#33 what is broadcast join in spark

In Apache Spark, a "broadcast join" is a type of join operation used to optimize performance when joining large and small datasets.

When performing a join operation between two datasets, Typically normal join is a wide transformation and shuffles data across the network to ensure that matching records are brought together.?

However, if one of the datasets is small enough to fit entirely in memory on each executor node, it can be more efficient to broadcast that dataset to all executor nodes rather than shuffling it across the network.?

This is particularly useful when joining a large dataset with a small dataset, as it reduces the amount of data that needs to be shuffled and improves performance.

Here's how a broadcast join works in Spark:

1. The larger dataset is distributed across the cluster.?
2. Whereas, the smaller dataset, the complete copy of the dataset, is made available on every machine on the cluster.?
3. This hugely reduces the shuffling of Data and thereby optimizing the performance.

Ex: spark.sparkContext.broadcast(<dataset name>.collect())

Broadcast joins can significantly improve the performance of join operations, especially when dealing with large datasets, by minimizing data shuffling and network communication overhead. However, it's important to use broadcast joins judiciously, as broadcasting large datasets can consume a significant amount of memory and may lead to out-of-memory errors if not managed properly.