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Programming Hive pdf, Free Programming Hive Ebook Download, Free Programming programming hive introduces hive, an essential tool in the hadoop. The Free Apache Hive Book explains how to access big data with Hadoop This free and open ebook is written for SQL savvy business users. Read "Programming Hive Data Warehouse and Query Language for Hadoop" by Edward Capriolo available from Rakuten Kobo. Sign up today and get $5 off.

This free and open ebook is written for SQL savvy business users, data analysts, data scientists, developers and with some advanced tips for devops. You can find the markdown source for the book on GitHub. You are free to share copy, distribute and transmit the book , you can change the book extend, fix, shorten, translate, …. However, you need to attribute the work to the author, Christian Prokopp prokopp. Lastly, you can not use the work commercially. You can contact me if you like to do so though. This book was sparked by the need to give some tutorial material to business users at Rangespan.

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Let us reduce the selection to a specific indicator. We can further restrict the result to return only the country name and the indicator result of the year Which countries have the largest and smallest percentage of trade to GDP ratio? Consequently, ordering a large set of data can take a very long time. It sorts the data by reducer and not globally, which can be much faster for large data sets.

If you ran the example on the Hortonworks VM or any other setup with one reducer your query result will look like the rows are not organised by indicator names.

It ensures that all rows with the same indicator are sent to the same reducer but it does not sort them. This can be useful if you write a streaming job with a custom reducer.

The reducer, for example, could aggregate data based on the distribution key and does not require the data in order as long as it is complete in regard to the distribution key. The result ensures that all rows with the same indicator name are grouped and all groups of a reducer are sorted but the global order of the groups is not guaranteed.

This is still a very useful combination of commands since we can extend it by additional fields. The above query will return all results of all countries for the two indicators where the data is available, i. The result will be sorted by indicator and since the input was sorted by country already the result is also sorted by country within each indicator.

It is limited by the cardinality of the key though. If you have only two keys then only two reducers can work in parallel independent of you cluster size. Imagine sorting orders by category and then analyse each category of orders. You may have millions of orders and hundreds of categories. Clustering the sorting would provide a tremendous performance improvement since the sort can potentially be done by hundreds of cluster nodes in parallel.

Joins are very common operations to combine related tables and join them on a shared value. Until recently duplication of columns in tables was seen as wasteful and hard to change. The wdi table has a country name and country code column. Since storage has become cheap and plentiful we can observe a tendency to duplicate commonly used information in tables like country name to reduce the need for JOINs. This simplifies daily operations, analytics, and saves computation on JOINs on the expense of storage.

However, there are plenty of examples where JOINs are needed. The most common example is the inner JOIN. We declared the aliases o and n after mentioning the tables in the query to simplify the query The results above show only rows that satisfy the JOIN, i.

An outer join ensures that the result contains a row for each input table. It does not matter if the left or the right side of the join has null values for a right or left part of the join. It uses four delimiters to split an output or input file into rows, columns and complex data types. It describes the default splitting of rows by newline character.

Unfortunately, this delimiter can not be set to anything else but newline. The default for collection items is 0x02 and 0x03 for map keys.

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It is important understand that Hive does not support escaped characters. For example, a sheet saved as a CSV file from Excel may be formated with double quotation to escape commas in fields:.

It is optimised for fast and block size IO.

Most tools in the Hadoop ecosystem can read SequenceFile format. Hive stored its row data in the value part of the pairs. This format is more efficient than plain text and it can be compresses with the available formats in Hadoop either by value row or block data chunk. The latter is usually more efficient since a compression is more likely to find reducible patterns in a large data block than in a row.

The disadvantage of this format is that Hive has to read and parse every row for every query presuming no partitioning to execute the query conditions against it. This can lead to unnecessary read operations if only some rows or some columns are relevant to the query.

Each file stores one or more row groups, which is a number of rows. The row groups themselves are stored in a columnar structure. For example, a file may store rows 1—1, in the first group and row 1, to 2, in the next and both groups in one RCFile. The row group itself stores all the columns together. The first group then would save the first column of row 1 to 1, and then next column and so forth.

The benefit of grouping columns is a more efficient compression since similar data is near to each other. More importantly query conditions can be pushed down to read only relevant parts of a table. This is especially helpful with wide tables and queries that only apply to a few columns. In these cases Hive can skip large parts of the data to save IO and computing time.

The ORC file format became generally available with Hive 0. Hi Christian, Thanks for writing this book. It is one of the best introductions i could find on internet. Would recommend it to anyone who wants to learn hive and is looking for resources to begin from. I can not agree more to the views from Surabhi.

Absolutely, bang on content for anyone who wants to dive into the Hive world. Thanks for the book. It helped me get insight into how Hive works. Is there settings changes required to execute Distributed by etc.. Number of reduce tasks not specified. Estimated from input data size: In order to change the average load for a reducer in bytes: In order to limit the maximum number of reducers: In order to set a constant number of reducers: You can ignore this.

These are advanced settings and you can leave it to Hive and Hadoop to manage the distribution of tasks unless you want to fine tune it for specific reasons. Thanks Amish. Unfortunately, I have not done anything like it for HBase.

You may find the reference guide very helpful though. It is really a nice compilation of what is HIVE. It has provided all the basic information for any one who wants to learn HIVE. Thanks a ton Christian. Is there any extension for this book? There is none planned at the moment merely due to lack of time on my side.

However, the documents are open source and on github for anyone to extend and send pull requests. You can get it from the Github repository: Hi Huzefa, You can find them in the linked Github repository: Author required.

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Email will not be published required. This site uses Akismet to reduce spam. Learn how your comment data is processed. The Free Hive Book. Thank you. Why this book?

Table of Contents The book is work in progress and the TOC as well as the actual chapters will evolve. Accessing Big Data The downside, which Facebook encountered, was that data stored in Hadoop is inaccessible to business users. Democratising Big Data Hive is a success story. What this book will teach You will learn how to access data with Hive.

Installing Hive for evaluation purposes is best done with a virtual machine provided by one of the popular Hadoop distributions: Creating an empty table Let us create a table, which will be a simple list of all names of all countries. Go to the Beeswax query editor and execute the following query: Describing a table We can get all the information we need about a table through a query too.

Go back to the query editor and execute: Go tot he query editor and execute: Go to the query editor and execute the equivalent query to drop the table: Only the detailed description reveals a difference: If you drop the table the data will remain: JOIN Joins are very common operations to combine related tables and join them on a shared value. For example, a sheet saved as a CSV file from Excel may be formated with double quotation to escape commas in fields: I am eagerly waiting to see the next chapters too.

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