By Balaswamy Vaddeman
Learn how to use Apache Pig to enhance light-weight immense facts purposes simply and fast. This booklet indicates you several optimization strategies and covers each context the place Pig is utilized in vast info analytics. starting Apache Pig indicates you ways Pig is straightforward to benefit and calls for rather little time to improve monstrous info functions. The e-book is split into 4 components: the entire positive aspects of Apache Pig integration with different instruments how one can resolve advanced enterprise difficulties and optimization of instruments. Youll detect themes reminiscent of MapReduce and why it can't meet each company want the gains of Pig Latin similar to information varieties for every load, shop, joins, teams, and ordering how Pig workflows will be created filing Pig jobs utilizing Hue and dealing with Oozie. Youll additionally see the right way to expand the framework through writing UDFs and customized load, shop, and clear out services. eventually youll hide assorted optimization innovations resembling collecting facts a couple of Pig script, becoming a member of thoughts, parallelism, and the function of information codecs in stable functionality. What you are going to study Use all of the beneficial properties of Apache Pig combine Apache Pig with different instruments expand Apache Pig Optimize Pig Latin code clear up various use situations for Pig Latin Who This e-book Is For All degrees of IT execs: architects, colossal information lovers, engineers, builders, and large info directors
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Extra resources for Beginning Apache Pig Big Data Processing Made Easy
But it also has some additional features. One feature is that commands used in the script are saved in history. Thus, they can be executed using aliases from the script after running it. Here’s the syntax: run [–param] [–param_file] piglatinscript options are same as in command exec. • -param: This specifies extra parameters such as a name-value pair. It is not mandatory because some scripts may not have parameters. • -paramfile: This specifies all property names and their values in a file when handling multiple parameters.
Split is used to tokenize sentences into words after applying a comma as a delimiter. explode is a table-generating function that converts every line of words into rows and names new column data as words. This creates a new temporary table called temp, generates a word-wise count using the group by and count functions from the temp table, and creates an alias called count. This query output is displayed on the console. You can create a new table from this table by prepending the create table as select statement like below.
HiveServer2 is a Thrift client that enables BI tools to connect to Hive and retrieve results. Here is how to write a word count program in Apache Hive: select word,count(word) as count from (SELECT explode(split(sentence, ',')) AS word FROM texttable)temp group by word This writes a Hive query that filters the word pear and generates the word count. split is used to tokenize sentences into words after applying a comma as a delimiter. explode is a table-generating function that converts every line of words into rows and names new column data as words.