By Balaswamy Vaddeman
Learn to take advantage of Apache Pig to strengthen light-weight massive info functions simply and speedy. This booklet exhibits you several optimization options and covers each context the place Pig is utilized in enormous facts analytics. Beginning Apache Pig indicates you the way Pig is simple to profit and calls for particularly little time to improve massive information applications.The publication is split into 4 components: the whole gains of Apache Pig; integration with different instruments; tips to remedy advanced enterprise difficulties; and optimization of tools.You'll detect issues reminiscent of MapReduce and why it can't meet each enterprise want; the positive aspects of Pig Latin comparable to information forms for every load, shop, joins, teams, and ordering; how Pig workflows may be created; filing Pig jobs utilizing Hue; and dealing with Oozie. you are going to additionally see tips on how to expand the framework by means of writing UDFs and customized load, shop, and clear out capabilities. eventually you will conceal varied optimization concepts reminiscent of amassing records a few Pig script, becoming a member of ideas, parallelism, and the position of information codecs in strong performance.
What you are going to Learn• Use the entire gains of Apache Pig• combine Apache Pig with different instruments• expand Apache Pig• Optimize Pig Latin code• remedy varied use circumstances for Pig LatinWho This publication Is ForAll degrees of IT pros: architects, sizeable information fanatics, engineers, builders, and massive information administrators
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Additional resources for Beginning Apache Pig: Big Data Processing Made Easy
Here’s an example: [empname#Bala] emp = load '/data/employees' as (M:map); 25 Chapter 2 ■ Data Types Here’s another example: emp = load '/data/employees' as (M:map[chararray]); The second example states the value of the data type is chararray. If your data is not in the map data type format, you can convert the two existing fields into the map data type using the TOMAP function. The following code converts the employee name and year of joining a company to the map data type: emp = load 'employees' as (empname:chararray, year:int); empmap = foreach emp generate TOMAP(empname, year); tuple A tuple is an ordered set of fields and is enclosed in parentheses.
To perform arithmetic operations, bytearray casting is performed to double. Similarly, casting from bytearray to datetime, chararray, and boolean data types occurs when the user performs the respective operations. bytearray can also be cast to the complex data types of map, tuple, and bag. Casting Error Both implicit casting and explicit casting throw an error if they cannot perform casting. For example, if you are performing a sum operation on two fields and one of them does not contain a numeric value, then implicit casting will throw an error.
The following is an example that uses the bytearray data type: emp = load '/data/employees' as (eid,ename,salary); Here’s another example: emp = load '/data/employees' as (eid:bytearray,ename:bytearray,salary:bytearray); datetime The only date-based data type available in Pig Latin is datetime, which is used to represent the date and time. The data before T is the date, the data after the T is the time, the data after + is time zone. 000+00:00. emp = load '/data/employees' as (dateofjoining:datetime); biginteger The biginteger data type is the same as the biginteger in Java.