How do you handle data skewness in Spark?

Question

Accepted Answer

Data skewness in Spark can be handled by partitioning, bucketing, or using salting techniques.

Partitioning the data based on a key column can distribute the data evenly across the nodes.
Bucketing can group the data into buckets based on a key column, which can improve join performance.
Salting involves adding a random prefix to the key column, which can distribute the data evenly.
Using broadcast joins for small tables can also help in reducing skewness.
Using dynamic allocation can help in balancing the workload across the nodes.
Using Spark's skew join optimization can also help in handling skewness.

Accepted Answer

if one executer got the lot of load in work node after the data shuffling we call it as a data skewness.

Accepted Answer

1. Repartition by Column(s)

The first solution is to logically re-partition your data based on the transformations in your script. In short, if you’re grouping or joining, partitioning by the groupBy/join columns can improve shuffle efficiency.

2. Salt

If you’re not sure what columns would lead to even workload by your app, you can use a random salt to evenly distribute data across cores. All we do is create a column with a random value the partition by that column.

Accepted Answer

We handle skewness via

1) log transform

2) square root transform

Accepted Answer

We can drop the tables including back-up tables associated with that db to reduce skewness

How do you handle data skewness in Spark?

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