Note: When examining the performance of join queries and the effectiveness of the join order optimization, make sure the query involves enough data and cluster resources to see a difference depending on the query plan. JOIN is same as OUTER JOIN in SQL. First, let's discuss how join works in Hive. A JOIN condition is to be raised using the primary keys and foreign keys of the tables. For big data, this simple operation can turn out to be resource-intensive. By definition, self join is a join in which a table is joined itself. Cross joins are used to return every combination of rows from two or multi-tables. The default for hive.auto.convert.join.noconditionaltask is true which means auto conversion is enabled. 10. To assist with optimality, you can structure the queries for parallel implementation of the cross-join. Vectorization feature is introduced into hive for the first time in hive-0.13.1 release only. Enable Vectorization. The size configuration enables the user to control what size table can fit in memory. (Originally the default was false – see HIVE-3784 – but it was changed to true by HIVE-4146 before Hive 0.11.0 was released.). Left Outer Join: Hive query language LEFT OUTER JOIN returns all the rows from the left table even though there are no matches in right table; If ON Clause matches zero records in the right table, the joins still return a record in the result with NULL in each column from the right table; From the above screenshot, we can observe the following As performant as Hive and Hadoop are, there is always room for improvement. Joins play a important role when you need to get information from multiple tables but when you have 1.5 Billion+ records in one table and joining it … ... the overall Hive … August, 2017 adarsh Leave a comment. Optimizing Hive cross-joins to avoid excessive computation time / resources. How Joins Work Today. The common join is also called reduce side join. In this article, we will check how to write self join query in the Hive, its performance issues and how to optimize it. A common join operation will be compiled to a MapReduce task, as shown in figure 1. It is a basic join in Hive and works for most of the time. Bucketing can also improve the join performance if the join keys are also bucket keys because bucketing ensures that the key is present in a certain bucket. The following query executes JOIN on the CUSTOMER and ORDER tables, and retrieves the records: hive> SELECT c.ID, c.NAME, c.AGE, o.AMOUNT FROM CUSTOMERS c JOIN ORDERS o ON (c.ID = o.CUSTOMER_ID); Hive tutorial 9 – Hive performance tuning using join optimization with common, map, bucket and skew join. FULL JOIN (FULL OUTER JOIN) – Selects all records that match either left or right table records. Self joins are usually used only when there is a parent child relationship in the given data. 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