Table Joins Query in Hive | Hive Tutorial Part-10 | Left Join | Right Join | Inner Join
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Today, we'll delve into the world of joins in Hive. Whether you're new to Hive or looking to enhance your skills, understanding joins is crucial for effective data analysis. In this tutorial, we explore left, right, inner, and full outer joins in Hive.
Overview of Joins in Hive:
Hadoop supports various types of joins, much like SQL joins. We can perform left joins, right joins, inner joins, and even full outer joins on Hive. However, there are certain restrictions, such as only equi-joins or equality joins being possible. This means we can join tables based on conditions like 'A.key = B.key'.
Setting Up Data for Joins:
To demonstrate joins, we'll work with two datasets: 'online retail customer' and 'online retail invoice'. These datasets need to be first loaded into our Hive environment. We'll walk through the process of copying the data from local to HDFS (Hadoop Distributed File System) and creating tables in Hive.
Performing Left Join in Hive:
A left join, also known as a left outer join, retrieves all records from the left table (first table mentioned) and the matched records from the right table. We create a new table called 'left join' by combining data from 'online retail customer' and 'online retail invoice'. The resulting table will have records from the left table and matching records from the right table.
Executing Right Outer Join:
Similar to the left join, a right outer join retrieves all records from the right table and the matched records from the left table. We create a table named 'right outer join' to combine data from 'online retail customer' and 'online retail invoice'. This join type ensures that all records from the right table are included in the result.
Exploring Inner Join in Hive:
An inner join in Hive fetches records that have matching values in both tables. It creates a new table with only the common observations between 'online retail customer' and 'online retail invoice'. The resulting table will contain rows where the unique IDs match in both datasets.
Understanding Full Outer Join:
Lastly, we dive into the full outer join, which combines the results of both left and right outer joins. It includes all rows from both tables, merging data where there are matching values and also retaining unmatched rows from each table. The resultant table named 'full outer join' contains all observations from 'online retail customer' and 'online retail invoice'.
Step-by-Step Execution:
Preparing Data Sets:
Copy 'online retail customer' and 'online retail invoice' data to HDFS.
Create tables in Hive using the schemas provided.
Performing Joins:
Execute left, right, inner, and full outer joins in Hive terminal.
Monitor the MapReduce processes as the joins are executed.
Checking Resultant Tables:
After each join, we check the number of rows in the resultant tables to verify the successful execution of the joins. The count of rows gives us insights into how the data has been merged based on the join conditions.
Conclusion:
Congratulations on mastering joins in Hive! You've learned how to perform left, right, inner, and full outer joins, essential for combining and analyzing large datasets in Hive. Experiment with different join types to gain deeper insights into your data.
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