Are you tired of waiting for your big data processing to finish? Do you want to unlock the full potential of Databricks and take your performance from zero to hero? Look no further! In this guide, we’ll take you on a fast-paced journey through the world of Databricks performance optimization. We’ll show you how to fine-tune your queries, optimize your clusters, and leverage cutting-edge features like External shuffling to achieve lightning-fast processing speeds. With our expert tips and tricks, you’ll be well on your way to mastering Databricks performance optimization and achieving big data success in record time. Get ready to hit the fast lane and leave sluggish performance behind!

Map side Join to avoid shuffling

Suppose we have two large datasets, transactions and products, and we want to join them on the product_id column to compute the total revenue generated by each product. The transactions the dataset contains information about each transaction, including the product_id and the quantity and price of the product sold. The products dataset contains information about each product, including the product_id and the name and category of the product.

To perform the join, we can use the following SQL query:

SELECT p.product_id, p.name, p.category, SUM(t.quantity * t.price) AS revenue
FROM transactions t
JOIN products p ON t.product_id = p.product_id
GROUP BY p.product_id, p.name, p.category

However, this query may be slow due to the large size of the transactions dataset. In particular, the join operation may require shuffling and sorting of data, which can be computationally expensive.

To speed up the query, we can use a map side join. A map side join is a join operation where one of the datasets is small enough to fit in memory, so we can load it into memory and perform the join entirely in memory without shuffling or sorting the data.

In this case, the products dataset is likely to be much smaller than the transactions dataset, so we can load it into memory and use a map side join to join it with the transactions dataset.

Here’s an example of how we can use a map side join in Databricks:

-- Load the products dataset into a broadcast variable
val products = spark.read.parquet("dbfs:/path/to/products")
  .select("product_id", "name", "category")
  .cache()
  .broadcast()

-- Perform the mapside join
val result = transactions
  .join(broadcast(products), Seq("product_id"), "inner")
  .groupBy("product_id", "name", "category")
  .agg(sum("quantity" * "price").as("revenue"))

In this example, we first load the products dataset into a broadcast variable using the broadcast() function. The cache() function is used to cache the dataset in memory for faster access. Since the products dataset is small enough to fit in memory, this should not cause any memory issues.

We then perform the map side join by joining the transactions dataset with the broadcasted products dataset using the join() function. The broadcast() function ensures that the products dataset is loaded into memory and used for the join operation.

Finally, we group the data by the product_id, name, and category columns and compute the sum of the quantity times price columns to get the total revenue generated by each product.

By using a map side join, we can avoid shuffling and sorting the data and perform the join entirely in memory, which can significantly speed up the query.

External Shuffle

External Shuffle Service is a Databricks feature that offloads the shuffle operations from the Spark executors to a separate service that runs outside the executor’s JVM process. The shuffle service helps to improve the performance and stability of large-scale Spark workloads by reducing the memory pressure on the Spark executors and improving the overall utilization of cluster resources.

Here’s an example of how External Shuffle Service works:

Suppose we have a Spark application that performs a large-scale group-by-aggregation operation on a dataset. The group-by operation involves a shuffle stage, where data is partitioned and transferred between Spark executors for processing. The shuffle stage can be resource-intensive, as it requires significant memory and network resources to transfer and process the data.

With External Shuffle Service, the shuffle stage is offloaded from the Spark executors to a separate service that runs on dedicated nodes in the cluster. The shuffle service manages the shuffle data and transfers it between the executors, freeing up the executor’s memory for other tasks.

When the Spark application runs, the shuffle service is automatically started by Databricks and is used for all shuffle operations in the application. The service runs on dedicated nodes that are separate from the Spark executors, ensuring that the shuffle operations do not compete with other tasks for resources.

By using External Shuffle Service, Spark applications can benefit from improved performance, stability, and scalability. The service helps to reduce the memory pressure on the Spark executors and improves the overall utilization of cluster resources, allowing Spark to handle larger workloads with better performance and efficiency.

Reducing partitions with Coalesce

Coalesce is a method in Spark that reduces the number of partitions in a dataset without shuffling the data. This can help to improve performance in certain scenarios, such as when the data is skewed or when there are too many small partitions that create overhead.

Here’s an example of how Coalesce can help to tune performance in Databricks:

Suppose we have a large dataset that we want to process using Spark. The dataset has 1000 partitions, but we know that the data is skewed, with some partitions containing much more data than others. This can lead to slow performance, as the executors will be waiting for the skewed partitions to finish processing before moving on to the next stage.

To address this issue, we can use Coalesce to reduce the number of partitions in the dataset. For example, we can coalesce the dataset into 100 partitions instead of the original 1000 partitions. This will help to ensure that the data is evenly distributed across the partitions, reducing the impact of data skew.

Here’s an example of how to use Coalesce in Databricks:

e# Load the dataset
data = spark.read.csv("s3://my-bucket/data.csv")

# Check the number of partitions
print("Number of partitions:", data.rdd.getNumPartitions())

# Coalesce the dataset into 100 partitions
coalesced_data = data.coalesce(100)

# Check the number of partitions again
print("Number of partitions after coalesce:", coalesced_data.rdd.getNumPartitions())

In this example, we load the dataset from an S3 bucket and then check the number of partitions using the getNumPartitions() method. We then use the coalesce() method to reduce the number of partitions to 100 and check the number of partitions again.

By using Coalesce, we can reduce the impact of data skew and improve performance by ensuring that the data is evenly distributed across the partitions. This can help to reduce the amount of time that executors spend waiting for skewed partitions to finish processing, improving overall performance.

Hope you like the blog. Please refer to my other Databricks performance optimization blogs:
Boost Databricks Performance for Maximum Results
From Slow to Go: How to Optimize Databricks Performance Like a Pro
Turbocharge Your Data: The Ultimate Databricks Performance Optimization Guide