Question

What function would you use to control sharding in Spark

A) partitionBy()
B) shard()
C) clusterBy()
D) distribute()

Answer 1

In Spark, the partitionBy() function is used to control sharding by specifying how data is distributed across the cluster. This function is key for performance optimization by minimizing data shuffling.

Step-by-step explanation:

To control sharding in Spark, the function you would use is partitionBy(). This function is part of the DataFrame API and allows you to specify how data is partitioned across the cluster. Partitioning can be done based on one or more columns of the DataFrame, which determines how the data is distributed or sharded across the nodes of a Spark cluster.

Proper partitioning is essential for optimizing performance, as it can minimize data shuffling when performing distributed computations.

In Spark, the function used to control sharding is partitionBy().

Sharding in Spark refers to the process of distributing data across multiple nodes in a cluster to improve parallelism. The partitionBy() function allows you to partition the data based on a specific key. For example, if you have a large dataset and you want to partition it by a certain column, you can use partitionBy() to achieve that.

Here's an example:

DataFrame partitionedData = data.partitionBy("columnName");

Therefore correct option is A) partitionBy().