By Balaswamy Vaddeman

Learn to take advantage of Apache Pig to increase light-weight titanic information functions simply and speedy. This booklet indicates you several optimization strategies and covers each context the place Pig is utilized in significant facts analytics. Beginning Apache Pig indicates you ways Pig is simple to benefit and calls for fairly little time to enhance substantial information applications.The booklet is split into 4 components: the full good points of Apache Pig; integration with different instruments; tips on how to resolve complicated company difficulties; and optimization of tools.You'll observe issues reminiscent of MapReduce and why it can't meet each enterprise desire; the good points of Pig Latin similar to facts kinds for every load, shop, joins, teams, and ordering; how Pig workflows should be created; filing Pig jobs utilizing Hue; and dealing with Oozie. you are going to additionally see the way to expand the framework through writing UDFs and customized load, shop, and clear out capabilities. eventually you will conceal diverse optimization options akin to collecting statistics a couple of Pig script, becoming a member of techniques, parallelism, and the function of knowledge codecs in stable performance.

What you'll Learn• Use the entire beneficial properties of Apache Pig• combine Apache Pig with different instruments• expand Apache Pig• Optimize Pig Latin code• clear up diverse use situations for Pig LatinWho This ebook Is ForAll degrees of IT pros: architects, enormous info lovers, engineers, builders, and large facts administrators

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2222212145218886998. Summary of Simple Data Types Table 2-1 summarizes all the simple data types. Table 2-1. 00 Complex Data Types Complex data types in Pig Latin are used to process more than one data point. Complex data is classified as a map, tuple, or bag, as specified in Figure 2-2. 24 Chapter 2 ■ Data Types Figure 2-2. Complex data types map A map data type holds a set of key-value pairs. Maps are enclosed in straight brackets. The key and value are separated by the # character. The key should be the chararray data type and should be unique.

Here is an example of data with an inner bag: (1,{( Bala, 1972, Software Engineer)}) You can convert fields with simple data types into bag data types using the TOBAG function. The following lines of code convert existing fields into bag data types. emp = load '/data/employees' as (ename:chararray, empid:int, desg:charray); empbag=foreach emp generate TOBAG(ename,empid,desg); Dump empbag; ({(Bala),(1972),(Software Engineer)}) Summary of Complex Data Types Table 2-2 summarizes the complex data types.

An example of load follows: emp = load '/data/employee' using PigStorage(',') as (eno:int,ename:chararra y,salary:int,deptno:int); The as operator defines the schema, and using specifies the function that you applied while reading data. By default, Pig Latin chooses PigStorage() for both the schema and the function. bytearray is the default data type in the default schema. The number list starting at 0 is taken as the default field name. Pigstorage('\t') applies a tab as the default delimiter, but you can specify any other character as the delimiter.

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