![]() We have 3 stages for all jobs as there is shuffle exchange happening. When not using bucketing, the analysis will run ‘shuffle exchange’ as seen in the above screenshot. Without Bucketing:- We will create two datasets without bucketing and perform join, groupBy, and distinct transformation. Alternatively, we can start with the number that is the same as the number of the executor we have in our cluster and then adjust it till we get the best performance. Alternatively, we can perform a hit and try to get the best number of buckets. ![]() When we enable the buckets, it is critical that we specify the bucket number, for this, one needs to have an insight into the data. It is beneficial to bucketing when pre-shuffled bucketed tables are used once within the query. It’s also beneficial when there are frequent join operations requiring large and small tables.īucketing is commonly used to optimize the performance of a join query by avoiding shuffles of tables participating in the join. This technique benefits dimension tables, which are frequently used tables containing primary keys. Improved sampling: The data is already split up into smaller chunks so sampling is improved.īucketing involves sorting and shuffling the data prior to the operation which needs to be performed on data like joins.īucketing boosts performance by sorting and shuffling data before performing downstream operations, such as table joins. In a map-side join, the left-hand side table bucket will exactly know the dataset contained by the right-hand side bucket to perform a table join in a well-structured format. ![]() Since each bucket contains an equal size of data, map-side joins perform better than a non-bucketed table on a bucketed table. Improved query performance: At the time of joins, we can specify the number of buckets explicitly on the same bucketed columns. Although not mandatory, using a partitioned table to do the bucketing will give the best results. At the time of loading, the data processing engine will calculate the hash value for that column, based on which it will reside in one of the buckets. When we start using a bucket, we first need to specify the number of the buckets for the bucketing column (column name). Based on the value of one or more bucketing columns, the data is allocated to a predefined number of buckets. In bucketing buckets ( clustering columns) determine data partitioning and prevent data shuffle. What is bucketing?īucketing is a technique in both Spark and Hive used to optimize the performance of the task. Tapping into Clairvoyant’s expertise in bucketing, this blog discusses how the technique can help to enhance the Spark job performance. It helps our clients lower the cost of the cluster while running jobs. Clairvoyant utilizes the bucketing technique to improve the spark job performance, no matter how small or big the job is.
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