In Hive, a skew join occurs when one or more keys in a table have significantly more values than other keys. This can lead to performance issues, as the join operation becomes much slower due to the uneven distribution of data. To address this problem, Hive provides several techniques that can be used to reduce skew join and improve query performance.
Techniques to Reduce Skew Join in Hive:
- Bucketing:
Bucketing is a technique that can be used to evenly distribute data across multiple buckets. By dividing the data into smaller, evenly sized buckets, Hive can perform join operations more efficiently, as the data is distributed more evenly. To use bucketing, you must first define the number of buckets and the columns to use as the bucketing keys. Once the table is created with the bucketing property, you can insert data into the table with the “CLUSTER BY” clause, which specifies the bucketing keys.
For example, the following query creates a table with three buckets and bucketing keys of “id” and “date”:
CREATE TABLE table1 (id int, name string, date string)
CLUSTERED BY (id, date) INTO 3 BUCKETS;
- Map-side Join:
A map-side join is a technique that can be used to reduce the amount of data that needs to be shuffled during a join operation. In a map-side join, the smaller table is loaded into memory and used to build a hash table. Then, for each row in the larger table, Hive looks up the corresponding value in the hash table and performs the join operation. This technique is much faster than a regular join, as it reduces the amount of data that needs to be shuffled.
To use a map-side join, the smaller table must fit into memory. Additionally, both tables must be sorted on the join keys to ensure that the data is partitioned correctly.
- Sampling:
Sampling is a technique that can be used to estimate the size of a join key and adjust the join operation accordingly. By sampling a portion of the data and analyzing the distribution of values, Hive can determine whether the join key is skewed and adjust the join operation accordingly. For example, if the join key is skewed, Hive can use a different join algorithm, such as a map-side join, to improve performance.
To use sampling in Hive, you can use the “TABLESAMPLE” clause in your query. For example, the following query uses a sample size of 10% to estimate the size of the join key:
SELECT /*+ MAPJOIN(t2) */ t1.*
FROM table1 t1 JOIN table2 t2
ON t1.id = t2.id
TABLESAMPLE (10 PERCENT);
Skew join can significantly impact the performance of join operations in Hive. By using techniques such as bucketing, map-side join, and sampling, you can reduce skew join and improve query performance. When designing your Hive queries, it is important to consider the distribution of data and choose the appropriate technique to address skew join. By doing so, you can ensure that your queries run efficiently and deliver the results you need.
Hive important pages to refer
