##Tutorial 3: Data Processing With Hive - Processing Baseball Stats With Hive
This tutorial is from the Hortonworks Sandbox 2.0 - a single-node Hadoop cluster running in a virtual machine. Download the Hortonworks Sandbox to run this and other tutorials in the series.
This tutorial was derived from one of the lab problems in the Hortonworks Developer training class. The developer training class covers uses of the tools in the Hortonworks Data Platform and how to develop applications and projects using the Hortonworks Data Platform. You can find more information about the course at Hadoop Training for Developers
Apache Hive (running time: about 16 minutes)
Hive is a component of Hortonworks Data Platform(HDP). Hive provides a SQL-like interface to data stored in HDP. In the previous tutorial we used Pig which is a scripting language with a focus on dataflows. Hive provides a database query interface to Apache Hadoop.
People often ask why do Pig and Hive exist when they seem to do much of the same thing. Hive because of its SQL like query language is often used as the interface to an Apache Hadoop based data warehouse. Hive is considered friendlier and more familiar to users who are used to using SQL for querying data. Pig fits in through its data flow strengths where it takes on the tasks of bringing data into Apache Hadoop and working with it to get it into the form for querying. A good overview of how this works is in Alan Gates posting on the Yahoo Developer blog titled Pig and Hive at Yahoo! From a technical point of view both Pig and Hive are feature complete so you can do tasks in either tool. However you will find one tool or the other will be preferred by the different groups that have to use Apache Hadoop. The good part is they have a choice and both tools work together.
We are going to do the same data processing task as we just did with Pig in the previous tutorial. We have several files of baseball statistics and we are going to bring them into Hive and do some simple computing with them. We are going to find the player with the highest runs for each year. This file has all the statistics from 1871-2011 and contains more that 90,000 rows. Once we have the highest runs we will extend the script to translate a player id field into the first and last names of the players.
The data files we are using comes from the site www.seanlahman.com. You can download the data file from:
http://seanlahman.com/files/database/lahman591-csv.zip
Once you have the file you will need to unzip it into a directory. We will be uploading just the Master.csv and Batting.csv files from the dataset.
We start by selecting the File Browser from the top tool bar. The File Browser shows you the files in the HDP file store. In this case the file store resides in the Hortonworks Sandbox VM.
Click on the Upload button
You want to select Files. Then you will get a dialog box.
When you click on the Upload a file button you will get a dialog box. Navigate to where you stored the Batting.csv file on your local disk and select Batting.csv. Do the same thing for Master.csv. When you are done you will see there are two files in your directory.
Lets start Beeswax by clicking on the bee icon in the top bar. Beeswax is a user interface to the Hive data warehouse system for Hadoop.
Beeswax provides a GUI to Hive. On right is a query editor. There is a limit of one query per execute cycle. A query may span multiple lines. At the bottom there are buttons to Execute the query, Save the query with a name, Explain the query and to start a new query.
Before we get started let's take a look at how Pig and Hive data models differ. In the case of Pig all data objects exist and are operated on in the script. Once the script is complete all data objects are deleted unless you stored them. In the case of Hive we are operating on the Apache Hadoop data store. Any query you make, table that you create, data that you copy persists from query to query. You can think of Hive as providing a data workbench where you can examine, modify and manipulate the data in Apache Hadoop. So when we perform our data processing task we will execute it one query or line at a time. Once a line successfully executes you can look at the data objects to verify if the last operation did what you expected. All your data is live, compared to Pig, where data objects only exist inside the script unless they are copied out to storage. This kind of flexibility is Hive's strength. You can solve problems bit by bit and change your mind on what to do next depending on what you find.
The first task we will do is create a table to hold the data. We will type the query into the composition area on the right like this. Once you have typed in the query hit the Execute button at the bottom.
create table temp_batting (col_value STRING); 
The query returns "No data available in the table" because at this point we just created an empty table and we have not copied any data in it.
Once the query has executed we can click on Tables at the top of the composition area and we will see we have a new table called temp_batting.
Clicking on the Browse Data button will let us see the data and right now the table is empty. This is a good example of the interactive feel you get with using Hive.
The next line of code will load the data file Batting.csv into the table temp_batting. We can start typing the code and we will notice there is a helper feature that helps us fill in the correct path to our file.
The complete query looks like this.
LOAD DATA INPATH '/user/hue/Batting.csv' OVERWRITE INTO TABLE temp_batting;
After executing the query we can look at the Tables again and when we browse the data for temp_batting we see that the data has been read in. Note Hive consumed the data file Batting.csv during this step. If you look in the File Browser you will see Batting.csv is no longer there.
Now that we have read the data in we can start working with it. The next thing we want to do extract the data. So first we will type in a query to create a new table called batting to hold the data. That table will have three columns for player_id, year and the number of runs.
create table batting (player_id STRING, year INT, runs INT); 
Then we extract the data we want from temp_batting and copy it into batting. We will do this with a regexp pattern. To do this we are going to build up a multi-line query. The first line of the query create the table batting. The three regexp_extract calls are going to extract the player_id, year and run fields from the table temp_batting. When you are done typing the query it will look like this. Be careful as there are no spaces in the regular expression pattern.
insert overwrite table batting SELECT regexp_extract(col_value, '^(?:([^,]*)\,?){1}', 1) player_id, regexp_extract(col_value, '^(?:([^,]*)\,?){2}', 1) year, regexp_extract(col_value, '^(?:([^,]*)\,?){9}', 1) run from temp_batting;
Execute the query and look at the batting table. You should see data that looks like this.
Now we have the data fields we want. The next step is to group the data by year so we can find the highest score for each year. This query first groups all the records by year and then selects the player with the highest runs from each year.
SELECT year, max(runs) FROM batting GROUP BY year; 
The results of the query look like this.
Now we need to go back and get the player_id(s) so we know who the player(s) was. We know that for a given year we can use the runs to find the player(s) for that year. So we can take the previous query and join it with the batting records to get the final table.
SELECT a.year, a.player_id, a.runs from batting a JOIN (SELECT year, max(runs) runs FROM batting GROUP BY year ) b ON (a.year = b.year AND a.runs = b.runs) ;
The resulting data looks like:
This query may take a couple of minutes. While you're waiting, if you have internet access, take a look at this video from Alan Gates to hear about the future of HCatalog: Future of HCatalog
So now we have our results. As described earlier we solved this problem using Hive step by step. At any time we were free to look around at the data, decide we needed to do another task and come back. At all times the data is live and accessible to us.
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