Access to files stored either directly in Apache HDFSTM or in other data storage systems such as Apache HBaseTM
  • Query execution via MapReduce

Hive defines a simple SQL-like query language, called QL, that enables users familiar with SQL to query the data. At the same time, this language also allows programmers who are familiar with the MapReduce framework to be able to plug in their custom mappers and reducers to perform more sophisticated analysis that may not be supported by the built-in capabilities of the language. QL can also be extended with custom scalar functions (UDF's), aggregations (UDAF's), and table functions (UDTF's).

Hive does not mandate read or written data be in the "Hive format"---there is no such thing. Hive works equally well on Thrift, control delimited, or your specialized data formats. Please see File Format and SerDe in the Developer Guide for details.

Hive is not designed for OLTP workloads and does not offer real-time queries or row-level updates. It is best used for batch jobs over large sets of append-only data (like web logs). What Hive values most are scalability (scale out with more machines added dynamically to the Hadoop cluster), extensibility (with MapReduce framework and UDF/UDAF/UDTF), fault-tolerance, and loose-coupling with its input formats.

TechNet’s How to Import Data to Hadoop on Windows Azure from Windows Azure Marketplace wiki article of 1/18/2012, last revised 1/19/2012, appears to be out of date and doesn’t conform to the current Apache Hadoop on Windows Azure and MarketPlace DataMarket portal implementations. This tutorial is valid for the designs as of 4/3/2012 and will be updated for significant changes thereafter.

Prerequisites

The following demonstration of the Interactive Hive console with datasets from the DataMarket requires an invitation code to gain access to the portal. If you don’t have access, complete this Microsoft Connect survey to obtain an invitation code and follow the instructions in the welcoming email to gain access to the portal. Read the Windows Azure Deployment of Hadoop-based services on the Elastic Map Reduce (EMR) Portal for a detailed description of the signup process (as of 2/23/2012).

You also need an Windows Azure Marketplace Data Market account with subscriptions for one or more (preferably free) OData datasets, such as Microsoft’s Utility Rate Service. To subscribe to this service:

1. Navigate to the DataMarket home page, log in with your Windows Live ID and click the My Account link to confirm your User Name (email address) and obtain your Primary Account Key:

image

2. Open Notepad, copy the Primary Account Key to the clipboard, and paste it to Notepad’s window for use in step ? below.

3. Search for utility, which displays the following screen:

image

4. Click the Utility Rate Service to open its Data page:

image

5. Click the Sign Up button to open the Subscription page, mark the Accept the Offer Terms and Privacy Policy check box:

image

6. Click the Sign Up button to display the Purchase page:

image

Obtaining Values Required by the Interactive Hive Console

The Interactive Hive Console requires your DataMarket User ID, Primary Account Key, and text of the OData query.

1. If you’re returning to the DataMarket, confirm your User ID and save your Primary Account Key as described in step 1 of the preceding section.

2. If you’re returning to the DataMarket, click the My Data link under the My Account link to display the page of the same name and click the Use button to open the interactive DataMarket Service Explorer.

image

If you’ve continuing from the preceding section, click the Explore This DataSet link to open the interactive DataMarket Service Explorer.

3. In the DataMarket Service Explorer, accept the default CustomerPlans query and click Run Query to display the first 100 records:

image

4. Click the Develop button to display the OData query URL to retrieve the first 100 records to a text box:

image

5. Copy the query text and paste it to the Notepad window with your Primary Account Key.

Loading the Hive Table with Data from the Utility Rate Service

1. Navigate to the

Apache Hadoop on Windows Azure portal, log in with the Windows Live ID you used in your application for an invitation code, and scroll to the bottom of the Welcome Back page:

image

Note: By default, your cluster’s lifespan is 48 hours. You can extend it’s life when 6 hours or fewer remain by clicking the Extend Cluster button.

2. Click the Manage Cluster tile to open that page:

image

3. Click the DataMarket button to open the Import from DataMarket page. Fill in the first three text boxes with your Live ID and the data you saved in the preceding section; remove the $top=100 parameter from the query to return all rows, and type UtilityRateService as the Hive Table Name:

image

4. Click the Import Data button to start the import process and wait until import completes, as indicated by the DataLoader progress = 100[%] message:

image

The text of the generated data import Command with its eight required arguments is:

    c:\apps\dist\bin\dataloader.exe
    -s datamarket
    -d ftp
    -u "roger_jennings@compuserve.com"
    -k "2ZPQEl2CouvtQw+..............................Qud0E="
    -q "https://api.datamarket.azure.com/Data.ashx/Microsoft/UtilityRateService/ Prices?"
    -f "ftp://d5061cb5f84c9ea089bfa052f1a0a3f2:da835ebe965c87b428923032057014f7@
    localhost:2222/uploads/UtilityRateService/UtilityRateService"
    -h "UtilityRateService"

 

Querying the UtilityRateService Hive Database

Hive QL query syntax is similar, but not identical, to ANSI SQL. Hive translates QL queries into MapReduce job(s) and executes them in the cluster. This causes Hive query batches to exhibit substantial latency.

Following are a few of the basic similarities and differences in Hive and T-SQL DQL statements:

  • Hive supports basic SQL SELECT syntax but not IN, EXISTS or subqueries in the WHERE clause.
  • LIMIT n with a DESC sort substitutes for T-SQL’s TOP(n) modifier.
  • Hive supports LEFT, LEFT SEMI, RIGHT, FULL and OUTER equality JOINS between two or more tables but not inequality joins.
  • Hive offers a wide variety of aggregate functions, including count(col), sum(col), avg(col), min(col), max(col), variance(col), var_pop(col), var_samp(col), stddev_pop(col), stddev_samp(col), and many others


To execute a few sample DQL queries with Hive QL, do the following:

1. Return to the home (Welcome Back) page and click the Interactive Console tile to open the JavaScript console and click the Hive button.

2. Wait a few seconds for the Table and Column list boxes to populate and select the utilityrateservice table, which populates the the Columns list with the first column name.

3. To verify that DataMarket’s default interactive page size of 100 records doesn’t apply to DataMarket API queries, type SELECT Count(*) FROM utilityrateservice in the text box (Hive QL isn’t case-sensitive).

4. Click the Evaluate button, and wait about a minute for the query to complete, which displays the result and Hive history:

image

5. Click the Clear screen button, type SELECT * FROM utilityrateservice LIMIT 5 in the text box and click the Evaluate button to display five random rows:

image

Note: Evaluating SELECT * FROM utilityrateservice ORDER BY id DESC LIMIT 5 takes 15 or more minutes because all rows must be loaded, sorted and then the top five displayed. Returned data sets larger than 1 MB are truncated for display.

6. To display the table schema with data types, evaluate the DESCRIBE utilityrateservice statement:

image

7. To add detailed information about the table, evaluate DESCRIBE EXTENDED utilityrateservice:

image

Viewing Job History

To view the history, execution time and status of the MapReduce jobs you’ve run, return to the home page and click the Job History tile:

image

Attempting to Load ContentItem Records from the Microsoft Codename “Social Analytics” DataSet

Microsoft Codename “Social Analytics” is an SQL Azure Labs incubator project that is “aimed at developers who want to integrate social web information into business applications. This lab includes:

  1. The Engagement Client, used to browse and view analytics on a social dataset
  2. Two pre-defined, actively streaming datasets which include data from top social sources such as Twitter, Facebook, blogs and forums
  3. The Social Analytics API, based on the Open Data Protocol and delivered through the DataMarket on the Windows Azure Marketplace” [Emphasis added.]

The “Social Analytics” team maintains a blog here.

Interactive queries attempted as described in the earlier Obtaining Values Required by the Interactive Hive Console section above don’t appear to work.

My Codename “Social Analytics” WinForms Client sample application summarizes consumer engagement with and sentiment about Windows 8 based on a combination of Twitter tweets, retweets, replies, and direct messages, as well as messages from a few other social Web sites. This screen capture shows data for about four weeks in October/November 2011:

image

The ContentItems dataset contained 1,674,190 rows on 4/3/2012 at 11:00 AM PDT.

A C# LINQ query generates the following OData URL query (captured by Fiddler2) to return selected columns of the ContentItems table in 500-row batches:

https://api.datamarket.azure.com/Vancouver/VancouverWindows8/ContentItems()?$filter=CalculatedToneId%20ne%20null&$orderby=PublishedOn%20desc&$skip=0&$top=500&$expand=ContentItemType&$select=Id,ContentItemTypeId, ContentItemType/Name,Title,PublishedOn,CalculatedToneId,ToneReliability, Summary,HtmlUrl

 

Pasting my User Name, Account Key, and the above query into the Import from DataMarket page to create a Hive ContentItems table, as shown here:

image

Clicking the Import Data button results in the following error message:

image

The Debug Output is:

Caused by: The remote server returned an error: (400) Bad Request.
Stack trace:
at System.Net.HttpWebRequest.GetResponse()
at Microsoft.Hadoop.DataLoader.ODataSource.ExportSchema()
at Microsoft.Hadoop.DataLoader.DataLoaderMediator.StartDataTransfer(IDataSource source, IHdfsChannel destination)
at Microsoft.Hadoop.DataLoader.DataLoaderProgram.Main(String[] args)

 

I’ve notified the Codename “Social Analytics” team of this problem and will update this article if and when I receive information from them.

For more details about Codename “Social Analytics,” see:

Conclusion

Data import from the Windows Azure Marketplace DataMarket to user-specified Hive tables is one of three prebuilt data import options for Hadoop on Azure. The other two on the Manage Cluster page are:

  • ASV (Windows Azure blob storage)
  • S3 (Amazon Simple Storage Service)

Import of simple DataMarket datasets is a relatively simple process. As demonstrated by the problems with the VancouverWindows8 dataset’s ContentItems table, more complex datasets might fail for unknown reasons.

Writing Java code for MapReduce jobs isn’t for novices or faint-hearted code jockeys. The interactive Hive console and the HiveQL syntax simplify the query process greatly.

 

Published at DZone with permission of its author, Roger Jennings. (source)

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