This document discusses using Pig to process XML data from the Northwind database. It demonstrates loading XML data from HDFS, transforming the XML structure into a Pig-readable format, and performing queries on the data such as filtering by dates, counting orders by year/month, finding orders matching country criteria, and sorting by date fields.

1. Pig -
Working with XML
Ram Kedem

2. Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
ramkedem.com
Working with XML
• This presentation is intended to demonstrate how to
processes XML data using PIG.
• The data used to illustrate this topic is taken from MSSQL
Northwind database
• This presentation is based on
• hadoop-1.1.2
• pig-0.11.1
Results may vary under different version of Pig or Hadoop

3. Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Sample Data
<orders>
<OrderID>10248</OrderID>
<CustomerID>VINET</CustomerID>
<EmployeeID>5</EmployeeID>
<OrderDate>1996-07-04T14:15:14.257</OrderDate>
<RequiredDate>1996-08-01T14:15:14.257</RequiredDate>
<ShippedDate>1996-07-16T14:15:14.257</ShippedDate>
<ShipCity>Reims</ShipCity>
<ShipCountry>France</ShipCountry>
</orders>

4. Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
ramkedem.com
Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Load Data – Move into HDFS
Our first step is to move the XML file to the HDFS as we’re
not working in Local Mode
• hadoop fs -put ORDERS_XML.xml
/user/hduser/orders/orders_xml.xml

5. Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Load XML Using Pig
Next, from Pig Shell we’ll register Piggybank and load the XML using
XMLLoader
REGISTER '/home/hduser/Downloads/piggybank.jar' ;
LOAD_ORDERS = LOAD
'/user/hduser/orders/orders_xml.xml' USING
org.apache.pig.piggybank.storage.XMLLoader('orders')
AS (mydoc:chararray);

6. Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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XML to Pig Structure
• Next we’ll translate the XML structure into a format Pig can
understand.
• This phase involves two steps :
• Using Regular Expression translate the XML structure into a Pig
“table” (GENERATE FLATTEN)
• Map each column in that table and name it (AS)

7. Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
ramkedem.com
Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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XML to Pig Structure
CLEAN = FOREACH LOAD_ORDERS GENERATE
FLATTEN
(REGEX_EXTRACT_ALL(mydoc,
'<orders>s*<OrderID>(.*)</OrderID>s*<Cu
stomerID>(.*)</CustomerID>s*<EmployeeID>(
.*)</EmployeeID>s*<OrderDate>(.*)</OrderD
ate>s*<RequiredDate>(.*)</RequiredDate>
s*<ShippedDate>(.*)</ShippedDate>s*<ShipC
ity>(.*)</ShipCity>s*<ShipCountry>(.*)</S
hipCountry>s*</orders>'))
AS
(OrderID:chararray,CustomerID:chararray,Emp
loyeeID:chararray,OrderDate:chararray,Requi
redDate:chararray,ShippedDate:chararray,Shi
pCity:chararray,ShipCountry:chararray) ;

8. Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Filter by Id (int)
• From this step we can start and query our data.
• First query is filter by INT, As we didn't map the columns to
the right datatype, we'll use conversion functions.
CONV_CLEAN = FOREACH CLEAN GENERATE
(int)OrderID, ShipCity, ShipCountry ;
FILTER_CONV_CLEAN = FILTER CONV_CLEAN
BY OrderID == 11066 ;

9. Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
ramkedem.com
Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Filter by date, using GetYear
• Same goes with dates, only this time we'll use the GetYear
function as well
CONV_CLEAN = FOREACH CLEAN GENERATE
(int)OrderID, ShipCity, ShipCountry,
GetYear(ToDate(ShippedDate)) AS
YearShippedDate;
FILTER_CONV_CLEAN = FILTER CONV_CLEAN
BY YearShippedDate == 2014 ;
DUMP FILTER_CONV_CLEAN ;

10. Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Comparing Dates using
DaysBetween
• The value can be either :
• Negative (ShippedDate is before 1998-05-04)
• Positive (ShippedDate is after 1998-05-04)
• Equal to zero (ShippedDate is equal to 1998-05-04)
CONV_CLEAN = FOREACH CLEAN GENERATE
(int)OrderID,
ShipCity,
ShipCountry,
ToDate(ShippedDate) AS GeneralDate,
DaysBetween(
ToDate(ShippedDate) ,
ToDate('1998-05-04', 'yyyy-MM-dd')
) ;

11. Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Comparing Dates using
DaysBetween
CONV_CLEAN = FOREACH CLEAN GENERATE
(int)OrderID, ShipCity, ShipCountry,
ToDate(ShippedDate) AS GeneralDate;
FILTER_CONV_CLEAN = FILTER CONV_CLEAN BY
DaysBetween
(GeneralDate ,
ToDate('2014-11-29', 'yyyy-MM-dd')
) == (long)0;
DUMP FILTER_CONV_CLEAN ;

12. Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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How many orders shipped per
year and month ?
GENERATE_CLEAN = FOREACH CLEAN GENERATE
(int)OrderID,
GetYear(ToDate(ShippedDate)) AS YearShippedDate ,
GetMonth(ToDate(ShippedDate)) AS MonthShippedDate;
GROUP_CLEAN = GROUP GENERATE_CLEAN BY
(YearShippedDate,MonthShippedDate) ;
COUNT_ORDERS = FOREACH GROUP_CLEAN
GENERATE group,
COUNT(GENERATE_CLEAN.OrderID) AS Count ;
ORDER_COUNT_ORDERS = ORDER COUNT_POSTS BY Count ;

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Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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List occurrences of "Germany" / "GER"
(case insensitive search)
GENERATE_CLEAN = FOREACH CLEAN GENERATE
(int)OrderID, ShipCity, ShipCountry ;
FILTER_BY_SC = FILTER GENERATE_CLEAN
BY
UPPER(ShipCountry) == 'GERMANY'
OR
UPPER(ShipCountry) MATCHES '.*GER.*' ;

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Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Top 10 Orders
(by ShippedDate)
GENERATE_CLEAN = FOREACH CLEAN GENERATE
(int)OrderID, ShipCity, ShipCountry ,
ToDate(ShippedDate) AS ShippedDate ;
ORDER_LIST = ORDER GENERATE_CLEAN BY
ShippedDate DESC ;
TOP_10 = LIMIT ORDER_LIST 10 ;

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Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Top 3 ShippedDate per year
GENERATE_CLEAN = FOREACH CLEAN GENERATE
GetYear(ToDate(ShippedDate)) AS
YearCreationDate , ShipCity, ShipCountry,
ToDate(ShippedDate) AS ShipDates;
GROUP_CLEAN = GROUP GENERATE_CLEAN BY
YearCreationDate ;
TOP_3 = FOREACH GROUP_CLEAN
{
RESULT = TOP(3, 3, GENERATE_CLEAN) ;
GENERATE group, RESULT;
} ;

16. Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Copyright 2014 © Ram Kedem. All rights reserved. Not to be reproduced without written consent
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Using DaysBetween
GENERATE_CLEAN = FOREACH CLEAN GENERATE OrderID,
ShipCity, ShipCountry, RequiredDate, OrderDate,
DaysBetween(ToDate(RequiredDate),
ToDate(OrderDate)) AS DaysBetween ;
GENERATE_CLEAN = LIMIT GENERATE_CLEAN 1000 ;
ORDER_LIST =
ORDER GENERATE_CLEAN BY DaysBetween DESC ;
TOP_10 = LIMIT ORDER_LIST 10 ;