By Balaswamy Vaddeman
Discover ways to use Apache Pig to enhance light-weight huge info functions simply and fast. This ebook indicates you several optimization recommendations and covers each context the place Pig is utilized in significant info analytics. starting Apache Pig exhibits you ways Pig is simple to profit and calls for fairly little time to enhance gigantic facts functions. The e-book is split into 4 elements: the whole gains of Apache Pig integration with different instruments the best way to clear up advanced company difficulties and optimization of instruments. Youll notice subject matters corresponding to MapReduce and why it can't meet each company want the beneficial properties of Pig Latin corresponding to info forms for every load, shop, joins, teams, and ordering how Pig workflows may be created filing Pig jobs utilizing Hue and dealing with Oozie. Youll additionally see how you can expand the framework by way of writing UDFs and customized load, shop, and filter out capabilities. ultimately youll disguise varied optimization suggestions equivalent to accumulating facts a couple of Pig script, becoming a member of concepts, parallelism, and the position of knowledge codecs in reliable functionality. What you'll research Use the entire positive aspects of Apache Pig combine Apache Pig with different instruments expand Apache Pig Optimize Pig Latin code remedy diversified use circumstances for Pig Latin Who This booklet Is For All degrees of IT execs: architects, monstrous info fanatics, engineers, builders, and massive facts directors
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Extra resources for Beginning Apache Pig Big Data Processing Made Easy
It supports indexing and ACID properties. Hive has some useful components such as the metastore, Hive Query Language, HCatalog, and Hive Server. • The metastore stores table metadata and stats in an RDBMS such as MySQL, Postgres, or Oracle. By default it stores metadata in the embedded RDBMS Apache Derby. • The Hive Query Language (HQL) is a SQL interface to Hadoop that is compiled into MapReduce code. Queries can be submitted through the command-line interface (CLI), the web interface, a Thrift client, an ODBC interface, or a JDBC interface.
Cascading Cascading extensions are available in different languages. Scalding is Scala-based, Cascalog is Clojure-based, and PyCascading is Python-based. All are programming language–based. Though it takes relatively less development time than MapReduce, it cannot be used for ad hoc querying. In Pig Latin, the programming language is required only for advanced analytics, not for simple functions. The word count program in Cascading requires 30 lines of code, and Pig requires only five lines of code.
Most of the time, you will not remember job IDs, so you can use the mapred job -list command to get currently running jobs. Grunt> kill job_201512120001_001; set The set command applies a value to a property name, for both Pig and Hadoop. It can be used in the Grunt shell and Pig Latin scripts. This section shows Pig properties for which you can set values. The set default_parallel command specifies the default number of reducers; in this example, it sets the default number of reducers to 20: Grunt>set default_parallel 20; The set debug command enables and disables debugging in a Pig Latin script.
Beginning Apache Pig Big Data Processing Made Easy by Balaswamy Vaddeman