Optimizing KNN Joins with Broadcast in Apache Sedona

One of the key challenges in performing k-Nearest Neighbors (KNN) joins in distributed systems is the performance overhead that arises when dealing with large datasets. In a typical distributed KNN join, each partition must compute the neighbors for a subset of the data, often resulting in a lot of communication and unnecessary computation. Apache Sedona, a powerful spatial analytics library built on top of Apache Spark, has introduced a new feature that optimizes KNN joins by utilizing broadcasting techniques.

What is a Broadcast KNN Join?

A broadcast k-Nearest Neighbors (KNN) join is an optimization technique in spatial join operations. It aims to improve the performance of KNN joins by broadcasting the smaller of the two datasets to all partitions, rather than performing the full partitioning of both datasets. This reduces the overhead of partitioning and shuffling data, thus improving the overall efficiency of the operation.

In a traditional KNN join, the system computes the neighbors between two datasets, which is computationally expensive. Broadcasting helps by sending the smaller dataset to all partitions of the larger dataset, reducing the need for partitioning the smaller dataset and significantly speeding up the operation.

When the query side (red points) is small, broadcasting all query geometries to each partition of the object side (green points) can dramatically reduce computation time.

In the physical plan, the query-side broadcast operation is added, where all query geometries are sent to each partition of the object side:

BroadcastQuerySideKNNJoin GEOM#41: geometry, GEOM#86: geometry, LeftSide, Inner, 4        

How it works:

? The objects dataset can be partitioned normally (non-spatial).

? All query geometries are broadcasted to each partition of the object side.

? Each partition performs a local KNN join, which is then “reduced” to find the top K nearest neighbors.

? This approach eliminates the need for spatial partitioning of the query side, which significantly improves performance.

On the other hand, when the object side (green points) is small, broadcasting all object geometries to each partition of the query side (red points) is a more efficient approach.

In the physical plan, the object-side broadcast operation is introduced, where all object geometries are sent to each partition of the query side:

BroadcastObjectSideKNNJoin GEOM#41: geometry, GEOM#86: geometry, RightSide, Inner, 4        

How it works:

? The query dataset can be partitioned normally (non-spatial).

? All object geometries are broadcasted to each partition of the query side.

? Not like the query-side broadcast, the local KNN join results do NOT need to be reduced to obtain the top K nearest neighbors.

? This eliminates the need for spatial partitioning of the object side.

Performance Gains from Broadcasting

The key performance improvement from broadcasting is eliminating spatial partitioning, which can introduce unnecessary communication and computation when partitioning is not required. By broadcasting the smaller dataset, we reduce the overhead of managing partitions and allow each partition to focus on local computations, which leads to faster KNN join operations.

The broadcast optimization for KNN joins in Apache Sedona allows for more efficient distributed computing by leveraging the size difference between the query and object sides. By broadcasting the smaller dataset to all partitions, Sedona reduces the need for spatial partitioning, improving performance significantly.

This feature is particularly useful in scenarios where the query side or the object side is much smaller than the other, making the operation faster and more resource-efficient.

If you want to try this out, you can run it on Apache Sedona or use Wherobots Cloud for a seamless experience.